history and research methods in cognitive psychology

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Cognitive Psychology

History of cognitive psychology.

The history of cognitive psychology can be parsed into four periods: philosophical, early experimental, the cognitive revolution, and modern cognitive psychology.

Philosophical Period

Ancient Egyptian hieroglyphics suggest that thoughtful people were concerned with processes such as thought, memory, and most of all the “ka”, or soul, Great energy was directed toward preserving the soul but also some theorized that knowledge was localized in the heart. Greek philosophers were obsessed with knowledge and cognitive matters and current models of cognition often have some ties to ancient Greece. Aristotle’s views on the locus of knowledge were similar to the Egyptians. However, Plato postulated that the brain was the true locus of knowledge. Renaissance scholars considered thinking, logic, and the nature of the soul and, al­though divergent views were expressed, the locus of the knowledge and rationality was thought to be in the brain.

During the eighteenth century, philosophic debate over the source of knowledge took place between the empiricist and the nativist. A British empiricist believed knowledge came from experience. However, the nativist believed knowledge was innate and based on structural characteristics and properties inherent in the brain. Modern cognitive psychologists continue to argue these matters, although usually with scientific data.

The philosophic period provided a context for un­derstanding the mind and its processes. In addition, these early thinkers identified some major theoretical issues that would later be studied empirically using scientific research methods.

Early Experimental Period

Cognition has been studied scientifically since the end of the nineteenth century. In 1879, the philosophical aspects of mental processes gave way to empirical ob­servations when Wundt founded the first psychological laboratory in Germany in 1879. Psychology began to break away from philosophy and form a discipline based on objective science rather than on speculation, logic, and conjecture. Many forces propelled the break with moral philosophy, but certainly the development of new methods that allowed for the examination of mental events changed the way cognition was studied. Intro­spection, or looking within, was one such method that allowed the observer to examine consciousness and the structure of mental representation by breaking down an experience into sensations and images. By detecting patterns within introspective reports, the mind’s con­tents were presumed to be revealed.

Theories of knowledge representation became di­vided between introspectionists who studied observable sensations, and act psychologists, led by Brentano, who studied the activities of the mind. Brentano considered internal representations meaningless to psychology and chose to study mental acts of comparing, judging, and feeling physical objects.

By the beginning of the twentieth century American psychology was beginning to take a distinctive form with a wide range of topics under investigation. Lead­ing this expanded experimental psychology was Wil­liam James, the first president of the American Psycho­logical Association. His ideas on philosophy, religion, and psychology shaped the intellectual history of these topics throughout the twentieth century. No less im­portant were his thoughts about attention and memory, and his distinction of a dichotomy memory store—pri­mary and secondary memory—led directly to experi­ments in the 1960s on that topic. Clearly, James’s ideas were important in shaping modern cognitive psychol­ogy.

During this time, American psychologists became in­terested in educational matters and were greatly influ­enced by the objective nature of act psychology. Psy­chologists such as Thorndike were concerned with the effects of reward and punishment on learning and less concerned with consciousness. The introspective tech­nique, in which a subject asks himself what sensations he might experience, for example, were considered by American psychologists as being sterile and leading to inconsistent results. There was, argued many, a need for a purely objective and scientific psychology in which mental processes, such as memory, sensations, and learning, could be reliably measured. Behaviorism, led by John Watson, was predicated on the idea that overt behavior could be objectively observed, offered an at­tractive scientific approach to psychology, and was an appropriate foil to the rapidly developing interest in psy­choanalysis.

Despite interest in overt behavior, cognitive process were not totally neglected. During the early 1900s Donders and Cattell were conducting perception experiments on imageless thought using brief visual displays to examine the time required for mental operations to take place and using reaction time data as dependent measures.

In several laboratories in America interesting re­search was being done on memory, attention, percep­tion, language, concept formation, and problem solving that was the preformal stage of cognitive psychology. In addition to these efforts within psychology, several forces outside of traditional experimental psychology helped shape cognitive psychology. Among these forces are the considerable influence of the Swiss psychologist, Jean Piaget, whose central idea was that there are dis­tinctive cognitive stages through which children de­velop. In Russia, the brilliant young savant, Lev Vygotsky, suggested a model of development psychology in which learning precedes development. Another impor­tant influence was the work of Frederic Bartlett, from England, who investigated memory from a naturalistic viewpoint and was particularly concerned with the re­membering of stories. From recall of stories, Bartlett hypothesized that memory is largely determined by schemata, or the way knowledge is organized and rep­resented in the brain. Even some animal studies were beginning to embrace cognitive themes. In 1932, Tolman, a well-known behavioral psychologist, observed that rats learned a cognitive map of their environment while learning to run a maze.

Although cognition was not the dominant school of psychological thought in America during this time, some experimental psychologists demonstrated that scientific methodology could be used in the study of men­tal events. The techniques, subject matter, procedures, and even the interpretations used by these researchers anticipated the emergence of a cognitive discipline.

Concepts such as sensation, thinking, and mental imagery were anathema under the behaviorist’s influ­ence, as they were considered subjective. Internal states were considered intervening variables and not neces­sary to understand human behavior. Psychology had been concentrated on observable behaviors and human subjects were largely replaced with rats and pigeons.

Gestalt psychology offered an alternative way to study sensory perception to the problematic method of introspection that diffused the research on cognition. Concurrently the behaviorists attempted to create a purely objective psychology by successfully attacking the cognitive psychologists and Gestaltists as well.

Cognitive Revolution

Cognitive psychology began to take form as a new way of understanding the science of the mind during the late 1950s. These formative events were spurred on by research discoveries in memory, learning, and attention as well as ideas outside of the mainstay of experimental psychology, such as communication theory , develop­mental psychology, social psychology, linguistics, and computer science, which gave cognitive psychologists additional breadth to deal with the complexity of hu­man information processing and thinking.

The reemergence of cognitive psychology during this period is commonly referred to as the Cognitive Revo­lution, emerging in 1956 with a conference on com­munication theory at Massachusetts Institute of Tech­nology (MIT) (Solso, 1998) in which seminal papers were presented by Noam Chomsky, Jerome Bruner, Al­len Newell and Herbert Simon, and George Miller. The coalescence of cognitive psychology during this period was probably not due to a single group of people (and certainly no precise date of a movement is possible) but was a reflection of a larger Zeitgeist in which psychol­ogists appreciated the complexity of the thinking hu­man. At the same time, cognitive psychologists rejected the traditional, simplistic theories of the mind, but in many cases held on to the scientific methodology as had developed in the early part of the twentieth century. The paradigm that offered a pertinent methodol­ogy and embraced a sufficiently wide latitude of intel­lectual topics was cognitive psychology, which enjoyed widespread acceptance and growth.

Research in verbal learning and semantic organi­zation led to the development of testable models of memory and cognition, providing another empirical base for the study of mental processes. George A. Miller made a distinction between short-term and long-term memory and his influential paper The Magic Number Seven, Plus or Minus Two (Miller, 1956) addressed the limited capacity of short-term memory and introduced the concept of chunking—the idea that the limits of short-term memory could be extended by grouping in­formation into larger units of information. In 1958, Pe­terson and Peterson in America and John Brown in England found a rapid loss or decay of memory after the study of nonsense syllables after a few seconds when verbal rehearsal was absent, thus promoting the idea of a separate stage of short-term memory. In 1960, Sperling showed that a very transitory memory (or in­formation storage system) held information for a very brief period of time. This discovery further advanced the notion that humans were complex information-processing creatures who processed incoming infor­mation through a series of stages. That simple idea was a perfect model for researchers and theorists interested in memory, and several models appeared about this time by Atkinson and Shiffrin, Waugh and Norman, and later by Craik and Tulving.

Prior to this period, information theory was intro­duced by Shannon and Weaver, who used box diagrams to describe how information is communicated and transformed along a series of stages. Donald Broadbent, a psychologist at Cambridge, began applying Shannon and Weaver’s ideas to selective attention processes and introduced the concept of information flow to psychol­ogy and used box diagrams to describe cognitive pro­cesses. Broadbent’s information flow referred to the se­ries of operations that analyze, transform, or change mental events such as memory encoding, forgetting, thinking, concept formation, etc. As such, Broadbent provided “a language to talk about what happened in­side a man which was not a mentalistic introspective language” (Cohen, 1986, p. 23).

Elsewhere, technological advances in computer sci­ence called for reexamination of basic postulates of cognition. In 1955, Simon and Newell developed a computer capable of solving a mathematical proof. Cogni­tive psychologists were excited that machines could simulate human thought and computers could possibly be operating according to the same rules and proce­dures as the human mind. Furthermore, since com­puters were seen as intelligent, it required us to analyze our own intelligence so that the intelligence of a ma­chine could be determined. As a result the hypothetical Turing test was devised to determine if observers could discriminate the output of a computer from that of hu­man responses.

Meanwhile, the behaviorists came under attack from Chomsky, a linguist from MIT, who developed a method of analyzing the structure of language. Chomsky ar­gued that language was too complicated to learn and produce via behavioral principles of reinforcement and postulated the existence of a cognitive structure of an innate language acquisition device.

Another influence that aided cognitive psychology’s foothold was World War II. Financial support in areas of military interest became readily available during the war. Because of the military’s interest in developing and using new technology, research in vigilance, cre­ativity, and human factors was encouraged. One out­come was a seminal report in 1954 by Tanner and Swets on signal detection demonstrating that cognitive processes can have a mediating effect on sensory thresholds. Another outcome of the war was that many soldiers suffered from brain injuries. A vast amount of clinical data in perception, memory, and language was a by-product of these victims’ afflictions.

In the 1950s, interest turned to attention, memory, pattern recognition, images, semantic organization, language processes, thinking, and even consciousness (the most dogmatically eschewed concept), as well as other cognitive topics once considered outside the boundary of experimental psychology. Behaviorism and its dogma failed to account for the richness and diver­sity of human experience. Behaviorists could not ac­count for the results found by Piaget’s and Chomsky’s developmental studies. And information theory and computer science gave psychologists new ways to con­ceptualize and discuss cognition.

Modern Cognitive Psychology

By the 1960s, cognitive psychology had experienced a renaissance. Cognitive Psychology, which systematized the new science, was written by Ulric Neisser and was published in America (1967). Neisser’s book was cen­tral to the solidification of cognitive psychology as it gave a label to the field and defined the topical areas. Neisser used the computer metaphor for selecting, stor­ing. Recovering, combining, outputting, and manipu­lating information. And in 1966 Hilgard and Bower in­troduced a chapter in their Theories of Learning (New York) that developed the idea of using computer pro­grams to serve as models on theories of cognition.

The 1970s saw the emergence of professional jour­nals devoted to cognitive psychology such as Cognitive Psychology, Cognition, Memory & Cognition, and a series of symposia volumes, including the Loyola Symposium on Cognition edited by Solso and the Carnegie-Mellon series edited by Chase and others, based on the Car­negie Symposium on Cognition. In the 1970s and 1980s cognitive laboratories were beginning to be built, symposia and conferences appeared at national and re­gional meetings, courses in cognitive psychology and related topics were being added to curricula, grants were awarded to people investigating memory, lan­guage processing, attention, and like topics, new text­books were written on the theme of cognition, and uni­versities recruited professors of cognitive psychology to replace those of traditional experimental psychology. In the 1980s and 1990s serious efforts were made to find corresponding neural components that were linked to cognitive constructs. Thus, the cerebral location for a word, like hammer, as a noun, might be far different than the location for the same word if the word were used as a verb. Furthermore, influential memory the­ories (such as Tulving’s semantic and episodic memory theory) were manifest in cerebral localization experi­ments using brain imaging technology. The science of human cognition is still undergoing transformation due to major changes in computer technology and brain science. As a result cognitive psychology has converged with computer science and neuroscience to create a new discipline called cognitive science.

Finally, with the advent of new ways to see the brain (e.g. functional magnetic resonance imaging [fMRI], positron emission tomography [PET], electroencepha­logram [EEG]) cognitive psychologists have expanded their operations to neuroscience, which promises to empirically display the parts of the brain involved in cognition that were hypothesized by twentieth-century psychologists.

• Cognitive Psychology Theories
• Cognitive Psychology Research Methods

Cognitive Approach in Psychology

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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Cognitive psychology is the scientific study of the mind as an information processor. It concerns how we take in information from the outside world, and how we make sense of that information.

Cognitive psychology studies mental processes, including how people perceive, think, remember, learn, solve problems, and make decisions.

Cognitive psychologists try to build cognitive models of the information processing that occurs inside people’s minds, including perception, attention, language, memory, thinking, and consciousness.

Cognitive psychology became of great importance in the mid-1950s. Several factors were important in this:

• Dissatisfaction with the behaviorist approach in its simple emphasis on external behavior rather than internal processes.
• The development of better experimental methods.
• Comparison between human and computer processing of information . Using computers allowed psychologists to try to understand the complexities of human cognition by comparing it with computers and artificial intelligence.

The emphasis of psychology shifted away from the study of conditioned behavior and psychoanalytical notions about the study of the mind, towards the understanding of human information processing using strict and rigorous laboratory investigation.

Summary Table

Theoretical Assumptions

Mediational processes occur between stimulus and response:

The behaviorist approach only studies external observable (stimulus and response) behavior that can be objectively measured.

They believe that internal behavior cannot be studied because we cannot see what happens in a person’s mind (and therefore cannot objectively measure it).

However, cognitive psychologists consider it essential to examine an organism’s mental processes and how these influence behavior.

Cognitive psychology assumes a mediational process occurs between stimulus/input and response/output. 

These are mediational processes because they mediate (i.e., go-between) between the stimulus and the response. They come after the stimulus and before the response.

Instead of the simple stimulus-response links proposed by behaviorism, the mediational processes of the organism are essential to understand.

Without this understanding, psychologists cannot have a complete understanding of behavior.

The mediational (i.e., mental) event could be memory , perception , attention or problem-solving, etc. 

• Perception : how we process and interpret sensory information.
• Attention : how we selectively focus on certain aspects of our environment.
• Memory : how we encode, store, and retrieve information.
• Language : how we acquire, comprehend, and produce language.
• Problem-solving and decision-making : how we reason, make judgments, and solve problems.
• Schemas : Cognitive psychologists assume that people’s prior knowledge, beliefs, and experiences shape their mental processes. 

For example, the cognitive approach suggests that problem gambling results from maladaptive thinking and faulty cognitions, which both result in illogical errors.

Gamblers misjudge the amount of skill involved with ‘chance’ games, so they are likely to participate with the mindset that the odds are in their favour and that they may have a good chance of winning.

Therefore, cognitive psychologists say that if you want to understand behavior, you must understand these mediational processes.

Psychology should be seen as a science:

This assumption is based on the idea that although not directly observable, the mind can be investigated using objective and rigorous methods, similar to how other sciences study natural phenomena. 

Controlled experiments

The cognitive approach believes that internal mental behavior can be scientifically studied using controlled experiments . It uses the results of its investigations to make inferences about mental processes.  Cognitive psychology uses highly controlled laboratory experiments to avoid the influence of extraneous variables . This allows the researcher to establish a causal relationship between the independent and dependent variables. These controlled experiments are replicable, and the data obtained is objective (not influenced by an individual’s judgment or opinion) and measurable. This gives psychology more credibility.

Operational definitions

Cognitive psychologists develop operational definitions to study mental processes scientifically. These definitions specify how abstract concepts, such as attention or memory, can be measured and quantified (e.g., verbal protocols of thinking aloud). This allows for reliable and replicable research findings.

Falsifiability

Falsifiability in psychology refers to the ability to disprove a theory or hypothesis through empirical observation or experimentation. If a claim is not falsifiable, it is considered unscientific.

Cognitive psychologists aim to develop falsifiable theories and models, meaning they can be tested and potentially disproven by empirical evidence.

This commitment to falsifiability helps to distinguish scientific theories from pseudoscientific or unfalsifiable claims.

Empirical evidence

Cognitive psychologists rely on empirical evidence to support their theories and models. They collect data through various methods, such as experiments, observations, and questionnaires, to test hypotheses and draw conclusions about mental processes.

Cognitive psychologists assume that mental processes are not random but are organized and structured in specific ways. They seek to identify the underlying cognitive structures and processes that enable people to perceive, remember, and think.

Cognitive psychologists have made significant contributions to our understanding of mental processes and have developed various theories and models, such as the multi-store model of memory , the working memory model , and the dual-process theory of thinking.

Humans are information processors:

The idea of information processing was adopted by cognitive psychologists as a model of how human thought works.

