similarity hypothesis simple definition

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Similarity Hypothesis

The basic idea.

Consider the closest friends you meet while backpacking abroad. You likely share many similarities; perhaps a thrill for spontaneity, hobbies, appreciation for culture, music preferences, or food choices. During the trip, you find yourself effortlessly interacting with other backpackers: sharing a relatively-unknown scenic route, a local exhibition to visit, or the best bed and breakfast in town. We often relate and empathize easily with similar individuals – this is a result of the similarity hypothesis.

The similarity hypothesis suggests that we tend to be drawn towards those who are similar to ourselves. Similarities can refer to shared attitudes and values, as well as political opinions, cultural background, or even minute details like posture. 1

The experience of interacting with similar individuals jumpstarts cognitive processing, like learning, memory, attention, and reasoning. An aspiring musician might remember all the lyrics to their favorite band’s albums. An employee might pick up skills more quickly when assisted by a mentor they admire or identify with. Even when it comes to making comparisons with others, we tend to look for individuals who share similar attitudes and beliefs because it can be difficult to make accurate comparisons when others are too different from us. 2

Why do we tend to be drawn towards individuals who share similar attitudes and values?

Similarity Hypothesis: A hypothesis which states that we tend to be attracted towards individuals who share similar important traits, such as attitudes and values.

Cognitive Processing: A general term to describe any mental function involved in acquiring, storing, interpreting and manipulating information. These functions can be conscious or unconscious, such as attention, memory storage, learning, and reasoning.

Empathy: Understanding an individual from their point of view and experiencing that individual’s feelings, thoughts and perceptions.

In 1954, Leon Festinger proposed in his social comparison theory: when individuals are uncertain of their abilities and opinions, they tend to make comparisons with other similar individuals to assess the accuracy of their own opinion. Festinger’s influential social comparison theory introduced the similarity hypothesis. Since its introduction in A Theory of Social Comparison Processes , a large amount of evidence has supported the hypothesis. 3

Festinger’s hypothesis has been used to explain phenomena in a diverse array of fields, from political science to marketing. For instance, in the 1971 The Attraction Paradigm , psychologist Donn Byrne introduced the similarity-attraction theory. Byrne’s theory was based on the similarity hypothesis. He suggested that individuals who share similar “important attitudes” (opinions on family and values) are generally more likely to be attracted to each other, compared to individuals who share similar “less important” attitudes (opinions on a specific type of sink). 4 This holds for friendships as well as romantic partners. Byrne further outlined that individuals associate with those who have similar personality characteristics, such as self-esteem , optimism, and conscientiousness.

According to Byrne, personality similarity has a key role to play in the longevity and happiness of a marriage. 5 Byrne’s similarity-attraction theory stated that individuals are generally romantically attracted to others who share similar physical characteristics and levels of physical attractiveness. Byrne’s work on similarity-attraction was so influential that further research has supported his theory, with individuals’ preference for similarity being demonstrated in various other aspects such as social habits and socioeconomic status. 5

The similarity hypothesis then made its way into the field of economics and decision-making in Amos Tversky ’s 1972 book, Elimination by Aspects: A Theory of Choice . 6 Tversky influenced choice theory in economics by applying the similarity hypothesis to decision-making, changing the way modern economists approached the field. Based on the hypothesis, he suggested that when a new product enters a market, it will take more demand from the share of a similar product than a dissimilar one. This has important implications for brands: when creating a new line of products, they should make it as dissimilar as possible from their current offering to prevent market cannibalization. Tversky’s work influenced marketing managers, who started adopting his use of the similarity hypothesis to help make marketing entry decisions. 7

Leon Festinger

An influential American social psychologist, most renowned for his work on social comparison theory in his 1954 book, A Theory of Social Comparison Processes . Festinger introduced the similarity hypothesis in this book, which has been followed by an enormous amount of data which has provided evidence to support the hypothesis. Several of Festinger’s theories and research also renounced previously dominant behaviorist views of social psychology.

An American psychologist and influential contributor of foundational theory in interpersonal attraction. His work on similarity-attraction theory, based on the similarity hypothesis, was groundbreaking for exploring the relationship between similar attitudes and attraction. Byrne was also an early contributor on the psychology of human sexuality. 8

Amos Tversky

One of the founders of behavioral science who helped revolutionize the field of economics and decision-making. Tversky was an influential psychologist who applied the similarity hypothesis to decision-making and choice theory in economics. Along with Daniel Kahneman , Tversky was also a pioneer in loss aversion and prospect theory .

Consequences

When it comes to attraction, Byrne’s similarity-attraction theory remains relevant today as it provides reassurance that an individual is not alone in their belief. Being attracted to individuals with similar attitudes also enables one to more accurately predict the other’s behaviors in different scenarios, providing an insight into the other’s predilections and “pet peeves” based on similarity. 5

Similarly, when we empathize with a target, such as a novel, our enhanced cognitive processing enables us to facilitate reading comprehension. Our reading accelerates and our memory increases. Likewise, when we fail to empathize with a target, such as a film, we evoke a perception of dissimilarity. This creates the opposite effect, and our cognitive processing is inhibited: we lose focus easily, finding it difficult to recall the plot of the film. 1

Our enhanced cognitive processing is a result of empathy, which arises from our perception of similarity. This affects the way we interact with other individuals, as the perception of similarity can implicitly evoke empathy between two individuals. The perception of similarity is the reason why an employee may be able to learn new techniques more quickly when assisted by a mentor they empathize with.

Understanding the similarity hypothesis can allow us to better design inclusive educational curricula, particularly in scenarios where it is important to understand individuals or experiences which are not necessarily similar to most learners. This can be especially useful in cross-cultural education, history, minority education, and special-needs classes. 1 Applying the similarity hypothesis in these fields of education can help overcome the effort involved in understanding experiences or individuals which are dissimilar.

Controversies

Despite the repeated evidence upholding the similarity hypothesis, one criticism is that individuals frequently seek novelty and difference, with such experiences providing just as much certainty when it comes to self-evaluation. 3

Scholars who disagree with the similarity-attraction theory tend to adopt the complementarity view of attraction. This view states that individuals are more likely to prefer partners who have attributes that are complementary, rather than those who possess replicating attributes. This can be seen when an individual with a certain perceived negative attribute, such as impatience, is more attracted to someone who does not possess that same attribute. The complementarity view of attraction suggests that individuals prefer not to be reminded of their faults by being with someone similar, and therefore they are more attracted to those who will complement and bring out the best in them. 5

Emerging studies are also starting to define more clearly that it is perceived similarity, rather than actual similarity, that influences attraction. A 2012 study by American psychologists at Texas A&M and Northwestern University found that, unlike previous findings, actual similarity did not predict romantic attraction as effectively as previously thought. 9

There are alternative views when addressing how the similarity hypothesis influences opinion comparisons between individuals. Some argue that comparisons with other similar individuals depend on the type of opinion being evaluated. A study in 2000 by Jerry Suls, René Martin, and Ladd Wheeler highlights results which suggest that we prefer comparing with other similar individuals when it comes to the evaluation of preferences. Think about how you are more likely to care about what your best friend thinks of your outfit, compared to the Lyft driver who dropped you off this morning. In contrast, other studies have suggested that we prefer to compare ourselves with dissimilar individuals when it comes to belief assessment, 3 such as evaluating whether a certain statement or proposition is true.

The effects of the similarity hypothesis on memory retrieval.

In 2015, Hidetsugu Komeda conducted a study to observe memory retrieval in typically developing (TD) individuals and individuals with Autism Spectrum Disorder (ASD). The similarity hypothesis predicts that individuals with ASD will be able to easily retrieve other individuals with ASD from their memory. Participants were carefully selected and read 24 stories, before completing a recognition task. The results showed that ASD individuals demonstrated the same level of accuracy as TD individuals, but memory-retrieval patterns between the two groups were different. 1

Individuals with ASD were able to retrieve ASD-consistent stories more easily than ASD-inconsistent stories. TD individuals were also able to retrieve TD-consistent stories more easily than ASD-protagonist stories. These results are consistent with the similarity hypothesis, suggesting that individuals with ASD characteristics are able to help other ASD individuals due to empathy arising from their similarities. 1

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Similarities and Differences Between Hypothesis and Theory

In the realm of scientific inquiry, two terms that are often used interchangeably but hold distinct meanings are “hypothesis” and “theory.” Both play crucial roles in the scientific method, contributing to the understanding and advancement of knowledge. This article delves into the similarities and differences between these two fundamental scientific concepts.

Hypothesis: The Starting Point

A hypothesis is a proposed explanation for a phenomenon. It is an educated guess or a tentative solution to a problem based on existing knowledge. Scientists formulate hypotheses to guide their research and make predictions that can be tested through experimentation or observation.

Characteristics

Testability: A good hypothesis is testable, meaning it can be investigated through empirical methods.
Falsifiability: It should be possible to prove the hypothesis false through experimentation or observation.
Specificity: The hypothesis must be clear and specific, outlining the expected outcome of the experiment.

If plants receive more sunlight, then their growth rate will increase.

Theory: A Comprehensive Explanation

On the other hand, a theory is a well-substantiated explanation of some aspect of the natural world. Unlike a hypothesis, a theory has withstood extensive testing and scrutiny, providing a comprehensive framework for understanding a particular phenomenon.

Explanatory Power: Theories explain a wide range of phenomena and observations.
Predictive Capability: They can predict future observations and experiments accurately.
Consistency: The components of a theory are internally consistent and align with existing scientific knowledge.

The theory of evolution explains the biodiversity of life through the processes of natural selection and genetic variation.

Similarities

1. both guide scientific inquiry.

Both hypotheses and theories play integral roles in the scientific method, guiding researchers in the pursuit of knowledge. Hypotheses set the initial direction for experiments, while theories provide overarching frameworks.

2. Subject to Revision

Scientific knowledge is dynamic, and both hypotheses and theories are subject to revision based on new evidence. As more data becomes available, scientists may refine or even discard hypotheses and theories.

Differences

1. level of certainty.

The primary distinction lies in the level of certainty associated with each term. A hypothesis is a tentative explanation that requires testing, while a theory is a well-established explanation supported by a substantial body of evidence.

Hypotheses are narrow in scope, addressing specific questions or problems, while theories have a broader scope, encompassing a wide range of related phenomena.

In conclusion, hypotheses and theories are essential components of the scientific process, each serving distinct roles. Hypotheses initiate investigations, while theories provide robust explanations for observed phenomena. Recognizing the differences and similarities between these concepts is crucial for understanding how scientific knowledge evolves and progresses.

Related References:

Scientific Method – Wikipedia
Understanding Science – University of California Museum of Paleontology
The Difference Between Hypothesis and Theory – ThoughtCo

Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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Assumption vs. Hypothesis

What's the difference.

Assumption and hypothesis are both concepts used in research and reasoning, but they differ in their nature and purpose. An assumption is a belief or statement that is taken for granted or accepted as true without any evidence or proof. It is often used as a starting point or a premise in an argument or analysis. On the other hand, a hypothesis is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated to be tested and verified through empirical research or experimentation. While assumptions are often subjective and can be biased, hypotheses are more objective and aim to provide a basis for scientific investigation.

Attribute	Assumption	Hypothesis
Definition	A belief or statement taken for granted without proof as a basis for reasoning or action.	An educated guess or proposed explanation based on limited evidence, which is subject to testing and verification.
Role	Provides a starting point or foundation for further analysis or investigation.	Serves as a proposed explanation or prediction that can be tested through experimentation or observation.
Proof	Assumptions are not proven, but are accepted as true for the sake of argument or analysis.	Hypotheses are tested and supported or rejected based on evidence and data.
Level of Certainty	Assumptions are often made with varying degrees of certainty, ranging from highly probable to speculative.	Hypotheses are formulated with a certain level of confidence, but can be revised or rejected based on evidence.
Testing	Assumptions are not typically tested, but are used as a starting point for further analysis.	Hypotheses are tested through experimentation, observation, or data analysis to determine their validity.
Scope	Assumptions can be broad and encompassing, providing a foundation for multiple hypotheses.	Hypotheses are specific and focused, addressing a particular question or problem.

Further Detail

Introduction.

Assumptions and hypotheses are fundamental concepts in the fields of logic, science, and research. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of assumptions and hypotheses, their roles in different contexts, and how they contribute to the process of knowledge acquisition and problem-solving.

Assumptions

An assumption is a belief or statement that is taken for granted or accepted as true without any proof or evidence. It serves as a starting point for reasoning or argumentation. Assumptions can be based on personal experiences, cultural norms, or generalizations. They are often used to fill in gaps in knowledge or to simplify complex situations.

One key attribute of assumptions is that they are not necessarily true or proven. They are subjective and can vary from person to person. Assumptions can be implicit, meaning they are not explicitly stated, or explicit, where they are clearly expressed. They can also be conscious or unconscious, depending on whether we are aware of them or not.

Assumptions play a crucial role in everyday life, decision-making, and problem-solving. They help us make sense of the world and navigate through uncertain situations. However, it is important to recognize that assumptions can introduce biases and limit our understanding if they are not critically examined or challenged.

A hypothesis, on the other hand, is a tentative explanation or prediction that is based on limited evidence or prior knowledge. It is formulated as a testable statement that can be supported or refuted through empirical observation or experimentation. Hypotheses are commonly used in scientific research to guide investigations and generate new knowledge.

Unlike assumptions, hypotheses are grounded in evidence and are subject to verification. They are formulated based on existing theories, observations, or logical reasoning. Hypotheses are often stated in the form of "if-then" statements, where the independent variable (the "if" part) is manipulated or observed to determine its effect on the dependent variable (the "then" part).

Hypotheses are essential in the scientific method, as they provide a framework for conducting experiments and gathering data. They allow researchers to make predictions and draw conclusions based on empirical evidence. If a hypothesis is supported by the data, it can lead to the development of theories or further research. If it is refuted, it may prompt the formulation of new hypotheses or the revision of existing ones.

Comparison of Attributes

While assumptions and hypotheses share the commonality of being statements or beliefs, they differ in several key attributes:

Assumptions are often based on personal beliefs, experiences, or cultural norms. They can be influenced by subjective factors and may not have a solid foundation in evidence or logic. In contrast, hypotheses are grounded in existing knowledge, theories, or observations. They are formulated based on logical reasoning and are subject to empirical testing.

2. Verifiability

Assumptions are not easily verifiable since they are often subjective or based on incomplete information. They are accepted as true without rigorous testing or evidence. On the other hand, hypotheses are formulated to be testable and verifiable. They can be supported or refuted through empirical observation or experimentation.

Assumptions are primarily used to simplify complex situations, fill in gaps in knowledge, or provide a starting point for reasoning. They are often employed in everyday life, decision-making, and problem-solving. Hypotheses, on the other hand, serve the purpose of generating new knowledge, guiding scientific research, and making predictions about the relationship between variables.

4. Role in Knowledge Acquisition

Assumptions can limit knowledge acquisition if they are not critically examined or challenged. They can introduce biases and prevent us from exploring alternative explanations or perspectives. Hypotheses, on the other hand, contribute to knowledge acquisition by providing a structured approach to testing and refining ideas. They encourage critical thinking, data collection, and analysis.

5. Testability

Assumptions are often difficult to test since they are not formulated as specific statements or predictions. They are more subjective in nature and may not lend themselves to empirical verification. Hypotheses, on the other hand, are designed to be testable. They are formulated as specific statements that can be supported or refuted through observation or experimentation.

Assumptions and hypotheses are both important concepts in reasoning, problem-solving, and scientific research. While assumptions provide a starting point for reasoning and decision-making, hypotheses offer a structured approach to generating new knowledge and making predictions. Understanding the attributes and differences between assumptions and hypotheses is crucial for critical thinking, avoiding biases, and advancing our understanding of the world.

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Hypothesis vs. Theory: Understanding the Differences

“Hypothesis” and “theory” are two terms often used in science, but they have different meanings. Understanding the distinction between these two words can help us make sense of scientific explanations. In this article, we will explore the differences between “hypothesis” and “theory” in a way that is easy to understand. By the end, you’ll have a clearer grasp of these concepts and be able to use them confidently in scientific discussions.

Hypothesis vs. Theory

A hypothesis is a preliminary assumption to be tested.
A theory is a well-supported explanation for a broad range of phenomena.

Hypothesis vs. Theory: The Definition

What does hypothesis mean.

A hypothesis is a proposed explanation for a phenomenon or a scientific question that can be tested through experimentation or observation. It is an essential part of the scientific method, which involves formulating a hypothesis, conducting experiments to test it, and analyzing the results to draw conclusions.

In scientific research, a hypothesis serves as a tentative solution to a problem or a preliminary explanation for an observed phenomenon. It is based on existing knowledge and is formulated to be tested and potentially refuted through empirical evidence. A well-constructed hypothesis is specific, testable, and falsifiable, meaning that it can be proven false through experimentation or observation.

Example of a hypothesis : “If a person consumes more vitamin C, then their immune system will be stronger and they will have a lower likelihood of catching a cold.”

What Does Theory Mean?

A theory is a well-substantiated explanation of some aspect of the natural world that is based on a body of evidence, observations, and experimentation. In the scientific context, a theory is more than just a guess or a hypothesis; it is a comprehensive framework that has been rigorously tested and supported by a substantial amount of empirical data.

Scientific theories are developed through the scientific method, which involves formulating hypotheses, conducting experiments, and analyzing the results. As evidence accumulates and supports a particular explanation, it may be elevated to the status of a theory. Importantly, scientific theories are not static or unchangeable; they are subject to modification or even rejection in light of new evidence or more comprehensive explanations.

Example of a theory: The theory of evolution, which explains how species change over time through the process of natural selection.

Hypothesis vs. Theory: Usage

You employ hypotheses during the early stages of research to develop experiments. For instance, you might hypothesize that a plant given more sunlight will grow faster.

A theory , like the Theory of Evolution, summarizes a group of tested hypotheses and facts to explain a complex set of patterns and behaviors.

For a better understanding of the differences between the two terms, let’s take a look at the table below:

Feature	Hypothesis	Theory
Definition	A proposed explanation for a phenomenon	Well-substantiated explanation of some aspect
Basis	Based on limited evidence and observations	Based on extensive research and evidence
Testability	Can be tested through experiments and research	Has been extensively tested and supported
Scope	Narrow in scope, specific to a particular phenomenon	Broader in scope, applicable to multiple phenomena
Status	Preliminary and subject to change	Established and widely accepted in the scientific community

Tips to Remember the Differences

Think of a hypothesis as a “hunch” to be tested.
View a theory as a “tapestry” of well-tested ideas.
Use the phrase “hypothesis for testing” and “theory for explaining” to keep them distinct in your mind.

Hypothesis vs. Theory: Examples

Example sentences using hypothesis.

She formulated a hypothesis to explain the observed pattern in the data.
The researchers tested their hypothesis through a series of carefully controlled experiments.
The hypothesis proposed by the scientist led to a new understanding of the chemical reaction.
It is essential to develop a clear and testable hypothesis before conducting the research.
The hypothesis was supported by the experimental results, providing valuable insights into the phenomenon.

Example Sentences Using Theory

Einstein ‘s theory of relativity has fundamentally altered our understanding of space and time.
Darwin’s theory of natural selection provides a framework for understanding the evolution of species.
The germ theory of disease is fundamental in developing medical hygiene practices.
The Big Bang theory is widely accepted as the leading explanation for the origin of the universe.
The kinetic molecular theory explains the behavior of gases, including their volume and temperature relationships.

Related Confused Words

Hypothesis vs thesis.

A hypothesis is a specific, testable prediction that is proposed before conducting a research study, while a thesis is a statement or theory put forward to be maintained or proved. In essence, a hypothesis is a tentative assumption made in order to draw out and test its logical or empirical consequences, while a thesis is a proposition that is maintained by argument.

Both play distinct roles in the scientific and academic realms, with hypotheses guiding research and theses forming the central point of an argument or discussion.

Theory vs. Law

The primary difference between a scientific theory and a scientific law lies in their scope and function. A scientific theory is a well-substantiated explanation of some aspect of the natural world that is based on a body of evidence and has undergone rigorous testing and validation. In contrast, a scientific law describes a concise statement or mathematical equation that summarizes a wide variety of observations and experiments, often expressing a fundamental principle of nature.

While a theory provides an overarching framework for understanding a phenomenon, a law describes a specific, observable relationship. Both theory and law are vital components of scientific understanding, with theories offering explanations and laws providing concise descriptions of natural phenomena.

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experiments disproving spontaneous generation

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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Scientific Theory Definition and Examples

A scientific theory is a well-established explanation of some aspect of the natural world. Theories come from scientific data and multiple experiments. While it is not possible to prove a theory, a single contrary result using the scientific method can disprove it. In other words, a theory is testable and falsifiable.

Examples of Scientific Theories

There are many scientific theory in different disciplines:

Astronomy : theory of stellar nucleosynthesis , theory of stellar evolution
Biology : cell theory, theory of evolution, germ theory, dual inheritance theory
Chemistry : atomic theory, Bronsted Lowry acid-base theory , kinetic molecular theory of gases , Lewis acid-base theory , molecular theory, valence bond theory
Geology : climate change theory, plate tectonics theory
Physics : Big Bang theory, perturbation theory, theory of relativity, quantum field theory

Criteria for a Theory

In order for an explanation of the natural world to be a theory, it meets certain criteria:

A theory is falsifiable. At some point, a theory withstands testing and experimentation using the scientific method.
A theory is supported by lots of independent evidence.
A theory explains existing experimental results and predicts outcomes of new experiments at least as well as other theories.

Difference Between a Scientific Theory and Theory

Usually, a scientific theory is just called a theory. However, a theory in science means something different from the way most people use the word. For example, if frogs rain down from the sky, a person might observe the frogs and say, “I have a theory about why that happened.” While that theory might be an explanation, it is not based on multiple observations and experiments. It might not be testable and falsifiable. It’s not a scientific theory (although it could eventually become one).

Value of Disproven Theories

Even though some theories are incorrect, they often retain value.

For example, Arrhenius acid-base theory does not explain the behavior of chemicals lacking hydrogen that behave as acids. The Bronsted Lowry and Lewis theories do a better job of explaining this behavior. Yet, the Arrhenius theory predicts the behavior of most acids and is easier for people to understand.

Another example is the theory of Newtonian mechanics. The theory of relativity is much more inclusive than Newtonian mechanics, which breaks down in certain frames of reference or at speeds close to the speed of light . But, Newtonian mechanics is much simpler to understand and its equations apply to everyday behavior.

Difference Between a Scientific Theory and a Scientific Law

The scientific method leads to the formulation of both scientific theories and laws . Both theories and laws are falsifiable. Both theories and laws help with making predictions about the natural world. However, there is a key difference.

A theory explains why or how something works, while a law describes what happens without explaining it. Often, you see laws written in the form of equations or formulas.

Theories and laws are related, but theories never become laws or vice versa.

Theory vs Hypothesis

A hypothesis is a proposition that is tested via an experiment. A theory results from many, many tested hypotheses.

Theory vs Fact

Theories depend on facts, but the two words mean different things. A fact is an irrefutable piece of evidence or data. Facts never change. A theory, on the other hand, may be modified or disproven.

Difference Between a Theory and a Model

Both theories and models allow a scientist to form a hypothesis and make predictions about future outcomes. However, a theory both describes and explains, while a model only describes. For example, a model of the solar system shows the arrangement of planets and asteroids in a plane around the Sun, but it does not explain how or why they got into their positions.

Frigg, Roman (2006). “ Scientific Representation and the Semantic View of Theories .” Theoria . 55 (2): 183–206.
Halvorson, Hans (2012). “What Scientific Theories Could Not Be.” Philosophy of Science . 79 (2): 183–206. doi: 10.1086/664745
McComas, William F. (December 30, 2013). The Language of Science Education: An Expanded Glossary of Key Terms and Concepts in Science Teaching and Learning . Springer Science & Business Media. ISBN 978-94-6209-497-0.
National Academy of Sciences (US) (1999). Science and Creationism: A View from the National Academy of Sciences (2nd ed.). National Academies Press. doi: 10.17226/6024 ISBN 978-0-309-06406-4.
Suppe, Frederick (1998). “Understanding Scientific Theories: An Assessment of Developments, 1969–1998.” Philosophy of Science . 67: S102–S115. doi: 10.1086/392812

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attitude similarity hypothesis

Quick reference.

The proposition that people tend to be attracted to others who share their attitudes and values in important areas. This hypothesis has received strong and consistent support from empirical investigations. Also called the similarity-attraction hypothesis . Compare need complementarity hypothesis.

From: attitude similarity hypothesis in A Dictionary of Psychology »

Subjects: Science and technology — Psychology

Relationship Theories Revision Notes

Will Goulder

Psychology A-level Teacher

BSc (Hons), Psychology

Psychology and performing arts teacher in Canterbury. Deputy head of language and arts, and digital technology leader.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

What do the examiners look for?

Accurate and detailed knowledge
Clear, coherent, and focused answers
Effective use of terminology (use the “technical terms”)

In application questions, examiners look for “effective application to the scenario,” which means that you need to describe the theory and explain the scenario using the theory making the links between the two very clear. If there is more than one individual in the scenario you must mention all of the characters to get to the top band.

Difference between AS and A level answers

The descriptions follow the same criteria; however, you have to use the issues and debates effectively in your answers. “Effectively” means that it needs to be linked and explained in the context of the answer.

Read the model answers to get a clearer idea of what is needed.

Exam Paper Advice

In the exam, you will be asked a range of questions on relationships, which may include questions about research methods or using mathematical skills based on research into relationships.

As in Paper One and Two, you may be asked a 16-mark question, which could include an item (6 marks for AO1 Description, 4 marks for AO2 Application, and 6 marks for AO3 Evaluation) or simply to discuss the topic more generally (6 marks AO1 Description and ten marks AO2 Evaluation).

There is no guarantee that a 16-mark question will be asked on this topic, though, so it is important to have a good understanding of all of the different areas linked to the topic.

There will be 24 marks for relationship questions, so you can expect to spend about 30 minutes on this section, but this is not a strict rule.

The evolutionary explanations for partner preferences

The relationship between sexual selection and human reproductive behavior.

Evolutionary approaches state that animals are motivated to select a ‘mate’ with the best possible genes who will best be able to ensure the offspring’s future health and survival.

Anisogamy means two sex cells (or gametes) that are different coming together to reproduce. Men have sperm cells, which can reproduce quickly with little energy expenditure, and once they start being produced, they do not usually stop until the man dies.

Female gametes (eggs or ova) are, in contrast, much less plentiful; they are released in a limited time frame (between puberty and menopause) and require much more energy to produce.

This difference (anisogamy) means that men and women use different strategies when choosing partners.

Inter-sexual Selection

Intersexual selection is the preferred strategy of the female. They value quality over quantity.

Intersexual selection is when one gender makes mate choices based on a specific characteristic of the other gender: e.g., peahens choosing peacocks with larger tails. As a result, peacock tails become larger across the population because peacocks with larger tails will mate more, thus passing these characteristics on.

Females lose more resources than men if they choose a sub-standard partner, so they are pickier about who they select. They are more likely to pick a partner who is genetically fit and willing to offer the maximum resources to raise their offspring (a man who will remain by her side as the child grows to protect them both and potentially provide more children).

Females tend to seek a man who displays physical health characteristics and is a high-status individual who controls resources within the social group. Thus male partners are able to protect, provide and control food and resources. Although this ability may have equated to muscular strength in our evolutionary past, in modern society, it is more likely to relate to occupation, social class, and wealth.

If they have made a good choice, then their offspring will inherit the positive features of their father and are therefore also more likely to be chosen by women or men in the next generation.

Intra-sexual Selection

Intrasexual selection is the preferred strategy of the male. They value quantity over quality. Anisogamy suggests that men’s best evolutionary strategy is to have as many partners as possible.

To succeed, men must compete with other males to present themselves as the most attractive mate, encouraging features such as muscles that indicate to the opposite sex an ability to protect both themselves and their offspring.

Intrasexual selection refers to competition between members of the same sex for access to a mate of the opposite sex. Whatever characteristics led to success in mating will be passed on to the next generation, thus becoming more widespread in the gene pool.

Buss (1989) surveyed over 10,000 adults in 33 countries and found that females reported valuing resource-based characteristics when choosing a male (such as their jobs) whilst men valued good looks and preferred younger partners more than females did.

Although the size and scale of Buss’s work are impressive, his use of questionnaires could lead to social desirability bias, with participants answering in socially desirable ways rather than honestly. Also, 77% of participants were from Western industrial nations, meaning Buss might have been measuring the effects of culture rather than an evolutionary-determined behavior.

Clark and Hatfield (1989) conducted a now infamous study where male and female psychology students were asked to approach fellow students of Florida State University (of the opposite sex) and ask them for one of three things; to go on a date, to go back to their apartment, or to go to bed with them.

About 50% of men and women agreed to the date, but 69% of men agreed to visit the apartment, and 75% agreed to go to bed with them; only 6% of women agreed to go to the apartment, and 0% accepted the more intimate offer.

The evolutionary approach is determinist suggesting that we have little free will in partner choice. However, everyday experience tells us we have some control over our preferences. Evolutionary approaches to mate preferences are socially sensitive in that they promote traditional (sexist) views regarding what are ‘natural’ male and female roles and behaviors.

Gender bias – In today’s society, women are more career orientated and, therefore,, will not look for resourceful partners as much – Evolutionary theory does not apply to modern society.

Finally, the evolutionary theory makes little attempt to explain other types of relationships, e.g., gay and lesbian relationships, and cultural variations in relationships that exist across the world, e.g., arranged marriages.

Factors Affecting Attraction

Self disclosure.

This refers to the extent to which a person reveals thoughts, feelings, and behaviors which they would usually keep private from a potential partner. This increases feelings of intimacy.

In the initial stages of a relationship, couples often seek to learn as much as they can about their new partner and feel that this sharing of information brings them closer together. But can too much sharing scare your partner away? Is not sharing very much information intriguing or frustrating?

Altman and Taylor (1973) identified breadth and depth as important factors of self-disclosure . At the start of a relationship, self-disclosure is likely to cover a range of topics as you seek to explore the key facts about your new partner. “What do you do for work” and “Where did you last go on holiday” but these topics are relatively superficial.

As the relationship develops, people tend to share more detailed and personal information, such as past traumas and desires for the future. If this sharing happens too soon, however, an incompatibility may be found before the other person has reached a suitable level of investment in the relationship. Altman and Taylor referred to this sharing of information as social penetration .

An important aspect of this is the reciprocity of the process; if one person shares more than the other is willing to, there may be a breakdown of trust as one person establishes themselves as more invested than the other.

Aron et al. (1997) found that by providing a list of questions to pairs of people that start with superficial information (Who would be your perfect dinner party guest) and moving over 36 questions to more intimate information (Of all the people in your family, whose death would you find the most disturbing) people grew closer and more intimate as the questions progressed.

Aron’s research also included a four-minute stare at the end of the question sequence, which may have also contributed to the increased intimacy.

Sprecher and Hendrick (2004) observed couples on dates and found a close correlation between the amount of satisfaction each person felt and the overall self-disclosure that occurred between the partners.

However, much of the research into self-disclosure is correlational, which means that a causal relationship cannot be easily determined; in short, it may be that it is the attraction between partners which leads to greater self-disclosure, rather than the sharing of information, that leads to greater intimacy.

Physical attractiveness: including the matching hypothesis

Physical attractiveness is viewed by society as one of the most important factors of relationship formation, but is this view supported by research?

Physical appearance can be seen as a range of indicators of underlying characteristics. Women with a favorable waist-to-hip ratio are seen as attractive because they are perceived to be more fertile (Singh, 2002), and people with more symmetrical features are seen to be more genetically fit.

This is because our genes are designed to make us develop symmetrically, but diseases and infections during physical development can cause these small imperfections and asymmetries (Little and Jones, 2003).

The halo effect is a cognitive bias (mental shortcut) that occurs when a person assumes that a person has positive traits in terms of personality and other features because they have a pleasing appearance.

Dion, Berscheid, and Walster (1972) asked participants to rate photographs of three strangers for a number of different categories, including personality traits such as overall happiness and career success.

When these results were compared to the physical attraction rating of each participant (from a rating of 100 students), the photographs which were rated the most physically attractive were also rated higher on the other positive traits.

Walster et al. proposed The Matching Hypothesis that similar people end up together. The more physically desirable someone is, the more desirable they would expect their partner to be. An individual would often choose to date a partner of approximately their own attractiveness.

The matching hypothesis (Walster et al., 1966) suggests that people realize at a young age that not everybody can form relationships with the most attractive people, so it is important to evaluate their own attractiveness and, from this, partners who are the most attainable.

If a person always went for people “out of their league” in terms of physical attractiveness, they may never find a partner, which would be evolutionarily foolish. This identification of those who have a similar level of attraction, and therefore provide a balance between the level of competition (intra-sexual) and positive traits, is referred to as matching.

Modern dating in society is increasingly visual, with the rise of online dating, particularly using apps such as Tinder.

In Dion et al.’s (1972) study, those who were rated to be the most physically attractive were not rated highly on the statement “Would be a good parent,” which could be seen to contradict theories about inter and intra-sexual selection.

Landy and Aronson (1969) show how the halo effect occurs in other contexts. They found that when victims of crime were perceived to be more attractive, defendants in court cases were more likely to be given longer sentences by a simulated jury.

When the defendants were unattractive, they were more likely to be sentenced by the jury, which supports the idea that we generalize physical attractiveness as an indicator of other, less visual traits such as trustworthiness.

Feingold (1988) conducted a meta-analysis of 17 studies and found a significant correlation between the perceived attractiveness of actual partners rated by independent participants.

Individual differences – Towhey et al. found that some people are less sensitive to physical attractiveness when making judgments of personality and likeability – The effects of physical attractiveness can be moderated by other factors and is not significant.

The Filter Theory

Kerckhoff and Davis (1962) suggested that when selecting partners from a range of those who are potentially available to them (a field of availability), people will use three filters to “narrow down” the choice to those who they have the best chance of a sustainable relationship with.

The filter model speaks about three “levels of filters” which are applied to partners.

The first filter proposed when selecting partners were social demography . Social variables such as age, social background, ethnicity, religion, etc., determine the likelihood of individuals meeting and socializing, which will, in turn, influence the likelihood of a relationship being formed.

We are also more likely to prefer potential partners with whom we share social demography as they are more similar to us, and we share more in common with them in terms of norms, attitudes, and experiences.

The second filter that Kerckhoff and Davis suggested was similarity in attitudes . Psychological variables to do with shared beliefs and attitudes are the best predictor of a relationship becoming stable. Disclosure is essential at this stage to ensure partners really do share genuine similarities.

This was supported by their original 1962 longitudinal study of two groups of student couples (those who had been together for more or less than 18 months).

Over seven months, the couples completed questionnaires based on their views and attitudes, which were then compared for similarities. Kerckhoff and Davis suggested that the similarity of attitudes was the most important factor in the group that had been together for less than 18 months. This is supported by the self-disclosure research described elsewhere on this topic.

The third filter was complementarity which goes a step further than similarity. Rather than having the same traits and attitudes, such as dominance or humor, a partner who complements their spouse has traits that the other lacks. For example, one partner may be good at organization, whilst the other is poor at the organization but very good at entertaining guests.

Kerchoff and Davis found that this level of the filter was the most important for couples who had been together for more than 18 months. This may be the origin of the classic phrase “opposites attract,” though we may add the condition “although not for the first 18 months of the relationship.

This theory may be interpreted as similar to the matching hypothesis but for personality rather than physical traits.

Some stages of this model may now be seen as less relevant; for example, as modern society is much more multicultural and interconnected (by things such as the internet) than in the 1960s, we may now see social demography as less of a barrier to a relationship. This may lead to the criticism that the theory lacks temporal validity.

This lack of temporal validity is supported by Levinger (1978), who, even only 16 years after the study, pointed out that many studies had failed to replicate Karchkoff and Davis’ original findings, although this may be down to methodological issues with operationalizing factor such as the success of a relationship or complementarity of traits.

Again, investigating the second and third levels of the filter theory looks at correlation which cannot easily explain causality. Both Davis and Rusbult (2001) and Anderson et al. (2003) found that people become more similar in different ways the more time that they spend in a relationship together.

So it may be that the relationship leads to an alignment of attitudes and also a greater complementarity as couples assign each other roles: “He does the cooking, and I do the hoovering.”

Theories of Romantic Relationships

Social exchange theory.

This is an economic theory of romantic relationships. Many psychologists believe that the key to maintaining a relationship is that it is mutually beneficial.

Psychologists Thibault and Kelley (1959) proposed the Social Exchange Theory , which stipulates that one motivation to stay in a romantic relationship, and a large factor in its development, is the result of a cost-benefit analysis that people perform, either consciously or unconsciously.

Thibaut and Kelley assume that people try to maximize the rewards they obtain from a relationship and minimize the costs (the minimax principle).

In a relationship, people gain rewards (such as attention from their partner, sex, gifts, and a boost to their self-esteem) and incur costs (paying money for gifts, compromising on how to spend their time or stress).

There is also an opportunity cost in relationships, as time spent with a partner that does not develop into a lasting relationship could have been spent with another partner with better long-term prospects.

How much value is placed on each cost and benefit is subjective and determined by the individual. For example, whilst some people may want to spend as much time as possible with their partner in the early stages of the relationship and see this time together as a reward of the relationship, others may value their space and see extended periods spent together as more of a necessary investment to keep the other person happy.

Thibault and Kelley also identified a number of different stages of a relationship which progress from the sampling stage, where couples experiment with the potential costs and rewards of a relationship through direct or indirect interactions, through the bargaining and commitment stages as negotiations of each partner’s role in the relationship occur.

The rewards and costs are established and become more predictable, and finally arriving at the institutionalization stage, where the couple is settled. The norms of the relationship are heavily embedded.

Comparison Levels (CL) and (CLalt)

The comparison level (CL) in a relationship is a judgment of how much profit an individual is receiving (benefits minus costs). The acceptable CL needed to continue to pursue a relationship changes as a person matures and can be affected by a number of external and internal factors.

External factors may include the media (younger people may want more from a relationship after being socialized by images of romance on films and television), seeing friends and families in relationships (people who have divorced or separated parents may have a different CL to those with parents who are still married), or experiences from prior relationships, which have taught the person to expect more or less from a partner. Internal perceptions of self-worth, such as self-esteem, will directly affect the CL that a person believes they are entitled to in a relationship.

CLalt stands for the Comparison Level for Alternatives and refers to a person’s judgment of if they could be getting fewer costs and greater rewards from another alternative relationship with another partner. Steve Duck (1994) suggested that a person’s CLalt is dependent on the level of reward and satisfaction in their current relationship. If the CL is positive, then the person may not consider the potential benefits of a relationship with another person.

Operationalizing rewards and costs are hugely subjective, making comparisons between people and relationships in controlled settings very difficult. Most studies that are used to support Social Exchange Theory account for this by using artificial procedures in laboratory settings, reducing the external validity of the findings.

Michael Argyle (1987) questions whether it is the CL that leads to dissatisfaction with the relationship or dissatisfaction which leads to this analysis. It may be that Social Exchange Theory serves as a justification for dissatisfaction rather than the cause of it.

Social Exchange Theory ignores the idea of social equity explained by the next relationship theory concerning equality in a relationship – would a partner really feel satisfied in a relationship where they received all of the rewards and their partner incurred all of the costs?

Real-world application – Social Exchange Theory is used in Integrated Behavioural Couples Therapy where couples are taught how to increase the proportion of positive exchanges and decrease negative exchanges – This shows high mundane realism in terms of the practical, real-world application of the theory therefore, SET is really beneficial at improving real relationships.

Equity Theory

This is an economic theory of romantic relationships. Equity means fairness.

Equity Theory (Walster ‘78) is an extension of Social Exchange Theory but argues that rather than simply trying to maximize rewards/minimize losses. Couples will experience satisfaction in their relationship if there is an equal ratio of rewards to losses between both partners: i.e., there is equity/fairness.

If one partner is benefiting from more profit (benefits-costs) than the other, then both partners are likely to feel unsatisfied.

If one partner’s reward: loss ratio is far greater than their partner’s, they may experience guilt or shame (they are giving nothing and getting lots in return).

If one partner’s reward: loss ratio is far lower than their partner’s, they may experience anger or resentment (they are giving a lot and getting little in return).

A partner who feels that they are receiving less profit in an inequitable relationship may respond by either working hard to make the relationship more equitable or by shifting their own perception of rewards and costs to justify the relationship continuing.

Principles of equity theory:

Distribution – Trade-offs and compensations are negotiated to achieve fairness in a relationship e.g., one partner may cook and the other may clean; each has their own role.
Dissatisfaction – The greater the perceived inequity, the greater the dissatisfaction e.g., someone who over-benefits in their relationship will feel guilty, and one who under-benefits will feel angry.
Realignment – The more unfair the relationship feels, the harder the partner will work to restore equity. Or they may revise their perceptions of rewards and costs, e.g., what was once seen as a cost (abuse, infidelity) is now accepted as the norm.

Huseman et al. (1987) suggested that individual differences are an important factor in equity theory. They make a distinction between entitleds who feel that they deserve to gain more than their partner in a relationship and benevolents who are more prepared to invest by working harder to keep their partner happy.

Clark and Mills (2011) argue that we should differentiate between the role of equity in romantic relationships and other types of relationships, such as business or casual, friendly relationships. They found in a meta-analysis that there is more evidence that equity is a deciding factor in non-romantic relationships, the evidence being more mixed in romantic partnerships.

Social Equity Theory does not apply to all cultures; couples from collectivist cultures (where the group needs are more important than those of the individual) were more satisfied when over-benefitting than those from individualistic cultures (where the needs of the individual are more important than those of the individual) in a study conducted by Katherine Aumer-Ryan et al. (2007).

Some cultures have traditions and expectations that one member of a romantic relationship should benefit more from the partnership. The traditional nuclear family, typical in the early to mid-20th century, was patriarchal, and the woman was often expected to contribute to more tasks, such as housework and raising the children, than the man for whom providing money to the family was perceived to be the primary role.

Rusbult’s Investment Model

Rusbult et al.’s (2011) model of commitment in a romantic relationship builds upon the Social Exchange Theory discussed above and proposes that three factors contribute to the level of commitment in a relationship.

Satisfaction level . The sum total of positive and negative emotions experienced and how much each partner fulfills the other’s needs (financial, sexual, etc.)

Investment size . This relates to the number of investments made in the relationship to date in terms of time, money, and effort, which would be lost if the relationship stopped. Investments increase dependency on the relationship due to the costs caused by the loss of what has been invested. Therefore, investments are a powerful influence in preventing relationship breakdown.

Commitment level . This refers to the likelihood the relationship will continue. In new romantic relationships, partners tend to have high levels of commitment as they have (i) high levels of satisfaction, (ii) they would lose a lot if the relationship ended, (iii) they don’t expect any gains, (iv) they tend not to be interested in alternative relationships. However, as the relationship continues, these factors may change, resulting in lower levels of commitment.

Le and Agnew’s (2003) meta-analysis of studies relating to similar investment models found that satisfaction, comparison with alternatives, and investment were all strong indicators of commitment to a relationship. This importance was the same across cultures and genders and also applied to homosexual relationships.

Many of the studies relating to an investment in relationships rely on self-report techniques. Whilst this would be perceived as a less reliable and overly-subjective method in other areas when looking at the amount an individual feels they are committed to a relationship, their own opinion and the value that they place on behaviors and attributes are more relevant than objective observations.

Again, investment models tend to give correlational data rather than causal; it may be that a commitment established at an earlier stage leads inevitably to the partner viewing comparisons more favorably and investing more into the relationship.

Rusbult’s investment model has important real-world applications in that it can help explain why partners suffering abuse continue to stay in abusive relationships – although satisfaction may be very low, investment size (for example, children) may be very high, and they may lack alternative potential partners.

Rusbult (1995) found that for women living in a shelter for abused women, lack of alternatives and high investment were the major factors underlying why women returned to abusive relationships.

Duck’s Phase Model

Duck’s (2007) phase model suggests that the breakdown of a relationship is not a single event but rather a system of stages or phases in which a couple progresses, incorporating the end of the relationship.

Intra-Psychic Phase

Literally ‘within one’s own mind.’ In this phase, one of the partners begins to have doubts about the relationship. They spend time thinking about the pros and cons of the relationship and possible alternatives, including being alone. They may either internalize these feelings or confide in a trusted friend.

Dyadic Phase

The partners discuss their feelings about the relationship; this usually leads to hostility and may take place over a number of days or weeks. Over this period, the discussions will often focus on the equity in the relationship and will either culminate in a renewed resolution to invest in the relationship or the realization that the relationship has broken down.

Social Phase

Other people are involved in the process; friends are encouraged to choose a side and may urge for reconciliation with their partner or may encourage the breakdown through the expression of opinion or hidden facts (“I heard they did this…”). Each partner may seek approval from their friends at the expense of their previous romantic partner. At this point, the relationship is unlikely to be repaired as each partner has invested in the breakdown to their friends, and any retreat from this may be met with disapproval.

Grave-Dressing Phase

When the relationship has completely ended, each partner will seek to create a favorable narrative of the events, justifying to themselves and others why the relationship breakdown was not their fault, thus retaining their social value and not lowering their chances of future relationships.

Their internal narrative will focus more on processing the events of the relationship, perhaps reframing memories in the context of new discoveries about the partner. For example, an initial youthfulness may now be seen as immaturity.

Duck’s model may be a relevant description of the breakdown of relationships, but it does not explain what leads to the initial stages of the model, which other models of relationships discussed earlier attempt to do.

Duck’s phase model has useful real-life applications. When relationship therapists can identify the phase of a breakdown that a couple are in, they can identify strategies that target the issues at that particular stage. Duck (1994) recommends that couples in the intra-psychic phase should be encouraged to think about the positive rather than the negative aspects of their partner.

Rollie and Duck (2006) added a fifth stage to the model, the resurrection phase, where people take the experiences and knowledge gained from the previous relationship and apply it to future relationships they have. When Rollie and Duck revisited the model, they also emphasized that progression from one stage to the next is not inevitable and effective interventions can prevent this.

Virtual Relationships in Social Media

The development of social media sites since Facebook launched in 2004 has meant that people can initiate, maintain and dissolve relationships online without ever physically meeting the other person.

Research indicates important differences in the way in which people conduct virtual relationships compared to face-to-face relationships in terms of:

Self-Disclosure

This tends to vary according to whether the individual feels they are presenting information privately (e.g., private messaging) or publicly (e.g., their Facebook account). Disclosures to a public audience where the author’s identity is known are usually heavily edited.

Disclosures to ‘private’ audiences, particularly when the author’s identity is anonymous, are often marked by quicker and more revealing disclosures.

Online anonymity means that people do not fear the negative social consequences of disclosure in that they will not be judged negatively/punished for what would normally be judged as socially inappropriate disclosures.

Rubin (’75) found a similar phenomenon when studying personal disclosure of information in normal relationships, with people being far more likely to disclose highly personal information to strangers as they knew (a) they would probably never see the person again and (b) the stranger could not report disclosures to the individual’s social group.

Absence of Gating

A gate is any feature/obstacle that could interfere with the development of a relationship.

Gating in relationships refers to a peripheral feature becoming a barrier to the connection between people. This gate could be a physical feature, such as somebody’s weight or disfigurement, or a feature of one’s personality, such as introversion or shyness.

It may be that two people’s personalities are very compatible, and attraction would occur if they spoke for any length of time, but a gate prevents this from happening.

In face-to-face relationships, various factors influence the likelihood of a relationship starting in the 1st place: e.g., geographic location, social class, ethnicity, attractiveness, etc. These ‘gates’ are not present in virtual relationships and, in fact, people may mislead others online to form a false impression of their true identity: e.g., fake/photoshopped photos, females posing as males, etc.

McKenna and Bargh (1999) propose the idea that CmC relationships remove these gates and mean that there is little distraction from the connection between people that might not otherwise have occurred. Some people use the anonymity available on the internet to compensate for these gates by portraying themselves differently than they would do in FtF relationships.

People who lack confidence may use the extra time available in messaging to consider their responses more carefully, and those who perceive themselves to be unattractive may choose an avatar or edited picture which does not show this trait.

Gender bias – Theory assumes that gates affect people in the same way, but age and level of physical attractiveness are probably more gating factors for females seeking male partners than males seeking female partners – Research has suffered from a beta bias and oversimplified how gates are used in virtual relationships and are therefore less valid.

Zhao (2008) found that Facebook users often present highly edited, fictional representations of their true identity, presenting a false version of their ‘ideal’ self which they consider more likely to be attractive to others. Yurchisin (’05) interviewed online daters and found that although people would ‘stretch’ the truth about their true selves, they did not present completely imaginary identities to others for fear of rejection and ridicule if and when they met someone for a physical date.

Baker (2010) found that online relationships allowed shy people to overcome the lack of confidence that normally prevented them from forming face-to-face relationships. A survey of 207 male and female students found that high shyness and use of Facebook scores correlated with a higher perception of friend quality.

Low shyness and high Facebook use were not correlated with friendship quality. This seems to indicate that shy people may find virtual relationships particularly rewarding, presumably as the negative emotions brought about by face-to-face relationships are lessened or removed.

McKenna (2000) surveyed 568 internet users and found that just under 10% had gone on to physically meet friends who they had met online, and just over 10% had talked on the phone. After a 2-year gap, 57% revealed that their virtual relationship had increased intimacy. In terms of romantic relationships, 70% lasted 2 years or more compared to only 50% of relationships formed face-to-face.

A current danger in society relates to individuals assuming false identities online to deceive others into disclosing private information/images and then, possibly, blackmailing the individual who disclosed. School-delivered and online awareness campaigns aim to highlight the dangers of disclosing too much and putting trust in online relationships that may turn out to be based on false identities and/or dangerous/exploitative.

Parasocial Relationships

Levels of Parasocial Relationships

Parasocial relationships are one-sided relationships where one partner is unaware that they are apart of it.

Parasocial relationships may be described as those which are one-sided, Horton and Wohl (1956) defined them as relationships where the ‘fan’ is extremely invested in the relationships but the celebrity is unaware of their existence.

Parasocial relationships may occur with any dynamic which elevates someone above the population in a community, making it difficult for genuine interaction; this could be anyone from fictitious characters to teachers.

PSRs are usually directed toward media figures (musicians, bloggers, TV presenters, etc.). The object of the PSR becomes a meaningful figure in the individual’s life, and the ‘relationship’ may occupy a lot of the individual’s time.

PSRs are often formed because the individual lacks the social skills or opportunities to form a real relationship. PSRs do not involve risks present in real relationships, such as criticism or rejection.

PSRs are likely to form because the individual views the object of the PSR as (i) attractive and (ii) similar to themselves.

The Attachment Theory Explanation

Bowlby’s theory of attachment suggests that those who do not have a secure attachment earlier in life will have emotional difficulties and attachment disorders when they grow up.

Parasocial relationships are often associated with teenagers and young adults who may have had less genuine relationships to build an internal working model which allows them to recognize parasocial relationships as abnormal.

For example, it may be that those with insecure resistant attachment types are drawn to parasocial relationships because they do not offer the threat of rejection or abandonment.

The Absorption-Addiction Model

McCutcheon (2002) proposed that parasocial relationships form due to deficiencies in people’s lives. They look to the relationship to escape from reality, perhaps due to traumatic events or to fill the gap left by a real-life attachment ending.

Absorption refers to behavior designed to make the person feel closer to the celebrity. This could be anything from researching facts about them, both their personal life and their career, to repeatedly experiencing their work, playing their music or buying tickets to see them live, or paying for their merchandise to strengthen the apparent relationship.

As with other Addictions, this refers to the escalation of behavior to sustain and strengthen the relationship. The person starts to believe that the ‘need’ for the celebrity and behaviors become more extreme and more delusional. Stalking is a severe example of this behavior.