The information processing approach is based on several assumptions, including:

• Information is processed by a series of systems : The information processing approach proposes that a series of cognitive systems, such as attention, perception, and memory, process information from the environment. Each system plays a specific role in processing the information and passing it along to the next stage.
• Processing systems transform information : As information passes through these cognitive systems, it is transformed or modified in systematic ways. For example, incoming sensory information may be filtered by attention, encoded into memory, or used to update existing knowledge structures.
• Research aims to specify underlying processes and structures : The primary goal of research within the information processing approach is to identify, describe, and understand the specific cognitive processes and mental structures that underlie various aspects of cognitive performance, such as learning, problem-solving, and decision-making.
• Human information processing resembles computer processing : The information processing approach draws an analogy between human cognition and computer processing. Just as computers take in information, process it according to specific algorithms, and produce outputs, the human mind is thought to engage in similar processes of input, processing, and output.

Computer-Mind Analogy

The computer-brain metaphor, or the information processing approach, is a significant concept in cognitive psychology that likens the human brain’s functioning to that of a computer.

This metaphor suggests that the brain, like a computer, processes information through a series of linear steps, including input, storage, processing, and output.

According to this assumption, when we interact with the environment, we take in information through our senses (input).

This information is then processed by various cognitive systems, such as perception, attention, and memory. These systems work together to make sense of the input, organize it, and store it for later use.

During the processing stage, the mind performs operations on the information, such as encoding, transforming, and combining it with previously stored knowledge. This processing can involve various cognitive processes, such as thinking, reasoning, problem-solving, and decision-making.

The processed information can then be used to generate outputs, such as actions, decisions, or new ideas. These outputs are based on the information that has been processed and the individual’s goals and motivations.

This has led to models showing information flowing through the cognitive system, such as the multi-store memory model.

The information processing approach also assumes that the mind has a limited capacity for processing information, similar to a computer’s memory and processing limitations.

This means that humans can only attend to and process a certain amount of information at a given time, and that cognitive processes can be slowed down or impaired when the mind is overloaded.

The Role of Schemas

A schema is a “packet of information” or cognitive framework that helps us organize and interpret information. It is based on previous experience.

Cognitive psychologists assume that people’s prior knowledge, beliefs, and experiences shape their mental processes. They investigate how these factors influence perception, attention, memory, and thinking.

Schemas help us interpret incoming information quickly and effectively, preventing us from being overwhelmed by the vast amount of information we perceive in our environment.

Schemas can often affect cognitive processing (a mental framework of beliefs and expectations developed from experience). As people age, they become more detailed and sophisticated.

However, it can also lead to distortion of this information as we select and interpret environmental stimuli using schemas that might not be relevant.

This could be the cause of inaccuracies in areas such as eyewitness testimony. It can also explain some errors we make when perceiving optical illusions.

1. Behaviorist Critique

B.F. Skinner criticizes the cognitive approach. He believes that only external stimulus-response behavior should be studied, as this can be scientifically measured.

Therefore, mediation processes (between stimulus and response) do not exist as they cannot be seen and measured.

Behaviorism assumes that people are born a blank slate (tabula rasa) and are not born with cognitive functions like schemas , memory or perception .

Due to its subjective and unscientific nature, Skinner continues to find problems with cognitive research methods, namely introspection (as used by Wilhelm Wundt).

2. Complexity of mental experiences

Mental processes are highly complex and multifaceted, involving a wide range of cognitive, affective, and motivational factors that interact in intricate ways.

The complexity of mental experiences makes it difficult to isolate and study specific mental processes in a controlled manner.

Mental processes are often influenced by individual differences, such as personality, culture, and past experiences, which can introduce variability and confounds in research .

3. Experimental Methods 

While controlled experiments are the gold standard in cognitive psychology research, they may not always capture real-world mental processes’ complexity and ecological validity.

Some mental processes, such as creativity or decision-making in complex situations, may be difficult to study in laboratory settings.

Humanistic psychologist Carl Rogers believes that using laboratory experiments by cognitive psychology has low ecological validity and creates an artificial environment due to the control over variables .

Rogers emphasizes a more holistic approach to understanding behavior.

The cognitive approach uses a very scientific method that is controlled and replicable, so the results are reliable.

However, experiments lack ecological validity because of the artificiality of the tasks and environment, so they might not reflect the way people process information in their everyday lives.

For example, Baddeley (1966) used lists of words to find out the encoding used by LTM.

However, these words had no meaning to the participants, so the way they used their memory in this task was probably very different from what they would have done if the words had meaning for them.

This is a weakness, as the theories might not explain how memory works outside the laboratory.

4. Computer Analogy

The information processing paradigm of cognitive psychology views the minds in terms of a computer when processing information.

However, although there are similarities between the human mind and the operations of a computer (inputs and outputs, storage systems, and the use of a central processor), the computer analogy has been criticized.

For example, the human mind is characterized by consciousness, subjective experience, and self-awareness , which are not present in computers.

Computers do not have feelings, emotions, or a sense of self, which play crucial roles in human cognition and behavior.

The brain-computer metaphor is often used implicitly in neuroscience literature through terms like “sensory computation,” “algorithms,” and “neural codes.” However, it is difficult to identify these concepts in the actual brain.

5. Reductionist

The cognitive approach is reductionist as it does not consider emotions and motivation, which influence the processing of information and memory. For example, according to the Yerkes-Dodson law , anxiety can influence our memory.

Such machine reductionism (simplicity) ignores the influence of human emotion and motivation on the cognitive system and how this may affect our ability to process information.

Early theories of cognitive approach did not always recognize physical ( biological psychology ) and environmental (behaviorist approach) factors in determining behavior.

However, it’s important to note that modern cognitive psychology has evolved to incorporate a more holistic understanding of human cognition and behavior.

1. Importance of cognitive factors versus external events

Cognitive psychology emphasizes the role of internal cognitive processes in shaping emotional experiences, rather than solely focusing on external events.

Beck’s cognitive theory suggests that it is not the external events themselves that lead to depression, but rather the way an individual interprets and processes those events through their negative schemas.

This highlights the importance of addressing cognitive factors in the treatment of depression and other mental health issues.

Social exchange theory (Thibaut & Kelly, 1959) emphasizes that relationships are formed through internal mental processes, such as decision-making, rather than solely based on external factors.

The computer analogy can be applied to this concept, where individuals observe behaviors (input), process the costs and benefits (processing), and then make a decision about the relationship (output).

2. Interdisciplinary approach

While early cognitive psychology may have neglected physical and environmental factors, contemporary cognitive psychology has increasingly integrated insights from other approaches.

Cognitive psychology draws on methods and findings from other scientific disciplines, such as neuroscience , computer science, and linguistics, to inform their understanding of mental processes.

This interdisciplinary approach strengthens the scientific basis of cognitive psychology.

Cognitive psychology has influenced and integrated with many other approaches and areas of study to produce, for example, social learning theory , cognitive neuropsychology, and artificial intelligence (AI).

3. Real World Applications

Another strength is that the research conducted in this area of psychology very often has applications in the real world.

By highlighting the importance of cognitive processing, the cognitive approach can explain mental disorders such as depression.

Beck’s cognitive theory of depression argues that negative schemas about the self, the world, and the future are central to the development and maintenance of depression.

These negative schemas lead to biased processing of information, selective attention to negative aspects of experience, and distorted interpretations of events, which perpetuate the depressive state.

By identifying the role of cognitive processes in mental disorders, cognitive psychology has informed the development of targeted interventions.

Cognitive behavioral therapy aims to modify the maladaptive thought patterns and beliefs that underlie emotional distress, helping individuals to develop more balanced and adaptive ways of thinking.

CBT’s basis is to change how people process their thoughts to make them more rational or positive.

Through techniques such as cognitive restructuring, behavioral experiments, and guided discovery, CBT helps individuals to challenge and change their negative schemas, leading to improvements in mood and functioning.

Cognitive behavioral therapy (CBT) has been very effective in treating depression (Hollon & Beck, 1994), and moderately effective for anxiety problems (Beck, 1993). 

Issues and Debates

Free will vs. determinism.

The cognitive approach’s position is unclear. It argues that cognitive processes are influenced by experiences and schemas, which implies a degree of determinism.

On the other hand, cognitive-behavioral therapy (CBT) operates on the premise that individuals can change their thought patterns, suggesting a capacity for free will.

Nature vs. Nurture

The cognitive approach takes an interactionist view of the debate, acknowledging the influence of both nature and nurture on cognitive processes.

It recognizes that while some cognitive abilities, such as language acquisition, may have an innate component (nature), experiences and learning (nurture) also shape the way information is processed.

Holism vs. Reductionism

The cognitive approach tends to be reductionist in its methodology, as it often studies cognitive processes in isolation.

For example, researchers may focus on memory processes without considering the influence of other cognitive functions or environmental factors.

While this approach allows for more controlled study, it may lack ecological validity, as in real life, cognitive processes typically interact and function simultaneously.

Idiographic vs. Nomothetic

The cognitive approach is primarily nomothetic, as it seeks to establish general principles and theories of information processing that apply to all individuals.

It aims to identify universal patterns and mechanisms of cognition rather than focusing on individual differences.

History of Cognitive Psychology

• Wolfgang Köhler (1925) – Köhler’s book “The Mentality of Apes” challenged the behaviorist view by suggesting that animals could display insightful behavior, leading to the development of Gestalt psychology.
• Norbert Wiener (1948) – Wiener’s book “Cybernetics” introduced concepts such as input and output, which influenced the development of information processing models in cognitive psychology.
• Edward Tolman (1948) – Tolman’s work on cognitive maps in rats demonstrated that animals have an internal representation of their environment, challenging the behaviorist view.
• George Miller (1956) – Miller’s paper “The Magical Number 7 Plus or Minus 2” proposed that short-term memory has a limited capacity of around seven chunks of information, which became a foundational concept in cognitive psychology.
• Allen Newell and Herbert A. Simon (1972) – Newell and Simon developed the General Problem Solver, a computer program that simulated human problem-solving, contributing to the growth of artificial intelligence and cognitive modeling.
• George Miller and Jerome Bruner (1960) – Miller and Bruner established the Center for Cognitive Studies at Harvard, which played a significant role in the development of cognitive psychology as a distinct field.
• Ulric Neisser (1967) – Neisser’s book “Cognitive Psychology” formally established cognitive psychology as a separate area of study, focusing on mental processes such as perception, memory, and thinking.
• Richard Atkinson and Richard Shiffrin (1968) – Atkinson and Shiffrin proposed the Multi-Store Model of memory, which divided memory into sensory, short-term, and long-term stores, becoming a key model in the study of memory.
• Eleanor Rosch’s (1970s) research on natural categories and prototypes, which influenced the study of concept formation and categorization.
• Endel Tulving’s (1972) distinction between episodic and semantic memory, which further developed the understanding of long-term memory.
• Baddeley and Hitch’s (1974) proposal of the Working Memory Model, which expanded on the concept of short-term memory and introduced the idea of a central executive.
• Marvin Minsky’s (1975) framework of frames in artificial intelligence, which influenced the understanding of knowledge representation in cognitive psychology.
• David Rumelhart and Andrew Ortony’s (1977) work on schema theory, which described how knowledge is organized and used for understanding and remembering information.
• Amos Tversky and Daniel Kahneman’s (1970s-80s) research on heuristics and biases in decision making, which led to the development of behavioral economics and the study of judgment and decision-making.
• David Marr’s (1982) computational theory of vision, which provided a framework for understanding visual perception and influenced the field of computational cognitive science.
• The development of connectionism and parallel distributed processing (PDP) models in the 1980s, which provided an alternative to traditional symbolic models of cognitive processes.
• Noam Chomsky’s (1980s) theory of Universal Grammar and the language acquisition device, which influenced the study of language and cognitive development.
• The emergence of cognitive neuroscience in the 1990s, which combined techniques from cognitive psychology, neuroscience, and computer science to study the neural basis of cognitive processes.

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Further Reading

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• Cognitive Psychology Historial Development

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1 Chapter 1. History and Research Methods

Philosophers have wondered about the mind at least as far back as Socrates. Yet the scientific study of mental processes only began much more recently. What changed, and what tools can we use to study mental processes?

RISE OF COGNITIVE PSYCHOLOGY

Precursors to American psychology can be found in philosophy and physiology. Philosophers such as John Locke (1632–1704) and Thomas Reid (1710–1796) promoted empiricism , the idea that all knowledge comes from experience. The work of Locke, Reid, and others emphasized the role of the human observer and the primacy of the senses in defining how the mind comes to acquire knowledge. In American colleges and universities in the early 1800s, these principles were taught as courses on mental and moral philosophy. Most often these courses taught about the mind based on the faculties of intellect, will, and the senses (Fuchs, 2000).

EARLY COGNITIVE RESEARCH

While the term cognitive psychology was not coined until 1967, there were many researchers who contributed to its development much earlier. In 1868, Dutch physiologist Franciscus Donders aimed to determine the length of time needed to make a decision. Donders devised a simple experiment measuring reaction time – how long it takes to respond to a stimulus. He measured it using a simple reaction time task , where participants were asked to press a button as soon as they saw a light stimulus. Additionally, he measured it using a choice reaction time task , where participants were asked to press a button corresponding to one of two lights – a right-side button for a right-side light or a left-side button for a left-side light. In a third task, Donders had participants complete a go/no go task , where participants saw a number of stimuli presented sequentially, and were only to respond (by pressing a button) when the “go” stimulus was presented, while withholding their response on the “no go” stimuli. Donders reasoned that the simple reaction time task included sensory reception of the stimulus as well as the physiological response time (the time it takes the brain to command the finger to press the button). The go/no go task included these same components, plus an identification process (whether the stimulus was a “go” or “no go” stimulus). The choice reaction time task included all of these components as well as a response selection process (deciding which button to press), which Donders deemed the decision process.

Donders had deduced that the difference in the reactions times of the tasks would indicate the length of time needed for the different processes, using a subtractive method (also referred to as mental chronometry ). So, the difference between a participant’s average time on the go/no go task and their average time on the simple reaction time task would indicate the length of time that the i dentification process takes. The difference between a participant’s average time on the choice reaction time task and their average time on the go/no go task would indicate the length of time that the response selection (decision) process takes.

ESTABLISHMENT OF PSYCHOLOGY

The formal development of modern psychology is usually credited to the work of German physician, physiologist, and philosopher Wilhelm Wundt (1832–1920). Wundt helped to establish the field of experimental psychology by serving as a strong promoter of the idea that psychology could be an experimental field and by providing classes, textbooks, and a laboratory for training students. In 1875, he joined the faculty at the University of Leipzig and quickly began to make plans for the creation of a program of experimental psychology. In 1879, he complemented his lectures on experimental psychology with a laboratory experience: an event that has served as the popular date for the establishment of the science of psychology.

The response to the new science was immediate and global. Wundt attracted students from around the world to study the new experimental psychology and work in his lab. Students were trained to offer detailed self- reports of their reactions to various stimuli, a procedure known as introspection , with the goal of identifying the components of consciousness. In addition to the study of sensation and perception, research was done on mental chronometry. The work of Wundt and his students further demonstrated that mental processes could be measured and the nature of consciousness could be revealed through scientific means. It was an exciting proposition, and one that found great interest in America. After the opening of Wundt’s lab in 1879, it took just four years for the first psychology laboratory to open in the United States (Benjamin, 2007).

THE GROWTH OF PSYCHOLOGY

Throughout the first half of the 20th century, psychology continued to grow and flourish in America. It was large enough to accommodate varying points of view on the nature of mind and behavior. Gestalt psychology is a good example. The Gestalt movement began in Germany with the work of Max Wertheimer (1880–1943). Opposed to the reductionist approach of Wundt’s laboratory, Wertheimer and his colleagues Kurt Koffka (1886–1941), Wolfgang Kohler (1887–1967), and Kurt Lewin (1890–1947) believed that studying the whole of any experience was richer than studying individual aspects of that experience. The saying “the whole is greater than the sum of its parts” is a Gestalt perspective. Consider that a melody is an additional element beyond the collection of notes that comprise it. The Gestalt psychologists proposed that the mind often processes information simultaneously rather than sequentially. For instance, when you look at a photograph, you see a whole image, not just a collection of pixels of color. Using Gestalt principles, Wertheimer and his colleagues also explored the nature of learning and thinking. Many of the German Gestalt psychologists were Jewish and were forced to flee the Nazi regime due to the threats posed on both academic and personal freedoms. In America, they were able to introduce a new audience to the Gestalt perspective, demonstrating how it could be applied to perception and learning (Wertheimer, 1938). In many ways, the work of the Gestalt psychologists served as a precursor to the rise of cognitive psychology in America (Benjamin, 2007).