The absorption-addiction model can be viewed as more of a description of parasocial relationships than an explanation; it states how a parasocial relationship may be identified and the form it may take, but not what it is caused by.

Methodologically, many studies into parasocial relationships, such as Maltby’s 2006 survey, rely on the self-report technique. This can often lack validity, whether this is due to accidental inaccuracies, due to a warped perception of the parasocial relationship by the participant, genuine memory lapses, or more deliberate actions.

For example, the social desirability bias makes the respondents under-report their abnormal behavior. There is often competition between fans of celebrities to see who is the ‘biggest’ fan, which may lead to an exaggeration of the behaviors and attitudes when reporting the relationship.

McCutcheon et al. (2006) used 299 participants to investigate the links between attachment types and attitudes toward celebrities. They found no direct relationship between the type of attachment and the likelihood that a parasocial relationship will be formed.

Portrays a negative view of human behavior – PSRs are portrayed as psychopathological behavior like calling them ‘borderline pathological’ – Theory may be socially sensitive as it implies that such behavior is a bad thing when it may actually provide support for those who struggle with real-life relationships, it may be more appropriate to adopt a positive, humanistic approach.

Altman, I., Taylor, D. A., & Actman, I. (1973). Social penetration: The development of interpersonal relationships (2nd ed.) . New York: Holt, Rinehart and Winston.

Anderson, C., Keltner, D., & John, O. P. (2003). Emotional convergence between people over time. Journal of Personality and Social Psychology, 84(5) , 1054–1068. doi:10.1037/0022-3514.84.5.1054

Aron, A., Melinat, E., Aron, E. N., Vallone, R. D., & Bator, R. J. (1997). The experimental generation of interpersonal closeness: A procedure and some preliminary findings. Personality and Social Psychology Bulletin, 23(4) , 363–377. doi:10.1177/0146167297234003

Buss, D. M. (1989). Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures. Behavioral and Brain Sciences, 12(01) , 1. doi:10.1017/s0140525x00023992

Clark, R. (1989). Gender differences in receptivity to sexual offers. Journal of Psychology & Human Sexuality, 2(1) , 39–55. doi:10.1300/j056v02n01_04

Davis, J. L., & Rusbult, C. E. (2001). Attitude alignment in close relationships. Journal of Personality and Social Psychology, 81(1) , 65–84. doi:10.1037/0022-3514.81.1.65

Dion, K., Berscheid, E., & Walster, E. (1972). What is beautiful is good. Journal of Personality and Social Psychology, 24(3) , 285–290. doi:10.1037/h0033731

Feingold, A. (1988). Matching for attractiveness in romantic partners and same-sex friends: A meta-analysis and theoretical critique. Psychological Bulletin, 104(2) , 226–235. doi:10.1037/0033-2909.104.2.226

Flanagan, C., Berry, D., & Jarvis, M. (2016). AQA psychology for A level year 2 – student book . United Kingdom: Illuminate Publishing.

Gallagher, M., Nelson, R., J, Y., & Weiner, I. B. (2003). Handbook of psychology: V. 3: Biological psychology . New York: John Wiley & Sons.

Huston, T. L., & Levinger, G. (1978). Interpersonal attraction and relationships. Annual Review of Psychology, 29(1) , 115–156. doi:10.1146/annurev.ps.29.020178.000555

Kerckhoff, A. C., & Davis, K. E. (1962). Value consensus and need Complementarity in mate selection. American Sociological Review,27(3) , 295. doi:10.2307/2089791

Landy, D., & Aronson, E. (1969). The influence of the character of the criminal and his victim on the decisions of simulated jurors. Journal of Experimental Social Psychology, 5(2) , 141–152. doi:10.1016/0022-1031(69)90043-2

Little, A. C., & Jones, B. C. (2003). Evidence against perceptual bias views for symmetry preferences in human faces. Proceedings of the Royal Society B: Biological Sciences, 270(1526) , 1759–1763. doi:10.1098/rspb.2003.2445

Singh, D. (1993). Adaptive significance of female physical attractiveness: Role of waist-to-hip ratio. Journal of Personality and Social Psychology, 65(2) , 293–307. doi:10.1037/0022-3514.65.2.293

Sprecher, S., & Hendrick, S. S. (2004). Self-disclosure in intimate relationships: Associations with individual and relationship characteristics over time. Journal of Social and Clinical Psychology,23(6) , 857–877. doi:10.1521/jscp.23.6.857.54803

Walster, E., Aronson, V., Abrahams, D., & Rottman, L. (1966). Importance of physical attractiveness in dating behavior. Journal of Personality and Social Psychology, 4(5) , 508–516. doi:10.1037/h0021188

Waynforth, D., & Dunbar, R. I. M. (1995). Conditional mate choice strategies in humans: Evidence from ‘lonely hearts’ advertisements. behavior, 132(9) , 755–779. doi:10.1163/156853995×001 andura’s Bobo Doll studies are laboratory experiments and therefore criticizable on the grounds of lacking ecological validity.’

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The simplicity principle in perception and cognition

Jacob feldman.

Department of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, NJ

The simplicity principle, traditionally referred to as Occam’s razor, is the idea that simpler explanations of observations should be preferred to more complex ones. In recent decades the principle has been clarified via the incorporation of modern notions of computation and probability, allowing a more precise understanding of how exactly complexity minimization facilitates inference. The simplicity principle has found many applications in modern cognitive science, in contexts as diverse as perception, categorization, reasoning, and neuroscience. In all these areas, the common idea is that the mind seeks the simplest available interpretation of observations— or, more precisely, that it balances a bias towards simplicity with a somewhat opposed constraint to choose models consistent with perceptual or cognitive observations. This brief tutorial surveys some of the uses of the simplicity principle across cognitive science, emphasizing how complexity minimization in a number of forms has been incorporated into probabilistic models of inference.

Occam’s razor

The principle of simplicity or parsimony —broadly, the idea that simpler explanations of observations should be preferred to more complex ones—is conventionally attributed to William of Occam, after whom it is traditionally referred to as Occam’s razor . 1 Since then philosophers of science have adopted a bias towards simpler explanations as a foundational principle of inference, guiding the selection of hypotheses whenever multiple hypothesis are consistent with data—as is nearly always the case.

But why should simpler theories be preferred? Practicing scientists have generally assumed it is because they are actually more likely to be correct. But it has never been clear exactly why this should be so. Hume’s principle of “Uniformity of Nature” suggests that simpler theories are preferable because they make a good match for a highly regular, lawful world. Conversely, some philosophers have assumed that the bias towards simplicity is an essentially aesthetic preference, akin to elegance or beauty that mathematicians prize in theorems, conveying no particular claim to correctness (see Sober, 1975 ). Simpler theories were seen as more manageable, more comprehensible, and more testable ( Popper, 1934/1959 ), but not necessarily more truthful. In the twentieth century some authors (e.g. Quine, 1965 ; Jeffreys, 1939/1961 ) began to argue that simpler theories were, in fact, more likely to be true, but until recently the precise connection between simplicity and truth remained, at best, extremely unclear. To understand the connection, we need at the very least a more precise definition of simplicity . Such a definition only arrived in the last few decades.

Mathematical definitions of simplicity and complexity

Historically it has generally been assumed that simplicity and complexity were inherently subjective notions, impervious to clear, rigorous, or universal definitions. A theory that is simple in one method of expression may seem complex in another, implying that simplicity lies “in the eye of the beholder.” A notorious example was the competition between Julian Schwinger’s elaborate formalization of quantum thermodynamics, and Richard Feynman’s apparently simpler account (based on “funny little diagrams”)—which, notwithstanding their apparent difference in complexity, were eventually shown by Freeman Dyson to be equivalent ( Krauss, 2011 ). Cases like this seemed to imply that complexity depends on the chosen method of expression, and thus that no quantification of complexity could be universal.

Kolmogorov complexity

This all changed in the 1960s with the introduction of a principled and convincing mathematical definition of complexity now known as Kolmogorov complexity or algorithmic complexity . Introduced in slightly different forms by Solomonoff (1964) , Kolmogorov (1965) , and Chaitin (1966) , the main idea is that the complexity of a string of characters reflects the degree of incompressibility , as measured by the length of a computer program required to faithfully express the string (see M. Li & Vitányi, 1997 ). Simple strings are those that can be expressed by brief computer programs, and complex datasets are those that cannot. The idea is usually formalized by considering a string S (a sequence of symbols), and then considering the length (in symbols) of the shortest computer program capable of generating it S . For example, the loop (in pseudocode) “ for i=1 to 1000: print '1' ” prints a string of 1000 characters, but the program itself contains only 26; this string is highly compressible. By contrast a “typical” random string of 1000 characters (e.g. “ 39383827262226 …”) can’t be compressed in this way, although it can be expressed by a program that is itself about 1000 characters long (e.g. “ Print '9486390348473969683…' ”, which has 1008 characters). More generally, a string that contains regularities or patterns—of any form that can be expressed in the computer language—can be faithfully reproduced by a short program that takes advantages of these patterns, while a relatively complex or “random” string cannot be similarly compressed. This measure of complexity is inherently capped at approximately the length of the original string, because any string, no matter how irregular, can be reproduced exactly simply by quoting it verbatim as in the example above. In this view, simplicity is essentially compressibility .

Critically, Kolmogorov complexity is universal in the sense that it does not depend “very much” on the computer language in which the program is written. Turing (1937) had demonstrated the existence of computers that can, in a well-defined sense, carry out any concretely specifiable algorithm, now referred to as universal Turing machines. In modern terminology, we can think of them as computer languages that are general enough to express any computable function—including, critically, to “simulate” other computer languages. Assume a string S that can be expressed by computer language L 1 in K 1 ( S ) steps, meaning that its Kolmogorov complexity is at least as low as K 1 ( S ). Assume that some other language L 2 can be expressed in language L 1 in a finite number | L 2 | of steps—in modern terms, | L 2 | is the length of a compiler for language L 2 written in language L 1 . It follows fairly immediately that string S can be expressed in language L 2 in | L 2 | + K 1 ( S ) steps, meaning that the complexity of S in language L 2 (i.e. K 2 ( S )) is at least as low as | L 2 |+ K 1 ( S )—because that’s how many steps it takes to translate L 2 into L 1 and then express S in L 2 . The “translation component” | L 2 | may be very large, if the computer languages are very different, but it is finite—and, critically, it does not depend on the length of the string S . This means that as strings get longer and longer, the translation component of their complexity matters less and less, and in this sense their complexity is asymptotically independent of the programming language. For this reason the Kolmogorov complexity K ( S ) of a string S is usually thought of as a universal measure of its inherent complexity or randomness.

Note that the actual value of the K ( S ) is uncomputable. 2 For long strings it can be approximated by effective string-compression algorithms such as Lempel-Ziv ( M. Li & Vitányi, 1997 ) implemented in the common utility gzip ( Ziv & Lempel, 1977 ), meaning that the Kolmogorov complexity of a long string S is approximately the length, in characters, of the gzipped version of S . For shorter strings such approximations are in principle less reliable, though recent work by Gauvrit, Singmann, Soler-Toscano, and Zenil (2016) has for the first time provided practical techniques for estimating the Kolmogorov complexity of short strings, opening an intriguing research avenue for evaluating the role of complexity in psychological models.

Information-theoretic Description Length

Another important approach to the quantification of complexity was initiated by Shannon (1948) . Shannon showed that in a set of messages m 1 , m 2 , … which occur with probability p 1 , p 2 …, each message conveys information given by −log p i , which quantifies the degree of “surprise” or unexpectedness entailed by the message. Consequently, if one seeks to convey a set of messages in the fewest symbols possible, one should adopt a coding language in which each message m i is assigned a code of length approximately −log p i symbols. Such a procedure will minimize the expected total code length, that is, achieve the most compressed expression possible. As a result, the quantity −log p i is sometimes referred to as the Description Length (DL). The DL of a message is in effect a measure of complexity, because it quantifies how many symbols are required to express m i in a maximally compressed code. That is, just like Kolmogorov complexity, the DL quantifies how many symbols are required to express a particular message after maximal compression . In this way Shannon showed that complexity is intimately related to probability, a profound insight that pervades the modern understanding of both concepts.

Rissanen (1989) took the next step by elevating this insight into a fundamental principle of inference, which he called the Minimum Description Length (MDL) principle (see Grünwald, 2005 , and compare the closely related approach referred to as Minimum Message Length ; Wallace, 2004 ). In its classic formulation, the MDL principle begins by imagining that we are trying to explain some data X via some set of alternative models Y i . For any given model Y , the joint probability p ( X ∧ Y ) that both model and data are true can be written as p ( X | Y ) p ( Y ), the product of the probability of the model and the probability of the data conditioned on the model. The DL of this conjunction, that is, its negative log probability, is simply

That is, the complexity (DL) of the model and data is the sum of the complexity of the model, plus the complexity of the data given the model—bearing in mind that, via Shannon’s definition, the “complexity” of the data is really its surprisingness given the model. This neat additive formulation captures something very basic about scientific theorizing: that we are trying to simultaneously minimize the complexity of our theories and the unexpectedness (surprise) of the data given our theories—that is, that we seek elegant models that also explain the data reasonably well. This perfectly encapsulates Einstein’s (perhaps aprocryphal) quip that our theories should be “as simple as possible, but no simpler.”

Bayesian inference

The intimate relationship between Occam’s razor and rational probabilistic inference was probably first pointed out by Jeffreys (1939/1961) , one of the principal developers of the modern conception of Bayesian inference. Jeffreys argued in some detail that the simplest interpretation was indeed the most likely one, and in particular advocated adopting priors that penalize complexity, that is, placing higher priors on simpler models and lower priors on more complex ones. More recently, Edwards (1972) has also argued that probability theory inherently favors simpler inductions, though on the basis of the likelihood rather than the prior.

The close connection between Occam’s razor and Bayes’ rule can be appreciated most directly simply by observing that the hypothesis with the highest posterior is, ipso facto, also the hypothesis iwth the minimum DL in Shannon’s sense. In a Bayesian framework, the posterior belief in hypothesis H after considering data D , notated p ( H | D ), is proportional to the product of its prior p ( H ) and its likelihood p ( D | H ),

(see Yuille & Bülthoff, 1996 or Feldman, 2014 for tutorial introductions). The hypothesis that maximizes this quantity, sometimes called the maximum a posteriori or MAP, is the hypothesis that is the most probable, in that it maximizes the tradeoff between prior plausibility and fit to the observed data. Hence in this simple sense, if one assumes an optimal description language in the sense defined by Shannon, the winning hypothesis is both the most probable and the simplest.

Even under broader set of assumptions, Bayesian inference inherently favors simpler hypotheses because of the way it assigns probability ( MacKay, 2003 ), a tendency often referred to as the “Bayes occam” factor. In practice, a Bayesian hypothesis space often consists of one more more parameterized families of hypotheses. The more parameters a family has, the smaller the probability volume devoted to each individual hypothesis (that is, each setting of the parameters), since the total probability assigned to all hypotheses must sum to one. Hence if one thinks of the number of parameters as a measure of the complexity of the model family, the prior necessarily decreases with complexity. A similar argument applies to the likelihood as well ( Tenenbaum & Griffiths, 2001 ), suggesting that Bayesian inference automatically favors more restrictive (i.e., simpler) hypotheses even without an overt prior bias.

In the modern literature on machine learning and statistical learning, the intimate connection between simplicity and probability is part of what is called the bias / variance trade-off , a terminology introduced by Geman, Bienenstock, and Doursat (1992) . Very broadly speaking, more complex theories are inherently—that is, by virtue of their complexity and thus flexibility—capable of more precise fits to training data, but simpler theories tend to generalize better. This leads to a trade-off in which optimal inference requires a balance between adhering to data and preferring simpler theories (see Duda, Hart, & Stork, 2001 or Hastie, Tibshirani, & Friedman, 2001 for discussion). This tradeoff is widely regarded as a central aspect of all probabilistic inference, and is yet another reason why probability and simplicity are intertwined; almost regardless of the nature of the inference problem, a bias towards simpler theories (in this context sometimes called regularization) is required in order to prevent overfitting the training data and thus generalizing poorly.

Finally, notice that just as one can create a complexity from a probability by taking a negative logarithm, one can create a probability from a complexity by exponentiation. Given a set of strings S each having Kolmogorov complexity K ( S ), one can construct a set of probabilities

(see M. Li & Vitányi, 1997 ). Such a distribution assigns higher probability to simpler strings (here thought of as models of data), and lower probability to more complex ones. This construction may seem contrived, but, as Solomonoff (1964) observed, it yields a set of probabilities—for example, a prior in the Bayesian sense—that is universal in precisely the same sense that Kolmogorov complexity itself is universal: namely, that is approximately correct regardless of the details of the coding language. Such a “universal prior” closes the loop connecting probability to complexity.

This connection between simplicity and probability has many nuances not mentioned here, and is not without controversy (see MacKay, 2003 for a more substantial discussion), and is viewed somewhat differently by those from Bayesian and information-theoretic traditions (see Burnham & Anderson, 2002 ). However, notwithstanding the many subtleties, it is important to understand that in the modern technical literature and in cognitive science, Bayesian inference and complexity minimization are usually treated as deeply intertwined, if not practically the same thing.

The simplicity principle in psychology

In psychology and cognitive science, the simplicity principle posits that the mind draws interpretations of the world—mental models or mental representations—that are as simple as possible, or, at least, that are biased towards simplicity ( Chater, 1997 ; Chater & Vitányi, 2003 ). The idea takes different forms in different areas of cognition, depending on the nature of the many perceptual and cognitive problems the mind encounters: perceptual interpretations of sense data, memory encodings of experience, causal interpretations of observations, and so forth. In MDL and Bayesian formulations, the principle can be extended to allow a tradeoff, inherent in these frameworks, between simplicity and consistency with sense data and experience—meaning that the interpretation drawn by the mind in light of simplicity may not actually be consistent with observation. Nevertheless in many areas of cognition, briefly surveyed in the next few sections, researchers have found that human thought incorporates a bias towards simplicity.

Note that complexity often arises in psychological experiments as a nuisance variable, simply because it can have such a salient effect on performance. Many experiments include simple and complex conditions (often labeled in other ways such as “high-load” and “low-load”, etc.), even when complexity per se is not the main topic of inquiry. Typically, “complex” conditions simply involve a larger number of items or features, although it should be noted that the sheer number of elements in a construct is not generally a good proxy for complexity, since (as will be seen below) patterns with an equal number of elements can vary widely in regularity or compressibility. This review will not generally include such studies, but will instead focus on studies in which the bias towards simplicity is the main topic of interest.

The principle of simplicity first arose in perceptual psychology via the Gestalt notion of Prägnanz , a broad term meant to encompass “such properties as regularity, symmetry, simplicity, and others” ( Koffka, 1935 ). The idea is that the mind prefers coherent and plausible interpretations of sensory data, for example interpreting contours as the boundaries of objects, completing shapes plausibly behind occluders, and so forth. Notwithstanding its somewhat vague definition, Prägnanz is often thought of as a kind of simplicity principle, sometimes under the rubric minimum principle (see Boselie & Wouterlood, 1989 ; Kanizsa, 1979 ). The idea is that more coherent or “Prägnant” interpretations are in some sense simpler than alternatives ( Hatfield & Epstein, 1985 ).

In the 1950s, following the introduction of computers and the dissemination of Shannon’s ideas about information, some psychologists began to take up information-theoretic quantifications of complexity and Prägnanz. Attneave (1954) ’s influential paper expressed the idea of simplicity in terms of “economy of perceptual description,” and for the first time compared Shannon’s formal information measure to human performance. Around the same time, Hochberg and McAlister (1953) quantified the complexity of a stimulus in a Kolmogorov-like way (a decade before Kolmogorov, Chaitin and Solomonoff), adding up the number of steps in the simplest generative procedure required to replicate a stimulus (e.g., the number of segments, turns, corners and bends required to recreate a given line drawing; see also Hochberg, 1964 ). They demonstrated that subjects shown an ambiguous figure preferred interpretations in inverse proportion to their complexity quantified in this manner.

The attempt to create a general complexity measure for perceptual interpretations reached a greater level of sophistication in the work of Leeuwenberg (1971) . Leeuwenberg, along with his followers in the tradition later known as Structural Information Theory, articulated a coding language based on pattern repetitions, symmetries, and, later, other kinds of regularities ( van der Helm, 2014 , 2015 ). Predictions derived from the theory have been used to account for various phenomena of visual completion (e.g. van Lier, Leeuwenberg, & van der Helm, 1995 ; van Lier, van der Helm, & Leeuwenberg, 1994 ) as well as motion interpretation ( Restle, 1979 ). While these results are impressive, it should be noted that complexity in a fixed coding language such as SIT’s cannot necessarily be assumed to be universal in the sense of Kolmogorov complexity unless the language has been shown to be able to express all visual patterns (including shading, color, texture, etc., which the SIT coding language does not usually include) which to the author’s knowledge never been demonstrated.

Regardless of the details of the complexity measure, the simplicity principle in visual perception has often been placed in opposition to the Likelihood principle (see Hatfield & Epstein, 1985 ; Pomerantz & Kubovy, 1986 ), which is the tendency of the visual system to see the most probable interpretation (see e.g. Boselie & Leeuwenberg, 1986 ; Leeuwenberg & Boselie, 1988 ; Moravec & Beck, 1986 ), which is in turn descended from notions of ecological probability in perception originated by Egon Brunswik ( Brunswik & Kamiya, 1953 ; Brunswik, 1956 ). However as discussed above, complexity minimiziation and probabilistic inference are now recognized to be closely aligned, and indeed not always clearly distinguishable from each other. In the perception literature this connection was first recognized in an influential paper by Chater (1996) , who argued that in many contexts the simplest visual interpretation is also the most likely to be veridical. The more specific connection between Bayesian inference and complexity minimization has been explored in a number of places since (e.g. Vitanyi & Li, 2000 ; Feldman, 2009 ).

The idea that the visual system chooses the simplest model consistent with visual input was expressed in a particularly memorable way in an influential paper by Adelson and Pentland (1996) . They imagined the process of scene model construction via a metaphor in which the scene must be created by a combination of a metalworker, a painter, and a lighting designer, each of whom charges fees for constructive operations such creating a surface, bending a surface, painting a surface, or adding a light source. The brain’s task is to construct a scene consistent with the image data for the least cost—in other words, to construct a scene model with minimum complexity in this particular “coding language.” Because the coding language is in principle capable of generating any observable scene (albeit possibly with an enormous number of surfaces and colors, etc.), this dollar cost is a close analog of the Kolmogorov complexity. Simple images are those that can be rendered via inexpensive models, and the simplest (cheapest) model of the image is the most likely hypothesis about what arrangement of surfaces in the real world actually generated it.

The role of simplicity in vision has been particularly prominent in relation to perceptual organization and vision, where it originated. The work of Leeuwenberg and his followers on simplicity in perceptual organization has already been mentioned. In the computational literature, Darrell, Sclaroff, and Pentland (1990) showed how the visual image can be parsed into coherent objects by choosing the decomposition with minimum DL. Feldman (1997) similarly showed how the most plausible grouping interpretation can be chosen via a suitably chosen complexity minimization. Similarly, configurations of dots are clustered in part based on simplicity criteria ( Gershman & Niv, 2013 ). Regardless of the specifics of the complexity measures, all these results suggest that the human visual system divides the image into coherent units in part based on the simplicity principle.

Similar principles govern how individual objects are represented. In the Bayesian shape representation framework of Feldman and Singh (2006) , individual shapes are parsed into individual parts by choosing the simplest (MDL) skeletal representation consistent with the bounding contour. A closely related complexity measure has been shown to influence the detectability of both open contours in noise ( Wilder, Feldman, & Singh, 2015a ) as well as closed contours, that is, whole shapes ( Wilder, Feldman, & Singh, 2015b ). Finally, a simplicity bias influences how the visual system interpets 3D structure in line drawings; the system apparently chooses the simplest 3D shape consistent with the configuration of line elements ( Y. Li & Pizlo, 2011 ).

Categorization and concept learning

The role of simplicity biases was recognized early in the machine learning literature (e.g. Medin, Wattenmaker, & Michalski, 1987 ; Iba, Wogulis, & Langley, 1988 ). Algorithmic approaches to learning have grown enoromously since then, diverging into a number of frameworks with their own complexity measures. PAC(“probably approximately correct”) learning, introduced by Valiant (1984) , often uses a complexity measure called VC (Vapnik-Chervonenkis) dimension (see Abu-Mostafa, 1989 ), which as with nearly all complexity measures relates to how model complexity needs to be constrained in order to ensure learnability. As mentioned above, statistical learning theory, including the theory of neural networks (e.g. Poggio, Rifkin, Mukherjee, & Niyogi, 2004 ) more generally assumes a tradeoff between model complexity and fit to training data in order to promote effective generalization.

In the psychological literature on concept learning, the role of simplicity was noticed early ( Neisser & Weene, 1962 ; Haygood, 1963 ; Looney & Haygood, 1968 ). Rosch (1978) articulated a “principle of cognitive economy” as a motivation for why the mind reflexively organizes the world into coherent categories. However this idea actually played relatively little role in the models that dominated the categorization literature for the next several decades, exemplar models (e.g. Nosofsky, 1986 ; Kruschke, 1992 ). Exemplar models assume that categorization is a by-product of the storage of specific examples, which are then used as standards against which to judge the category membership of future examples. Exemplar models do not have an overt simplicity bias, because they do not involve any abstraction process per se, although later analysis made it clear that they implicitly regularize to a degree modulated by certain parameter settings ( Jäkel, Schölkopf, & Wichmann, 2008 ; Briscoe & Feldman, 2006 ). Later “hybrid” (prototype plus exemplar) models, such as that of Nosofsky, Palmeri, and McKinley (1994) and others that followed, posited that learning proceeds by discovering collections of items that are well-described by a simple rule, which can be stored separately from “exceptional” (irregular) items; such a strategy obviously requires an overt simplicity bias. Pothos and Chater (2001) showed that unsupervised categorization too can be understood as a process of complexity minimization, using an MDL criterion that maximizes similarity within clusters and minimizes it between them. Similarly, Hahn, Chater, and Richardson (2003) showed how similarity , an essential construct in almost all categorization models, can be understood in terms of the Kolmogorov complexity of the transformation between objects.

The role of complexity in category learning has been studied the most directly in the context of Boolean categories, that is, categories built out of combinations of binary features ( Feldman, 2003 ). Because Boolean categories involve finite combinations of discrete features, it is possible to test them comprehensively, including every distinct logical type ( Shepard, Hovland, & Jenkins, 1961 ). In early work, several studies had suggested that the subjective difficulty of Boolean concepts could be tied to their logical complexity ( Haygood, 1963 ; Neisser & Weene, 1962 ). More recently, Feldman (2000) undertook a more comprehensive study incorporating a much larger set of concept types. The results show that subjects’ ability to learn Boolean concepts declines with their inherent logical complexity, suggesting (yet again) a bias towards simplicity in learning.

In its traditional definition (see Wegener, 1987 ), Boolean complexity is defined as the length (in variable names, or literals) of the shortest propositional formula equivalent to a given set of examples. For example, the propositional formula ( A ∧ B ) ∨ ( A ∧ B ′) describes two training examples, one with features A and B , the other with features A and not B . (In this notation, A and B are features, A ′ is the negation of feature A , ∧ means “and,” and ∨ means “or.”) This expression can be reduced to A ∧ ( B ∨ B ′) which in turn reduces to A , meaning that the original examples can be fully expressed simply by their common feature A ; its Boolean complexity is 1. By contrast, the examples ( A ∧ B ) ∨ ( A ′ ∧ B ′) cannot be reduced at all (it is “incompressible”) so its Boolean complexity is 4. This definition parallels that of Kolmogorov complexity, in that it quantifies the length of the shortest faithful representation of the original formula, and enjoys an analogous kind of universality: Boolean complexity is universal across logical bases (i.e. choice of connectives) up to a multiplicative factor.

The simplicity bias in Boolean concept learning has been corroborated in a number of studies, though there are still a number of distinct views about how to properly formulate the complexity measure ( Lafond, Lacouture, & Mineau, 2007 ; Vigo, 2009 ; Fass & Feldman, 2002 ; Mathy, 2010 ; Aitkin & Feldman, 2006 ; Feldman, 2006 ). Mathy and collaborators have measured complexity in terms of the length of a decision tree ( Mathy & Bradmetz, 2004 ), and even shown that response times can be tied to the process of decompression from a simplified representation ( Bradmetz & Mathy, 2008 ). Finally, Goodman, Tenenbaum, Feldman, and Griffiths (2008) were able to explain a large swath of concept learning data with a Bayesian model that assigns probability to logical formulae (i.e., models) in proportion to the length of their derivation in a context-free grammar, thus in effect favoring simple models over more complex ones.

Compressibility has a particularly direct connection with memory, because it is obviously advantageous for any memory system to compress material before storing it. The more information can be effectively compressed—that is, the lower its Kolmogorov complexity—the more can be stored. Mathy and Feldman (2012) demonstrated this connection fairly directly by manipulating the compressibility of digit sequences to be held by subjects in verbal short term memory (STM), for example including “runs” (chains of rising or falling digits, like 3-4-5-6-7 or 8-7-6) of various lengths. The more regular (and thus compressible) the sequence, the more digits ordinary subjects could retain correctly. The implication is that verbal STM incorporates an active compression or pattern-finding mechanism which allows it to minimize memory resources. Children, too, show an effect of compressibility, meaning that their verbal STM capacity also varies in direct proportion to the compressiblity of the material to be remembered ( Mathy, Fartoukh, Gauvrit, & Guida, 2016 ). Intriguingly, while their digit span rises with age, the effect of compressibility is apparently constant, meaning that as children develop they retain a fixed capacity to compress information.

A closely related debate involves the capacity of visual STM, the buffer in which visual information is briefly held. Like verbal STM, visual STM had traditionally been assumed to contain a fixed number (about 3 or 4) of slots without regard to information load. But this “fixed slots” view has been challenged in favor of a “continuous-resource” view in which memory resources are flexibly allocated depending on intrinsic information load. For example, several studies have found that the number of items stored depends on the complexity of each item ( Luria, Sessa, Gotler, Jolicoeur, & Dell’Acqua, 2010 ) or the precision with which each is represented ( Alvarez & Cavanagh, 2004 ), implying that capacity is bound by the total information content rather than by a fixed slot limit. Ma, Husain, and Bays (2014) provide a good recent summary, concluding that visual STM capacity depends on a continuous information limit rather than a set of discrete slots. All these findings relate directly to the simplicity bias, because they imply an underlying compression system in which information is reflexively represented in the most parsimonious way possible. Although the nature of the underlying neural code is not yet well understood, recent models assume a maximally compressed code that is efficient in the Shannon sense ( Sims, Jacobs, & Knill, 2012 ).

Causal reasoning

Another natural setting for a simplicity bias is in the inference of causal explanations from observations. When a doctor is confronted with a set of symptoms (say, fatigue, fever, and a runny nose) it is simpler and thus more reasonable to diagnose a single cause (the flu) rather than a set of distinct causes (anemia, sepsis, and seasonal allergies). Here again the simplicity bias can be described in Bayesian terms, as the assignment of a higher prior to a single cause than to a set of three distinct causes (which, being independent, would have a prior approximately proportional the third power of that of a single cause, a much lower number). The confidence inspired by a simple explanation of a complex set of facts ( Eureka! ) derives in part from the fact that it is unlikely for a simple theory to fit “by accident” ( Feldman, 2004 ). Accordingly, Little and Shiffrin (2009) found that subjects favor simple explanations of data (e.g., lower-degree polynomial models) over more complex (higher-degree) explanations. Similarly, studies of children’s explanations of causal relations have found that they too favor explanations that minimize the number of distinct causes ( Lombrozo, 2007 ; Bonawitz & Lombrozo, 2012 ).

Neuroscience

Finally, we briefly mention the role that complexity, and in particular minimization of coding length, plays in theoretical neuroscience. Barlow (1961) famously advocated efficient coding as a fundamental principle of sensory representation, arguing that the brain encodes sensory signals so as to minimize the redundancy of the raw stimulus array. This idea is essentially equivalent to the notion of compression later connected with Kolmogorov complexity, as it entails an Occam-like compression of the raw sensory signal in order to extract the regularities latent in the raw sensory signal.

Barlow’s idea has exerted a profound and far-reaching influence on neursocience in the decades since. The idea that neural networks extract regularity from sensory data, sometimes referred to as dimensionality reduction , is a central principle of theoretical neuroscience ( Hinton & Salakhutdinov, 2006 ). Similarly, neural receptive fields are now thought to be designed so as to optimally (that is, with maximal informational efficiency) encode visual stimuli ( Field, 1987 ). Another important development along the same lines is in the quantification of information along neural spike trains, which is based on the idea that the sequence of action potential constitutes an optimally efficient encoding of the information conveyed by sensory receptors ( Rieke, Warland, de Ruyter van Steveninck, & Bialek, 1996 ). All these developments have in common an Occam-like reduction of the complexity of the raw stimulus array in order to further the behavioral goals of the organism.

Finally note that the brain’s neural circuitry itself, viewed from the perspective of graph theory, seems to minimize circuit complexity and other computational costs ( Bullmore & Sporns, 2012 ). While this is admittedly speculative, it is possible that (in some not-yet-understood sense) the mental bias towards relatively simple interpretations of the world might be related to a neural bias towards simplicity in the underlying neural architecture.

As the many examples above illustrate, a bias towards simplicity pervades mental function. Examples can be found in perception, learning, categorization, reasoning, and neuroscience. Some of these findings involve Kolmogorov complexity directly; others involve information-theoretic concepts like DL (negative log probability) and information load; and others involve simplicity biases that arise in the context of Bayesian inference—all of which are closely related from a mathematical point of view. Broadly speaking, it may be that, as Hume first suggested, the mind cannot apprehend the world without assuming some form of underlying regularity, an idea sometimes called the “Principle of Natural Modes” ( Richards & Bobick, 1988 ).

However, notwithstanding the ubiquity of simplicity biases, it actually remains unclear whether Occam’s razor is, in fact, a primary driving principle of human inference. As discussed above, simplicity biases are deeply intertwined with—indeed scarcely separable from—information theory and Bayesian probability theory. From the point of view of modern theory, almost any form of rational inference will entail some kind of simplicity bias. Hence rather than being a foundational principle, the human simplicity bias may simply be an epiphenomenon of a more basic goal of mental function, such as veridicality ( Pizlo, Sawada, Li, Kropatsch, & Steinman, 2010 ), optimal estimation ( Knill, Kersten, & Yuille, 1996 ), or, perhaps most fundamentally, adaptive functionality ( Hoffman, Singh, & Prakash, 2015 ).

Acknowledgments

This research was supported by NIH (NEI) Grant EY021494. I am grateful to Manish Singh and the member of the Visual Cognition Lab for many stimulating discussions.

1 Occam himself was arguing against the existence of “universals” (i.e., generalizations), maintaining that one should not posit the existence of entities beyond those that can be directly observed; see Hannam, 2009 .

2 Kolmogorov complexity is uncomputable for essentially the same reason there is no “smallest uninteresting number”—if there were, that would indeed be very interesting. (See Chaitin, 1974 on what Bertrand Russell called the Berry paradox.) Similarly, if there were a computable procedure for computing K ( S ), then some strings of complexity at least K ( S ) could be reproduced by a program of the form “Print the shortest string with complexity K ( S ).” Such a program would take fewer than K ( S ) characters to encode—a contradiction. See Schöning and Pruim (1998) for a more careful discussion.

Abu-Mostafa YS. The Vapnik-Chervonenkis dimension: Information versus complexity in learning. Neural Computation. 1989; 1 :312–317. [ Google Scholar ]
Adelson EH, Pentland AP. The perception of shading and reflectance. In: Knill David C, Richards W, editors. Perception as Bayesian inference. Cambridge: Cambridge University Press; 1996. pp. 409–423. [ Google Scholar ]
Aitkin CD, Feldman J. Subjective complexity of categories defined over three-valued features; Proceedings of the 28th conference of the cognitive science society; 2006. pp. 961¢–966¢. [ Google Scholar ]
Alvarez GA, Cavanagh P. The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychol Sci. 2004; 15 (2):106–111. [ PubMed ] [ Google Scholar ]
Attneave F. Some informational aspects of visual perception. Psychological Review. 1954; 61 :183–193. [ PubMed ] [ Google Scholar ]
Barlow HB. In: Possible principles underlying the transformation of sensory messages. Rosenblith WA, editor. Cambridge: M.I.T. Press; 1961. pp. 217–234. [ Google Scholar ]
Bonawitz EB, Lombrozo T. Occam’s rattle: children’s use of simplicity and probability to constrain inference. Developmental Psychology. 2012; 48 (4):1156–1164. [ PubMed ] [ Google Scholar ]
Boselie F, Leeuwenberg E. A test of the minimum principle requires a perceptual coding system. Perception. 1986; 15 :331–354. [ PubMed ] [ Google Scholar ]
Boselie F, Wouterlood D. The minimum principle and visual pattern completion. Psychological Research. 1989; 51 :93–101. [ PubMed ] [ Google Scholar ]
Bradmetz J, Mathy F. Response times seen as decompression times in Boolean concept use. Psychological Research. 2008; 72 :211–234. [ PubMed ] [ Google Scholar ]
Briscoe E, Feldman J. Conceptual complexity and the bias-variance tradeoff; Proceedings of the conference of the cognitive science society; 2006. pp. 1038–1043. [ Google Scholar ]
Brunswik E. Perception and the representative design of psychological experiments. Berkeley: University of California Press; 1956. [ Google Scholar ]
Brunswik E, Kamiya J. Ecological cue-validity of ‘proximity’ and of other Gestalt factors. American Journal of Psychology. 1953; 66 :20–32. [ PubMed ] [ Google Scholar ]
Bullmore E, Sporns O. The economy of brain network organization. Nature Reviews Neuroscience. 2012; 13 (5):336–349. [ PubMed ] [ Google Scholar ]
Burnham KP, Anderson DR. Model selection and multi-model inference: A practical information-theoretic approach. New York: Springer; 2002. [ Google Scholar ]
Chaitin GJ. On the length of programs for computing finite binary sequences. Journal of the Association for Computing Machinery. 1966; 13 :547–569. [ Google Scholar ]
Chaitin GJ. Information-theoretic computational complexity. IEEE Transactions on Information Theory. 1974; IT-20 (1):10–15. [ Google Scholar ]
Chater N. Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review. 1996; 103 (3):566–581. [ PubMed ] [ Google Scholar ]
Chater N. Simplicity and the mind. Psychologist. 1997; 10 (11):495–498. [ Google Scholar ]
Chater N, Vitányi P. Simplicity: a unifying principle in cognitive science. Trends in Cognitive Sciences. 2003; 7 (1):19–22. [ PubMed ] [ Google Scholar ]
Darrell T, Sclaroff S, Pentland A. Proceedings third international conference on computer vision. Los Alamitos, CA: IEEE Computer Society Press; 1990. Segmentation by minimal description. [ Google Scholar ]
Duda RO, Hart PE, Stork DG. Pattern classification. New York: John Wiley and Sons, Inc; 2001. [ Google Scholar ]
Edwards AWF. Likelihood. Cambridge: Cambridge University Press; 1972. [ Google Scholar ]
Fass D, Feldman J. Categorization under complexity: a unified MDL account of human learning of regular and irregular categories. In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing 15. Cambridge: MIT Press; 2002. [ Google Scholar ]
Feldman J. Regularity-based perceptual grouping. Computational Intelligence. 1997; 13 (4):582–623. [ Google Scholar ]
Feldman J. Minimization of Boolean complexity in human concept learning. Nature. 2000; 407 :630–633. [ PubMed ] [ Google Scholar ]
Feldman J. The simplicity principle in human concept learning. Current Directions in Psychological Science. 2003; 12 (6):227–232. [ Google Scholar ]
Feldman J. How surprising is a simple pattern? Quantifying “Eureka!” Cognition. 2004; 93 :199–224. [ PubMed ] [ Google Scholar ]
Feldman J. An algebra of human concept learning. Journal of Mathematical Psychology. 2006; 50 :339–368. [ Google Scholar ]
Feldman J. Bayes and the simplicity principle in perception. Psychological Review. 2009; 116 (4):875–887. [ PubMed ] [ Google Scholar ]
Feldman J. Bayesian models of perceptual organization. In: Wagemans J, editor. Handbook of perceptual organization. Oxford: Oxford University Press; 2014. [ Google Scholar ]
Feldman J, Singh M. Bayesian estimation of the shape skeleton. Proceedings of the National Academy of Sciences. 2006; 103 (47):18014–18019. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Field DJ. Relations between the statistics of natural images and the response properties of cortical cells. J. Optical Society of America A. 1987; 4 (12):2379–2394. [ PubMed ] [ Google Scholar ]
Gauvrit N, Singmann H, Soler-Toscano F, Zenil H. Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method. Behavioral Research Methods. 2016; 48 (1):314–329. [ PubMed ] [ Google Scholar ]
Geman S, Bienenstock E, Doursat R. Neural networks and the bias/variance dilemma. Neural computation. 1992; 4 :1–58. [ Google Scholar ]
Gershman SJ, Niv Y. Perceptual estimation obeys Occam’s razor. Frontiers in Psychology. 2013; 4 :623. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Goodman ND, Tenenbaum JB, Feldman J, Griffiths TL. A rational analysis of rule-based concept learning. Cognitive Science. 2008; 32 (1):108–154. [ PubMed ] [ Google Scholar ]
Grünwald PD. A tutorial introduction to the minimum description length principle. In: Grünwald PD, Myung IJ, Pitt M, editors. Advances in minimum description length: Theory and applications. Cambridge, MA: MIT press; 2005. [ Google Scholar ]
Hahn U, Chater N, Richardson LB. Similarity as transformation. Cognition. 2003; 87 :1–32. [ PubMed ] [ Google Scholar ]
Hannam J. God’s philosophers: How the medieval world laid the foundations of modern science. Icon Books; 2009. [ Google Scholar ]
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer; 2001. [ Google Scholar ]
Hatfield G, Epstein W. The status of the minimum principle in the theoretical analysis of visual perception. Psychological Bulletin. 1985; 97 (2):155–186. [ PubMed ] [ Google Scholar ]
Haygood RC. Unpublished doctoral dissertation. University of Utah; 1963. Rule and attribute learning as aspects of conceptual behavior. [ Google Scholar ]
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006; 313 (5786):504–507. [ PubMed ] [ Google Scholar ]
Hochberg J. Perception. New Jersey: Pretince-Hall; 1964. [ Google Scholar ]
Hochberg J, McAlister E. A quantitative approach to figural “goodness” Journal of Experimental Psychology. 1953; 46 :361–364. [ PubMed ] [ Google Scholar ]
Hoffman DD, Singh M, Prakash C. The interface theory of perception. Psychonomic Bulletin and Review. 2015; 22 (6):1480–1506. [ PubMed ] [ Google Scholar ]
Iba W, Wogulis J, Langley P. Proceedings of the fifth international conference on machine learning. Ann Arbor, MI; 1988. Trading off simplicity and coverage in incremental concept learning; p. 7379. [ Google Scholar ]
Jäkel F, Schölkopf B, Wichmann F. Generalization and similarity in exemplar models of categorization: Insights from machine learning. Psychonomic Bulletin and Review. 2008; 15 (2):256–271. [ PubMed ] [ Google Scholar ]
Jeffreys H. Theory of probability. third. Oxford: Clarendon Press; 1939/1961. [ Google Scholar ]
Kanizsa G. Organization in vision: essays on Gestalt perception. New York: Praeger Publishers; 1979. [ Google Scholar ]
Knill DC, Kersten D, Yuille A. Introduction: a Bayesian formulation of visual perception. In: Knill DC, Richards W, editors. Perception as Bayesian inference. Cambridge: Cambridge University Press; 1996. pp. 123–162. [ Google Scholar ]
Koffka K. Principles of Gestalt psychology. New York: Harcourt; 1935. [ Google Scholar ]
Kolmogorov AN. Three approaches to the quantitative definition of information. Problems of Information Transmission. 1965; 1 (1):1–7. [ Google Scholar ]
Krauss LM. Quantum man: Richard feynman’s life in science. New York: Atlas & Co; 2011. [ Google Scholar ]
Kruschke J. ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review. 1992; 99 (1):22–44. [ PubMed ] [ Google Scholar ]
Lafond D, Lacouture Y, Mineau G. Complexity minimization in rule-based category learning: Revising the catalog of Boolean concepts and evidence for non-minimal rules. Journal of Mathematical Psychology. 2007; 51 :57–74. [ Google Scholar ]
Leeuwenberg ELJ. A perceptual coding language for visual and auditory patterns. American Journal of Psychology. 1971; 84 (3):307–349. [ PubMed ] [ Google Scholar ]
Leeuwenberg ELJ, Boselie F. Against the likelihood principle in visual form perception. Psychological Review. 1988; 95 :485–491. [ PubMed ] [ Google Scholar ]
Li M, Vitányi P. An introduction to Kolmogorov complexity and its applications. New York: Springer; 1997. [ Google Scholar ]
Li Y, Pizlo Z. Depth cues versus the simplicity principle in 3D shape perception. Topics in Cognitive Science. 2011; 3 (4):667–685. [ PubMed ] [ Google Scholar ]
Little DR, Shiffrin RM. Simplicity bias in the estimation of causal functions; Proceedings of the 31st annual conference of the cognitive science society; 2009. pp. 1157–1162. [ Google Scholar ]
Lombrozo T. Simplicity and probability in causal explanation. Cognitive Psychology. 2007; 55 (3):232–257. [ PubMed ] [ Google Scholar ]
Looney NJ, Haygood RC. Effects of number of relevant dimensions in disjunctive concept learning. Journal of Experimental Psychology. 1968; 78 (1):169–171. [ PubMed ] [ Google Scholar ]
Luria R, Sessa P, Gotler A, Jolicoeur P, Dell’Acqua R. Visual short-term memory capacity for simple and complex objects. J Cogn Neurosci. 2010; 22 (3):496–512. [ PubMed ] [ Google Scholar ]
Ma WJ, Husain M, Bays PM. Changing concepts of working memory. Nat. Neurosci. 2014; 17 (3):347–356. [ PMC free article ] [ PubMed ] [ Google Scholar ]
MacKay DJC. Information theory, inference, and learning algorithms. Cambridge: Cambridge University Press; 2003. [ Google Scholar ]
Mathy F. Assessing conceptual complexity and compressibility using information gain and mutual information. Tutorials in Quantitative Methods for Psychology. 2010; 6 (1):16–30. [ Google Scholar ]
Mathy F, Bradmetz J. A theory of the graceful complexification of concepts and their learnability. Current Psychology of Cognition. 2004; 22 :41–82. [ Google Scholar ]
Mathy F, Fartoukh M, Gauvrit N, Guida A. Developmental abilities to form chunks in immediate memory and its non-relationship to span development. Frontiers in Psychology. 2016; 7 :201. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Mathy F, Feldman J. What’s magic about magic numbers? Chunking and data compression in short-term memory. Cognition. 2012; 122 (3):346–362. [ PubMed ] [ Google Scholar ]
Medin DL, Wattenmaker WD, Michalski RS. Constraints and preferences in inductive learning: an experimental study of human and machine performance. Cognitive Science. 1987; 11 :299–339. [ Google Scholar ]
Moravec L, Beck J. A modal completion: simplicity is not the explanation. Bulletin of the Psychonomic Society. 1986; 24 (4):269–272. [ Google Scholar ]
Neisser U, Weene P. Hierarchies in concept attainment. Journal of Experimental Psychology. 1962; 64 (6):640–645. [ PubMed ] [ Google Scholar ]
Nosofsky RM. Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General. 1986; 115 (1):39–61. [ PubMed ] [ Google Scholar ]
Nosofsky RM, Palmeri TJ, McKinley SC. Rule-plus-exception model of classification learning. Psychological Review. 1994; 101 (1):53–79. [ PubMed ] [ Google Scholar ]
Pizlo Z, Sawada T, Li Y, Kropatsch WG, Steinman RM. New approach to the perception of 3D shape based on veridicality, complexity, symmetry and volume. Vision Res. 2010; 50 (1):1–11. [ PubMed ] [ Google Scholar ]
Poggio T, Rifkin R, Mukherjee S, Niyogi P. General conditions for predictivity in learning theory. Nature. 2004; 428 :419–422. [ PubMed ] [ Google Scholar ]
Pomerantz JR, Kubovy M. Theoretical approaches to perceptual organization. In: Boff KR, Kaufman L, Thomas JP, editors. Handbook of perception and human performance, vol. 2: Cognitive processes and performance. New York: John Wiley & Sons; 1986. pp. 36-1–36-46. [ Google Scholar ]
Popper KR. The logic of scientific discovery. New York: Basic Books; 1934/1959. [ Google Scholar ]
Pothos EM, Chater N. Categorization by simplicity: a minimum description length approach to unsupervised clustering. In: Hahn U, Ramscar M, editors. Similarity and categorization. Oxford: Oxford; 2001. pp. 51–72. [ Google Scholar ]
Quine WVO. On simple theories of a complex world. In: Foster MH, Martin ML, editors. Probability, confirmation, and simplicity: Readings in the philosophy of inductive logic. New York: Odyssey Press; 1965. pp. 250–252. [ Google Scholar ]
Restle F. Coding theory of the perception of motion configurations. Psychological Review. 1979; 86 (1):1–24. [ PubMed ] [ Google Scholar ]
Richards WA, Bobick A. Playing twenty questions with nature. In: Pylyshyn Z, editor. Computational processes in human vision: An interdisciplinary perspective. Norwood, NJ: Ablex Publishing Corporation; 1988. pp. 3–26. [ Google Scholar ]
Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes: exploring the neural code. Cambridge: M.I.T. Press; 1996. [ Google Scholar ]
Rissanen J. Stochastic complexity in statistical inquiry. Singapore: World Scientific; 1989. [ Google Scholar ]
Rosch E. Principles of categorization. In: Rosch E, Lloyd B, editors. Cognition and categorization. Lawrence Erlbaum; 1978. [ Google Scholar ]
Schöning U, Pruim R. Gems of theoretical computer science. Berlin: Springer; 1998. [ Google Scholar ]
Shannon C. A mathematical theory of communication. The Bell System Technical Journal. 1948; 27 :379–423. [ Google Scholar ]
Shepard RN, Hovland CL, Jenkins HM. Learning and memorization of classifications. Psychological Monographs: General and Applied. 1961; 75 (13):1–42. [ Google Scholar ]
Sims CR, Jacobs RA, Knill DC. An ideal observer analysis of visual working memory. Psychol Rev. 2012; 119 (4):807–830. [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sober E. Simplicity. London: Oxford University Press; 1975. [ Google Scholar ]
Solomonoff R. A formal theory of inductive inference: part II. Information and Control. 1964; 7 :224–254. [ Google Scholar ]
Tenenbaum JB, Griffiths TL. Generalization, similarity, and Bayesian inference. Behavioral and brain sciences. 2001; 24 (4):629–640. [ PubMed ] [ Google Scholar ]
Turing AM. On computable numbers, with an application to the entscheidungs problem. Proceedings of the London Mathematical Society. 1937; 2 (42):230–265. [ Google Scholar ]
Valiant L. A theory of the learnable. Communications of the ACM. 1984; 27 (11):1134–1142. [ Google Scholar ]
van der Helm PA. Simplicity in perceptual organization. In: Wagemans J, editor. Handbook of perceptual organization. Oxford: Oxford University Press; 2014. [ Google Scholar ]
van der Helm PA. Simplicity in vision. Cambridge: Cambridge University Press; 2015. [ Google Scholar ]
van Lier RJ, Leeuwenberg ELJ, van der Helm PA. Multiple completions primed by occlusion patterns. Perception. 1995; 24 :727–740. [ PubMed ] [ Google Scholar ]
van Lier RJ, van der Helm P, Leeuwenberg ELJ. Integrating global and local aspects of visual occlusion. Perception. 1994; 23 (8):883–903. [ PubMed ] [ Google Scholar ]
Vigo R. Categorical invariance and structural complexity in human concept learning. Journal of Mathematical Psychology. 2009; 53 :203–221. [ Google Scholar ]
Vitanyi PMB, Li M. Minimum description length induction, Bayesianism, and Kolmogorov complexity. IEEE Transactions on information theory. 2000; 46 (2):446–464. [ Google Scholar ]
Wallace CS. Statistical and inductive inference by minimum message length. Springer; 2004. [ Google Scholar ]
Wegener I. The complexity of Boolean functions. Chichester: John Wiley & Sons; 1987. [ Google Scholar ]
Wilder J, Feldman J, Singh M. Contour complexity and contour detection. Journal of Vision. 2015a; 15 (6) [ PMC free article ] [ PubMed ] [ Google Scholar ]
Wilder J, Feldman J, Singh M. The role of shape complexity in the detection of closed contours. Vision Research. 2015b [ PubMed ] [ Google Scholar ]
Yuille AL, Bülthoff HH. Bayesian decision theory and psychophysics. In: Knill DC, Richards W, editors. Perception as Bayesian inference. Cambridge: Cambridge University Press; 1996. pp. 123–162. [ Google Scholar ]
Ziv J, Lempel A. Auniversal algorithm for sequential data compression. IEEE Transactions on Information Theory. 1977; 23 (8):337–343. [ Google Scholar ]

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Article Contents

Study 1: the effects of when-similarity, study 2: when-similarity and who-similarity, general discussion, about the authors.