Behaviorism emerged early in the 20th century and became a major force in American psychology. Championed by psychologists such as John B. Watson (1878–1958) and B. F. Skinner (1904–1990), behaviorism rejected any reference to mind and viewed overt and observable behavior as the proper subject matter of psychology. Through the scientific study of behavior, it was hoped that laws of learning could be derived that would promote the prediction and control of behavior. Russian physiologist Ivan Pavlov (1849–1936) influenced early behaviorism in America. His work on conditioned learning, popularly referred to as classical conditioning, provided support for the notion that learning and behavior were controlled by events in the environment and could be explained with no reference to mind or consciousness (Fancher, 1987).

COGNITIVE REVOLUTION

Behaviorism’s emphasis on objectivity and focus on external behavior had pulled psychologists’ attention away from the mind for a prolonged period of time. The early work of the humanistic psychologists redirected attention to the individual human as a whole, and as a conscious and self-aware being. By the 1950s, new disciplinary perspectives in linguistics, neuroscience, and computer science were emerging, and these areas revived interest in the mind as a focus of scientific inquiry. This particular perspective has come to be known as the cognitive revolution (Miller, 2003). By 1967, Ulric Neisser published the first textbook entitled Cognitive Psychology , which served as a core text in cognitive psychology courses around the country (Thorne & Henley, 2005). Cognitive psychology is the study of mental processing such as attention, memory, perception, language use, problem solving, decision making, creativity, and thinking . Much of the work derived from cognitive psychology has been integrated into various other modern disciplines of psychological study including social psychology, personality psychology, abnormal psychology, developmental psychology, educational psychology, and economics.

Although no one person is entirely responsible for starting the cognitive revolution, Noam Chomsky was very influential in the early days of this movement. Chomsky (1928–), an American linguist, was dissatisfied with the influence that behaviorism had had on psychology. He believed that psychology’s focus on behavior was short-sighted and that the field had to re-incorporate mental functioning into its purview if it were to offer any meaningful contributions to understanding behavior (Miller, 2003).

European psychology had never really been as influenced by behaviorism as had American psychology; and thus, the cognitive revolution helped reestablish lines of communication between European psychologists and their American counterparts. Furthermore, psychologists began to cooperate with scientists in other fields, like anthropology, linguistics, computer science, and neuroscience, among others. This interdisciplinary approach often was referred to as the cognitive sciences, and the influence and prominence of this particular perspective resonates in modern-day psychology (Miller, 2003). Next, we will look at the research methods psychologists use to ask questions about the world.

RESEARCH METHODS IN PSYCHOLOGY

One of the important steps in scientific inquiry is to test our research questions, otherwise known as hypotheses. However, there are many ways to test hypotheses in psychological research. Which method you choose will depend on the type of questions you are asking, as well as what resources are available to you. All methods have limitations, which is why the best research uses a variety of methods. Most psychological research can be divided into two types: experimental and correlational research.

EXPERIMENTAL RESEARCH

Imagine you are taking notes in class. Should you take typed notes on your laptop, or longhand notes in a notebook? Which method of note taking will help you learn the most from lecture? As long as you’re taking notes, does it really matter?

Pam A Mueller and Daniel M. Oppenheimer, psychology researchers at Princeton University and UCLA, set out to test the difference between longhand and laptop note taking (Mueller & Oppenheimer, 2014). Participants in their experiment were told to take notes while they watched video lectures. Half of the participants were given a notebook to take notes, meaning they would take notes longhand, and the other half were given a laptop for note taking, meaning they would type their notes. Afterward, participants completed a test that measured how much participants learned from the lectures.

In an experiment, researchers manipulate, or cause changes, in the independent variable , and observe or measure any impact of those changes in the dependent variable . The independent variable is the one under the experimenter’s control, or the variable that is intentionally altered between groups. In the case of Mueller and Oppenheimer’s experiment, the independent variable was whether participants took notes longhand or using a laptop. The dependent variable is the variable that is not manipulated at all, or the one where the effect happens. One way to help remember this is that the dependent variable “depends” on what happens to the independent variable. In our example, the participants’ learning (the dependent variable in this experiment) depends on how the participants take notes (the independent variable). Thus, any observed changes or group differences in learning can be attributed to note taking method. What Mueller and Oppenheimer found was that the people who took notes longhand learned significantly more from the lectures than those who took notes using a laptop. In other words, the note taking method students use causes a difference in learning. Do you find this surprising?

But wait! Doesn’t learning depend on a lot of different factors—for instance, how intelligent someone is, or how much they already know about a topic? How can we accurately conclude that the note taking method causes differences in learning, as in the case of Mueller and Oppenheimer’s experiment? While the sample of participants was drawn from a population where everyone had an equal chance of being selected (also known as random selection ), the most important thing about experiments is random assignment . That is, participants don’t get to pick which condition they are in (e.g., participants didn’t choose whether they took notes using a laptop or notebook). The experimenter assigns them to a particular condition based on the flip of a coin or the roll of a die or any other random method. Why do researchers do this? Random assignment makes it so the groups, on average, are similar on all characteristics except what the experimenter manipulates.

By randomly assigning people to conditions (laptop versus longhand note taking), some people who already have some knowledge about the lecture topics should end up in each condition. Likewise, some people who have never heard of the lecture topics should end up in each condition. As a result, the distribution of all these factors will generally be consistent across the two groups, and this means that on average the two groups will be relatively equivalent on all these factors. Random assignment is critical to experimentation because if the only difference between the two groups is the independent variable, we can infer that the independent variable is the cause of any observable difference (e.g., in the amount they learn from the lecture).

So why do people learn more from a lecture when they take longhand rather than laptop notes? It turns out that when people take notes on a laptop, they tend to take verbatim notes , meaning that they try to type every single word the lecturer says. On the other hand, when people take longhand notes, they tend to take summary notes , meaning that they reframe the ideas in their own words. This additional cognitive processing improves learning.

OTHER CONSIDERATIONS

In addition to using random assignment, you should avoid introducing confounds into your experiments. Confounds are things that could undermine your ability to draw causal inferences. For example, if you wanted to test if a new happy pill will make people happier, you could randomly assign participants to take the happy pill or not (the independent variable) and compare these two groups on their self-reported happiness (the dependent variable). However, if some participants know they are getting the happy pill, they might develop expectations that influence their self-reported happiness. This is sometimes known as a placebo effect . Sometimes a person just knowing that he or she is receiving special treatment or something new is enough to actually cause changes in behavior or perception: In other words, even if the participants in the happy pill condition were to report being happier, we wouldn’t know if the pill was actually making them happier or if it was the placebo effect

—an example of a confound. A related idea is participant demand . This occurs when participants try to behave in a way they think the experimenter wants them to behave. Placebo effects and participant demand often occur unintentionally. Even experimenter expectations can influence the outcome of a study. For example, if the experimenter knows who took the happy pill and who did not, and the dependent variable is the experimenter’s observations of people’s happiness, then the experimenter might perceive improvements in the happy pill group that are not really there.

One way to prevent these confounds from affecting the results of a study is to use a double- blind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. For example, when participants are given the happy pill or the fake pill, they don’t know which one they are receiving. This way the participants shouldn’t experience the placebo effect, and will be unable to behave as the researcher expects (participant demand). Likewise, the researcher doesn’t know which pill each participant is taking (at least in the beginning—later, the researcher will get the results for data-analysis purposes), which means the researcher’s expectations can’t influence his or her observations. Therefore, because both parties are “blind” to the condition, neither will be able to behave in a way that introduces a confound. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier.

CORRELATIONAL DESIGNS

When scientists passively observe and measure phenomena it is called correlational research. Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what.

So, what if you wanted to test whether spending on others is related to happiness, but you don’t have $20 to give to each participant? You could use a correlational design—which is exactly what Elizabeth Dunn, a professor at the University of British Columbia, did in a study (2008). She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.

Image: https://hbr.org/2020/09/does-more-money-really-makes-us-more-happy

MORE DETAILS ABOUT THE CORREL ATION

To find out how well two variables correspond, we can plot the relation between the two scores on what is known as a scatterplot (Figure 1). In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month (x-axis) and how happy the person felt in the past month (y-axis).

Figure 1. Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81). Each dot represents an individual.

Which variable is plotted on which axis does not matter. The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as r ). A correlation coefficient provides information about the direction and strength of the association between two variables. For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.

With a positive correlation, the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The r value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the r value is .81. A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 2 shows the association between the average height of males in a country (y-axis) and the pathogen prevalence (or commonness of disease; x-axis) of that country. In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus (–) sign in front of it. Here, it is –.83.

The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness: The more money people reported spending on others, the happier they reported to be. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable! Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.

Figure 2. Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = –.83). Each dot represents a country. (Chiao, 2009)

PROBLEMS WITH THE CORRELATION

If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can’t be certain. For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often-repeated phrase among psychologists.

QUALITATIVE DESIGNS

Just as correlational research allows us to study topics we can’t experimentally manipulate (e.g., whether you have a large or small income), there are other types of research designs that allow us to investigate these harder-to-study topics. Qualitative designs, including participant observation, case studies, and narrative analysis are examples of such methodologies.

QUASI-EXPERIMENTAL DESIGNS

What if you want to study the effects of marriage on a variable? For example, does marriage make people happier? Can you randomly assign some people to get married and others to remain single? Of course not. So how can you study these important variables? You can use a quasi- experimental design .

A quasi-experimental design is similar to experimental research, except that random assignment to conditions is not used. Instead, we rely on existing group memberships (e.g., married vs. single). We treat these as the independent variables, even though we don’t assign people to the conditions and don’t manipulate the variables. As a result, with quasi-experimental designs causal inference is more difficult. For example, married people might differ on a variety of characteristics from unmarried people. If we find that married participants are happier than single participants, it will be hard to say that marriage causes happiness, because the people who got married might have already been happier than the people who have remained single.

LONGITUDINAL STUDIES

Another powerful research design is the longitudinal study . Longitudinal studies track the same people over time. Some longitudinal studies last a few weeks, some a few months, some a year or more. Some studies that have contributed a lot to psychology followed the same people over decades. For example, one study followed more than 20,000 Germans for two decades. From these longitudinal data, psychologist Rich Lucas (2003) was able to determine that people who end up getting married indeed start off a bit happier than their peers who never marry. Longitudinal studies like this provide valuable evidence for testing many theories in psychology, but they can be quite costly to conduct, especially if they follow many people for many years.

TRADEOFFS IN RESEARCH

Even though there are serious limitations to correlational and quasi-experimental research, they are not poor cousins to experiments and longitudinal designs. In addition to selecting a method that is appropriate to the question, many practical concerns may influence the decision to use one method over another. One of these factors is simply resource availability— how much time and money do you have to invest in the research? (Tip: If you’re doing a senior honor’s thesis, do not embark on a lengthy longitudinal study unless you are prepared to delay graduation!) Often, we survey people even though it would be more precise—but much more difficult—to track them longitudinally. Especially in the case of exploratory research, it may make sense to opt for a cheaper and faster method first. Then, if results from the initial study are promising, the researcher can follow up with a more intensive method.

Beyond these practical concerns, another consideration in selecting a research design is the ethics of the study. For example, in cases of brain injury or other neurological abnormalities, it would be unethical for researchers to inflict these impairments on healthy participants. Nonetheless, studying people with these injuries can provide great insight into human psychology (e.g., if we learn that damage to a particular region of the brain interferes with emotions, we may be able to develop treatments for emotional irregularities). In addition to brain injuries, there are numerous other areas of research that could be useful in understanding the human mind but which pose challenges to a true experimental design— such as the experiences of war, long-term isolation, abusive parenting, or prolonged drug use. However, none of these are conditions we could ethically experimentally manipulate and randomly assign people to. Therefore, ethical considerations are another crucial factor in determining an appropriate research design.

RESEARCH METHODS: WHY YOU NEED THEM

Just look at any major news outlet and you’ll find research routinely being reported. Sometimes the journalist understands the research methodology, sometimes not (e.g., correlational evidence is often incorrectly represented as causal evidence). Often, the media are quick to draw a conclusion for you. After reading this module, you should recognize that the strength of a scientific finding lies in the strength of its methodology. Therefore, in order to be a savvy consumer of research, you need to understand the pros and cons of different methods and the distinctions among them. Plus, understanding how psychologists systematically go about answering research questions will help you to solve problems in other domains, both personal and professional, not just in psychology.

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CHAPTER 1 LICENSE & ATTRIBUTION

Rise of Cognitive Psychology

Source: Baker, D. B. & Sperry, H. (2019). History of psychology. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series:

Psychology . Champaign, IL: DEF publishers. Retrieved from http:// noba.to/j8xkgcz5

History of Psychology by David B. Baker and Heather Sperry is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Condensed from original Research Methods in Psychology

Source: Scollon, C. N. (2019). Research designs. In R. Biswas-Diener &

• Diener (Eds), Noba textbook series: Psychology . Champaign, IL: DEF publishers. Retrieved from http://noba.to/acxb2thy

Research Designs by Christie Napa Scollon is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Condensed from original; Example experiment under “Experimental research” changed to Mueller and Oppenheimer (2014)

Cover photo by Jean-Marc Côté, Wikimedia Commons.

ESSENTIALS OF COGNITIVE PSYCHOLOGY Copyright © 2023 by Christopher Klein is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cognitive Foundations

Chapter 1 history and research methods.

Figure 1.1: Around the turn of the 20th century, futurists imagined what a classroom might look like in the year 2000. Illustration by Jean-Marc Côté, Wikimedia Commons.

Philosophers have wondered about the mind at least as far back as Socrates. Yet the scientific study of the mind only began much more recently. What changed, and what tools can we use to study the mind?

LEARNING OBJECTIVES

• Describe the precursors to the establishment of the science of cognitive psychology.
• Identify key individuals and events in the history of cognitive psychology.
• Articulate the difference between correlational and experimental designs.
• Understand how experiments help us to infer causality.
• List a strength and weakness of different research designs.

1.1 Rise of Cognitive Psychology

Precursors to American psychology can be found in philosophy and physiology. Philosophers such as John Locke (1632–1704) and Thomas Reid (1710–1796) promoted empiricism , the idea that all knowledge comes from experience. The work of Locke, Reid, and others emphasized the role of the human observer and the primacy of the senses in defining how the mind comes to acquire knowledge. In American colleges and universities in the early 1800s, these principles were taught as courses on mental and moral philosophy. Most often these courses taught about the mind based on the faculties of intellect, will, and the senses ( Fuchs, 2000 ) .

Figure 1.2: The earliest records of a psychological experiment go all the way back to the Pharaoh Psamtik I of Egypt in the 7th Century B.C. *Image: Neithsabes, CC0 Public Domain

Analytic introspection

The formal development of modern psychology is usually credited to the work of German physician, physiologist, and philosopher Wilhelm Wundt (1832–1920). Wundt helped to establish the field of experimental psychology by serving as a strong promoter of the idea that psychology could be an experimental field and by providing classes, textbooks, and a laboratory for training students. In 1875, he joined the faculty at the University of Leipzig and quickly began to make plans for the creation of a program of experimental psychology. In 1879, he complemented his lectures on experimental psychology with a laboratory experience: an event that has served as the popular date for the establishment of the science of psychology.

Figure 1.3: Wilhelm Wundt is considered one of the founding figures of modern psychology. CC0 Public Domain

The response to the new science was immediate and global. Wundt attracted students from around the world to study the new experimental psychology and work in his lab. Students were trained to offer detailed self-reports of their reactions to various stimuli, a procedure known as introspection . The goal was to identify the elements of consciousness . In addition to the study of sensation and perception, research was done on mental chronometry, more commonly known as reaction time. The work of Wundt and his students demonstrated that the mind could be measured and the nature of consciousness could be revealed through scientific means. It was an exciting proposition, and one that found great interest in America. After the opening of Wundt’s lab in 1879, it took just four years for the first psychology laboratory to open in the United States ( L. T. Benjamin, 2007 ) .