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Extending the Similarity-Attraction Effect: The Effects of When-Similarity in Computer-Mediated Communication *

Accepted by previous editor Maria Bakardjieva

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Maurits Kaptein, Deonne Castaneda, Nicole Fernandez, Clifford Nass, Extending the Similarity-Attraction Effect: The Effects of When-Similarity in Computer-Mediated Communication, Journal of Computer-Mediated Communication , Volume 19, Issue 3, 1 April 2014, Pages 342–357, https://doi.org/10.1111/jcc4.12049

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The feeling of connectedness experienced in computer-mediated relationships can be explained by the similarity-attraction effect (SAE). Though SAE is well established in psychology, the effects of some types of similarity have not yet been explored. In 2 studies, we demonstrate similarity-attraction based on the timing of activities—“when-similarity.” We describe a novel experimental paradigm for manifesting when-similarity while controlling for the activities being performed (what-similarity). Study 1 (N = 24) shows when-similarity attraction in the evaluation of connectedness with others. Study 2 (N = 42) identifies an interaction between who-similarity—similarity in personal backgrounds—and when-similarity. Both studies show that real-time computer-mediated interaction can lead to greater feelings of connectedness between people when there is an opportunity to discover when-similarity.

In their early stages, social networking sites (SNSs) focused primarily on sharing personal information via user profiles, enabling users to discover similarities in demographics, interests, and attitudes. A great deal of empirical evidence indicates that when users discover these types of similarities, even when this discovery is mediated, they become more attracted to each other ( Montoya et al., 2008 )—the so-called similarity-attraction effect ( Byrne, 1971 ). Previous similarity-attraction effect manipulations include personality traits ( Banikiotes & Neimeyer, 1981 ; Bleda, 1974 ), attitudes (Yeong Tan & Singh, 1995 ), ethnic backgrounds ( Hu et al., 2008 ), facial features ( Bailenson et al., 2008 ), and voice features ( Nass & Brave, 2005 ), among others.

Similarity-attraction has been a topic of investigation for more than half a century, but the recent growth of mediated communication inspires new questions regarding similarity and interpersonal attraction. In this paper, we explore a previously unstudied type of interpersonal similarity: similarity in the timing of activities. That is, beyond knowing that someone else holds the same attitudes as oneself, shares a common background, or performs the same activities, is similarity-attraction amplified when two people know that they are doing the same activity at the same time? With the dramatic growth of real-time applications—such as Facebook and Twitter—one can be presented with contemporaneity information without actively interacting with the other person. Does this computer-mediated simultaneity influence people's feelings of connectedness and belonging? Does similarity-attraction extend to similarity in the timing of activities?

Background on The Similarity-Attraction Effect

The empirical evidence for similarity-attraction is so compelling that Byrne and Rhamey ( 1965 ) early on labeled the positive relationship between respondents' similarity and the attraction between respondents the Law of Attraction . After numerous replications in multiple domains using different similarity manipulations, Berger ( 1975 ) proclaimed that similarity-attraction is “one of the most robust relationships in all of the behavioral sciences.”

The explanations for the origin of similarity-attraction are, however, multifold and often disputed. Initially, no explanations for the effect were offered, and similarity-attraction was regarded to be self-evident—as we see even today by the lack of explanations for similarity-attraction in many social psychology textbooks ( Heine et al., 2009 ). When explanations are proposed, one of the most popular views is based on people's innate desire to be consistent with societal norms and values. This explanation assumes that the discovery of interpersonal similarity leads to the validation of one's own characteristics and views by providing consensus support ( Byrne & Clore, 1970 ). The validation then leads to a higher perceived appropriateness of one's current beliefs, attitudes, behaviors, and traits.

Byrne ( 1971 ) and Clore and Byrne ( 1974 ) extended this explanation in their formulation of the reinforcement-affect model. This model is based on the assumptions that: (a) people experience stimuli as rewarding or punishing and seek out those that are rewarding, (b) positive feelings—affect—are associated with rewarding stimuli, and (c) other people are liked or disliked according to their association with rewarding or punishing stimuli. That is, we learn to associate positive feelings with people that are linked to rewards. Instances of interpersonal similarity function as rewarding stimuli, which leads people to associate positive feelings with similar others, which in turn leads people to be more attracted to similar others.

Another popular explanation for similarity-attraction is the emergence of positive feelings stemming from smooth and rewarding interactions, which are more likely to arise when communicating with people who are similar to oneself ( Berscheid & Walster, 1978 ) than when communicating with dissimilar others. While this explanation is similar to the explanation of Clore and Byrne ( 1974 ) in its emphasis on rewards experienced by positive stimuli, Berscheid & Walster ( 1978 ) specifically focus on the smooth interactions that are likely to occur with similar others as opposed to the positive feelings stemming from discovering instances of interpersonal similarity. Through these smooth interactions with others, similarity-attraction partly satisfies a person's “Need to Belong” ( Baumeister, 1995 ).

Other explanations put forward for similarity-attraction include enhanced reciprocal liking towards similar others ( Condon & Crano, 1988 ) or a desire to satisfy implicit egoism ( Jones et al., 2004 ). One final explanation for the similarity-attraction effect inverts these arguments: A person's default state is to like everyone, and dissimilarity leads to repulsion ( Rosenbaum, 1986 ). Rosenbaum supports this view by showing several experiments in which the standard experimental paradigm of the SAE is extended by adding a “no-interaction” control group. His work shows that attraction ratings in the no-interaction and in the similar other groups are comparable, while the ratings in the nonsimilar other group are significantly lower.

Despite the overwhelming empirical evidence supporting the existence of similarity-attraction as well as the numerous plausible mechanisms identified to explain it, several questions have been raised regarding the importance and integrity of the effect. Some authors have discounted the effect as merely resulting from demand characteristics operating in experiments ( Sunnafrank, 1991 ) or other methodological flaws ( Bochner, 1991 ). Morry ( 2007 ) questions the claimed causality of similarity-attraction, and Sunnafrank and Miller ( 1981 ) demonstrate that similarity-attraction is diminished greatly when allowing for initial interactions between participants in laboratory studies. Overall, these results lead critics to conclude that similarity-attraction only exists in a laboratory setting using ad-hoc dyads and is of no practical importance in a real-world setting. Meta-analysis of numerous SAE studies both within and outside of the laboratory indeed shows that while similarity manipulations have strong effects on attraction in laboratory settings, these effects are limited in real long-term relationships ( Montoya et al., 2008 ). In the next section, we will explain why these criticisms do not discredit similarity-attraction as an important psychological effect in real-time computer-mediated interactions.

Types of Similarity in New Media

The most common laboratory paradigm in similarity-attraction research is the phantom-other technique ( Smith, 1957 ; Byrne, 1961 ). In laboratory experiments using the phantom-other technique, the target is often unknown to participants. Participants are then presented with details about the nonpresent target, such as age, personality, judgments, or social status. This phantom-other technique leads to the strongest similarity-attraction effects.

Coincidentally, these laboratory characteristics frequently hold for a wide variety of computer-mediated communications that take place in new media and SNSs: A large number of “friends” in people's social networks are relatively unfamiliar, not physically present, and their profiles present details not commonly discovered in face-to-face interactions. Thus, while similarity-attraction has been hard to replicate outside of the laboratory, the situation is now reversed: Real life, through SNSs and other mediated communication, has replicated the laboratory conditions in which similarity-attraction was first observed. It is thus plausible that the laboratory studies that support the similarity-attraction effect will possess high external validity in these new media contexts.

There are many types of similarity encountered by people when using new media like SNSs. Initially, SNSs enabled people to explore and experience “who-similarity": similarity in demographic features such as ethnic background or religious affiliation. As personal profiles and information streams on SNSs grew, people were also able to discover “what-similarity": similarity in attitudes, activities, and hobbies. The use of social GPS tracking, as is done by applications like Foursquare, has even increased the salience of “where-similarity": similarity in location.

Recent advancements—specifically, real-time social technologies—have created the ability to discover a new type of similarity that has not been previously examined either in the laboratory or in the field: when-similarity. While it was already possible to connect with remote others in real-time since the emergence of chat rooms and instant messaging (see e.g. Baker, 2008 ), experiencing co-occurrence of (remote) activities in time without an explicit conversation or shared activity is relatively novel. In this paper, we create a situation in which co-occurrence of remote activities is experienced without introducing additional confounds that would naturally arise during an explicit conversation.

Real-time services like Facebook and Twitter enable users to experience similarity with others in the timing of activities. With millions of people broadcasting their current activity status in real-time repeatedly during the day, it is highly likely users will experience some form of when-similarity: Users discover that at the point in time that they are carrying out a specific activity, someone in their extended social network is carrying out that same activity, without an active conversation or conscious joint activity of the two parties involved. Due to the growing prominence of real-time services both on the web as well as on other devices such as mobile phones, it is worthwhile to explore whether this new type of similarity also enhances attraction and thus supports the similarity-attraction effect.

Overview of the Studies

In this paper, we describe two studies in which when-similarity (Study 1) and both when- and who-similarity (Study 2) are manipulated. The effects on the participant's perception of a target other are measured. Based on the strong evidence that supports similarity-attraction in settings similar to those experienced by users of real-time services, we hypothesize that similarity-attraction will also hold for this new type of similarity. That is, we expect that users who discover when-similarity with a target through computer-mediated communication channels will feel closer to the target. This expectation is already supported by a number of sociological investigations into the effects of the timing of events such as religious festivities: When a group of individuals perform an activity or ritual at the same time, simultaneity plays a key role in the formation of feelings of connectedness within the social group. ( Durkheim, 1912 / 2008 ; Horton, 1967 ; Lee & Liebenau, 2000 ; Zerubavel, 1982 ).

In both of the presented studies, we use a novel method to manipulate when-similarity while controlling for other types of similarity. We are the first to isolate the effects of the timing of activities from confounds such as the type of activity being performed. The mediated nature of the interactions in the experiment enables us to manipulate the timing of activities while keeping the pattern of activity types constant and while avoiding confounds caused by physical proximity. Our manipulation of when-similarity differs from traditional experimental manipulations in that the two experimental groups—those high and low in when-similarity—are created dynamically based on the behavior of participants. We explain this method in detail.

This study tests the effects of when-similarity—while controlling for other types of similarity—on the participants' evaluations of others. Interpersonal attraction, after a weeklong intervention manipulating when-similarity, was measured using both social connectedness and intimacy scales. When-similarity was manipulated between participants.

Participants

Twenty-four United States college students participated in this balanced, between-participants experiment. Thirteen (54.2%) participants were female, and gender was balanced as much as possible across the conditions. The average age of respondents was 20.1 (S D = 1.61) years. Participants received partial course credit for their participation.

First, participants were asked to complete a brief personal profile, which asked for their gender, age, area(s) of academic study, and favorite pastimes. Participants were then ostensibly partnered with another participant of the same gender. Participants were provided with the name of their partner for the duration of the experiment, and no other information about the partner was disclosed. In reality, participants' partners were not actual participants, and partners' names were selected to provide a perceived gender match with the participant.

Next, participants received six text messages per day on their mobile phones over the course of five days. Each message contained the question: “What are you doing right now?” Participants were instructed to reply with a number from 1 to 5, which represented different behavioral categories: 1: Eating , 2: Studying , 3: Physical Activity , 4: Relaxing , and 5: Working . These categories were pretested to resonate with the participant population and were found exclusive as well as exhaustive: a 2-day pretest showed that for each moment in the day that they were queried, students ( N = 11) were able to pick exclusively one of the five provided categories as their current activity.

A few minutes after responding to the text message, participants received a follow-up message stating the activity their partner was ostensibly performing at that same point in time, and whether their own activities matched their partner's. Participants were not provided with any information about their partner other than these activity messages. After 5 days of text messages, summing to 5 × 6 = 30 messages, participants answered an online questionnaire to evaluate their partner and their perceived relationship with their partner.

We chose text messaging as our medium, as opposed to an SNS, since respondents would have direct access to their devices to be able to report on their activities at the moment the messages were received. Furthermore, the text messages allowed us to fully control the conversation with the ostensible other with less risk of directed online searches to get in contact via other means. Thus, text messaging was chosen mainly for methodological ease. We do not feel our results are restricted to text messaging but rather are representative of a much broader class of mediated real-time interactions.

When-Similarity Manipulation

To manipulate when-similarity while controlling for other types of similarity, half of the participants were assigned to the Similar Timing condition and half were assigned to the Dissimilar Timing condition. In both conditions, to keep constant what the partner was doing, the ostensible partner responded in such a way that after all six text messages had been sent for a specific day, the partner had studied twice and performed each of the other behavioral categories only once. This constraint on the activity pattern of the ostensible partner was imposed to prevent the actual activity from influencing the perceptions of the partner. By providing the same “activity profile” in both of our conditions, we minimize the effect of what-similarity.

The two conditions differed only in when the participants were told their partners were performing these activities. In the Dissimilar Timing condition, the response, when possible, consisted of a different behavioral category than the one performed by the participant at that point in time. For example, if the participant indicated she was “eating” at the time she received a message, the response message would be any (random) behavioral category other than eating—unless all other behavioral categories had already been exhausted that day, leaving “eating” to be the only valid remaining response. In the Similar Timing condition, the response was, when possible, in the same behavioral category. Since no background information about the participants' ostensible partners was presented, this experimental setup also controlled for possible confounds of who- or where-similarity (beyond the matching gender).

Validity of the When-Similarity Manipulation

Something to keep in mind is that ostensible partner responses generated by our algorithm depend on the activities that are performed by our participants. Thus, to evaluate the validity of our when-similarity manipulation, we need to determine whether there would always be a difference between the number of simultaneous activity occurrences for the Similar and Dissimilar Timing groups regardless of whether our participants' overall activity pattern were different. We addressed this by conducting simulations of our response algorithm for different possible participant activity patterns. For N = 20 participants per simulation ( M = 1000), we took six draws from a five-category multinomial—corresponding to the six activity messages sent daily. By changing the initial probabilities of the five activity categories, we were able to test the consistency of our algorithm's responses to different participant activity patterns (i.e., what are the generated responses from the ostensible partner if the participant performs the same activity at all times?).

Figure 1 shows the simulation results for the actual observed activity probabilities in our study (Row 1), a flat activity pattern in which the simulated participants performed each of the activities equally often (Row 2), and a severely skewed distribution of activities in which simulated participants spent far more time studying than performing any of the other activities (row 3). The distribution of responses by the ostensible partner (Column 2) is the same for all simulations due to the restricted daily activity pattern implemented to control for what-similarity (i.e., study twice a day and perform all other categories only once). Column 3 shows the distribution of the percentage of simultaneous activities in the Similar Timing and the Dissimilar Timing conditions—that is, the times in the simulation when the activities of the participant and the ostensible partner would match. In each of the simulated scenarios, including the severely skewed scenario (Row 3, Column 3), the number of matches in activity responses produced by the algorithm differed significantly between the Dissimilar Timing and Similar Timing conditions. Thus, our when-similarity manipulation creates distinct numbers of activity matches between the Similar Timing and the Dissimilar Timing groups while controlling for what-similarity even in situations where the activities of the participant are skewed 1 .

Evaluation by means of simulation of the when-similarity algorithm for different true population distributions of activities. The column on the right shows the distribution of the number of matches over all simulated experiments. Rows show different participant activity patterns: Row 1 shows the actual activity pattern of our participants, Row 2 shows an evenly distributed activity pattern, and Row 3 shows a highly skewed pattern. Even in the last case, the response generated by our matching algorithm led to a significant difference in the number of matches between the two conditions.

In this study, the activity of the ostensible partner matched that of the participant 50.6% of the time in the Similar Timing condition and 3.0% of the time in the Dissimilar Timing condition. Thus, the actual number of instances of experienced when-similarity in the two conditions is in accordance with those produced in the simulations.

Participants evaluated their partner after the one-week manipulation using three rating scales. The first scale was a 6-item, 7-point Social Connectedness Scale (Cronbach's α = 0.94) (Van Bel et al., 2009 ). The endpoints of the items were labeled “(1) Totally disagree” to “(7) Totally agree.” This social connectedness scale consisted of items addressing the feelings of closeness and shared thoughts between the participant and their partner (e.g. “ I often know what my partner feels ” and “ I feel that my partner often knows what I think ”).

The second scale was the Inclusion of the Other in the Self (IOS) Scale. The IOS measures perceived intimacy ( Aron et al., 2006 ) using a single, 7-point pictorial item. Each of the pictures shows two circles labeled “You” and “Your Partner.” In each picture, the circles overlap more and more—from nontouching to almost fully overlapping. Participants are asked which of the pictures most closely represents the relationship with their ostensible partner.

Finally, participants were asked to state how much they agreed with the statement “ I would like to meet my partner ” on a 7-point scale. The end-points of this scale—which was specifically designed for the purposes of this experiment—were labeled “(1) Completely disagree” to “(7) Completely agree.”

A MANOVA with when-similarity as an independent factor and the three attitudinal measures as dependent factors showed a strong multivariate main effect of when-similarity-attraction on the overall partner evaluations, F(3,20) = 10.58, p < 0.001, η 2 = 0.61. Table 1 presents the mean scores of each dependent variable and the outcomes of separate t-tests for the effect of when-similarity. For each of the three dependent measures, participants in the Similar Timing condition scored significantly higher than participants in the Dissimilar Timing condition.

Comparisons of attitudes towards the ostensible partner in the Dissimilar Timing and Similar Timing conditions. N = 24

Measure .	(SD) .	(SD) .	.	.
Connectedness	1.74 (0.76)	3.54 (0.94)	5.190	<.001
Intimacy	1.33 (0.49)	2.42 (0.99)	3.377	<.01
“Would like to meet…”	2.67 (1.23)	3.92 (1.51)	2.227	<.05

Measure .	(SD) .	(SD) .	.	.
Connectedness	1.74 (0.76)	3.54 (0.94)	5.190	<.001
Intimacy	1.33 (0.49)	2.42 (0.99)	3.377	<.01
“Would like to meet…”	2.67 (1.23)	3.92 (1.51)	2.227	<.05

Study 1 examined the effects of similarity in the timing of activities—when-similarity—on participants' evaluations of others using a method that controlled for other types of similarity. This experimental manipulation of when-similarity produced a large difference in the number of matching simultaneous activities between our experimental conditions while controlling for the patterns of activities performed during the day. The latter constraint controls for what-similarity but also ensures a realistic activity pattern performed by the ostensible partners of participants in this study. The ostensible partner-generated activity patterns are very close to the activity patterns performed by the participants themselves (compare Figure 1 , column 1, row 1 & 2).

Participants in the Similar Timing condition evaluated their partners more positively than those in the Dissimilar Timing condition: Participants that experienced a similar timing of activities felt more connected to their partner, felt more intimate, and were more eager to meet their partner than those who did not experience timing similarity. These findings support our hypothesis that similarity-attraction holds for this previously unstudied type of similarity. The finding extends similarity-attraction to when-similarity in a computer-mediated setting that is a common experience for people using real-time web services.

Besides the practical importance of showing that when-similarity can have positive effects on people's evaluations of others in SNSs, these results and the proposed method can be further used to study the interactions between different types of similarity and even to evaluate possible similarity-attraction explanations. Historically, similarity-attraction explanations were concerned largely with who- or what-similarity. The existence of a positive when-similarity attraction effect in cases for which who- and what-similarity is absent could partially invalidate explanations that are solely or heavily dependent on who people are and/or what they do. To explore this further and test the robustness of the when-similarity effect, Study 2 combines a manipulation of when-similarity with a more traditional manipulation of who-similarity.

In Study 2, we used a similar experimental protocol as detailed in Study 1 to examine the possible interaction between when-similarity and who-similarity. This interaction is of practical importance because these two types of similarity are often experienced together when using SNSs as well as other forms of computer-mediated communication. In addition to their practical importance, interactions between different types of similarity are also theoretically interesting: Hypotheses about these interactions would differ based on which explanation of similarity-attraction to which one subscribes. When following the popular explanation that similarity-attraction is caused by a desire to validate one's own beliefs, when-similarity would have no effect (which is not consistent with Study 1). If, however, an explanation of similarity-attraction is not necessarily tied to this type of similarity and rather encompasses a holistic view in which any type of additional similarity increases attraction, one would expect separate main effects of both when- and who-similarity.

Study 2 examined the effects of both when-similarity and who-similarity simultaneously. A 2 (when-similarity: Similar Timing vs. Dissimilar Timing) × 2 (who-similarity: Similar Profile vs. Dissimilar Profile) between-participants experiment was created to test the effects of both when-similarity and who-similarity on the evaluations of others.

Participants in this study consisted of 17 male (38.6%) and 27 female (61.4%) United States college students with an average age of 20.9 ( SD = 1.5) years. Gender was evenly balanced as much as possible across conditions, and participants again received partial course credit for their participation in this study. None of the participants had participated in Study 1.

The procedure in this study was very similar to the procedure in Study 1 with one minor change: after participants were told that they would be paired with another person—during the introduction questionnaire—they were shown a profile of their ostensible partner. The profile contained information about their partner's gender, age, academic focus, and three favorite pastimes. These variables were chosen to link directly to the types of information presented in profiles that are typically used in SNSs.

The when-similarity manipulation was similar to that described in Study 1. In this study, activities matched temporally 52.2% of the time in the Similar Timing condition and 2.5% of the time in the Dissimilar Timing condition. Again, keeping the ostensible partner's activity pattern constant within each day of the experiment controlled for what-similarity.

Who-Similarity Manipulation

Based on the participant's profile, their partner's profile was dynamically generated as employed in the phantom-other technique ( Smith, 1957 ). The partner's profile was adjusted to implement the two experimental conditions. In the Dissimilar Profile condition, the participant's partner was randomly 3 to 4 years older or younger, pursued a different academic focus, and had at least two different pastimes. In the Similar Profile condition, the partner was randomly one year older or younger, pursued the same academic focus, and had two similar pastimes. Who-similarity was thus manipulated on multiple dimensions. In all cases, the partner's gender matched the participant's gender.

This manipulation of who-similarity in which the difference in age, academic focus, and favorite pastimes are all simultaneously manipulated may seem overstated. However, to examine the robustness of when-similarity, it was important to have a strong manipulation of who-similarity and -dissimilarity. The manipulation was not unrealistic: frequently on SNSs, people are exposed to profiles of other people with whom they have a single or very few commonalities (e.g., a shared alma mater) but who are, in most other respects (e.g., their academic focus, age, and favorite pastimes), different from themselves.

Study 2 used the same dependent variables as Study 1: Connectedness ( Cronbach's α = 0.93), Intimacy, and Willingness to Meet. Different than in Study 1, participants answered these questions both directly after the who-similarity manipulation when they received their partner's profile—prior to the experience of when-similarity—and at the end of the text messaging intervention period—after the experience of when-similarity.

Pre-When-Similarity Manipulation

Participants were asked to rate their initial impressions of their partner directly after reading their profile and thus before the when-similarity manipulation. A MANOVA with who-similarity as an independent factor and the three attitudinal measures (i.e. Connectedness, Intimacy, and Willingness to Meet) as dependent factors showed a strong multivariate main effect of who-similarity on the overall partner evaluations, F(1,42) = 7.02, p < 0.001, η 2 = 0.36. Table 2 shows separate t-tests for each of the dependent measures. Participants in the Similar Profile condition felt more connected to and more intimate with their partner, consistent with previous findings using the phantom-other technique. The who-similarity manipulation failed to influence whether or not participants wanted to meet their partner, although the difference was in the expected direction.

Comparisons of attitudes in the Dissimilar Profile and Similar Profile conditions after reading the profile. N = 44

Measure .	(SD) .	(SD) .	.	.
Connectedness	3.26 (1.28)	4.69 (1.18)	3.847	<.001
Intimacy	2.09 (0.87)	3.23 (0.92)	4.209	<.001
“Would like to meet…”	3.77 (1.23)	4.09 (1.51)	1.059	.30

Measure .	(SD) .	(SD) .	.	.
Connectedness	3.26 (1.28)	4.69 (1.18)	3.847	<.001
Intimacy	2.09 (0.87)	3.23 (0.92)	4.209	<.001
“Would like to meet…”	3.77 (1.23)	4.09 (1.51)	1.059	.30

Post-When-Similarity Manipulation

Participants also rated their impressions of their partner after the weeklong text messaging intervention—the experimental manipulation of when-similarity. In the 2x2 MANOVA, there was a significant multivariate main effect of when-similarity, F (3,38) = 6.61, p < 0.001, η 2 = 0.34, such that participants who worked with ostensible partners who had similar timing of activities were much more attracted to their partners than those participants who had a partner with dissimilar timing. There was no multivariate main effect of who-similarity, F(3,38) = 1.26, p > 0.05, η 2 = 0.09, although the means were in the expected direction.

There was a significant interaction effect between who- and when-similarity, F (3,38) = 7.46, p < 0.001, η 2 = 0.37, presented in Figure 2 . The effect of when-similarity is strong and positive when the partner is initially perceived as dissimilar based on the presented profile information. If, however, attraction is already established based on a similar profile, the additional when-similarity does not increase attraction.

Estimated marginal means and standard errors of the similarity-attraction scores for the four experimental groups.

Table 3 presents separate univariate results to provide a more detailed look at the data. Participants' feelings of Connectedness and Perceived Intimacy with their partners were influenced by the when-similarity manipulations. Again, the Willingness to Meet question—as was the case after the who-similarity manipulation—does not exhibit an effect of the similarity manipulations, although the means were in the expected direction.

Results of both the Who- and When-similarity manipulations

.				.	.	.
Measure .	.	.	.	.	.	.
Connectedness	Who	3.1 (0.23)	3.4 (0.23)	0.887	0.022	.352
	When	2.7 (0.23)	3.8 (0.23)	9.330	0.189	.004
	Interaction			11.397	0.222	.002
Intimacy	Who	1.8 (0.19)	2.3 (0.19)	2.825	0.066	.101
	When	1.5 (0.19)	2.6 (0.19)	19.096	0.323	.001
	Interaction			0.113	0.003	.739
“Would like to meet…”	Who	3.0 (0.28)	3.6 (0.28)	1.870	0.045	.179
	When	3.1 (0.28)	3.5 (0.28)	0.468	0.012	.498
	Interaction			0.831	0.020	.367

.				.	.	.
Measure .	.	.	.	.	.	.
Connectedness	Who	3.1 (0.23)	3.4 (0.23)	0.887	0.022	.352
	When	2.7 (0.23)	3.8 (0.23)	9.330	0.189	.004
	Interaction			11.397	0.222	.002
Intimacy	Who	1.8 (0.19)	2.3 (0.19)	2.825	0.066	.101
	When	1.5 (0.19)	2.6 (0.19)	19.096	0.323	.001
	Interaction			0.113	0.003	.739
“Would like to meet…”	Who	3.0 (0.28)	3.6 (0.28)	1.870	0.045	.179
	When	3.1 (0.28)	3.5 (0.28)	0.468	0.012	.498
	Interaction			0.831	0.020	.367

The results of the who-similarity manipulation directly after the presentation of the partner profiles replicate the laboratory-setting finding that who-similarity affects people's evaluations of others positively: Sharing of a common background leads to more attraction when no other information about the partner is provided.

In Study 2, no results were found regarding the willingness to meet the ostensible partner based on both who- and when-similarity, but this item has limited validity and reliability: It queries a behavioral intention of the participants instead of a judgment about the ostensible partner and is a single item. After a week of interactions geared for the discovery of when-similarity, while controlling for who- and what-similarity, the main effect of who-similarity was weakened. This is probably due to the fact that throughout the week, the ostensible partner only matched the participant's activities 50% of the time, leading to a weakening of the initial manipulation of extremely high similarity.

The reinforcement-affect model provides an interesting means for understanding these results. The discovery of similarity or dissimilarity functions as a reward or punishment, which in turn leads to a change in affect towards similar or dissimilar others. A classical finding in the operant conditioning literature shows that rewards or punishments are not additive but rather elicit satiety: the effectiveness of reinforcement reduces once an individual's need for that reinforcement has been satisfied ( see, e.g., Guttman, 1953 ). Under this view, while who-similarity acts as a reward and in turn produces positive affect, any potentially positive effects of additional similarity are limited.

The results of Study 2 do however indicate that a weak attraction to others based on encountering a dissimilar profile in SNSs can be overcome by the synchronous timing of activities. In cases of initial dissimilarity (a dissimilar profile), the reward of discovering when-similarity leads to more positive evaluations.

The opportunity for people to experience when-similarity—without experiencing other types of similarity simultaneously—has emerged due to the recent growth of real-time technology. Services like Twitter enable people to discover what people who are essentially strangers are doing at exactly that moment in time. The mediated nature of these services enables a separation of when-similarity from what-similarity (and physical proximity) that was virtually unimaginable before the existence of real-time mediated interactions. Current communication practices, mediated through computers, mobile phones, and other devices, affect the nature and structure of our encounters with interpersonal similarity—in some respects bringing them closer to the encounters that occurred in past laboratory studies examining similarity-attraction. Our two studies show that when no other information about the other is provided (Study 1) or when the other is regarded as dissimilar based on a number of demographics and interests (Study 2), when-similarity leads to attraction and a more positive evaluation of the other. Greater feelings of connectedness towards the other are reported, and the perceived intimacy between people is increased upon encountering when-similarity.

We introduce a new method to study when-similarity in a setting that has high external validity. The presented manipulation enables control over other types of similarity. In both of our studies, this manipulation of when behaviors occurred while controlling for daily behavioral patterns.

When-Similarity Manipulation and Interactions

The when-similarity manipulation presented in this paper is qualitatively different from most manipulations found in experimental psychology or communication studies. While participants were randomly assigned to a fixed condition, the stimuli they received in these respective conditions were not deterministic. Due to our imposed constraint, which controlled for what-similarity—and thus ensured that participants' partners had the same activity pattern in both conditions—the generated responses differed based on the activity patterns of our participants. However, through simulations including severely skewed activity distributions for participants, we showed that the manipulation consistently led to differing numbers of time-matched activities between the two conditions. We hope that this manipulation can be useful in further research into the effects of similar timing of activities within the similarity-attraction paradigm.

Our when-similarity manipulation also created a clear distinction between different types of similarity that can be experienced by people on SNSs. While within the similarity-attraction literature, numerous moderating variables have been investigated ( Pilkington & Lydon, 1997 ), there is no work detailing interactions between the different types of similarity as introduced in our studies. However, these interactions are of great interest because they enable studying a possible additive effect of increased similarity and can also be used to further examine the explanations offered for similarity-attraction.

Explanations for Similarity-Attraction

As noted in the introduction, a popular explanation for similarity-attraction put forward by Byrne and Clore ( 1970 ) is based on people's need for accuracy: Finding people who share similar attitudes or background corroborates one's own beliefs and as such, positively enforces one's feeling of accuracy. While this explanation is very plausible for what-similarity—similarity based on shared attitudes or beliefs—given that finding similar others indeed reinforces the accuracy of consciously chosen and held beliefs, it is debatable that this explanation holds for every type of similarity. Already for who-similarity, in which characteristics that are not consciously chosen or selected by respondents are manipulated, it is harder to argue that, for example, being of the same gender enforces one's motivation towards accuracy. In Study 2, the who-similarity manipulation included several factors and thus its effectiveness does not necessarily invalidate the motivation-for-accuracy explanation. However, for the when-similarity manipulation employed in this study, it is not very plausible that the need for accuracy is an appropriate explanation for the established effect: One would have to subscribe to the assumption that people strive for accuracy in their timing of relatively trivial activities—such as eating—and that finding someone eating at some other time threatens their own accuracy perception. Future work, through manipulating different similarity types, can test conflicting hypotheses elicited from the different similarity-attraction explanations. In this way, the new types of similarity emerging in computer-mediated communication can aid our understanding of important social phenomena.

Our results are best explained not by a need for accuracy but rather by the predictions one would make based on the reinforcement-affect model. In this model, similarity-attraction is explained by the idea that similarity (or dissimilarity) functions as a reward (or punishment). Rewards are then associated with positive feelings which in turn lead to both positive affect towards similar others as well as a tendency to actively seek out similar others. The literature on operant conditioning however shows that both rewards and punishment are not additive: Once a person's need for a certain reward has been satisfied, the effectiveness of that reward reduces. This explains the interaction between who- and when-similarity observed in Study 2: Both who- and when-similarity can increase people's evaluations of others; however, once one is established, the additional effect of more similarity (or different types of similarity) is reduced.

Future Work

In this article, we presented when-similarity and the idea that this particular type of similarity is emergent in mediated communication. Next, we described a method to study this type of similarity while controlling for other types of similarity and have shown the method's effects on interpersonal evaluations. However, the results presented in this article still require further explanation: Possible conflicting explanations for the origins of the interaction between different types of similarity need to be examined in more detail. In particular, a more in-depth examination of the interactions between other types of similarity (e.g. what-similarity and who-similarity) is clearly called for. A better understanding of the effects demonstrated in this paper would also emerge if the exact nature of the reinforcing process that is likely in play when experiencing when-similarity is clarified: Does similar timing reinforce one's own previous choices? Or does similar timing create a bond through more subtle means identical to the sharing of physical space (e.g. Gibson, 1984 )?

Finally, we believe it is also important for future research to look at the cognitive processes that are activated when experiencing when-similarity: these processes might provide a more developed explanation for the interaction effects observed in Study 2.

Practical Implications

Web services like Twitter and Facebook show the activities of other users at a given moment. Critics of the impact of social real-time technologies have argued that people's urge to post status updates to SNSs can be explained merely by an egocentric urge for self-exhibition. Our two studies of the effects of when-similarity, the type of similarity that is primarily experienced using services like Twitter, suggest that another effect could be in play: people's need to belong ( Baumeister, 1995 ) is partly satisfied by the discovery of when-similarity. Hence, real-time services can serve a very social goal: enhancing connectedness by emphasizing when-similarity .

The exact algorithm to generate the replies is available in [R] (used for the simulation study) or PHP (used for the empirical studies) upon request.

Aron , A. , Aron , E. N. , & Smollan , D. ( 2006 ). Inclusion of other in the self scale and the structure of interpersonal closeness . Journal of Personality and Social Psychology , 63 , 596 – 612 .

Google Scholar

Bailenson , J. N. , Iyengar , S. , Yee , N. , & Collins , N. A. ( 2008 ). Facial similarity between voters and candidates causes influence . Public Opinion Quarterly . 72 ( 5 ), 935 – 961 .

Baker , A. J. ( 2008 ). Down the rabbit hole: The role of place in the initiation and development of online relationships . In A. Barak (Ed.), Psychological aspects of cyberspace: Theory, research, applications . Cambridge, UK : Cambridge University Press .

Google Preview

Banikiotes , P. G. , & Neimeyer , G. J. ( 1981 ). Construct importance and rating similarity as determinants of interpersonal attraction . British Journal of Social Psychology , 20 , 259 – 263 .

Baumeister , R. F. ( 1995 ). The need to belong: Desire for interpersonal attachments as a fundamental human motivation . Psychological bulletin , 3 , 497 – 529 .

Berger , C. R. ( 1975 ). Task performance and attributional communication as determinants of interpersonal attraction . Speech Monographs , 40 , 280 – 286 .

Berscheid , E. , & Walster , E. H. ( 1978 ). Interpersonal attraction . McGraw-Hill College .

Bleda , P. R. ( 1974 ). Toward a clarification of the role of cognitive and affective processes in the similarity-attratction relationship . Journal of Personality and Social Psychology , 29 , 368 – 373 .

Bochner , A. P. ( 1991 ). On the paradigm that would not die . Communication Yearbook , 14 484 – 491 .

Byrne , D. ( 1961 ). Interpersonal attraction and attitude similarity . Journal of Abnormal and Social Psychology , 62 , 713 – 715 .

Byrne , D. ( 1971 ). The attraction paradigm . Academic Press .

Byrne , D. , & Clore , G. L. ( 1970 ). A reinforcement model of evaluative responses . Personality: An International Journal , 1 , 103 – 128 .

Byrne , D. , & Rhamey , R. ( 1965 ). Magnitude of positive and negative reinforcements as a determinant of attraction . Journal of Personality and Social Psychology , 2 , 884 – 889 .

Clore , G. L. , & Byrne , D. A reinforcement-affect model of attraction . In T. L. Huston (Ed.), Foundations of interpersonal attraction . New York : Academic Press , 1974 .

Condon , J. , & Crano , W. D. ( 1988 ). Inferred evaluation and the relation between attitude similarity and interpersonal attraction . Journal of Personality and Social Psychology , 54 , 789 – 797 .

Durkheim , E . ( 1912/2008 ). The elementary forms of religious life . trans. by C. Cosman . New York : Oxford University Press .

Gibson , W. ( 1984 ). Necromancer . New York : Ace .

Guttman , N. ( 1953 ). Operant conditioning, extinction, and periodic reinforcement in relation to concentration of sucrose used as reinforcing agent . Journal of Experimental Psychology , 46 ( 4 ), 213 – 224 .

Heine , S. J. , Foster , JA. B. , & Spina , R. ( 2009 ). Do birds of a feather universally flock together? Cultural variation in the similarity-attraction effect . Asian Journal of Social Psychology , 12 , 247 – 258 .

Horton , J. ( 1967 , April). Time and cool people . Society , 4 ( 5 ), 5 – 12 .

Hu , C. , Thomas , K. M. , & Lance , C. E. ( 2008 ). Intentions to initiate mentoring relationships: Understanding the impact of race, proactivity, feelings of deprivation, and relationship roles . The Journal of Social Psychology , 148 ( 6 ), 727 – 744 .

Jones , J. T ., Pelham, B. W., M., C., & Mirenberg , M. C . ( 2004 ). How do I love thee? let me count the ways: Implicit egotism and interpersonal attraction . Journal of Personality and Social Psychology , 87 , 665 – 683 .

Lee , H. , & Liebenau , J. ( 2000 ). Time and the internet at the turn of the millennium . Time and Society , 9 ( 1 ), 43 – 56 .

Montoya , R. M. , Horton , R. S. , & Kirchner , J. ( 2008 ). Is actual similarity necessary for attraction? A meta-analysis of actual and perceived similarity . Journal of Social and Personal Relationships , 25 ( 6 ), 889 – 922 .

Morry , M. M. ( 2007 ). Relationship satisfaction as a predictor of perceived similarity among cross-sex friends . Journal of Social and Personal Relationships , 24 , 117 – 138 .

Nass , C. & Brave , S. ( 2005 ). Wired for speech: How voice activates and advances the human-computer relationship . Cambridge, MA : MIT Press .

Pilkington , N. W. , & Lydon , J. E. ( 1997 ). The relative effect of attitude similarity and attitude dissimilarity on interpersonal attraction: Investigating the moderating roles of prejudice and group membership . Personality and Social Psychology Bulletin , 23 ( 2 ), 107 – 122 .

Rosenbaum , M. E. ( 1986 ). The repulsion hypothesis: On the nondevelopment of relationships . Journal of Personality and Social Psychology , 51 , 1156 – 1166 .

Smith , A. J. ( 1957 ). Similarity of values and its relation to acceptance and the projection of similarity . Journal of Psychology , 43 , 251 – 260 .

Sunnafrank , M. J. ( 1991 ). Interpersonal attraction and attitude similarity: A communication-based assessment . In Communication yearbook 14 . Sage .

Sunnafrank , M. J. , & Miller , G. R. ( 1981 ). The role of initial conversations in determining attraction to similar and dissimilar strangers . Human Communication Research , 8 , 16 – 25 .

Van Bel , D. T ., Smolders , K. C. H. J ., IJsselsteijn , W. A ., & De Kort , Y. A. W . ( 2009 ). Social connectedness: concept and measurement . In V. Callaghan , A. Kameas , A. Reyes , D. Royo , & M. Weber (Eds.), Proceedings of the 5th international conference on intelligent environments . (p. 67 – 74 ). Amsterdam : IOS Press .

Yeong Tan , D. T. , & Singh , R. ( 1995 ). Attitudes and attraction: A developmental study of the similarity-attraction and dissimilarity-repulsion hypotheses . Personality and Social Psychology Bulletin , 21 ( 9 ), 975 – 986 .

Zerubavel , E. ( 1982 , July). The standardization of time: A sociohistorical perspective . The American Journal of Sociology , 88 ( 1 ), 1 – 23 .

Maurits Kaptein is an assistant professor at the Department of Methodology and Statistics at the Tilburg school of Social and Behavioral Sciences. Maurits is also Chief Scientist at PersuasionAPI.

Address : Department of Methodology and Statistics, Room P1.106. Tilburg University. PO Box 90153, 5000LE Tilburg, the Netherlands.

Deonne Castaneda is a User Experience Designer in California. She received an M.S. in Computer Science and a B.A. in Sociology from Stanford University.

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Nicole Fernandez is a User Experience Researcher at Google in New York. She received her Masters in Human-Computer Interaction from Carnegie Mellon University and a B.S. in Symbolic Systems from Stanford University.

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Clifford Nass is the Thomas M. Storke Professor at Stanford University; he has been a professor at Stanford since 1986. His primary appointment is in Communication, but he also has appointments by courtesy in Computer Science, Education, Law, and Sociology, and is affiliated with the programs in Science, Technology, and Society and Symbolic Systems (cognitive science).

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Month:	Total Views:
December 2017	10
January 2018	4
February 2018	3
March 2018	35
April 2018	30
May 2018	24
June 2018	31
July 2018	35
August 2018	38
September 2018	33
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January 2019	39
February 2019	47
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April 2019	120
May 2019	75
June 2019	61
July 2019	57
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October 2019	88
November 2019	75
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November 2020	57
December 2020	69
January 2021	76
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March 2021	170
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May 2021	364
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Not Just a Matter of Semantics: The Relationship Between Visual and Semantic Similarity

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Clemens-Alexander Brust ORCID: orcid.org/0000-0001-5419-1998 11 &
Joachim Denzler 11 , 12

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11824))

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German Conference on Pattern Recognition

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Knowledge transfer, zero-shot learning and semantic image retrieval are methods that aim at improving accuracy by utilizing semantic information, e.g., from WordNet. It is assumed that this information can augment or replace missing visual data in the form of labeled training images because semantic similarity correlates with visual similarity.

This assumption may seem trivial, but is crucial for the application of such semantic methods. Any violation can cause mispredictions. Thus, it is important to examine the visual-semantic relationship for a certain target problem. In this paper, we use five different semantic and visual similarity measures each to thoroughly analyze the relationship without relying too much on any single definition.

We postulate and verify three highly consequential hypotheses on the relationship. Our results show that it indeed exists and that WordNet semantic similarity carries more information about visual similarity than just the knowledge of “different classes look different”. They suggest that classification is not the ideal application for semantic methods and that wrong semantic information is much worse than none.

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Robust image features for classification and zero-shot tasks by merging visual and semantic attributes

Semantic embedding: scene image classification using scene-specific objects

Barz, B., Denzler, J.: Hierarchy-based image embeddings for semantic image retrieval. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 638–647. IEEE (2019)

Google Scholar

Bilal, A., Jourabloo, A., Ye, M., Liu, X., Ren, L.: Do convolutional neural networks learn class hierarchy? 24 (1), 152–162. https://doi.org/10.1109/TVCG.2017.2744683

Van den Branden Lambrecht, C.J., Verscheure, O.: Perceptual quality measure using a spatiotemporal model of the human visual system. In: Digital Video Compression: Algorithms and Technologies 1996, vol. 2668, pp. 450–462. International Society for Optics and Photonics (1996)

Brust, C.A., et al.: Towards automated visual monitoring of individual gorillas in the wild. In: International Conference on Computer Vision Workshop (ICCV-WS) (2017)

Chen, G., Han, T.X., He, Z., Kays, R., Forrester, T.: Deep convolutional neural network based species recognition for wild animal monitoring. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 858–862. IEEE (2014)

Deselaers, T., Ferrari, V.: Visual and semantic similarity in ImageNet. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1777–1784. IEEE (2011)

Freytag, A., Rodner, E., Simon, M., Loos, A., Kühl, H., Denzler, J.: Chimpanzee faces in the wild: Log-Euclidean CNNs for predicting identities and attributes of primates. In: German Conference on Pattern Recognition (GCPR), pp. 51–63 (2016)

Frome, A., et al.: Devise: a deep visual-semantic embedding model. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp. 2121–2129. Curran Associates, Inc. (2013)

Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic similarity from natural language and ontology analysis. Synth. Lect. Hum. Lang. Technol. 8 (1), 1–254 (2015)

Article Google Scholar

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR) (2016)

Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint arXiv:cmp-lg/9709008 (1997)

Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vis. (IJCV) 123 (1), 32–73 (2017)

Article MathSciNet Google Scholar

Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report 4, University of Toronto (2009)

Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Industr. Electron. 55 (1), 348–363 (2008)

Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42 , 60–88 (2017)

Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40 (1), 262–282 (2007)

Maedche, A., Staab, S.: Comparing ontologies-similarity measures and a comparison study. Technical report, Institute AIFB, University of Karlsruhe (2001)

Malamas, E.N., Petrakis, E.G., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21 (2), 171–188 (2003)

Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38 (11), 39–41 (1995). https://doi.org/10.1145/219717.219748

Niemann, H.: Pattern Analysis. Springer Series in Information Sciences. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-96650-7 . https://books.google.de/books?id=mdOoCAAAQBAJ

Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42 (3), 145–175 (2001)

Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19 (1), 17–30 (1989)

Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint arXiv:cmp-lg/9511007 (1995)

Rohrbach, M., Stark, M., Schiele, B.: Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1641–1648. IEEE (2011)

Ross, S.M.: A First Course in Probability. Macmillan, New York (1976)

MATH Google Scholar

Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision (IJCV) 115 (3), 211–252 (2015)

Salem, M.A.M., Atef, A., Salah, A., Shams, M.: Recent survey on medical image segmentation. In: Computer Vision: Concepts, Methodologies, Tools, and Applications, pp. 129–169. IGI Global (2018)

Sánchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39 (9), 7718–7728 (2012)

Silla, C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Disc. 22 (1), 31–72 (2011)

Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15 (1), 72–101 (1904)

Thevenot, J., López, M.B., Hadid, A.: A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22 (5), 1497–1511 (2018)

Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. Trans. Pattern Anal. Mach. Intell. (PAMI) 30 (11), 1958–1970 (2008)

Tversky, A.: Features of similarity. Psychol. Rev. 84 (4), 327 (1977)

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13 (4), 600–612 (2004)

Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. arXiv preprint arXiv:1801.03924

Zhou, Z., Wang, Y., Gu, J.: A new model of information content for semantic similarity in WordNet. In: Second International Conference on Future Generation Communication and Networking Symposia, 2008, FGCNS 2008, vol. 3, pp. 85–89. IEEE (2008)

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This work was supported by the DAWI research infrastructure project, funded by the federal state of Thuringia (grant no. 2017 FGI 0031), including access to computing and storage facilities.

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Brust, CA., Denzler, J. (2019). Not Just a Matter of Semantics: The Relationship Between Visual and Semantic Similarity. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_29

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Principle of Similarity

The principle of similarity is a concept in cognitive psychology and design that suggests that elements that share visual similarities are perceived as being related or grouped together. This principle states that people tend to group or organize visual elements based on their similarities, such as shape, size, color, texture, or orientation.

Visual Similarity

Visual similarity refers to the perception of elements that have similar visual characteristics. When objects or elements share similarities in their appearance, they are more likely to be perceived as belonging to the same group or category. For example, a collection of red circles would be considered visually similar.

Grouping and Organization

The principle of similarity influences how we perceive and organize visual information. When elements appear similar, our brain automatically groups them together, allowing us to process information more efficiently. This grouping and organization based on similarity help in creating structure, hierarchy, and visual harmony in design.

Application in Design

In graphic design, the principle of similarity is often used to create visual unity, establish relationships, and communicate information effectively. Designers can manipulate visual attributes, such as color, shape, or texture, to highlight similarities between elements or to differentiate them. By utilizing this principle, designers can create visually appealing and easily understandable designs.

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There is a great deal of similarity between Caroline and her mother .
His situation has several similarities with our own.
The teacher found several suspicious similarities between their work .
The painting bears a striking similarity to one in the Louvre.
be no better than (a) something idiom
equivalence
equivalency
equivalent of something
equivalent to something
non-distinctive
not make any difference idiom
of the kind idiom

similarity | American Dictionary

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to delay dealing with something, especially because you want people to forget about it

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The state-of-the-art of mycobacterium chimaera infections and the causal link with health settings: a systematic review.