The Growth of Psychology

Throughout the first half of the 20th century, psychology continued to grow and flourish in America. It was large enough to accommodate varying points of view on the nature of mind and behavior. Gestalt psychology is a good example. The Gestalt movement began in Germany with the work of Max Wertheimer (1880–1943). Opposed to the reductionist approach of Wundt’s laboratory psychology, Wertheimer and his colleagues Kurt Koffka (1886–1941), Wolfgang Kohler (1887–1967), and Kurt Lewin (1890–1947) believed that studying the whole of any experience was richer than studying individual aspects of that experience. The saying “the whole is greater than the sum of its parts” is a Gestalt perspective. Consider that a melody is an additional element beyond the collection of notes that comprise it. The Gestalt psychologists proposed that the mind often processes information simultaneously rather than sequentially. For instance, when you look at a photograph, you see a whole image, not just a collection of pixels of color. Using Gestalt principles, Wertheimer and his colleagues also explored the nature of learning and thinking. Most of the German Gestalt psychologists were Jewish and were forced to flee the Nazi regime due to the threats posed on both academic and personal freedoms. In America, they were able to introduce a new audience to the Gestalt perspective, demonstrating how it could be applied to perception and learning ( Wertheimer, 1938 ) . In many ways, the work of the Gestalt psychologists served as a precursor to the rise of cognitive psychology in America ( L. T. Benjamin, 2007 ) .

Behaviorism emerged early in the 20th century and became a major force in American psychology. Championed by psychologists such as John B. Watson (1878–1958) and B. F. Skinner (1904–1990), behaviorism rejected any reference to mind and viewed overt and observable behavior as the proper subject matter of psychology. Through the scientific study of behavior, it was hoped that laws of learning could be derived that would promote the prediction and control of behavior. Russian physiologist Ivan Pavlov (1849–1936) influenced early behaviorism in America. His work on conditioned learning, popularly referred to as classical conditioning, provided support for the notion that learning and behavior were controlled by events in the environment and could be explained with no reference to mind or consciousness ( Fancher, 1987 ) .

Cognitive Revolution

Behaviorism’s emphasis on objectivity and focus on external behavior had pulled psychologists’ attention away from the mind for a prolonged period of time. The early work of the humanistic psychologists redirected attention to the individual human as a whole, and as a conscious and self-aware being. By the 1950s, new disciplinary perspectives in linguistics, neuroscience, and computer science were emerging, and these areas revived interest in the mind as a focus of scientific inquiry. This particular perspective has come to be known as the cognitive revolution ( Miller, 2003 ) . By 1967, Ulric Neisser published the first textbook entitled Cognitive Psychology, which served as a core text in cognitive psychology courses around the country ( Henley & Thorne, 2005 ) . Cognitive psychology is the study of mental processes such as attention, memory, perception, language use, problem solving, creativity, and thinking. Much of the work derived from cognitive psychology has been integrated into various other modern disciplines of psychological study including social psychology, personality psychology, abnormal psychology, developmental psychology, educational psychology, and economics.

Although no one person is entirely responsible for starting the cognitive revolution, Noam Chomsky was very influential in the early days of this movement. Chomsky (1928–), an American linguist, was dissatisfied with the influence that behaviorism had had on psychology. He believed that psychology’s focus on behavior was short-sighted and that the field had to re-incorporate mental functioning into its purview if it were to offer any meaningful contributions to understanding behavior ( Miller, 2003 ) .

European psychology had never really been as influenced by behaviorism as had American psychology; and thus, the cognitive revolution helped reestablish lines of communication between European psychologists and their American counterparts. Furthermore, psychologists began to cooperate with scientists in other fields, like anthropology, linguistics, computer science, and neuroscience, among others. This interdisciplinary approach often was referred to as the cognitive sciences, and the influence and prominence of this particular perspective resonates in modern-day psychology ( Miller, 2003 ) . Next, we will look at the research methods psychologists use to ask questions about the world.

1.2 Research Methods in Psychology

One of the important steps in scientific inquiry is to test our research questions, otherwise known as hypotheses. However, there are many ways to test hypotheses in psychological research. Which method you choose will depend on the type of questions you are asking, as well as what resources are available to you. All methods have limitations, which is why the best research uses a variety of methods.

Most psychological research can be divided into two types: experimental and correlational research.

Experimental Research

Imagine you are taking notes in class. Should you take typed notes on your laptop, or longhand notes in a notebook? Which method of note taking will help you learn the most from lecture? As long as you’re taking notes, does it really matter?

Figure 1.4: Does note taking medium matter? Experiments can help us find out. Photo from Unsplash.

Pam A. Mueller and Daniel M. Oppenheimer, psychology researchers at Princeton University and UCLA, set out to test the difference between longhand and laptop note taking ( Mueller & Oppenheimer, 2014 ) . Participants in their experiment were told to take notes while they watched video lectures. Half of the participants were given a notebook to take notes, meaning they would take notes longhand, and the other half were given a laptop for note taking, meaning they would type their notes. Afterward, participants completed a test that measured how much participants learned from the lectures.

In an experiment, researchers manipulate, or cause changes, in the independent variable , and observe or measure any impact of those changes in the dependent variable . The independent variable is the one under the experimenter’s control, or the variable that is intentionally altered between groups. In the case of Mueller and Oppenheimer’s experiment, the independent variable was whether participants took notes longhand or using a laptop. The dependent variable is the variable that is not manipulated at all, or the one where the effect happens. One way to help remember this is that the dependent variable “depends” on what happens to the independent variable. In our example, the participants’ learning (the dependent variable in this experiment) depends on how the participants take notes (the independent variable). Thus, any observed changes or group differences in learning can be attributed to note taking method. What Mueller and Oppenheimer found was that the people who took notes longhand learned significantly more from the lectures than those who took notes using a laptop. In other words, the note taking method students use causes a difference in learning. Do you find this surprising?

But wait! Doesn’t learning depend on a lot of different factors—for instance, how intelligent someone is, or how much they already know about a topic? How can we accurately conclude that the note taking method causes differences in learning, as in the case of Mueller and Oppenheimer’s experiment? The most important thing about experiments is random assignment . Participants don’t get to pick which condition they are in (e.g., participants didn’t choose whether they took notes using a laptop or notebook). The experimenter assigns them to a particular condition based on the flip of a coin or the roll of a die or any other random method. Why do researchers do this? Random assignment makes it so the groups, on average, are similar on all characteristics except what the experimenter manipulates.

By randomly assigning people to conditions (laptop versus longhand note taking), some people who already have some knowledge about the lecture topics should end up in each condition. Likewise, some people who have never heard of the lecture topics should end up in each condition. As a result, the distribution of all these factors will generally be consistent across the two groups, and this means that on average the two groups will be relatively equivalent on all these factors. Random assignment is critical to experimentation because if the only difference between the two groups is the independent variable, we can infer that the independent variable is the cause of any observable difference (e.g., in the amount they learn from the lecture).

So why do people learn more from a lecture when they take longhand rather than laptop notes? It turns out that when people take notes on a laptop, they tend to take verbatim notes, meaning that they try to type every single word the lecturer says. On the other hand, when people take longhand notes, they tend to take summary notes, meaning that they reframe the ideas in their own words. This additional cognitive processing improves learning.

Other considerations

In addition to using random assignment, you should avoid introducing confounds into your experiments. Confounds are things that could undermine your ability to draw causal inferences. For example, if you wanted to test if a new happy pill will make people happier, you could randomly assign participants to take the happy pill or not (the independent variable) and compare these two groups on their self-reported happiness (the dependent variable). However, if some participants know they are getting the happy pill, they might develop expectations that influence their self-reported happiness. This is sometimes known as a placebo effect . Sometimes a person just knowing that he or she is receiving special treatment or something new is enough to actually cause changes in behavior or perception: In other words, even if the participants in the happy pill condition were to report being happier, we wouldn’t know if the pill was actually making them happier or if it was the placebo effect—an example of a confound. A related idea is participant demand . This occurs when participants try to behave in a way they think the experimenter wants them to behave. Placebo effects and participant demand often occur unintentionally. Even experimenter expectations can influence the outcome of a study. For example, if the experimenter knows who took the happy pill and who did not, and the dependent variable is the experimenter’s observations of people’s happiness, then the experimenter might perceive improvements in the happy pill group that are not really there.

One way to prevent these confounds from affecting the results of a study is to use a double-blind procedure. In a double-blind procedure, neither the participant nor the experimenter knows which condition the participant is in. For example, when participants are given the happy pill or the fake pill, they don’t know which one they are receiving. This way the participants shouldn’t experience the placebo effect, and will be unable to behave as the researcher expects (participant demand). Likewise, the researcher doesn’t know which pill each participant is taking (at least in the beginning—later, the researcher will get the results for data-analysis purposes), which means the researcher’s expectations can’t influence his or her observations. Therefore, because both parties are “blind” to the condition, neither will be able to behave in a way that introduces a confound. At the end of the day, the only difference between groups will be which pills the participants received, allowing the researcher to determine if the happy pill actually caused people to be happier.

Correlational Designs

When scientists passively observe and measure phenomena it is called correlational research. Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.

So, what if you wanted to test whether spending on others is related to happiness, but you don’t have $20 to give to each participant? You could use a correlational design—which is exactly what Elizabeth Dunn, a professor at the University of British Columbia, did in a study ( Dunn et al., 2008 ) . She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.

More details about the correlation

To find out how well two variables correspond, we can plot the relation between the two scores on what is known as a scatterplot (Figure 1.5 ). In the scatterplot, each dot represents a data point. (In this case it’s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month (x-axis) and how happy the person felt in the past month (y-axis). Which variable is plotted on which axis does not matter.

Figure 1.5: Scatterplot of the association between happiness and ratings of the past month, a positive correlation ( r = .81). Each dot represents an individual.

The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as r). A correlation coefficient provides information about the direction and strength of the association between two variables. For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.

With a positive correlation, the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The r value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the r value is .81.

A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 1.6 shows the association between the average height of males in a country (y-axis) and the pathogen prevalence (or commonness of disease; x-axis) of that country. In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus (–) sign in front of it. Here, it is –.83.

Figure 1.6: Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = –.83). Each dot represents a country. ( Chiao, 2009 )

The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn’s correlational study, spending on others positively correlated with happiness: The more money people reported spending on others, the happier they reported to be. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable! Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.

Problems with the correlation

If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can’t be certain. For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists.

Qualitative Designs

Just as correlational research allows us to study topics we can’t experimentally manipulate (e.g., whether you have a large or small income), there are other types of research designs that allow us to investigate these harder-to-study topics. Qualitative designs, including participant observation, case studies, and narrative analysis are examples of such methodologies.

Quasi-Experimental Designs

What if you want to study the effects of marriage on a variable? For example, does marriage make people happier? Can you randomly assign some people to get married and others to remain single? Of course not. So how can you study these important variables? You can use a quasi-experimental design .

A quasi-experimental design is similar to experimental research, except that random assignment to conditions is not used. Instead, we rely on existing group memberships (e.g., married vs. single). We treat these as the independent variables, even though we don’t assign people to the conditions and don’t manipulate the variables. As a result, with quasi-experimental designs causal inference is more difficult. For example, married people might differ on a variety of characteristics from unmarried people. If we find that married participants are happier than single participants, it will be hard to say that marriage causes happiness, because the people who got married might have already been happier than the people who have remained single.

Figure 1.7: What is a reasonable way to study the effects of marriage on happiness? Image: Nina Matthews Photography, https://goo.gl/IcmLqg , CC BY-NC-SA, https://goo.gl/HSisdg

Longitudinal Studies

Another powerful research design is the longitudinal study . Longitudinal studies track the same people over time. Some longitudinal studies last a few weeks, some a few months, some a year or more. Some studies that have contributed a lot to psychology followed the same people over decades. For example, one study followed more than 20,000 Germans for two decades. From these longitudinal data, psychologist Rich Lucas et al. ( 2003 ) was able to determine that people who end up getting married indeed start off a bit happier than their peers who never marry. Longitudinal studies like this provide valuable evidence for testing many theories in psychology, but they can be quite costly to conduct, especially if they follow many people for many years.

Tradeoffs in Research

Even though there are serious limitations to correlational and quasi-experimental research, they are not poor cousins to experiments and longitudinal designs. In addition to selecting a method that is appropriate to the question, many practical concerns may influence the decision to use one method over another. One of these factors is simply resource availability—how much time and money do you have to invest in the research? (Tip: If you’re doing a senior honor’s thesis, do not embark on a lengthy longitudinal study unless you are prepared to delay graduation!) Often, we survey people even though it would be more precise—but much more difficult—to track them longitudinally. Especially in the case of exploratory research, it may make sense to opt for a cheaper and faster method first. Then, if results from the initial study are promising, the researcher can follow up with a more intensive method.

Beyond these practical concerns, another consideration in selecting a research design is the ethics of the study. For example, in cases of brain injury or other neurological abnormalities, it would be unethical for researchers to inflict these impairments on healthy participants. Nonetheless, studying people with these injuries can provide great insight into human psychology (e.g., if we learn that damage to a particular region of the brain interferes with emotions, we may be able to develop treatments for emotional irregularities). In addition to brain injuries, there are numerous other areas of research that could be useful in understanding the human mind but which pose challenges to a true experimental design—such as the experiences of war, long-term isolation, abusive parenting, or prolonged drug use. However, none of these are conditions we could ethically experimentally manipulate and randomly assign people to. Therefore, ethical considerations are another crucial factor in determining an appropriate research design.

Key Takeaways

• People have asked questions about the mind for centuries, but only relatively recently took a scientific approach. Psychology, and cognitive psychology especially, is a young science.
• In order to be a savvy consumer of research, you need to understand the pros and cons of different methods and the distinctions among them. Plus, understanding how psychologists systematically go about answering research questions will help you to solve problems in other domains, both personal and professional, not just in psychology.
• Discussion: How were early researchers important to the development of psychology as a science?
• Practice: Make a list of the schools of thought that preceded the cognitive revolution and write a short description of each.
• Compare: What are some key differences between experimental and correlational research?

1.3 Glossary

Behaviorism.

The study of behavior.

Factors that undermine the ability to draw causal inferences from an experiment.

consciousness

Awareness of ourselves and our environment.

correlation

Measures the association between two variables, or how they go together.

dependent variable

The variable the researcher measures but does not manipulate in an experiment.

The belief that knowledge comes from experience.

experimenter expectations

When the experimenter’s expectations influence the outcome of a study.

independent variable

The variable the researcher manipulates and controls in an experiment.

introspection

A method of focusing on internal processes.

longitudinal study

A study that follows the same group of individuals over time.

participant demand

When participants behave in a way that they think the experimenter wants them to behave.

placebo effect

When receiving special treatment or something new affects human behavior.

quasi-experimental design

An experiment that does not require random assignment to conditions.

random assignment

Assigning participants to receive different conditions of an experiment by chance.

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What Is Cognitive Psychology?

The Science of How We Think

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Topics in Cognitive Psychology

• Current Research
• Cognitive Approach in Practice

Careers in Cognitive Psychology

How cognitive psychology differs from other branches of psychology, frequently asked questions.

Cognitive psychology is the study of internal mental processes—all of the workings inside your brain, including perception, thinking, memory, attention, language, problem-solving, and learning. Learning about how people think and process information helps researchers and psychologists understand the human brain and assist people with psychological difficulties.

This article discusses what cognitive psychology is—its history, current trends, practical applications, and career paths.

Findings from cognitive psychology help us understand how people think, including how they acquire and store memories. By knowing more about how these processes work, psychologists can develop new ways of helping people with cognitive problems.

Cognitive psychologists explore a wide variety of topics related to thinking processes. Some of these include: 

• Attention --our ability to process information in the environment while tuning out irrelevant details
• Choice-based behavior --actions driven by a choice among other possibilities
• Decision-making
• Information processing
• Language acquisition --how we learn to read, write, and express ourselves
• Problem-solving
• Speech perception -how we process what others are saying
• Visual perception --how we see the physical world around us

History of Cognitive Psychology

Although it is a relatively young branch of psychology , it has quickly grown to become one of the most popular subfields. Cognitive psychology grew into prominence between the 1950s and 1970s.

Prior to this time, behaviorism was the dominant perspective in psychology. This theory holds that we learn all our behaviors from interacting with our environment. It focuses strictly on observable behavior, not thought and emotion. Then, researchers became more interested in the internal processes that affect behavior instead of just the behavior itself. 

This shift is often referred to as the cognitive revolution in psychology. During this time, a great deal of research on topics including memory, attention, and language acquisition began to emerge. 

In 1967, the psychologist Ulric Neisser introduced the term cognitive psychology, which he defined as the study of the processes behind the perception, transformation, storage, and recovery of information.

Cognitive psychology became more prominent after the 1950s as a result of the cognitive revolution.

Current Research in Cognitive Psychology

The field of cognitive psychology is both broad and diverse. It touches on many aspects of daily life. There are numerous practical applications for this research, such as providing help coping with memory disorders, making better decisions , recovering from brain injury, treating learning disorders, and structuring educational curricula to enhance learning.

Current research on cognitive psychology helps play a role in how professionals approach the treatment of mental illness, traumatic brain injury, and degenerative brain diseases.

Thanks to the work of cognitive psychologists, we can better pinpoint ways to measure human intellectual abilities, develop new strategies to combat memory problems, and decode the workings of the human brain—all of which ultimately have a powerful impact on how we treat cognitive disorders.

The field of cognitive psychology is a rapidly growing area that continues to add to our understanding of the many influences that mental processes have on our health and daily lives.

From understanding how cognitive processes change as a child develops to looking at how the brain transforms sensory inputs into perceptions, cognitive psychology has helped us gain a deeper and richer understanding of the many mental events that contribute to our daily existence and overall well-being.

The Cognitive Approach in Practice

In addition to adding to our understanding of how the human mind works, the field of cognitive psychology has also had an impact on approaches to mental health. Before the 1970s, many mental health treatments were focused more on psychoanalytic , behavioral , and humanistic approaches.

The so-called "cognitive revolution" put a greater emphasis on understanding the way people process information and how thinking patterns might contribute to psychological distress. Thanks to research in this area, new approaches to treatment were developed to help treat depression, anxiety, phobias, and other psychological disorders .

Cognitive behavioral therapy and rational emotive behavior therapy are two methods in which clients and therapists focus on the underlying cognitions, or thoughts, that contribute to psychological distress.

What Is Cognitive Behavioral Therapy?

Cognitive behavioral therapy (CBT) is an approach that helps clients identify irrational beliefs and other cognitive distortions that are in conflict with reality and then aid them in replacing such thoughts with more realistic, healthy beliefs.

If you are experiencing symptoms of a psychological disorder that would benefit from the use of cognitive approaches, you might see a psychologist who has specific training in these cognitive treatment methods.

These professionals frequently go by titles other than cognitive psychologists, such as psychiatrists, clinical psychologists , or counseling psychologists , but many of the strategies they use are rooted in the cognitive tradition.

Many cognitive psychologists specialize in research with universities or government agencies. Others take a clinical focus and work directly with people who are experiencing challenges related to mental processes. They work in hospitals, mental health clinics, and private practices.

Research psychologists in this area often concentrate on a particular topic, such as memory. Others work directly on health concerns related to cognition, such as degenerative brain disorders and brain injuries.

Treatments rooted in cognitive research focus on helping people replace negative thought patterns with more positive, realistic ones. With the help of cognitive psychologists, people are often able to find ways to cope and even overcome such difficulties.

Reasons to Consult a Cognitive Psychologist

• Alzheimer's disease, dementia, or memory loss
• Brain trauma treatment
• Cognitive therapy for a mental health condition
• Interventions for learning disabilities
• Perceptual or sensory issues
• Therapy for a speech or language disorder

Whereas behavioral and some other realms of psychology focus on actions--which are external and observable--cognitive psychology is instead concerned with the thought processes behind the behavior. Cognitive psychologists see the mind as if it were a computer, taking in and processing information, and seek to understand the various factors involved.

A Word From Verywell

Cognitive psychology plays an important role in understanding the processes of memory, attention, and learning. It can also provide insights into cognitive conditions that may affect how people function.

Being diagnosed with a brain or cognitive health problem can be daunting, but it is important to remember that you are not alone. Together with a healthcare provider, you can come up with an effective treatment plan to help address brain health and cognitive problems.

Your treatment may involve consulting with a cognitive psychologist who has a background in the specific area of concern that you are facing, or you may be referred to another mental health professional that has training and experience with your particular condition.

Ulric Neisser is considered the founder of cognitive psychology. He was the first to introduce the term and to define the field of cognitive psychology. His primary interests were in the areas of perception and memory, but he suggested that all aspects of human thought and behavior were relevant to the study of cognition.

A cognitive map refers to a mental representation of an environment. Such maps can be formed through observation as well as through trial and error. These cognitive maps allow people to orient themselves in their environment.

While they share some similarities, there are some important differences between cognitive neuroscience and cognitive psychology. While cognitive psychology focuses on thinking processes, cognitive neuroscience is focused on finding connections between thinking and specific brain activity. Cognitive neuroscience also looks at the underlying biology that influences how information is processed.

Cognitive psychology is a form of experimental psychology. Cognitive psychologists use experimental methods to study the internal mental processes that play a role in behavior.

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Ruggiero GM, Spada MM, Caselli G, Sassaroli S. A historical and theoretical review of cognitive behavioral therapies: from structural self-knowledge to functional processes .  J Ration Emot Cogn Behav Ther . 2018;36(4):378-403. doi:10.1007/s10942-018-0292-8

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cognitive psychology

Curator: Barbara Anne Dosher

Zhong-Lin Lu

Eugene M. Izhikevich

Robert P. O'Shea

Benjamin Bronner

Tobias Denninger

Max Coltheart

Dr. Zhong-Lin Lu , Neuroscience Graduate Program, University of Southern California, Los Angeles, CA

Dr. Barbara Anne Dosher , Department of Cognitive Science, University of California, Irvine

Cognitive psychology is the scientific investigation of human cognition, that is, all our mental abilities – perceiving, learning, remembering, thinking, reasoning, and understanding. The term “cognition” stems from the Latin word “ cognoscere” or "to know". Fundamentally, cognitive psychology studies how people acquire and apply knowledge or information. It is closely related to the highly interdisciplinary cognitive science and influenced by artificial intelligence, computer science, philosophy, anthropology, linguistics , biology, physics, and neuroscience .

Cognitive psychology in its modern form incorporates a remarkable set of new technologies in psychological science. Although published inquiries of human cognition can be traced back to Aristotle’s ‘’De Memoria’’ (Hothersall, 1984), the intellectual origins of cognitive psychology began with cognitive approaches to psychological problems at the end of the 1800s and early 1900s in the works of Wundt, Cattell, and William James (Boring, 1950).

Cognitive psychology declined in the first half of the 20th century with the rise of “ behaviorism " –- the study of laws relating observable behavior to objective, observable stimulus conditions without any recourse to internal mental processes (Watson, 1913; Boring, 1950; Skinner, 1950). It was this last requirement, fundamental to cognitive psychology, that was one of behaviorism's undoings. For example, lack of understanding of the internal mental processes led to no distinction between memory and performance and failed to account for complex learning (Tinklepaugh, 1928; Chomsky, 1959). These issue led to the decline of behaviorism as the dominant branch of scientific psychology and to the “Cognitive Revolution”.

The Cognitive Revolution began in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures (Miller, 1956; Broadbent, 1958; Chomsky, 1959; Newell, Shaw, & Simon, 1958). Cognitive psychology became predominant in the 1960s (Tulving, 1962; Sperling, 1960). Its resurgence is perhaps best marked by the publication of Ulric Neisser’s book, ‘’Cognitive Psychology’’, in 1967. Since 1970, more than sixty universities in North America and Europe have established cognitive psychology programs.

Assumptions

Cognitive psychology is based on two assumptions: (1) Human cognition can at least in principle be fully revealed by the scientific method, that is, individual components of mental processes can be identified and understood, and (2) Internal mental processes can be described in terms of rules or algorithms in information processing models. There has been much recent debate on these assumptions (Costall and Still, 1987; Dreyfus, 1979; Searle, 1990).

Very much like physics, experiments and simulations/modelling are the major research tools in cognitive psychology. Often, the predictions of the models are directly compared to human behaviour. With the ease of access and wide use of brain imaging techniques, cognitive psychology has seen increasing influence of cognitive neuroscience over the past decade. There are currently three main approaches in cognitive psychology: experimental cognitive psychology, computational cognitive psychology, and neural cognitive psychology. Experimental cognitive psychology treats cognitive psychology as one of the natural sciences and applies experimental methods to investigate human cognition. Psychophysical responses, response time, and eye tracking are often measured in experimental cognitive psychology. Computational cognitive psychology develops formal mathematical and computational models of human cognition based on symbolic and subsymbolic representations, and dynamical systems . Neural cognitive psychology uses brain imaging (e.g., EEG , MEG , fMRI , PET, SPECT, Optical Imaging) and neurobiological methods (e.g., lesion patients) to understand the neural basis of human cognition. The three approaches are often inter-linked and provide both independent and complementary insights in every sub-domain of cognitive psychology.

Sub-domains of Cognitive Psychology

Traditionally, cognitive psychology includes human perception , attention , learning , memory , concept formation , reasoning , judgment and decision-making , problem solving , and language processing . For some, social and cultural factors, emotion , consciousness , animal cognition , evolutionary approaches have also become part of cognitive psychology.

• Perception: Those studying perception seek to understand how we construct subjective interpretations of proximal information from the environment. Perceptual systems are composed of separate senses (e.g., visual, auditory, somatosensory) and processing modules (e.g., form, motion; Livingston & Hubel, 1988; Ungerleider & Mishkin, 1982; Julesz, 1971) and sub-modules (e.g., Lu & Sperling, 1995) that represent different aspects of the stimulus information. Current research also focuses on how these separate representations and modules interact and are integrated into coherent percepts. Cognitive psychologists have studied these properties empirically with psychophysical methods and brain imaging. Computational models, based on physiological principles, have been developed for many perceptual systems (Grossberg & Mingolla, 1985; Marr, 1982; Wandell, 1995).
• Attention : Attention solves the problem of information overload in cognitive processing systems by selecting some information for further processing, or by managing resources applied to several sources of information simultaneously (Broadbent, 1957; Posner, 1980; Treisman, 1969). Empirical investigation of attention has focused on how and why attention improves performance, or how the lack of attention hinders performance (Posner, 1980; Weichselgartner & Sperling, 1987; Chun & Potter, 1995; Pashler, 1999). The theoretical analysis of attention has taken several major approaches to identify the mechanisms of attention: the signal-detection approach (Lu & Dosher, 1998) and the similarity-choice approach (Bundesen, 1990; Logan, 2004). Related effects of biased competition have been studied in single cell recordings in animals (Reynolds, Chelazzi, & Desimone, 1999). Brain imaging studies have documented effects of attention on activation in early visual cortices, and have investigated the networks for attention control (Kanwisher & Wojciulik, 2000).
• Learning: Learning improves the response of the organism to the environment. Cognitive psychologists study which new information is acquired and the conditions under which it is acquired. The study of learning begins with an analysis of learning phenomena in animals (i.e., habituation, conditioning , and instrumental, contingency, and associative learning) and extends to learning of cognitive or conceptual information by humans (Kandel, 1976; Estes, 1969; Thompson, 1986). Cognitive studies of implicit learning emphasize the largely automatic influence of prior experience on performance, and the nature of procedural knowledge (Roediger, 1990). Studies of conceptual learning emphasize the nature of the processing of incoming information, the role of elaboration, and the nature of the encoded representation (Craik, 2002). Those using computational approaches have investigated the nature of concepts that can be more easily learned, and the rules and algorithms for learning systems (Holland, Holyoak, Nisbett, & Thagard, 1986). Those using lesion and imaging studies investigate the role of specific brain systems (e.g., temporal lobe systems) for certain classes of episodic learning, and the role of perceptual systems in implicit learning (Tulving, Gordon Hayman, & MacDonald, 1991; Gabrieli, Fleischman, Keane, Reminger, & Morell, 1995; Grafton, Hazeltine, & Ivry, 1995).
• Memory : The study of the capacity and fragility of human memory is one of the most developed aspects of cognitive psychology. Memory study focuses on how memories are acquired, stored, and retrieved. Memory domains have been functionally divided into memory for facts, for procedures or skills, and working and short-term memory capacity. The experimental approaches have identified dissociable memory types (e.g., procedural and episodic; Squire & Zola, 1996) or capacity limited processing systems such as short-term or working memory (Cowan, 1995; Dosher, 1999). Computational approaches describe memory as propositional networks, or as holographic or composite representations and retrieval processes (Anderson, 1996, Shiffrin & Steyvers, 1997). Brain imaging and lesion studies identify separable brain regions active during storage or retrieval from distinct processing systems (Gabrieli, 1998).
• Concept Formation: Concept or category formation refers to the ability to organize the perception and classification of experiences by the construction of functionally relevant categories. The response to a specific stimulus (i.e., a cat) is determined not by the specific instance but by classification into the category and by association of knowledge with that category (Medin & Ross, 1992). The ability to learn concepts has been shown to depend upon the complexity of the category in representational space, and by the relationship of variations among exemplars of concepts to fundamental and accessible dimensions of representation (Ashby, 2000). Certain concepts largely reflect similarity structures, but others may reflect function, or conceptual theories of use (Medin, 1989). Computational models have been developed based on aggregation of instance representations, similarity structures and general recognition models, and by conceptual theories (Barsalou, 2003). Cognitive neuroscience has identified important brain structures for aspects or distinct forms of category formation (Ashby, Alfonso-Reese, Turken, and Waldron, 1998).
• Judgment and decision: Human judgment and decision making is ubiquitous – voluntary behavior implicitly or explicitly requires judgment and choice. The historic foundations of choice are based in normative or rational models and optimality rules, beginning with expected utility theory (von Neumann & Morgenstern 1944; Luce, 1959). Extensive analysis has identified widespread failures of rational models due to differential assessment of risks and rewards (Luce and Raiffa, 1989), the distorted assessment of probabilities (Kahneman & Tversky, 1979), and the limitations in human information processing (i.e., Russo & Dosher, 1983). New computational approaches rely on dynamic systems analyses of judgment and choice (Busemeyer & Johnson, 2004), and Bayesian belief networks that make choices based on multiple criteria (Fenton & Neil, 2001) for more complex situations. The study of decision making has become an active topic in cognitive neuroscience (Bechara, Damasio and Damasio, 2000).
• ‘’’Reasoning:’’’ Reasoning is the process by which logical arguments are evaluated or constructed. Original investigations of reasoning focused on the extent to which humans correctly applied the philosophically derived rules of inference in deduction (i.e., A implies B; If A then B), and the many ways in which humans fail to appreciate some deductions and falsely conclude others. These were extended to limitations in reasoning with syllogisms or quantifiers (Johnson-Laird, Byne and Schaeken, 1992; Rips and Marcus, 1977). Inductive reasoning, in contrast, develops a hypothesis consistent with a set of observations or reasons by analogy (Holyoak and Thagard, 1995). Often reasoning is affected by heuristic judgments, fallacies, and the representativeness of evidence, and other framing phenomena (Kahneman, Slovic, Tversky, 1982). Computational models have been developed for inference making and analogy (Holyoak and Thagard, 1995), logical reasoning (Rips and Marcus, 1977), and Bayesian reasoning (Sanjana and Tenenbaum, 2003).
• Problem Solving: The cognitive psychology of problem solving is the study of how humans pursue goal directed behavior. The computational state-space analysis and computer simulation of problem solving of Newell and Simon (1972) and the empirical and heuristic analysis of Wickelgren (1974) together have set the cognitive psychological approach to problem solving. Solving a problem is conceived as finding operations to move from the initial state to a goal state in a problem space using either algorithmic or heuristic solutions. The problem representation is critical in finding solutions (Zhang, 1997). Expertise in knowledge rich domains (i.e., chess) also depends on complex pattern recognition (Gobet & Simon, 1996). Problem solving may engage perception, memory, attention, and executive function, and so many brain areas may be engaged in problem solving tasks, with an emphasis on pre-frontal executive functions.
• Language Processing: While linguistic approaches focus on the formal structures of languages and language use (Chomsky, 1965), cognitive psychology has focused on language acquisition, language comprehension, language production, and the psychology of reading (Kintsch 1974; Pinker, 1994; Levelt, 1989). Psycholinguistics has studied encoding and lexical access of words, sentence level processes of parsing and representation, and general representations of concepts, gist, inference, and semantic assumptions. Computational models have been developed for all of these levels, including lexical systems, parsing systems, semantic representation systems, and reading aloud (Seidenberg, 1997; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Just, Carpenter, and Woolley, 1982; Thorne, Bratley & Dewar, 1968; Schank and Abelson, 1977; Massaro, 1998). The neuroscience of language has a long history in the analysis of lesions (Wernicke, 1874; Broca, 1861), and has also been extensively studied with cognitive imaging (Posner et al, 1988).

Applications

Cognitive psychology research has produced an extensive body of principles, representations, and algorithms. Successful applications range from custom-built expert systems to mass-produced software and consumer electronics: (1) Development of computer interfaces that collaborate with users to meet their information needs and operate as intelligent agents, (2) Development of a flexible information infrastructure based on knowledge representation and reasoning methods, (3) Development of smart tools in the financial industry, (4) Development of mobile, intelligent robots that can perform tasks usually reserved for humans, (5) Development of bionic components of the perceptual and cognitive neural system such as cochlear and retinal implants.