1. Introduction

2. materials and methods, 4. discussion, 4.1. mycobacterium chimaera’s characteristics and ecosystem, 4.2. heater-cooler units, medical devices, water, and air-conditioned implants, 4.3. incubation period and symptoms presentation, 4.4. presence in the lung system, 4.5. modality of transmission, 4.6. detection, 4.7. disinfection, 4.8. causal link assessment, 5. limitations, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

MAC	mycobacterium avium complex
NTM	non-tuberculosis mycobacterium
M. chimaera	Mycobacterium chimaera
HCU	heater-cooler units
OPPP	opportunistic premise plumbing pathogens
ECMO	extra-corporal mechanical oxygenation
HAI	healthcare-associated infection

Vendramin, I.; Peghin, M.; Tascini, C.; Livi, U. Longest Incubation Period of Mycobacterium chimaera Infection after Cardiac Surgery. Eur. J. Cardio-Thoracic Surg. 2021 , 59 , 506–508. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Biswas, M.; Rahaman, S.; Biswas, T.K.; Haque, Z.; Ibrahim, B. Association of Sex, Age, and Comorbidities with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis. Intervirology 2020 , 64 , 36–47. [ Google Scholar ] [ CrossRef ]
Treglia, M.; Pallocci, M.; Passalacqua, P.; Sabatelli, G.; De Luca, L.; Zanovello, C.; Messineo, A.; Quintavalle, G.; Cisterna, A.M.; Marsella, L.T. Medico-Legal Aspects of Hospital-Acquired Infections: 5-Years of Judgements of the Civil Court of Rome. Healthcare 2022 , 10 , 1336. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bolcato, V.; Tronconi, L.P.; Odone, A.; Blandi, L. Healthcare-Acquired Sars-Cov-2 Infection: A Viable Legal Category? Int. J. Risk Saf. Med. 2023 , 34 , 129–134. [ Google Scholar ] [ CrossRef ]
Tattoli, L.; Dell’erba, A.; Ferorelli, D.; Gasbarro, A.; Solarino, B. Sepsis and Nosocomial Infections: The Role of Medico-Legal Experts in Italy. Antibiotics 2019 , 8 , 199. [ Google Scholar ] [ CrossRef ]
Barranco, R.; Caristo, I.; Spigno, F.; Ponzano, M.; Trevisan, A.; Signori, A.; Di Biagio, A.; Ventura, F. Management of the Medico-Legal Dispute of Healthcare-Related SARS-CoV-2 Infections: Evaluation Criteria and Case Study in a Large University Hospital in Northwest Italy from 2020 to 2021. Int. J. Environ. Res. Public Health 2022 , 19 , 16764. [ Google Scholar ] [ CrossRef ]
Goldenberg, S.D.; Volpé, H.; French, G.L. Clinical Negligence, Litigation and Healthcare-Associated Infections. J. Hosp. Infect. 2012 , 81 , 156–162. [ Google Scholar ] [ CrossRef ]
Rizzo, N. La Causalità Civile ; Jus Civile; Giappichelli Editore: Torino, Italy, 2022. [ Google Scholar ]
Riccardi, N.; Monticelli, J.; Antonello, R.M.; Luzzati, R.; Gabrielli, M.; Ferrarese, M.; Codecasa, L.; Di Bella, S.; Giacobbe, D.R. Mycobacterium chimaera Infections: An Update. J. Infect. Chemother. 2020 , 26 , 199–205. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Natanti, A.; Palpacelli, M.; Valsecchi, M.; Tagliabracci, A.; Pesaresi, M. Mycobacterium chimaera : A Report of 2 New Cases and Literature Review. Int. J. Leg. Med. 2021 , 135 , 2667–2679. [ Google Scholar ] [ CrossRef ]
Wetzstein, N.; Kohl, T.A.; Diricks, M.; Mas-Peiro, S.; Holubec, T.; Kessel, J.; Graf, C.; Koch, B.; Herrmann, E.; Vehreschild, M.J.G.T.; et al. Clinical Characteristics and Outcome of Mycobacterium chimaera Infections after Cardiac Surgery: Systematic Review and Meta-Analysis of 180 Heater-Cooler Unit-Associated Cases. Clin. Microbiol. Infect. 2023 , 29 , 1008–1014. [ Google Scholar ] [ CrossRef ]
Desai, A.N.; Hurtado, R.M. Infections and Outbreaks of Nontuberculous Mycobacteria in Hospital Settings. Curr. Treat. Options Infect. Dis. 2018 , 10 , 169–181. [ Google Scholar ] [ CrossRef ]
van Ingen, J.; Kohl, T.A.; Kranzer, K.; Hasse, B.; Keller, P.M.; Katarzyna Szafrańska, A.; Hillemann, D.; Chand, M.; Schreiber, P.W.; Sommerstein, R.; et al. Global Outbreak of Severe Mycobacterium chimaera Disease after Cardiac Surgery: A Molecular Epidemiological Study. Lancet Infect. Dis. 2017 , 17 , 1033–1041. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bisognin, F.; Messina, F.; Butera, O.; Nisii, C.; Mazzarelli, A.; Cristino, S.; Pascale, M.R.; Lombardi, G.; Cannas, A.; Dal Monte, P. Investigating the Origin of Mycobacterium chimaera Contamination in Heater-Cooler Units: Integrated Analysis with Fourier Transform Infrared Spectroscopy and Whole-Genome Sequencing. Microbiol. Spectr. 2022 , 10 , e0289322. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Pinzauti, D.; De Giorgi, S.; Fox, V.; Lazzeri, E.; Messina, G.; Santoro, F.; Iannelli, F.; Ricci, S.; Pozzi, G. Complete Genome Sequences of Mycobacterium chimaera Strains 850 and 852, Isolated from Heater-Cooler Unit Water. Microbiol. Resour. Announc. 2022 , 11 , e0102121. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Hasan, N.A.; Warren, R.L.; Elaine Epperson, L.; Malecha, A.; Alexander, D.C.; Turenne, C.Y.; MacMillan, D.; Birol, I.; Pleasance, S.; Coope, R.; et al. Complete Genome Sequence of Mycobacterium chimaera SJ42, a Nonoutbreak Strain from an Immunocompromised Patient with Pulmonary Disease. Genome Announc. 2017 , 5 , e00963-17. [ Google Scholar ] [ CrossRef ]
Wallace, R.J.; Iakhiaeva, E.; Williams, M.D.; Brown-Elliott, B.A.; Vasireddy, S.; Vasireddy, R.; Lande, L.; Peterson, D.D.; Sawicki, J.; Kwait, R.; et al. Absence of Mycobacterium Intracellulare and Presence of Mycobacterium chimaera in Household Water and Biofilm Samples of Patients in the United States with Mycobacterium avium Complex Respiratory Disease. J. Clin. Microbiol. 2013 , 51 , 1747–1752. [ Google Scholar ] [ CrossRef ]
Falkinham, J.O.; Hilborn, E.D.; Arduino, M.J.; Pruden, A.; Edwards, M.A. Epidemiology and Ecology of Opportunistic Premise Plumbing Pathogens: Legionella pneumophila , Mycobacterium avium , and Pseudomonas aeruginosa . Environ. Health Perspect. 2015 , 123 , 749–758. [ Google Scholar ] [ CrossRef ]
European Centre for Disease Prevention and Control. EU Protocol for Testing of M. chimaera Infections Potentially Associated with Heater-Cooler Units Environmental Microbiology Investigations ; Technical Document; European Centre for Disease Prevention and Control: Solna, Sweden, 2015. [ Google Scholar ]
Ministero della Salute. Raccomandazioni per Il Controllo Dell’infezione da Mycobacterium chimaera in Italia ; Ministero della Salute: Roma, Italy, 2019.
Bolcato, M.; Rodriguez, D.; Aprile, A. Risk Management in the New Frontier of Professional Liability for Nosocomial Infection: Review of the Literature on Mycobacterium chimaera . Int. J. Environ. Res. Public Health 2020 , 17 , 7328. [ Google Scholar ] [ CrossRef ]
Achermann, Y.; Rössle, M.; Hoffmann, M.; Deggim, V.; Kuster, S.; Zimmermann, D.R.; Bloemberg, G.; Hombach, M.; Hasse, B. Prosthetic Valve Endocarditis and Bloodstream Infection Due to Mycobacterium chimaera . J. Clin. Microbiol. 2013 , 51 , 1769–1773. [ Google Scholar ] [ CrossRef ]
Zabost, A.T.; Szturmowicz, M.; Brzezińska, S.A.; Klatt, M.D.; Augustynowicz-Kopeć, E.M. Mycobacterium chimaera as an Underestimated Cause of NTM Lung Diseases in Patients Hospitalized in Pulmonary Wards. Pol. J. Microbiol. 2021 , 70 , 315–320. [ Google Scholar ] [ CrossRef ]
Truden, S.; Žolnir-Dovč, M.; Sodja, E.; Starčič Erjavec, M. Nationwide Analysis of Mycobacterium chimaera and Mycobacterium intracellulare Isolates: Frequency, Clinical Importance, and Molecular and Phenotypic Resistance Profiles. Infect. Genet. Evol. 2020 , 82 , 104311. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021 , 372 , 71. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bills, N.D.; Hinrichs, S.H.; Aden, T.A.; Wickert, R.S.; Iwen, P.C. Molecular Identification of Mycobacterium chimaera as a Cause of Infection in a Patient with Chronic Obstructive Pulmonary Disease. Diagn. Microbiol. Infect. Dis. 2009 , 63 , 292–295. [ Google Scholar ] [ CrossRef ]
Cohen-Bacrie, S.; David, M.; Stremler, N.; Dubus, J.-C.; Rolain, J.-M.; Drancourt, M. Mycobacterium chimaera Pulmonary Infection Complicating Cystic Fibrosis: A Case Report. J. Med. Case Rep. 2011 , 5 , 473. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Alhanna, J.; Purucker, M.; Steppert, C.; Grigull-Daborn, A.; Schiffel, G.; Gruber, H.; Borgmann, S. Mycobacterium chimaera Causes Tuberculosis-like Infection in a Male Patient with Anorexia Nervosa. Int. J. Eat. Disord. 2012 , 45 , 450–452. [ Google Scholar ] [ CrossRef ]
Gunaydin, M.; Yanik, K.; Eroglu, C.; Sanic, A.; Ceyhan, I.; Erturan, Z.; Durmaz, R. Distribution of Nontuberculous Mycobacteria Strains. Ann. Clin. Microbiol. Antimicrob. 2013 , 12 , 33. [ Google Scholar ] [ CrossRef ]
Boyle, D.P.; Zembower, T.R.; Reddy, S.; Qi, C. Comparison of Clinical Features, Virulence, and Relapse among Mycobacterium avium Complex Species. Am. J. Respir. Crit. Care Med. 2015 , 191 , 1310–1317. [ Google Scholar ] [ CrossRef ]
Mwikuma, G.; Kwenda, G.; Hang’ombe, B.M.; Simulundu, E.; Kaile, T.; Nzala, S.; Siziya, S.; Suzuki, Y. Molecular Identification of Non-Tuberculous Mycobacteria Isolated from Clinical Specimens in Zambia. Ann. Clin. Microbiol. Antimicrob. 2015 , 14 , 1. [ Google Scholar ] [ CrossRef ]
Moon, S.M.; Kim, S.Y.; Jhun, B.W.; Lee, H.; Park, H.Y.; Jeon, K.; Huh, H.J.; Ki, C.S.; Lee, N.Y.; Shin, S.J.; et al. Clinical Characteristics and Treatment Outcomes of Pulmonary Disease Caused by Mycobacterium chimaera . Diagn. Microbiol. Infect. Dis. 2016 , 86 , 382–384. [ Google Scholar ] [ CrossRef ]
Moutsoglou, D.M.; Merritt, F.; Cumbler, E. Disseminated Mycobacterium chimaera Presenting as Vertebral Osteomyelitis. Case Rep. Infect. Dis. 2017 , 2017 , 9893743. [ Google Scholar ] [ CrossRef ]
Bursle, E.; Playford, E.G.; Coulter, C.; Griffin, P. First Australian Case of Disseminated Mycobacterium chimaera Infection Post-Cardiothoracic Surgery. Infect. Dis. Health 2017 , 22 , 1–5. [ Google Scholar ] [ CrossRef ]
Kim, S.-Y.; Shin, S.H.; Moon, S.M.; Yang, B.; Kim, H.; Kwon, O.J.; Huh, H.J.; Ki, C.-S.; Lee, N.Y.; Shin, S.J.; et al. Distribution and Clinical Significance of Mycobacterium avium Complex Species Isolated from Respiratory Specimens. Diagn. Microbiol. Infect. Dis. 2017 , 88 , 125–137. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Chand, M.; Lamagni, T.; Kranzer, K.; Hedge, J.; Moore, G.; Parks, S.; Collins, S.; Del Ojo Elias, C.; Ahmed, N.; Brown, T.; et al. Insidious Risk of Severe Mycobacterium chimaera Infection in Cardiac Surgery Patients. Clin. Infect. Dis. 2017 , 64 , 335–342. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Truden, S.; Žolnir-Dovč, M.; Sodja, E.; Starčič Erjavec, M. Retrospective Analysis of Slovenian Mycobacterium avium Complex and Mycobacterium abscessus Complex Isolates and Molecular Resistance Profile. Russ. J. Infect. Immun. 2018 , 8 , 447–451. [ Google Scholar ] [ CrossRef ]
Larcher, R.; Lounnas, M.; Dumont, Y.; Michon, A.L.; Bonzon, L.; Chiron, R.; Carriere, C.; Klouche, K.; Godreuil, S. Mycobacterium chimaera Pulmonary Disease in Cystic Fibrosis Patients, France, 2010–2017. Emerg. Infect. Dis. 2019 , 25 , 611–613. [ Google Scholar ] [ CrossRef ]
Shafizadeh, N.; Hale, G.; Bhatnagar, J.; Alshak, N.S.; Nomura, J. Mycobacterium chimaera Hepatitis: A New Disease Entity. Am. J. Surg. Pathol. 2019 , 43 , 244–250. [ Google Scholar ] [ CrossRef ]
Rosero, C.I.; Shams, W.E. Mycobacterium chimaera Infection Masquerading as a Lung Mass in a Healthcare Worker. IDCases 2019 , 15 , e00526. [ Google Scholar ] [ CrossRef ]
Watanabe, R.; Seino, H.; Taniuchi, S.; Igusa, R. Mycobacterium chimaera -Induced Tenosynovitis in a Patient with Rheumatoid Arthritis. BMJ Case Rep. 2020 , 13 , e233868. [ Google Scholar ] [ CrossRef ]
Chen, L.C.; Huang, H.N.; Yu, C.J.; Chien, J.Y.; Hsueh, P.R. Clinical Features and Treatment Outcomes of Mycobacterium chimaera Lung Disease and Antimicrobial Susceptibility of the Mycobacterial Isolates. J. Infect. 2020 , 80 , 437–443. [ Google Scholar ] [ CrossRef ]
Maalouly, C.; Devresse, A.; Martin, A.; Rodriguez-Villalobos, H.; Kanaan, N.; Belkhir, L. Coinfection of Mycobacterium malmoense and Mycobacterium chimaera in a Kidney Transplant Recipient: A Case Report and Review of the Literature. Transpl. Infect. Dis. 2020 , 22 , e13241. [ Google Scholar ] [ CrossRef ]
de Melo Carvalho, R.; Nunes, A.L.; Sa, R.; Ramos, I.; Valente, C.; Saraiva da Cunha, J. Mycobacterium chimaera Disseminated Infection. J. Med. Cases 2020 , 11 , 35–36. [ Google Scholar ] [ CrossRef ]
Sharma, K.; Sharma, M.; Modi, M.; Joshi, H.; Goyal, M.; Sharma, A.; Ray, P.; Rowlinson, M.C. Mycobacterium chimaera and Chronic Meningitis. QJM Int. J. Med. 2020 , 113 , 563–564. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Kim, M.J.; Kim, K.M.; Shin, J.I.; Ha, J.H.; Lee, D.H.; Choi, J.G.; Park, J.S.; Byun, J.H.; Yoo, J.W.; Eum, S.; et al. Identification of Nontuberculous Mycobacteria in Patients with Pulmonary Diseases in Gyeongnam, Korea, Using Multiplex PCR and Multigene Sequence-Based Analysis. Can. J. Infect. Dis. Med. Microbiol. 2021 , 2021 , 8844306. [ Google Scholar ] [ CrossRef ]
Kavvalou, A.; Stehling, F.; Tschiedel, E.; Kehrmann, J.; Walkenfort, B.; Hasenberg, M.; Olivier, M.; Steindor, M. Biofilm Infection of a Central Venous Port-Catheter Caused by Mycobacterium avium Complex in an Immunocompetent Child with Cystic Fibrosis. BMC Infect. Dis. 2022 , 22 , 321. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Robinson, B.; Chaudhri, M.; Miskoff, J.A. A Case of Cavitary Mycobacterium chimaera . Cureus 2022 , 14 , e26984. [ Google Scholar ] [ CrossRef ]
Ahmad, M.; Yousaf, A.; Khan, H.M.W.; Munir, A.; Chandran, A. Mycobacterium chimaera Lung Infection and Empyema in a Patient without Cardiopulmonary Bypass. Bayl. Univ. Med. Cent. Proc. 2022 , 35 , 817–819. [ Google Scholar ] [ CrossRef ] [ PubMed ]
George, M.; Afra, T.P.; Santhosh, P.; Nandakumar, G.; Balagopalan, D.; Sreedharan, S. Ulcerating Nodules on the Face Due to Mycobacterium chimaera in a Patient with Diabetes. Clin. Exp. Dermatol. 2022 , 47 , 587–589. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Lin, Y.F.; Lee, T.F.; Wu, U.I.; Huang, C.F.; Cheng, A.; Lin, K.Y.; Hung, C.C. Disseminated Mycobacterium chimaera Infection in a Patient with Adult-Onset Immunodeficiency Syndrome: Case Report. BMC Infect. Dis. 2022 , 22 , 665. [ Google Scholar ] [ CrossRef ]
Łyżwa, E.; Siemion-Szcześniak, I.; Sobiecka, M.; Lewandowska, K.; Zimna, K.; Bartosiewicz, M.; Jakubowska, L.; Augustynowicz-Kopeć, E.; Tomkowski, W. An Unfavorable Outcome of M. Chimaera Infection in Patient with Silicosis. Diagnostics 2022 , 12 , 1826. [ Google Scholar ] [ CrossRef ]
McLaughlin, C.M.; Schade, M.; Cochran, E.; Taylor, K.F. A Case Report of a Novel Atypical Mycobacterial Infection: Mycobacterium chimaera Hand Tenosynovitis. JBJS Case Connect. 2022 , 12 , e22. [ Google Scholar ] [ CrossRef ]
Gross, J.E.; Teneback, C.C.; Sweet, J.G.; Caceres, S.M.; Poch, K.R.; Hasan, N.A.; Jia, F.; Epperson, L.E.; Lipner, E.M.; Vang, C.K.; et al. Molecular Epidemiologic Investigation of Mycobacterium intracellulare Subspecies Chimaera Lung Infections at an Adult Cystic Fibrosis Program. Ann. Am. Thorac. Soc. 2023 , 20 , 677–686. [ Google Scholar ] [ CrossRef ]
Azzarà, C.; Lombardi, A.; Gramegna, A.; Ori, M.; Gori, A.; Blasi, F.; Bandera, A. Non-Tuberculous Mycobacteria Lung Disease Due to Mycobacterium chimaera in a 67-Year-Old Man Treated with Immune Checkpoint Inhibitors for Lung Adenocarcinoma: Infection Due to Dysregulated Immunity? BMC Infect. Dis. 2023 , 23 , 573. [ Google Scholar ] [ CrossRef ]
Pradhan, A.; Martinez, E.; Sintchenko, V.; Post, J.; Overton, K. Case of Mycobacterium chimaera Vertebral Osteomyelitis Diagnosed 7 Years after Cardiac Surgery. Intern. Med. J. 2023 , 53 , 150–151. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Garcia-Prieto, F.; Rodríguez Perojo, A.; Río Ramírez, M.T. Endobronchial Fibroanthracosis Associated with Mycobacterium chimaera Infection: An Exceptional Case. Open Respir. Arch. 2024 , 6 , 100309. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Paul, S.; MacNair, A.; Lostarakos, V.; Capstick, R. Non-Tuberculous Mycobacterial Pulmonary Infection Presenting in a Patient with Unilateral Pulmonary Artery Agenesis. BMJ Case Rep. 2024 , 17 , e259125. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bittner, M.J.; Preheim, L.C. Other Slow-Growing Nontuberculous Mycobacteria. Microbiol. Spectr. 2016 , 4 , 767–776. [ Google Scholar ] [ CrossRef ]
Tortoli, E.; Rindi, L.; Garcia, M.J.; Chiaradonna, P.; Dei, R.; Garzelli, C.; Kroppenstedt, R.M.; Lari, N.; Mattei, R.; Mariottini, A.; et al. Proposal to Elevate the Genetic Variant MAC-A Included in the Mycobacterium avium Complex, to Species Rank as Mycobacterium chimaera sp. nov. Int. J. Syst. Evol. Microbiol. 2004 , 54 , 1277–1285. [ Google Scholar ] [ CrossRef ]
Turankar, R.P.; Singh, V.; Gupta, H.; Pathak, V.K.; Ahuja, M.; Singh, I.; Lavania, M.; Dinda, A.K.; Sengupta, U. Association of Non-Tuberculous Mycobacteria with Mycobacterium leprae in Environment of Leprosy Endemic Regions in India. Infect. Genet. Evol. 2019 , 72 , 191–198. [ Google Scholar ] [ CrossRef ]
Makovcova, J.; Slany, M.; Babak, V.; Slana, I.; Kralik, P. The Water Environment as a Source of Potentially Pathogenic Mycobacteria. J. Water Health 2014 , 12 , 254–263. [ Google Scholar ] [ CrossRef ]
Falkinham, J.O. Ecology of Nontuberculous Mycobacteria-Where Do Human Infections Come From? Semin. Respir. Crit. Care Med. 2013 , 34 , 95–102. [ Google Scholar ] [ CrossRef ]
Norton, G.J.; Williams, M.; Falkinham, J.O., III; Honda, J.R. Physical Measures to Reduce Exposure to Tap Water-Associated Nontuberculous Mycobacteria. Front. Public Health 2020 , 8 , 190. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Durnez, L.; Eddyani, M.; Mgode, G.F.; Katakweba, A.; Katholi, C.R.; Machang’u, R.R.; Kazwala, R.R.; Portaels, F.; Leirs, H. First Detection of Mycobacteria in African Rodents and Insectivores, Using Stratified Pool Screening. Appl. Environ. Microbiol. 2008 , 74 , 768. [ Google Scholar ] [ CrossRef ]
Sax, H.; Bloemberg, G.; Hasse, B.; Sommerstein, R.; Kohler, P.; Achermann, Y.; Rössle, M.; Falk, V.; Kuster, S.P.; Böttger, E.C.; et al. Prolonged Outbreak of Mycobacterium chimaera Infection after Open-Chest Heart Surgery. Clin. Infect. Dis. 2015 , 61 , 67–75. [ Google Scholar ] [ CrossRef ]
Falkinham, J.O.; Williams, M.D. Desiccation-Tolerance of Mycobacterium avium , Mycobacterium intracellulare , Mycobacterium chimaera , Mycobacterium abscessus and Mycobacterium chelonae . Pathogens 2022 , 11 , 463. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Gebert, M.J.; Delgado-Baquerizo, M.; Oliverio, A.M.; Webster, T.M.; Nichols, L.M.; Honda, J.R.; Chan, E.D.; Adjemian, J.; Dunn, R.R.; Fierer, N. Ecological Analyses of Mycobacteria in Showerhead Biofilms and Their Relevance to Human Health. mBio 2018 , 9 , e01614–e01618. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Cao, Y.; Yuan, S.; Pang, L.; Xie, J.; Gao, Y.; Zhang, J.; Zhao, Z.; Yao, S. Study on Microbial Diversity of Washing Machines. Biodegradation 2024 , 1–13. [ Google Scholar ] [ CrossRef ]
Liu, H.; Jiao, P.; Guan, L.; Wang, C.; Zhang, X.X.; Ma, L. Functional Traits and Health Implications of the Global Household Drinking-Water Microbiome Retrieved Using an Integrative Genome-Centric Approach. Water Res. 2024 , 250 , 121094. [ Google Scholar ] [ CrossRef ]
Choi, J.Y.; Sim, B.R.; Park, Y.; Yong, S.H.; Shin, S.J.; Kang, Y.A. Identification of Nontuberculous Mycobacteria Isolated from Household Showerheads of Patients with Nontuberculous Mycobacteria. Sci. Rep. 2022 , 12 , 8648. [ Google Scholar ] [ CrossRef ]
Shen, Y.; Haig, S.J.; Prussin, A.J.; Lipuma, J.J.; Marr, L.C.; Raskin, L. Shower Water Contributes Viable Nontuberculous Mycobacteria to Indoor Air. PNAS Nexus 2022 , 1 , pgac145. [ Google Scholar ] [ CrossRef ]
Struelens, M.J.; Plachouras, D. Mycobacterium chimaera Infections Associated with Heater-Cooler Units (HCU): Closing Another Loophole in Patient Safety. Eurosurveillance 2016 , 21 , 30397. [ Google Scholar ] [ CrossRef ]
Trudzinski, F.C.; Schlotthauer, U.; Kamp, A.; Hennemann, K.; Muellenbach, R.M.; Reischl, U.; Gärtner, B.; Wilkens, H.; Bals, R.; Herrmann, M.; et al. Clinical Implications of Mycobacterium chimaera Detection in Thermoregulatory Devices Used for Extracorporeal Membrane Oxygenation (ECMO), Germany, 2015 to 2016. Eurosurveillance 2016 , 21 , 30398. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Schnetzinger, M.; Heger, F.; Indra, A.; Kimberger, O. Bacterial Contamination of Water Used as Thermal Transfer Fluid in Fluid-Warming Devices. J. Hosp. Infect. 2023 , 141 , 49–54. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Barker, T.A.; Dandekar, U.; Fraser, N.; Dawkin, L.; Sweeney, P.; Heron, F.; Simmons, J.; Parmar, J. Minimising the Risk of Mycobacterium chimaera Infection during Cardiopulmonary Bypass by the Removal of Heater-Cooler Units from the Operating Room. Perfusion 2018 , 33 , 264–269. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Barnes, S.; Twomey, C.; Carrico, R.; Murphy, C.; Warye, K. OR Air Quality: Is It Time to Consider Adjunctive Air Cleaning Technology? AORN J. 2018 , 108 , 503–515. [ Google Scholar ] [ CrossRef ]
Walker, J.T.; Lamagni, T.; Chand, M. Evidence That Mycobacterium chimaera Aerosols Penetrate Laminar Airflow and Result in Infections at the Surgical Field. Lancet Infect. Dis. 2017 , 17 , 1019. [ Google Scholar ] [ CrossRef ]
Schlotthauer, U.; Hennemann, K.; Gärtner, B.C.; Schäfers, H.-J.; Becker, S.L. Microbiological Surveillance of Heater-Cooler Units Used in Cardiothoracic Surgery for Detection of Mycobacterium chimaera . Thorac. Cardiovasc. Surg. 2022 , 72 , 59–62. [ Google Scholar ] [ CrossRef ]
Gross, J.E.; Caceres, S.; Poch, K.; Epperson, L.E.; Hasan, N.A.; Jia, F.; de Moura, V.C.N.; Strand, M.; Lipner, E.M.; Honda, J.R.; et al. Prospective Healthcare-Associated Links in Transmission of Nontuberculous Mycobacteria among People with Cystic Fibrosis (PHALT NTM) Study: Rationale and Study Design. PLoS ONE 2023 , 18 , e0291910. [ Google Scholar ] [ CrossRef ]
Nakamura, S.; Azuma, M.; Sato, M.; Fujiwara, N.; Nishino, S.; Wada, T.; Yoshida, S. Pseudo-Outbreak of Mycobacterium chimaera through Aerators of Hand-Washing Machines at a Hematopoietic Stem Cell Transplantation Center. Infect. Control Hosp. Epidemiol. 2019 , 40 , 1433–1435. [ Google Scholar ] [ CrossRef ]
Kanamori, H.; Weber, D.J.; Rutala, W.A. Healthcare-Associated Mycobacterium chimaera Transmission and Infection Prevention Challenges: Role of Heater-Cooler Units as a Water Source in Cardiac Surgery. Clin. Infect. Dis. 2017 , 64 , 343–346. [ Google Scholar ] [ CrossRef ]
Rao, M.; Silveira, F.P. Non-Tuberculous Mycobacterial Infections in Thoracic Transplant Candidates and Recipients. Curr. Infect. Dis. Rep. 2018 , 20 , 14. [ Google Scholar ] [ CrossRef ]
Walker, J.; Moore, G.; Collins, S.; Parks, S.; Garvey, M.I.; Lamagni, T.; Smith, G.; Dawkin, L.; Goldenberg, S.; Chand, M. Microbiological Problems and Biofilms Associated with Mycobacterium chimaera in Heater–Cooler Units Used for Cardiopulmonary Bypass. J. Hosp. Infect. 2017 , 96 , 209–220. [ Google Scholar ] [ CrossRef ]
Born, F.; Wieser, A.; Oberbach, A.; Oberbach, A.; Ellgass, R.; Peterss, S.; Kur, F.; Grabein, B.; Hagl, C. Five Years without Mycobacterium chimaera . Thorac. Cardiovasc. Surg. 2020 , 68 , S1–S72. [ Google Scholar ] [ CrossRef ]
Scriven, J.E.; Scobie, A.; Verlander, N.Q.; Houston, A.; Collyns, T.; Cajic, V.; Kon, O.M.; Mitchell, T.; Rahama, O.; Robinson, A.; et al. Mycobacterium chimaera Infection Following Cardiac Surgery in the United Kingdom: Clinical Features and Outcome of the First 30 Cases. Clin. Microbiol. Infect. 2018 , 24 , 1164–1170. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Olatidoye, O.A.; Samat, S.H.; Yin, K.; Bates, M.J. Pulmonary Valve Infective Endocarditis Caused by Mycobacterium abscessus . J. Cardiothorac. Surg. 2023 , 18 , 221. [ Google Scholar ] [ CrossRef ]
Ganatra, S.; Sharma, A.; D’Agostino, R.; Gage, T.; Kinnunen, P. Mycobacterium chimaera Mimicking Sarcoidosis. Methodist Debakey Cardiovasc. J. 2018 , 14 , 301–302. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Buchanan, R.; Agarwal, A.; Mathai, E.; Cherian, B. Mycobacterium chimaera : A Novel Pathogen with Potential Risk to Cardiac Surgical Patients. Natl. Med. J. India 2020 , 33 , 284–287. [ Google Scholar ] [ CrossRef ]
McHugh, J.; Saleh, O.A. Updates in Culture-Negative Endocarditis. Pathogens 2023 , 12 , 1027. [ Google Scholar ] [ CrossRef ]
Delgado, V.; Ajmone Marsan, N.; De Waha, S.; Bonaros, N.; Brida, M.; Burri, H.; Caselli, S.; Doenst, T.; Ederhy, S.; Erba, P.A.; et al. 2023 ESC Guidelines for the Management of Endocarditis. Eur. Heart J. 2023 , 44 , 3948–4042. [ Google Scholar ] [ CrossRef ]
Kohler, P.; Kuster, S.P.; Bloemberg, G.; Schulthess, B.; Frank, M.; Tanner, F.C.; Rössle, M.; Böni, C.; Falk, V.; Wilhelm, M.J.; et al. Healthcare-Associated Prosthetic Heart Valve, Aortic Vascular Graft, and Disseminated Mycobacterium chimaera Infections Subsequent to Open Heart Surgery. Eur. Heart J. 2015 , 36 , 2745–2753. [ Google Scholar ] [ CrossRef ]
Wyrostkiewicz, D.; Opoka, L.; Filipczak, D.; Jankowska, E.; Skorupa, W.; Augustynowicz-Kopeć, E.; Szturmowicz, M. Nontuberculous Mycobacterial Lung Disease in the Patients with Cystic Fibrosis—A Challenging Diagnostic Problem. Diagnostics 2022 , 12 , 1514. [ Google Scholar ] [ CrossRef ]
Virdi, R.; Lowe, M.E.; Norton, G.J.; Dawrs, S.N.; Hasan, N.A.; Epperson, L.E.; Glickman, C.M.; Chan, E.D.; Strong, M.; Crooks, J.L.; et al. Lower Recovery of Nontuberculous Mycobacteria from Outdoor Hawai’i Environmental Water Biofilms Compared to Indoor Samples. Microorganisms 2021 , 9 , 224. [ Google Scholar ] [ CrossRef ]
Schweickert, B.; Goldenberg, O.; Richter, E.; Göbel, U.B.; Petrich, A.; Buchholz, P.; Moter, A. Occurrence and Clinical Relevance of Mycobacterium chimaera sp. nov., Germany. Emerg. Infect. Dis. 2008 , 14 , 1443–1446. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Boyle, D.P.; Zembower, T.R.; Qi, C. Evaluation of Vitek MS for Rapid Classification of Clinical Isolates Belonging to Mycobacterium avium Complex. Diagn. Microbiol. Infect. Dis. 2015 , 81 , 41–43. [ Google Scholar ] [ CrossRef ]
Sommerstein, R.; Rüegg, C.; Kohler, P.; Bloemberg, G.; Kuster, S.P.; Sax, H. Transmission of Mycobacterium chimaera from Heater-Cooler Units during Cardiac Surgery despite an Ultraclean Air Ventilation System. Emerg. Infect. Dis. 2016 , 22 , 1008–1013. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Balsam, L.B.; Louie, E.; Hill, F.; Levine, J.; Phillips, M.S. Mycobacterium chimaera Left Ventricular Assist Device Infections. J. Card. Surg. 2017 , 32 , 402–404. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Sanchez-Nadales, A.; Diaz-Sierra, A.; Mocadie, M.; Asher, C.; Gordon, S.; Xu, B. Advanced Cardiovascular Imaging for the Diagnosis of Mycobacterium chimaera Prosthetic Valve Infective Endocarditis After Open-Heart Surgery: A Contemporary Systematic Review. Curr. Probl. Cardiol. 2022 , 47 , 101392. [ Google Scholar ] [ CrossRef ]
Cannas, A.; Campanale, A.; Minella, D.; Messina, F.; Butera, O.; Nisii, C.; Mazzarelli, A.; Fontana, C.; Lispi, L.; Maraglino, F.; et al. Epidemiological and Molecular Investigation of the Heater–Cooler Unit (HCU)-Related Outbreak of Invasive Mycobacterium chimaera Infection Occurred in Italy. Microorganisms 2023 , 11 , 2251. [ Google Scholar ] [ CrossRef ]
Schreiber, P.W.; Kohl, T.A.; Kuster, S.P.; Niemann, S.; Sax, H. The Global Outbreak of Mycobacterium chimaera Infections in Cardiac Surgery—A Systematic Review of Whole-Genome Sequencing Studies and Joint Analysis. Clin. Microbiol. Infect. 2021 , 27 , 1613–1620. [ Google Scholar ] [ CrossRef ]
Rubinstein, M.; Grossman, R.; Nissan, I.; Schwaber, M.J.; Carmeli, Y.; Kaidar-Shwartz, H.; Dveyrin, Z.; Rorman, E. Mycobacterium intracellulare Subsp. Chimaera from Cardio Surgery Heating-Cooling Units and from Clinical Samples in Israel Are Genetically Unrelated. Pathogens 2021 , 10 , 1392. [ Google Scholar ] [ CrossRef ]
Mercaldo, R.A.; Marshall, J.E.; Prevots, D.R.; Lipner, E.M.; French, J.P. Detecting Clusters of High Nontuberculous Mycobacteria Infection Risk for Persons with Cystic Fibrosis—An Analysis of U.S. Counties. Tuberculosis 2023 , 138 , 102296. [ Google Scholar ] [ CrossRef ]
Asadi, T.; Mullin, K.; Roselli, E.; Johnston, D.; Tan, C.D.; Rodriguez, E.R.; Gordon, S. Disseminated Mycobacterium chimaera Infection Associated with Heater-Cooler Units after Aortic Valve Surgery without Endocarditis. J. Thorac. Cardiovasc. Surg. 2018 , 155 , 2369–2374. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Clemente, T.; Spagnuolo, V.; Bottanelli, M.; Ripa, M.; Del Forno, B.; Busnardo, E.; Di Lucca, G.; Castagna, A.; Danise, A. Disseminated Mycobacterium chimaera Infection Favoring the Development of Kaposi’s Sarcoma: A Case Report. Ann. Clin. Microbiol. Antimicrob. 2022 , 21 , 57. [ Google Scholar ] [ CrossRef ]
Schaeffer, T.; Kuster, S.; Koechlin, L.; Khanna, N.; Eckstein, F.S.; Reuthebuch, O. Long-Term Follow-Up after Mycobacterium chimaera Infection Following Cardiac Surgery: Single-Center Experience. J. Clin. Med. 2023 , 12 , 948. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Trauth, J.; Matt, U.; Kohl, T.A.; Niemann, S.; Herold, S. Blind Spot in Endocarditis Guidelines: Mycobacterium chimaera Prosthetic Valve Endocarditis after Cardiac Surgery—A Case Series. Eur. Heart J. Case Rep. 2023 , 7 , ytad400. [ Google Scholar ] [ CrossRef ]
Sanavio, M.; Anna, A.; Bolcato, M. Mycobacterium chimaera : Clinical and Medico-Legal Considerations Starting from a Case of Sudden Acoustic Damage. Leg. Med. 2020 , 47 , 101747. [ Google Scholar ] [ CrossRef ] [ PubMed ]
van Ingen, J. Microbiological Diagnosis of Nontuberculous Mycobacterial Pulmonary Disease. Clin. Chest Med. 2015 , 36 , 43–54. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Wang, H.; Bédard, E.; Prévost, M.; Camper, A.K.; Hill, V.R.; Pruden, A. Methodological Approaches for Monitoring Opportunistic Pathogens in Premise Plumbing: A Review. Water Res. 2017 , 117 , 68–86. [ Google Scholar ] [ CrossRef ]
Hasse, B.; Hannan, M.M.; Keller, P.M.; Maurer, F.P.; Sommerstein, R.; Mertz, D.; Wagner, D.; Fernández-Hidalgo, N.; Nomura, J.; Manfrin, V.; et al. International Society of Cardiovascular Infectious Diseases Guidelines for the Diagnosis, Treatment and Prevention of Disseminated Mycobacterium chimaera Infection Following Cardiac Surgery with Cardiopulmonary Bypass. J. Hosp. Infect. 2020 , 104 , 214–235. [ Google Scholar ] [ CrossRef ]
Schreiber, P.W.; Köhler, N.; Cervera, R.; Hasse, B.; Sax, H.; Keller, P.M. Detection Limit of Mycobacterium chimaera in Water Samples for Monitoring Medical Device Safety: Insights from a Pilot Experimental Series. J. Hosp. Infect. 2018 , 99 , 284–289. [ Google Scholar ] [ CrossRef ]
Daley, C.L.; Iaccarino, J.M.; Lange, C.; Cambau, E.; Wallace, R.J.; Andrejak, C.; Böttger, E.C.; Brozek, J.; Griffith, D.E.; Guglielmetti, L.; et al. Treatment of Nontuberculous Mycobacterial Pulmonary Disease: An Official ATS/ERS/ESCMID/IDSA Clinical Practice Guideline. Clin. Infect. Dis. 2020 , 71 , e1–e36. [ Google Scholar ] [ CrossRef ]
Lecorche, E.; Haenn, S.; Mougari, F.; Kumanski, S.; Veziris, N.; Benmansour, H.; Raskine, L.; Moulin, L.; Cambau, E.; Aubry, A.; et al. Comparison of Methods Available for Identification of Mycobacterium chimaera . Clin. Microbiol. Infect. 2018 , 24 , 409–413. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Lyamin, A.V.; Ereshchenko, A.A.; Gusyakova, O.A.; Yanchenko, A.V.; Kozlov, A.V.; Khaliulin, A.V. Comparison of Laboratory Methods for Identifying Members of the Family Mycobacteriaceae. Int. J. Mycobacteriol 2023 , 12 , 129–134. [ Google Scholar ] [ CrossRef ]
Togawa, A.; Chikamatsu, K.; Takaki, A.; Matsumoto, Y.; Yoshimura, M.; Tsuchiya, S.; Nakamura, S.; Mitarai, S. Multiple Mutations of Mycobacterium ntracellulare Subsp. Chimaera Causing False-Negative Reaction to the Transcription-Reverse Transcription Concerted Method for Pathogen Detection. Int. J. Infect. Dis. 2023 , 133 , 14–17. [ Google Scholar ] [ CrossRef ]
Kuehl, R.; Banderet, F.; Egli, A.; Keller, P.M.; Frei, R.; Döbele, T.; Eckstein, F.; Widmer, A.F. Different Types of Heater-Cooler Units and Their Risk of Transmission of Mycobacterium chimaera during Open-Heart Surgery: Clues from Device Design. Infect. Control Hosp. Epidemiol. 2018 , 39 , 834–840. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Quintás Viqueira, A.; Pérez Romero, C.; Toro Rueda, C.; Sánchez Calles, A.M.; Blázquez González, J.A.; Alejandre Leyva, M. Mycobacterium chimaera in Heater-Cooler Devices: An Experience in a Tertiary Hospital in Spain. New Microbes New Infect. 2021 , 39 , 100757. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Falkinham, J.O., III. Disinfection and Cleaning of Heater-Cooler Units: Suspension- and Biofilm-Killing. J. Hosp. Infect. 2020 , 105 , 552–557. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Hammer-Dedet, F.; Dupont, C.; Evrevin, M.; Jumas-Bilak, E.; Romano-Bertrand, S. Improved Detection of Non-Tuberculous Mycobacteria in Hospital Water Samples. Infect. Dis. Now 2021 , 51 , 488–491. [ Google Scholar ] [ CrossRef ]
Romano-Bertrand, S.; Evrevin, M.; Dupont, C.; Frapier, J.M.; Sinquet, J.C.; Bousquet, E.; Albat, B.; Jumas-Bilak, E. Persistent Contamination of Heater-Cooler Units for Extracorporeal Circulation Cured by Chlorhexidine-Alcohol in Water Tanks. J. Hosp. Infect. 2018 , 99 , 290–294. [ Google Scholar ] [ CrossRef ]
Colangelo, N.; Giambuzzi, I.; Moro, M.; Pasqualini, N.; Aina, A.; De Simone, F.; Blasio, A.; Alfieri, O.; Castiglioni, A.; De Bonis, M. Mycobacterium chimaera in Heater–Cooler Units: New Technical Approach for Treatment, Cleaning and Disinfection Protocol. Perfusion 2019 , 34 , 272–276. [ Google Scholar ] [ CrossRef ]
Ditommaso, S.; Giacomuzzi, M.; Memoli, G.; Garlasco, J.; Curtoni, A.; Iannaccone, M.; Zotti, C.M. Chemical Susceptibility Testing of Non-Tuberculous Mycobacterium strains and Other Aquatic Bacteria: Results of a Study for the Development of a More Sensitive and Simple Method for the Detection of NTM in Environmental Samples. J. Microbiol. Methods 2022 , 193 , 106405. [ Google Scholar ] [ CrossRef ]
Shrimpton, N.Y.R. Evaluation of Disinfection Processes for Water Heater Devices Used for Extracorporeal Life Support. Perfusion 2019 , 34 , 428–432. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Sarink, M.J.; van Cappellen, W.A.; Tielens, A.G.M.; van Dijk, A.; Bogers, A.J.J.C.; de Steenwinkel, J.E.M.; Vos, M.C.; Severin, J.A.; van Hellemond, J.J. Vermamoeba Vermiformis Resides in Water-Based Heater–Cooler Units and Can Enhance Mycobacterium chimaera Survival after Chlorine Exposure. J. Hosp. Infect. 2023 , 132 , 73–77. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Bengtsson, D.; Westerberg, M.; Nielsen, S.; Ridell, M.; Jönsson, B. Mycobacterium chimaera in Heater-Cooler Units Used during Cardiac Surgery–Growth and Decontamination. Infect. Dis. 2018 , 50 , 736–742. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Weitkemper, H.H.; Spilker, A.; Knobl, H.J.; Körfer, R. The Heater-Cooler Unit-A Conceivable Source of Infection. J. Extra Corpor. Technol. 2016 , 48 , 62–66. [ Google Scholar ] [ CrossRef ]
Foltan, M.; Nikisch, A.; Dembianny, J.; Miano, A.L.; Heinze, J.; Klar, D.; Göbölös, L.; Lehle, K.; Schmid, C. A Solution for Global Hygienic Challenges Regarding the Application of Heater-Cooler Systems in Cardiac Surgery. Perfusion 2023 , 38 , 28–36. [ Google Scholar ] [ CrossRef ]
Pradal, I.; Esteban, J.; Mediero, A.; García-Coca, M.; Aguilera-Correa, J.J. Contact Effect of a Methylobacterium sp. Extract on Biofilm of a Mycobacterium chimaera Strain Isolated from a 3T Heater-Cooler System. Antibiotics 2020 , 9 , 474. [ Google Scholar ] [ CrossRef ]
Masaka, E.; Reed, S.; Davidson, M.; Oosthuizen, J. Opportunistic Premise Plumbing Pathogens. A Potential Health Risk in Water Mist Systems Used as a Cooling Intervention. Pathogens 2021 , 10 , 462. [ Google Scholar ] [ CrossRef ]
Treglia, M.; Pallocci, M.; Ricciardi Tenore, G.; Castellani, P.; Pizzuti, F.; Bianco, G.; Passalacqua, P.; De Luca, L.; Zanovello, C.; Mazzuca, D.; et al. Legionella and Air Transport: A Study of Environmental Contamination. Int. J. Environ. Res. Public Health 2022 , 19 , 8069. [ Google Scholar ] [ CrossRef ]
Glassmeyer, S.T.; Burns, E.E.; Focazio, M.J.; Furlong, E.T.; Gribble, M.O.; Jahne, M.A.; Keely, S.P.; Kennicutt, A.R.; Kolpin, D.W.; Medlock Kakaley, E.K.; et al. Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies. Geohealth 2023 , 7 , e2022GH000716. [ Google Scholar ] [ CrossRef ]
Ortiz-Martínez, Y. Mycobacterium chimaera : An under-Diagnosed Pathogen in Developing Countries? J. Hosp. Infect. 2017 , 97 , 125–126. [ Google Scholar ] [ CrossRef ]
Becker, J.B.; Moisés, V.A.; Guerra-Martín, M.D.; Barbosa, D.A. Epidemiological Differences, Clinical Aspects, and Short-Term Prognosis of Patients with Healthcare-Associated and Community-Acquired Infective Endocarditis. Infect. Prev. Pract. 2024 , 6 , 100343. [ Google Scholar ] [ CrossRef ]
Ferrara, S.D.; Baccino, E.; Bajanowski, T.; Boscolo-Berto, R.; Castellano, M.; De Angel, R.; Pauliukevičius, A.; Ricci, P.; Vanezis, P.; Vieira, D.N.; et al. Malpractice and Medical Liability. European Guidelines on Methods of Ascertainment and Criteria of Evaluation. Int. J. Leg. Med. 2013 , 127 , 545–557. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