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Internal references

• Valentino Braitenberg (2007) Brain . Scholarpedia , 2(11):2918.
• Olaf Sporns (2007) Complexity . Scholarpedia, 2(10):1623.
• James Meiss (2007) Dynamical systems . Scholarpedia, 2(2):1629.
• Paul L. Nunez and Ramesh Srinivasan (2007) Electroencephalogram . Scholarpedia, 2(2):1348.
• Robert Kurzban (2007) Evolutionary psychology . Scholarpedia, 2(8):3161.
• William D. Penny and Karl J. Friston (2007) Functional imaging . Scholarpedia, 2(5):1478.
• Seiji Ogawa and Yul-Wan Sung (2007) Functional magnetic resonance imaging . Scholarpedia, 2(10):3105.
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The Place of Development in the History of Psychology and Cognitive Science

In this article, I analyze how the relationship of developmental psychology with general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art. I shall argue that the study of human mind is inherently connected with the study of its development. From the beginning of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separated paths. This separation between adult and child studies has also persisted with the emergence of cognitive science. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry. At present, things have evolved in many ways. Psychology and cognitive science have enlarged their scope to include change process and the interaction between mind and environment. On the other hand, the possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Introduction

In this paper, I intend to analyze how the relationship of developmental psychology to general psychology and cognitive science has unfolded. This historical analysis will provide a background for a critical examination of the present state of the art.

Psychology emerged as a scientific discipline with the founding of Wundt’s Laboratory in Leipzig at the end of the nineteenth century (1879) 1 . Wundt’s method, both experimental and introspective, was directed to the study of an adult’s mind and behavior. It is less well-known that only 10 years later, James Baldwin, who had attended Wundt’s seminars in Germany, founded a laboratory of experimental psychology in Toronto in which experiments devoted to the study of mental development were performed. If the occasion that aroused Baldwin’s interest was the birth of his first daughter, actually, “that interest in the problems of genesis–origin, development, evolution–became prominent; the interest which was to show itself in all the subsequent years” ( Baldwin, 1930 ). Baldwin’s work was a source of inspiration for Piaget, certainly one of the most prominent figures in developmental psychology ( Morgan and Harris, 2015 ).

From the origins of psychology as a discipline, general psychology and developmental psychology have followed parallel and relatively separate paths. Two questions are particularly relevant to explain this fact.

From a theoretical point of view, developmental psychology has all along been greatly influenced by biology and evolutionary theory. The founders of developmental psychology have widely analyzed the relation between ontogenesis and phylogenesis ( Baldwin, 1895 ; Piaget, 1928 ). This analysis resulted in accepting the challenge of explaining development in a broad sense. In his autobiography, Baldwin affirms that already in the 10 years that he spent in Princeton between 1893 and 1903, where he founded another laboratory of experimental psychology, “the new interest in genetic psychology and general biology had become absorbing, and the meagerness of the results of the psychological laboratories (apart from direct work on sensation and movement) was becoming evident everywhere.” Thus, developmental psychology has followed an approach that in general psychology appeared much later 2 .

A second question regards method. Developmental researchers, while manifesting their attachment to experimental procedures, have been confronted with their insufficiency in the study of development. Both for deontological and practical reasons, many aspects of development, in particular in infants and young children, can hardly be investigated experimentally. Thus, a great number of studies in developmental psychology make use of observational methods based on different techniques such as ethnographic methods or parent reports, and the reliability of these methods has been questioned.

This relative separation between studies of adults and children has also persisted with the emergence of cognitive science. Actually, the primary aim of cognitive science, at least at the outset, was to model what we could call an adult static mind. Given a certain output, for instance an action, the task of the psychologist was to reconstruct the inference processes that were at the origin of this same action.

At the beginning of the twenty-first century, psychology and cognitive science have enlarged their scope to include change processes and the interaction between mind and environment, including other minds. Developmental psychology, for its part, has developed nonverbal methods such as looking measures and choice measures that also make it possible to carry out experiments with infants. These facts have paved the way for new possibilities of convergence, which are eliciting interesting results, despite a number of ongoing problems related to methods.

Psychology, Cognitive Science and Artificial Intelligence

The beginning of cognitive science.

According to the American psychologist George Miller, cognitive science was born on September 11, 1956, the second day of the Second Symposium on Information Theory held at MIT. That day began with a paper read by Allen Newell and Herbert Simon on the state of art of the Logic Theory Machine: a proof on computer of theorem 2.01 of Whitehead and Russell’s Principia Mathematica . That very same day ended with the first version of Chomsky’s The Structures of Syntax . Miller left the symposium convinced that experimental psychology, theoretical linguistics, and computer simulation of cognitive processes could become parts of a wider whole and that the future of research would be found in the elaboration of this composite whole (reported in Bruner, 1983a ). It is Miller who in 1960, together with Eugene Galanter and Karl Pribram, authored a text that may be considered the manifesto of cognitive science and that proclaimed the encompassing of cognitive psychology within the more general framework of information processing ( Miller et al., 1960 ). The assumption was that newly born information science could provide a unifying framework for the study of cognitive systems ( Schank and Abelson, 1977 ).

From a theoretical point of view, the core of this project is the concept of representation. Intentional mental states, such as beliefs and perceptions, are defined as relations to mental representations. The semantic properties of mental representations explain intentionality ( Pitt, 2017 ). Representations can be computed and thus constitute the basis for some forms of logic systems. According to the Cognitive Science Committee (1978) , which drew up a research project for the Sloan Foundation, all those disciplines, which belong to cognitive science, share the common goal of investigating the representational and computational capacities of the mind and the structural and functional realization of these capacities in the brain.

This point of view constitutes the foundation for what has been called functionalism in the philosophy of mind, i.e., the hypothesis that what defines the mind are those features that are independent of its natural realization. The classic functionalist stance is expressed by Pylyshyn in his book on computation and cognition ( Pylyshyn, 1984 ). He maintains that a clear distinction must be made between the functional architecture of the cognitive system and the rules and representations that the system employs.

Functionalism has been greatly discussed and criticized from the beginning ( Block, 1978 ; Dreyfus, 1979 ). Harnad (1990) identified what has been defined as the symbol grounding problem : “How can the semantic interpretation of a formal symbol system be made intrinsic to the system?”

The most exhaustive and most deeply argued critique of functionalism was advanced by Searle, who developed his arguments over time, publishing a number of essays which have given rise to heated debate ( Searle, 1980 , 1990 , 1992 ). The position taken up by functionalism is that the relationship between the brain and its products, that is to say conscious processes, is mediated by an intermediate level of unconscious rules. This intermediate level is, for functionalists, the level of the program. It is postulated that the rules are computational and that, consequently, the aim of research in cognitive science is to reconstruct these rules. Searle’s objection is that there are only two types of natural phenomena, the brain and the mental states that the brain brings into being and that humans experience. The brain produces mental states due to its specific biological characteristics. When we postulate the existence of unconscious rules, according to Searle, we invent a construct whose aim is to highlight a function, which we believe is especially significant. Such a function is not intrinsic and has no causal power. This argument is particularly interesting because it is founded on the impassable biological nature of the mind. Neither logic nor mathematical or statistical procedures may replace brain as a biological organ.

From another perspective, some scholars have emphasized that functionalism leads to a new form of behaviorism. Putnam (1988) claimed that reducing mental processes exclusively to their functional descriptions is tantamount to describe such processes in behavioristic terms 3 . In psychology, one of the most polemical critics of functionalism as a dangerous vehicle toward a new form of anti-mentalism, which would render vain all the battles waged by cognitivists against classic behaviorism, was a developmental psychologist, Bruner (1990) . The centrality of computability as the criterion for the construction of models in cognitive science leads naturally, in Bruner’s opinion, to abandoning “meaning making,” which was the central concern of the “Cognitive Revolution.”

Thus, at least at the outset, cognitive science was devoted to constructing computational models of human inference processes and of the knowledge that is used in performing these inferences. This definition of the object of cognitive science has led at first to designing and implementing problem-solving systems, where the complexity was located in the inference mechanisms, supposed to be the same for all problems ( Newell and Simon, 1972 ). Later, systems were implemented where reasoning was associated with specific and articulated knowledge representation ( Levesque and Brachman, 1985 ).

Notably, the aspect that was absent from this view of cognitive science was learning. This lack, according to Gentner (2010) , could be partly explained as a reaction to behaviorism, which was completely centered on learning. In fact, there were also philosophical reasons. Chomsky and Fodor, who were among the most influential members of the cognitive science community, were highly critical of the concept of learning. In their view, learning as a general mechanism does not exist, and Fodor even went so far as to state explicitly that no theory of development exists either ( Fodor, 1985 ).

Thus, cognitive science was born essentially as a reaction to behaviorism and took its legitimacy from the use of methodologies developed within artificial intelligence. These methodologies were supposed to make explicit how mental representations produced human activity in specific domains. However, this approach had a price: it separated the mind from its biological basis and from the context in which human activity takes place. There was no place for development, interaction, and variation due to biological or social causes 4 . This theoretical choice explains Bruner’s disillusion. For Bruner, cognitive science had fallen back into the behaviorism against which it originated, and no interesting relation could be established with developmental psychology. Developmental psychology is founded on the premise that a human being develops in interaction with the physical world and the society of other humans.

Cognitive Science in the Twenty-First Century

Cognitive science has changed considerably from its beginning. An obvious novelty concerns the increased importance assumed by learning with the emergence of connectionism ( Hinton, 1989 ).

When connectionist models were introduced, there was much debate regarding the relation of neural networks with the functioning of the human brain and their ability to address higher forms of thought ( Fodor and Pylyshyn, 1988 ; Quinlan, 1991 ; Chalmers, 1993 ). Later, philosophical discussion was replaced by empirical considerations. Networks are an efficient computational tool in some domains and are often used jointly with symbolic computations ( Wermter and Sun, 2000 ). Moreover, in recent advancements of artificial Intelligence, neural networks have been largely replaced by a variety of techniques of statistical learning ( Forbus, 2010 ).

More interesting for my purpose is the changes that the general philosophy of cognitive science has undergone due to the problems that have emerged with classic symbolic models. At its origin, the core of cognitive science was the relation between psychology and artificial intelligence. In the original project, this marriage was to be fruitful for both disciplines. Artificial intelligence expected from psychology the analysis of high-level mental mechanisms that, once simulated on a computer, could improve the efficiency of artificial systems. With computer simulation, psychology was to acquire a method to validate its models. However, this marriage, which for a while has been very productive and has generated many interesting ideas, ultimately failed. Artificial intelligence has evolved computing techniques that produce efficient systems without asking anymore if these techniques replicate human mental processes more or less faithfully. In psychology, the constraint to produce computational models has again restricted its scope ( Airenti and Colombetti, 1991 ).

Thus, the results of cognitive science of the twentieth century have led to a shift in cognitive science that has emerged with this century. Some researchers have proclaimed that the theoretical hypothesis that minds functionalities can be modeled disregarding the fact that they operate on the external world through the body could no longer be accepted. This new approach implies accounting for the biology of the mind/body unity and the interaction with the external world, both physical and social. One source of inspiration for this new turn came from Varela et al. (1991) , who proposed the concept of the embodied mind . Actually, the concept of embodiment includes many rather disparate inspirations, from Merleau-Ponty and phenomenology to Buddhism. I do not analyze these questions here. What interests me is the mere assumption that cognition is grounded in the world.

This new turn corresponds to the major importance assumed by robotics. It might be exaggerated to say that the role played by artificial intelligence in the past is now assumed by robotics. However, it is clear that the aim of constructing artificial actors that interact with the world and/or with humans has again established a link between the study of humans and the production of artificial systems. With respect to the past, the focus is no longer on the symbolic function of the mind, but on the mind embedded into a physical device that interacts with the external world. This evolution is linked to the enlarged scope of present robotics that goes well beyond traditional tasks such as farm automation. The ambition is to construct robots that may cooperate with humans in a multiplicity of tasks, including, for instance, assisting aged or disabled people or interacting with autistic children. Social robotics has then evolved toward biologically inspired systems, based on the notions of self-organization and embodiment ( Pfeifer et al., 2007 ). This new development has led to question once again psychologists about those characteristics that make humans what they are. If robots must be able to interact with humans, they should show those same characteristics ( Kahn et al., 2007 ). Can robots be endowed with intentionality, emotions, and possibly empathy?

Here, again a functionalist position appears. For some authors, the fact that the robot’s internal mechanisms are grounded in physical interactions with the external environment means that they truly have the potentiality of intrinsic intentionality ( Zlatev, 2001 ). This means, for them, that a mind is embodied in a robot. To the question of whether robots can have emotions, Arbib and Fellous (2004) answer that a better knowledge of biological systems will allow us in the future to single out “brain operating principles” independent of the physical medium in which they are implemented. This new form of functionalism is currently contrasted with an approach that considers that mental states and emotions are not intrinsic but can only be attributed to robots by humans ( Ziemke et al., 2015 ). Robots’ embodiment does not overcome the objection that was addressed to traditional artificial intelligence, namely that mental states and emotions can only be produced by a biological brain ( Ziemke, 2008 ). This latter position maintains that the relevant question for human-robot interaction is not that robots must be intentional beings, but that they must be perceived as such by humans ( Airenti, 2015 ; Wiese et al., 2017 ).

In conclusion, we can say that cognitive science was born as a way to renew psychology through a privileged connection with artificial intelligence. In the present state of research, it is social robotics that is attempting to establish a connection with biological sciences, psychology, and neuroscience, in order to build into robots those functionalities that should allow them to successfully interact with the external physical and social world. However, the main fundamental philosophical problems remain unchanged. One could still argue, as Searle did, that human mentality is an emergent feature of biological brains and no logical, mathematical or statistical procedure can produce it.

Present Questions for Cognitive Science

The question that we may raise today is this: what is cognitive science for? The relation that psychology has established with the sciences of the artificial has hidden the fact that a number of phenomena, which are essential for explaining the functioning of the human mind, have been largely ignored. This failure in explanation, which has concerned, for instance, the managing of mental states and emotions, and many complex communicative phenomena, is fundamentally linked to the fact that the mind is constantly in interaction with the physical and social world in a process of development. The primitive idea of cognitive science was to go beyond traditional psychology to enrich the study of mind with the contributions of other disciplines that also investigated human mind, such as linguistics, philosophy, and anthropology. This approach, which concerns the definition of the field of cognitive science, has been quite early reinterpreted as a problem of formalism. The question posed has been: how could psychology produce scientific models of human thought? Hence, the importance assumed by computer modeling as a means of replacing more traditional logical, mathematical, and statistical models. However, this theoretical choice has generated a major ambiguity, because computer models that are founded on logical, mathematical, or statistical formalisms have been seen as possibly equivalent to the mind. Once the fallacy of this equivalence appears—because no artificial model may replace the causal power of the human brain—we are left with some formal models with very limited psychological significance. What has been lost is the richness that cognitive science was supposed to acquire by connecting different disciplines. In particular, for many years, this approach has prevented general psychology from connecting with developmental psychology, a field of studies that, since Baldwin, had already posed the problem of the construction of the human mind as the result of biological development and social interaction.

The Study of Development

Biology and development in the debate between piaget and chomsky.

Studying development necessarily implies considering the fact that humans are biological systems that are certainly particularly complex but also share many characteristics with other living beings. Thus, in the field of developmental psychology, many questions have emerged concerning the link between development and evolution, the relation between genetic endowment and the influence on acquisition of environment (a concept that includes physical environment, parenting, social rules, etc.), and the nature of learning.

For Piaget, who came to developmental psychology from natural sciences, development had to be seen in the light of the theories of evolution. Intelligence, for him, is a particular case of biological adaptation, and knowledge is not a state but a process. Through action, children explore space and objects in the external world, and in this way, for instance, they learn the properties of the objects and their relations. These ideas, which sound rather contemporary to us, were considered as problematic in the past and prevented the establishment of a relationship between the study of development and the study of cognition in general. It is only in this century that development has been integrated into evolution studies via the so-called evo-devo approach and that these ideas have given rise to an interest in psychology ( Burman, 2013 ).

Actually, some aspects of Piaget’s perspective were problematic. Piaget supported his theory using what was considered a Lamarckian vision of evolution that assumed the inheritance of acquired characteristics. He had a well-known debate at the end of his life (1975) with Noam Chomsky on language acquisition, and outstanding biologists who also participated to the debate contested the validity of his use of the concept of phenocopy ( Piattelli-Palmarini, 1979/1980 ). In fact, on this point, Piaget had been influenced by Baldwin, who proposed what is known as Baldwin’s effect ( Simpson, 1953 ). This effect manifests in three stages: (1) Individual organisms interact with the environment in such a way as to produce nonhereditary adaptations; (2) genetic factors producing similar traits occur in the population; and (3) these factors increase in frequency under natural selection (taken from Waddington, 1953 ). Later, Piaget revised his own theory and updated Baldwin’s effect under the influence of Waddington ( Burman, 2013 ). Recently, epigenetic theories have emerged in biology, and the importance of development is generally accepted. On the developmental side, it has been proposed that Piaget’s theory might be replaced as a metatheory for cognitive development by evolutionary psychology ( Bjorklund, 2018 ).

The debate between Chomsky and Piaget is interesting because it is a clear example of the impossibility of dialogue between one of the fathers of cognitive science and the scholar who, at that moment, personified developmental psychology. Piaget was unable to justify his position that grammar rules could also be accounted for by sensorimotor schemata, and Chomsky appeared to have won the debate. At the same time, Chomsky presented the emergence of syntactic rules in the child’s mind, excluding in principle any possible form of learning. However, in hindsight, we know how the task of establishing abstract principles of universal grammar proved to be arduous, underwent many substantial changes and is not yet realized.

Another controversial aspect of Piaget’s position was his adherence to the recapitulation theory, i.e., the idea originally proposed by Haeckel, that ontogeny recapitulates phylogeny. It is this principle that motivated Piaget’s study of development as a way of contributing to the study of the evolution of human thought ( Koops, 2015 ). However, this position has as its consequence the idea that primitive populations would exist wherein we might find adult thought processes that in modern civilizations are typical of young children.

What is striking in this debate is that the specific biological model that Piaget adopted was not the only point of disagreement. What was questioned was in general the relevance of development for the study of a basic human ability such as language. Certainly, in the work of the first figures of developmental psychology, we find a baffling mix of very interesting ideas regarding the place of humans as biological entities in evolution and a difficulty in taking into account the complexities of actual biological theories and of social aspects such as cultural variation. At the same time, these scholars were confronted with objections from cognitive scientists who did not admit the relevance of investigating development for the study of the human mind.

The Interactionist Perspective

Piaget’s perspective was, in a sense, paradoxical. This perspective considered children’s development as the product of their action on the environment, but at the same time postulated a rather rigid succession of stages that led to adult thought and excluded the importance of the social aspects of this environment in the first years. In fact, infants and young children were considered closed in their egocentrism and unable to take advantage of their interactions with adults and peers.

These aspects have been criticized within developmental psychology, where a cultural turn, fathered by Vygotsky (1962/1986) and mainly interpreted in the United States by Bruner (1990) , has arisen. For both these authors, biological factors are considered an endowment of potentialities that develop in a society of co-specifics and are submitted to variability and to cultural variation.

Bruner was, at the outset, an enthusiastic supporter of cognitive science and in particular of the mentalist theory of language proposed by Chomsky ( Bruner, 1983b ). Later, however, the primacy that Chomsky assigns to syntax turned out to be unsatisfactory to Bruner, according to whom language is fundamentally a communicative device. The problem of language acquisition is thus redefined as the development of a communicative capacity that appears in the prelinguistic stage. This position was the result of Bruner’s work on preverbal communication carried out at the Center for Cognitive Studies at Harvard University starting in 1966.

For Bruner, language requires the maturation of cognitive structures, which underlie intentional action in general. His debt to Piaget with regard to the importance of action is evident. Language is “a specialized and conventionalized extension of cooperative action” ( Bruner, 1975 ). In this, he rejoins the communication theories proposed within the philosophy of language by Austin (1962) and Grice (1989) .

Bruner’s studies are part of a revolution in developmental studies in which more careful scrutiny and more sophisticated experimentation led to the discovery that children begin to engage in rather complex cognitive activity very early on. Prior to these studies, many of the aspects relating to infant cognition were not taken into consideration. The prejudice that saw human development as the slow acquisition of rationality prevented researchers from seeking elements of complexity in the cognition of a new-born.

In brief, since its origin, developmental psychology has undergone an important change. At the outset, the idea was that what characterized human cognition was adult rational thought, and studying development meant understanding the stages that led to this achievement. Later, the goal became understanding the development of the different faculties that characterize cognition starting from birth. This goal has also opened the door to comparative studies.

The Problems of Method

Developmental psychologists have always struggled with problems of method.

Piaget frequently discussed his observations of his three children. Studies on language acquisition have often benefited from researchers’ observations of their own children (see, for instance, Stern and Stern, 1928 ). These procedures, which have been considered as barely scientific by other psychologists, have provided useful inspiration for further research. Note that Darwin’s observations of his children were a fundamental source for his work on emotions ( Darwin, 1872/1965 ).

Ethical reasons forbid experiments, which may perturb children. Moreover, conceiving experiments that have ecological validity is even more difficult to do with young children than with adults. Hence, the necessity of using different methods in order to produce data that cannot be collected using classic experimental procedures. Without using observational methods, for instance, it is not possible to assess the spontaneous appearance of a given phenomenon ( Airenti, 2016 ). Furthermore, some behaviors may appear only in specific situations and would go unnoticed if they were not observed by caregivers who may see children at different moments of the day and in different situations. Thus, developmental psychologists have used different methodologies, classic experiments but also fieldwork, ethological observation, and parent reports.

A fundamental advancement was the development of techniques permitting to assess infants’ and young children’s abilities in experiments. A key element was the elaboration of the habituation paradigm ( Fantz, 1964 ; Bornstein, 1985 ). After repeated exposure to a stimulus, infants’ looking time decreases due to habituation and increases when a novel stimulus is presented. Habituation allows us to understand if infants discriminate among different stimuli.

In particular for language studies, nonnutritive sucking ( Siqueland and De Lucia, 1969 ) has been used. This is an experimental method based on operant conditioning allowing one to test infants’ discrimination of and preference for different stimuli. This technique has been used to show, for instance, that infants already acquire in the mother’s womb the ability to recognize and prefer the prosody of a language and of familiar voices ( DeCasper and Fifer, 1980 ).

Currently, the most utilized technique with infants is preferential looking or reaching. In this technique, two stimuli are presented together and what is measured is the infant’s preference. Specific types of this technique are used to claim surprise, anticipation, and preferences for novel or familiar stimuli and to evaluate preference over and above novelty or familiarity ( Hamlin, 2014 ) 5 .

Another technique presently used to investigate infant cognitive development is EEG recordings, even if creating infant-friendly laboratory environments, age-appropriate stimuli, and infant- friendly paradigms requires special care ( Hoehl and Wahl, 2012 ).

The development of these experimental techniques has vastly enlarged the scope of infant studies. In particular, a new research trend has emerged aimed at discovering what has been called the core knowledge ( Spelke, 2000 ; Spelke and Kinzler, 2007 ). The idea is that at the basis of human cognition, there is a set of competencies, such as representing objects, action, number and space, which are already present in infants and which underlie and constrain later acquisitions. Researchers have also been working on other possible basic competencies such as social cognition ( Baillargeon et al., 2016 ) and morality ( Wynn and Bloom, 2014 ).

In the literature, debate continues surrounding the replicability and robustness of findings obtained within these experimental paradigms, in particular with respect to infants’ and toddlers’ implicit false belief and morality ( Hamlin, 2014 ; Tafreshi et al., 2014 ; Baillargeon et al., 2018 ; Sabbagh and Paulus, 2018 ).

This debate also involves the relation between development and evolution. For Tafreshi and colleagues, for instance, the idea of core knowledge would involve a consideration of high-level cognitive capacities as biologically predetermined instead of constructed in interaction with the environment. This is not the perspective of those who consider that development does exist in the social environment but is constrained by a number of basic competencies ( Hamlin, 2014 ). An important element of this perspective is comparing human and animal capacities. In fact, research has shown that such basic competencies also exist in some form in animals. For instance, numerous studies have shown that adult nonhuman primates have the core systems of object, number, agent representations, etc. ( Spelke and Kinzler, 2007 ).

These preoccupations have also informed work by Tomasello and the Leipzig group. “All we can claim to have done so far–writes Tomasello–is to establish some comparative facts–organized by some theoretical speculations–that hopefully get us started in the right direction toward an evolutionary informed account of the ontogeny of uniquely human psychology” ( Tomasello, 2018 ). Comparing experimental work on great apes and young children has led him to formulate the hypothesis that the factors marking the difference between these two groups are different aspects of social cognition. Nonhuman primates have some basic capacities in these areas. In humans, the evolved capacity for shared intentionality transforms them in the species-unique human cognition and sociality ( Tomasello and Herrmann, 2010 ).

Tomasello’s work has also aroused criticism. In this case, the criticism is because his research, both with young children and primates, uses experimental methods and is carried out in a laboratory. Fieldwork primatologists have claimed that primates in captivity, tested by someone of another species, cannot display the abilities that their conspecifics display in their natural environment ( Boesch, 2007 ; De Waal et al., 2008 ). Tomasello answered this criticism by maintaining that the fact of being raised in a human environment enhances primates’ capacities ( Tomasello et al., 1993 ; Tomasello and Call, 2008 ).

In conclusion, in developmental psychology, a multiplicity of methods has been applied, and the debate over their respective validity and correct application continues. However, what is not in question is that development is a complex and multifaceted phenomenon that must be analyzed as such and from different points of view.

A paradigmatic case in the present research is the study of the theory of mind. Discovering how subjects represent their own mind and other minds was proposed in 1978 by Premack and Woodruff as a problem of research on primates, and in a short time, it has become one of the main topics in developmental research ( Premack and Woodruff, 1978 ). It is currently being studied in groups of different ages, from infants to the elderly, both in typical and clinical subjects and using different methodologies, from classic experiments to clinical observation. Moreover, a number of studies investigate individual and cross-cultural variation and its role in human-robots interactions. Philosophers have contributed to the definition of this phenomenon, and neuroscientists are working to discover its neural basis.

Computational Models of Development

Some researchers have pursued the goal of constructing computational models of cognitive development using different computational approaches (for a review, see Mareschal, 2010 ). However, as the author of this review remarks, all the models have explored cognition “as an isolated phenomenon”, i.e., they did not consider the physical and social context in which development unfolds.

Karmiloff-Smith, a developmental psychologist who proposed the most interesting theory about developmental change as an alternative to Piaget’s, considered that a number of features of her RR ( representational redescription ) model happened to map onto features of connectionist models ( Karmiloff-Smith, 1992 ; for a review of these models, see Plunkett et al., 1997 ). However, she also remarks that connectionist models have modeled tasks, while development is not simply task-specific learning, as it involves deriving and using previously acquired knowledge 6 .

One result of the dissatisfaction with the results deriving from the relation between cognitive psychology and artificial intelligence and the concomitant increase in interest in embodied cognition has been the growth of developmental robotics ( Lungarella et al., 2003 ). The aim of this field is to produce baby robots endowed with sensorimotor and cognitive abilities inspired by child psychology and to model developmental changes ( Cangelosi and Schlesinger, 2018 ). This approach has led to the comparison of results in experiments with robots and children. This is a promising field, even if it does not overcome the problems described above regarding the specificity of tasks that does not allow to account for infants’ ability to utilize previously differently acquired knowledge in the performance of a given task.

In conclusion, some approaches within cognitive science have acknowledged the usefulness of studying children in order to understand the mechanisms of development. Especially in the case of developmental robotics, this has allowed for studying the interaction of different capacities such as sensorimotor abilities, perception, and language. At the same time, the computational constraints do not allow for overcoming task specificity.

Concluding Remarks

I have argued that since their beginning, general psychology and developmental psychology have followed parallel paths that have only occasionally converged. The reason is due essentially to methodological problems that have involved not only research methods but also the very object of inquiry.

Psychology was founded with the ambition of becoming a science performed in laboratories and based on experimental work. However, as early as in 1934, Vygotsky had already deplored the attempt to achieve scientific standards by limiting the importance of general issues. “As long as we lack a generally accepted system incorporating all available psychological knowledge, any important factual discovery inevitably leads to the creation of a new theory to fit the newly observed facts” ( Vygotsky, 1962/1986 , p. 13).

The birth of cognitive science has taken important steps toward constructing links with other disciplines and also other ways to study cognition. However, this opening was soon transformed in the search for a unifying methodology, namely computer modeling, as a guarantee of scientific results. Many interesting ideas have been generated. However, after four decades of work in this direction, it has become impossible to ignore that too many important aspects of the human mind and activity have been eluded.

The relative isolation of developmental psychology came from the prejudice, also shared by eminent developmental psychologists like Piaget, that what characterizes human cognition are adult cognitive abilities.

However, from the start, developmental psychology was not limited to investiganting the specificity of children’s cognition. It devoted attention to what makes development possible, including biological endowment and cultural transmission; whether an infant should be considered a blank slate or if one can define some pre-existent basic abilities; what makes humans different from animals and nonhuman primates; and how specific human abilities such as language have evolved.

At present, a rapprochement between adult and child studies is made possible by different factors. The possibility of using experimental methods to study infancy has allowed us to realize the complexity of young humans. Moreover, development is increasingly being considered as a phenomenon not only characterizing childhood but also present over the life span, including both the acquisition and the decay of mental abilities ( Bialystok and Craik, 2006 ). Studying the human mind means studying how the human mind changes in interaction with the external environment all life long. In this sense, the study of human mind is inherently connected with the study of its development.

An important question of method emerges here. We have observed that over the years, developmental psychologists have sought to construct methods that can be reliable and at the same time can adequately address the topics under discussion here. The achievement of finding ways to carry out experiments with infants and nonhuman primates has been an important advancement in this perspective. This advancement has garnered both praise and criticism. To be reliable, experiments with infants require very rigorous procedures. Frequently, a detailed analysis of procedures is necessary to explain divergent results. However, it can be noted that reproducibility is an open problem for psychological science in general ( Open Science Collaboration, 2015 ). For nonhuman primates, the ecological validity of laboratory experiments has been questioned. More generally, it has been shown that in the field of developmental psychology, experimental studies do not completely replace other methodologies, but rather should coexist with them.

The human mind is complex, and all the methods that have been proposed in different disciplines may be useful in advancing our knowledge of it. The explanation of this complexity was the main goal underlying the proposal of cognitive science and is the perspective we must pursue in the future.

On this ground, the paths of psychology and developmental psychology may reconverge.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer MT declared a shared affiliation, with no collaboration, with the author to the handling editor at time of review.

1 The very earliest date was 1875 and that same year William James’ laboratory at Harvard in the United States was established ( Harper, 1950 ).

2 William James was influenced by Darwin and this appears in particular in his conceiving the mind as a function and not as a thing ( Bredo, 1998 ). However, his book The Principles of Psychology , first published in 1890 and later revised several times, ignored child development. In the chapter devoted to methods and snares in psychology, he adds to introspective observation and experimental method the comparative method. “So it has come to pass that instincts of animals are ransacked to throw light on our own; and that the reasoning faculties of bees and ants, the minds of savages, infants, madmen, idiots, the deaf and blind, criminals, and eccentrics, are invoked in support of this or that special theory about some part of our own mental life” ( James, 1983 , p. 193). If he admits that “information grows and results emerge”, he also cautions that “there are great sources of error in the comparative method” and that “comparative observation, to be definite, must usually be made to test some pre-existing hypothesis” ( James, 1983 ).

3 Putnam was actually the first to employ the term functionalism , and his aim in doing so was anti-reductionist. In his 1975 work he used the comparison with a computer program to show that psychological properties do not have a physical and chemical nature, even though they are realized by physical and chemical properties ( Putnam, 1975 ).

4 , Hewitt (1991) highlights the difficulties inherent in constructing artificial systems, which, like social systems, are founded on concepts such as commitment, cooperation, conflict, negotiation, and so forth.

5 Gaze and eye-tracking techniques are normally used in psychological research with adults ( Mele and Federici, 2012 ) but it is in developmental studies that they have had a dramatic impact on the possibilities of inquiry.

6 A different approach that has given origin to formal models and simulations is the paradigm that views the developmental process as a change within a complex dynamic system. Cognition in this perspective is embodied in the processes of perception and action ( Smith and Thelen, 2003 ).

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What Is the Cognitive Psychology Approach? 12 Key Theories

Maintaining focus on the oncoming traffic is paramount, yet I am barely aware of the seagulls flying overhead.

These noisy birds only receive attention when I am safely walking up the other side of the road, their cries reminding me of childhood seaside vacations.

Cognitive psychology focuses on the internal mental processes needed to make sense of the environment and decide on the next appropriate action (Eysenck & Keane, 2015).

This article explores the cognitive psychology approach, its origins, and several theories and models involved in cognition.