References	Author, Year	N. of Patients	Surgery	Mean Time of Presentation If Previous Surgery	Setting (Country)	Organ and/or Tissue Involved
[ ]	(Bills et al., 2009)	1	None	Na	Not healthcare (USA)	Lung, nodules in chronic obstructive pulmonary disease
[ ]	(Cohen-Bacrie et al., 2011)	1	None	Na	Possible frequent healthcare contact (Réunion Island, FR)	Lung infections in cystic fibrosis
[ ]	(Alhanna et al., 2012)	1	None	Na	Not healthcare (Germany)	Lung infection
[ ]	(Gunaydin et al., 2013)	5 (of 90)	None	Na	Possible healthcare contact (Turkey)	Lung (reassessment of sputum specimens)
[ ]	(Boyle et al., 2015)	125 (of 448)	None	Na	Possible healthcare contact (USA)	Lung (reassessment of sputum specimens)
[ ]	(Mwikuma et al., 2015)	1 (of 54)	None	Na	Not healthcare (Zambia)	Lung (reassessment of sputum specimens)
[ ]	(Moon et al., 2016)	11	None	Na	Not healthcare (South Korea)	Lung infection (reassessment of sputum specimens)
[ ]	(Moutsoglou et al., 2017)	1	None	Na	Not healthcare (USA)	Disseminated with spinal osteomyelitis and discitis
[ ]	(Bursle et al., 2017)	1	Tricuspid valve repair and mitral annuloplasty	13 months	Underwent surgery (Australia)	Disseminated
[ ]	Kim et al., 2017	8 (of 91)	None	Na	Possible healthcare contact (Korea)	Lung (reassessment of sputum specimens)
[ ]	(Chand et al., 2017) *	4	Valvular cardiac surgery	1.15 (0.25–5.1) years	Underwent surgery (UK)	1 osteomyelitis and 3 disseminated
[ ]	(Truden et al., 2018)	49 (of 102)	None	Na	Possible healthcare contact (Slovenia)	Lung (reassessment of sputum specimens)
[ ]	(Larcher et al., 2019)	4	None	Na	Possible frequent healthcare contact (France)	Lung (reassessment of sputum specimens in cystic fibrosis)
[ ]	(Shafizadeh et al., 2019) *	5	Valvular cardiac surgery	20.6 (14–29) months	Underwent surgery (USA)	Disseminated with liver infection
[ ]	(Rosero and Shams, 2019)	1	None but operating room nurse 10 years ago	>10 years	Possible frequent healthcare contact (USA)	Lung infection
[ ]	(Watanabe et al., 2020)	1	None	Na	Not healthcare (Japan)	Tendons, hand tenosynovitis
[ ]	(Chen et al., 2020)	28	None	Na	Not healthcare (Taiwan)	Lung infection (reassessment of sputum specimens)
[ ]	(Maalouly et al., 2020)	1	Kidney transplantation	One week	Underwent surgery (Belgium)	Kidney, urinary tract infection in a kidney transplant recipient with concomitant Mycobacterium malmoense lung infection and fibro anthracosis
[ ]	(de Melo Carvalho et al., 2020)	1	None	Na	Possible healthcare contact (Portugal)	Disseminated in B-cell lymphoma
[ ]	(Sharma et al., 2020)	2	None	Na	Not healthcare (India)	Meninges, meningitis
[ ]	(Zabost et al., 2021)	88 (of 200)	None	Na	Possible healthcare contact (Poland)	Lung infection (reassessment of sputum specimens)
[ ]	(Kim et al., 2021)	4 (of 320)	None	Na	Possible healthcare contact (Korea)	Lung infection (reassessment of sputum specimens)
[ ]	(Kavvalou et al., 2022)	1	None	Na	Possible healthcare contact (Germany)	Central venous catheter infection in cystic fibrosis
[ ]	(Robinson et al., 2022)	1	None	Na	Not healthcare (USA)	Lung infection in drug abuser
[ ]	(Ahmad et al., 2022)	1	None	Na	Not healthcare (USA)	Lung infection in sarcoidosis
[ ]	(George et al., 2022)	1	None	Na	Not healthcare (India)	Skin, periapical abscess with chin ulcer
[ ]	(Lin et al., 2022)	1	None	Na	Possible frequent healthcare contact (Taiwan)	Disseminated in adult-onset immunodeficiency syndrome
[ ]	(Łyżwa et al., 2022)	1	None	Na	Not healthcare (Poland)	Lung infection in silicosis
[ ]	(McLaughlin et al., 2022)	1	Coronary artery bypass grafting	1 year	Underwent surgery (USA)	Tendons, hand tenosynovitis in ipsilateral elbow wound in fisherman
[ ]	(Gross et al., 2023)	23	None	Na	Healthcare (USA)	Lung infections in cystic fibrosis (genomic analysis for cluster correlation to hospital outbreaks)
[ ]	(Azzarà et al., 2023)	1	None	Na	Possible healthcare contact (Italy)	Lung infection in lung adenocarcinoma treated with immune checkpoint inhibitors
[ ]	(Pradhan et al., 2023)	1	Bioprosthetic mitral valve replacement	7 years	Underwent surgery (Australia)	Spinal osteomyelitis and discitis
[ ]	(Garcia-Prieto et al., 2024)	1	None	Na	Not healthcare (Spain)	Lung infection in fibro anthracosis
[ ]	(Paul et al., 2024)	1	None	Na	Possible healthcare contact (UK)	Lung infection in unilateral pulmonary artery agenesis on the right side

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Bolcato, V.; Bassetti, M.; Basile, G.; Bianco Prevot, L.; Speziale, G.; Tremoli, E.; Maffessanti, F.; Tronconi, L.P. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare 2024 , 12 , 1788. https://doi.org/10.3390/healthcare12171788

Bolcato V, Bassetti M, Basile G, Bianco Prevot L, Speziale G, Tremoli E, Maffessanti F, Tronconi LP. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare . 2024; 12(17):1788. https://doi.org/10.3390/healthcare12171788

Bolcato, Vittorio, Matteo Bassetti, Giuseppe Basile, Luca Bianco Prevot, Giuseppe Speziale, Elena Tremoli, Francesco Maffessanti, and Livio Pietro Tronconi. 2024. "The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review" Healthcare 12, no. 17: 1788. https://doi.org/10.3390/healthcare12171788

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