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This Article Contains:

What is the cognitive psychology approach, a brief history of cognitive psychology, cognitive psychology vs behaviorism, 12 key theories, concepts, and models, fascinating research experiments, a look at positive cognitive psychology, interesting resources from positivepsychology.com, a take-home message.

The upsurge of research into the mysteries of the human brain and mind has been considerable in recent decades, with recognition of the importance of cognitive process in clinical psychology and social psychology  (Eysenck & Keane, 2015).

As a result, cognitive psychology has profoundly affected the field of psychology and our understanding of what it is to be human.

Perhaps more surprisingly, it has had such an effect without clear boundaries, an integrated set of assumptions and concepts, or a recognizable spokesperson (Gross, 2020).

So, what exactly is the cognitive psychology approach?

Cognitive psychology attempts to understand human cognition by focusing on what appear to be cognitive tasks that require little effort (Goldstein, 2011).

Let’s return to our example of walking down the road. Imagine now that we are also taking a call. We’re now combining several concurrent cognitive tasks:

• Perceiving the environment Distinguishing cars from traffic signals and discerning their direction and speed on the road as well as the people ahead standing, talking, and blocking the sidewalk.
• Paying attention Attending to what our partner is asking us on the phone, above the traffic noise.
• Visualizing Forming a mental image of items in the house, responding to the question, “Where did you leave your car keys?”
• Comprehending and producing language Understanding the real question (“I need to take the car. Where are your keys?”) from what is said and formulating a suitable reply.
• Problem-solving Working out how to get to the next appointment without the car.
• Decision-making Concluding that the timing of one meeting will not work and choosing to push it to another day.

While cognitive psychologists initially focused firmly on an analogy comparing the mind to a computer, their understanding has moved on.

There are currently four approaches, often overlapping and frequently combined, that science uses to understand human cognition (Eysenck & Keane, 2015):

• Cognitive psychology The attempt to “understand human cognition by using behavioral evidence” (Eysenck & Keane, 2015, p. 2).
• Cognitive neuropsychology Understanding ‘normal’ cognition through the study of patients living with a brain injury.
• Cognitive neuroscience Combining evidence from the brain with behavior to form a more complete picture of cognition.
• Computational cognitive science Using computational models to understand and test our understanding of human cognition.

Cognitive psychology plays a massive and essential role in understanding human cognition and is stronger because of its close relationships and interdependencies with other academic disciplines (Eysenck & Keane, 2015).

In 1868, a Dutch physiologist, Franciscus Donders, began to measure reaction time – something we would now see as an experiment in cognitive psychology (Goldstein, 2011).

Donders recognized that mental responses could not be measured directly but could be inferred from behavior. Not long after, Hermann Ebbinghaus began examining the nature and inner workings of human memory using nonsense syllables (Goldstein, 2011).

By the late 1800s, Wilhelm Wundt had set up the first laboratory dedicated to studying the mind scientifically. His approach became known as structuralism . His bold aim was to build a periodic table of the mind , containing all the sensations involved in creating any experience (Goldstein, 2011).

However, the use of analytical introspection to uncover hidden mental processes was gradually dropped when John Watson proposed a new psychological approach that became known as behaviorism (Goldstein, 2011).

Watson rejected the introspective approach and instead focused on observable behavior. His idea of classical conditioning – the connection of a new stimulus with a previously neutral one – was later surpassed by B. F. Skinner’s idea of operant conditioning , which focused on positive reinforcement (Goldstein, 2011).

Both theories sought to understand the relationship between stimulus and response rather than the mind’s inner workings (Goldstein, 2011).

Prompted by a scathing attack by linguist and cognitive scientist Noam Chomsky, by the 1950s behaviorism as the dominant psychological discipline was in decline. The introduction of the digital computer led to the information-processing approach , inspiring psychologists to think of the mind in terms of a sequence of processing stages (Goldstein, 2011).

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Moore (1996) recognized the tensions of the paradigm shift from behaviorism to cognitive psychology.

While research into cognitive psychology, cognitive neuropsychology, cognitive neuroscience , and computational cognitive science is now widely accepted as the driving force behind understanding mental processes (such as memory, perception, problem-solving, and attention), this was not always the case (Gross, 2020).

Moore (1996) highlighted the relationship between behaviorism and the relatively new field of cognitive psychology, and the sometimes mistaken assumptions regarding the nature of the former approach:

• Behaviorism is typically only associated with studying publicly observable behavior. Unlike behaviorism, cognitive psychology is viewed as free of the restrictions of logical positivism, which rely on verification through observation.

Since then, modern cognitive psychology has incorporated findings from many other disciplines, including evolutionary psychology , computer science, artificial intelligence , and neuroscience (Eysenck & Keane, 2015).

• Unlike behaviorism, cognitive psychology is theoretical and explanatory. Behaviorism is often considered merely descriptive, while cognitive psychology is seen as being able to explain what is behind behavior.

Particular ongoing advances in cognitive psychology include perception, language comprehension and production, and problem-solving (Eysenck & Keane, 2015).

• Behaviorism cannot incorporate theoretical terms. While challenged by some behaviorists at the time, it was argued that behaviorism could not incorporate theoretical terms unless related to directly observable behavior.

At the time, cognitive psychologists also argued that it was wrong of behaviorists to interpret mental states in terms of brain states.

Neuroscience advances, such as new imaging techniques like functional MRI, continue to offer fresh insights into the relationship between the brain and mental states (Eysenck & Keane, 2015).

Clearly, the relationship between behaviorism and the developing field of cognitive psychology has been complex. However, cognitive psychology has grown into a school of thought that has led to significant advances in understanding cognition, especially when teamed up with other developments in computing and neuroscience.

This may not have been possible without the shift in the dominant schools of thought in psychology (Gross, 2020; Goldstein, 2011; Eysenck & Keane, 2015).

And while it is beyond the scope of this article to cover the full breadth or depth of the areas of research, we list several of the most important and fascinating specialties and theories below.

It is hardly possible to imagine a world in which attention doesn’t play an essential role in how we interact with the environment, and yet, we rarely give it a thought.

According to cognitive psychology, attention is most active when driven by an individual’s expectations or goals, known as top-down processing . On the other hand, it is more passive when controlled by external stimuli, such as a loud noise, referred to as bottom-up processing (Eysenck & Keane, 2015).

A further distinction exists between focused attention (selective) and divided attention . Research into the former explores how we are able to focus on one item (noise, image, etc.) when there are several. In contrast, the latter looks at how we can maintain attention on two or more stimuli simultaneously.

Donald Broadbent proposed the bottleneck model to explain how we can attend to just one message when several are presented, for example, in dichotic listening experiments, where different auditory stimuli are presented to each ear. Broadbent’s model suggests multiple processing stages, each one progressively restricting the information flow (Goldstein, 2011).

As with all other areas of cognition, perception is far more complicated than we might first imagine. Take, for example, vision. While a great deal of research has “involved presenting a visual stimulus and assessing aspects of its processing,” there is also the time aspect to consider (Eysenck & Keane, 2015, p. 121).

We need to not only perceive objects, but also make sense of their movement and detect changes in the visual environment over time (Eysenck & Keane, 2015).

Research suggests perception, like attention, combines bottom-up and top-down processing. Bottom-up processing involves neurons that fire in response to specific elements of an image – perhaps aspects of a face, nose, eyebrows, jawline, etc. Top-down processing considers how the knowledge someone brings with them affects their perception.

Bottom-down processing helps explain why two people, presented with the same stimuli, experience different perceptions as a result of their expectations and prior knowledge (Goldstein, 2011).

Combining bottom-up and top-down processing also enables the individual to make sense of both static and moving images when limited information is available; we can track a person walking through a crowd or a plane disappearing in and out of clouds (Eysenck & Keane, 2015).

The mirror neuron system is incredibly fascinating and is proving valuable in our attempts to understand biological motion. Observing actions activates similar areas of the brain as performing them. The model appears to explain how we can imitate the actions of another person – crucial to learning (Eysenck & Keane, 2015).

Language comprehension

Whether written or spoken, understanding language involves a high degree of multi-level processing (Eysenck & Keane, 2015).

Comprehension begins with an initial analysis of sentence structure (larger language units require additional processing). Beyond processing syntax (the rules for building and analyzing sentences), analysis of sentence meaning ( semantics ) is necessary to understand if the interpretation should be literal or involve irony, metaphor, or sarcasm (Eysenck & Keane, 2015).

Pragmatics examines intended meaning. For example, shouting, “That’s the doorbell!” is not likely to be a simple observation, but rather a request to answer the door (Eysenck & Keane, 2015).

Several models have been proposed to understand the analysis and comprehension of sentences, known as parsing , including (Eysenck & Keane, 2015):

• Garden-path model This model attempts to explain why some sentences are ambiguous (such as, “The horse raced past the barn fell.”). It suggests they are challenging to comprehend because the analysis is performed on each individual unit of the sentence with little feedback, and correction is inhibited.
• Constraint-based model The interpretations of a sentence may be limited by several constraints, including syntactic, semantic, and general world knowledge.
• Unrestricted race model This model combines the garden-path and constraint-based model, and suggests all sources of information inform syntactic structure. One such interpretation is selected until it is discarded, with good reason, for another.
• Good-enough representation This model proposes that parsing provides a ‘good-enough’ interpretation rather than something detailed, accurate, and complete.

The research and theories above hint at the vast complexity of human cognition and explain why so many models and concepts attempt to answer what happens when it works and, equally important, when it doesn’t.

A level of psychology: the cognitive approach – Atomi

There are many research experiments in cognitive psychology that highlight the successes and failings of human cognition. Each of the following three offers insight into the mental processes behind our thinking and behavior.

Cocktail party phenomenon

Selective attention – or in this case, selective listening – is often exemplified by what has become known as the cocktail party phenomenon  (Eysenck & Keane, 2015).

Even in a busy room and possibly mid-conversation, we can often hear if someone else mentions our name. It seems we can filter out surrounding noise by combining bottom-up and top-down processing to create a “winner takes it all” situation where the processing of one high-value auditory input suppresses the brain activity of all others (Goldstein, 2011).

While people may believe that the speed of hand movement allows magicians to trick us, research suggests the main factor is misdirection (Eysenck & Keane, 2015).

A 2010 study of a trick involving the disappearance of a lighter identified that when the lighter was dropped (to hide it from a later hand-opening finale), it was masked by directing attention from the fixation point – known as covert attention – with surprising effectiveness.

However, subjects were able to identify the drop when their attention was directed to the fixation point – known as overt attention (Kuhn & Findlay, 2010).

In a thought-provoking study exploring freewill, participants were asked to consciously decide whether to move their finger left or right while a functional MRI scanner monitored their prefrontal cortex and parietal cortex (Soon, Brass, Heinze, & Haynes, 2008).

Brain activity predicted the direction of movement a full seven seconds before they consciously became aware of their decision. While follow-up research has challenged some of the findings, it appears that brain activity may come before conscious thinking (Eysenck & Keane, 2015).

Associations have been found between positive emotions, creative thinking, and overall wellbeing, suggesting environmental changes that may benefit staff productivity and innovation in the workplace (Yuan, 2015).

Factors explored include creating climates geared toward creativity, boosting challenge, trust, freedom, risk taking, low conflict, and even the beneficial effects of humor.

Undoubtedly, further innovation will be seen from marrying the two powerful and compelling new fields of positive psychology and cognitive psychology.

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• Skills for Regulating Emotion We can learn to manage our emotions by focusing on more positive experiences than negative ones.
• Emotional Repetition and Attention Remodeling Identifying phrases used to describe ourselves can help desensitize negative feelings .

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• Creating Savoring Rituals It’s possible to increase positive emotions by sharpening our sensory perceptions via savoring.

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Cognitive psychology is crucial in our search for understanding how we interact with and make sense of a constantly changing and potentially harmful environment.

Not only that, it offers insight into what happens when things go wrong and the likely impact on our wellbeing and ability to cope with life events.

Cognitive psychology’s strength is its willingness to embrace research findings from many other disciplines, combining them with existing psychological theory to create new models of cognition.

The tasks we appear to carry out unconsciously are a great deal more complex than they might first appear. Perception, attention, problem-solving, language comprehension and production, and decision-making often happen without intentional thought and yet have enormous consequences on our lives.

Use this article as a starting point to explore the many and diverse aspects of cognitive psychology. Consider their relationships with associated research fields and reflect on the importance of understanding cognition in helping clients overcome complex events or circumstances.

We hope you enjoyed reading this article. Don’t forget to download our three Positive Psychology Exercises for free .

• Eysenck, M. W., & Keane, M. T. (2015). Cognitive psychology: A student’s handbook . Psychology Press.
• Goldstein, E. B. (2011). Cognitive psychology . Wadsworth, Cengage Learning.
• Gross, R. D. (2020). Psychology: The science of mind and behaviour . Hodder and Stoughton.
• Kuhn, G., & Findlay, J. M. (2010). Misdirection, attention and awareness: Inattentional blindness reveals temporal relationship between eye movements and visual awareness. The Quarterly Journal of Experimental Psychology , 63 (1), 136–146.
• Moore, J. (1996). On the relation between behaviorism and cognitive psychology. Journal of Mind and Behavior , 17 (4), 345–367
• Soon, C. S., Brass, M., Heinze, H., & Haynes, J. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience , 11 (5), 543–545.
• Yuan, L. (2015). The happier one is, the more creative one becomes: An investigation on inspirational positive emotions from both subjective well-being and satisfaction at work. Psychology , 6 , 201–209.

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As a widowed Mother and Grandmother, whom was recently told by an adult child that maybe I should have “cognitive” testing done, I found this article to be very informative and refreshing. Having the ability to read and and learn about cognitive psychology is interesting as their are so many ways our brains are affected from the time we are born until the time we reach each and every stage in life. I have spent time with my grandchildren who are from age 19 months, through 15 years old , and spend time with children who are 35, 34, and 32, and my parents who are 88 and 84. I appreciate your article and your time in writing it. Sincerely,

Cognitive Psychology creates & build human capacity to push physical and mental limits. My concept of cognition in human behavior was judged by the most time I met my lawyer or the doctor. Most of the time while listening a pause, oh I see and it is perpetual transition to see. Cognition emergence is very vital support as we see & perceive. My practices in engineering solution are base on my cognitive sensibilities.You article provokes the same perceptions. Thank you

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Historical psychology.

• Noemi Pizarroso Lopez Noemi Pizarroso Lopez Psicología Básica I Juan del Rosal, National Distance Education University
• https://doi.org/10.1093/acrefore/9780190236557.013.467
• Published online: 30 April 2020

Historical psychology claims that the mind has a history, that is, that our ways of thinking, reasoning, perceiving, feeling, and acting are not necessarily universal or invariable, but are instead subject to modifications over time and space. The theoretical and methodological foundations of this movement were laid in France by psychologist Ignace Meyerson in his book Les fonctions psychologiques et les œuvres , published in 1948. His program stressed the active, experimental, constructive nature of human behavior, spanning behavioral registers as diverse as the linguistic, the religious, the juridical, the scientific/technical, and the artistic. All these behaviors involve aspects of different mental functions that we can infer through a proper analysis of “works,” considered as consolidated testimonies of human activity. As humanity’s successive achievements, constructed over the length of all the paths of the human experience, they are the materials with which psychology has to deal.

Meyerson refused to propose an inventory of functions to study. As unstable and imperfect products of a complex and uncertain undertaking, they can be analyzed only by avoiding the counterproductive prejudice of metaphysical fixism. Meyerson spoke in these terms of both deep transformations of feelings, of the person, or of the will, and of the so-called “basic functions,” such as perception and the imaginative function, including memory, time, space, and object.

Before Meyerson the term “historical psychology” had already been used by historians like Henri Berr and Lucien Febvre, a founding member of the Annales school, who firmly envisioned a sort of collective psychology of times past. Meyerson and his disciples eventually vied with their fellow historians of the Annales school for the label of “historical psychology” and criticized their notions of mentality and outillage mental . The Annales historians gradually abandoned the label, although they continued to cultivate the idea that mental operations and emotions have a history through the new labels of a “history of mentalities” and, more recently at the turn of the century, a “history of emotions.” While Meyerson and a few other psychologists kept using the “historical psychology” label, however, mainstream psychology remained quite oblivious to this historical focus. The greatest efforts made today among psychologists to think of our mental architecture in terms of transformation over time and space are probably to be found in the work of Kurt Danziger and Roger Smith.

• Ignace Meyerson
• Lucien Febvre
• Jean-Pierre Vernant
• psychological functions
• history of mentalities
• subjectivity
• outillage mental

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date: 28 June 2024

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