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• Introduction

Comparison with controlled study design

Natural experiments as quasi experiments, instrumental variables.

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natural experiment , observational study in which an event or a situation that allows for the random or seemingly random assignment of study subjects to different groups is exploited to answer a particular question. Natural experiments are often used to study situations in which controlled experimentation is not possible, such as when an exposure of interest cannot be practically or ethically assigned to research subjects. Situations that may create appropriate circumstances for a natural experiment include policy changes, weather events, and natural disasters. Natural experiments are used most commonly in the fields of epidemiology , political science , psychology , and social science .

Key features of experimental study design include manipulation and control. Manipulation, in this context , means that the experimenter can control which research subjects receive which exposures. For instance, subjects randomized to the treatment arm of an experiment typically receive treatment with the drug or therapy that is the focus of the experiment, while those in the control group receive no treatment or a different treatment. Control is most readily accomplished through random assignment, which means that the procedures by which participants are assigned to a treatment and control condition ensure that each has equal probability of assignment to either group. Random assignment ensures that individual characteristics or experiences that might confound the treatment results are, on average, evenly distributed between the two groups. In this way, at least one variable can be manipulated, and units are randomly assigned to the different levels or categories of the manipulated variables.

In epidemiology, the gold standard in research design generally is considered to be the randomized control trial (RCT). RCTs, however, can answer only certain types of epidemiologic questions, and they are not useful in the investigation of questions for which random assignment is either impracticable or unethical. The bulk of epidemiologic research relies on observational data, which raises issues in drawing causal inferences from the results. A core assumption for drawing causal inference is that the average outcome of the group exposed to one treatment regimen represents the average outcome the other group would have had if they had been exposed to the same treatment regimen. If treatment is not randomly assigned, as in the case of observational studies, the assumption that the two groups are exchangeable (on both known and unknown confounders) cannot be assumed to be true.

As an example, suppose that an investigator is interested in the effect of poor housing on health. Because it is neither practical nor ethical to randomize people to variable housing conditions, this subject is difficult to study using an experimental approach. However, if a housing policy change, such as a lottery for subsidized mortgages, was enacted that enabled some people to move to more desirable housing while leaving other similar people in their previous substandard housing, it might be possible to use that policy change to study the effect of housing change on health outcomes. In another example, a well-known natural experiment in Helena , Montana, smoking was banned from all public places for a six-month period. Investigators later reported a 60-percent drop in heart attacks for the study area during the time the ban was in effect.

Because natural experiments do not randomize participants into exposure groups, the assumptions and analytical techniques customarily applied to experimental designs are not valid for them. Rather, natural experiments are quasi experiments and must be thought about and analyzed as such. The lack of random assignment means multiple threats to causal inference , including attrition , history, testing, regression , instrumentation, and maturation, may influence observed study outcomes. For this reason, natural experiments will never unequivocally determine causation in a given situation. Nevertheless, they are a useful method for researchers, and if used with care they can provide additional data that may help with a research question and that may not be obtainable in any other way.

The major limitation in inferring causation from natural experiments is the presence of unmeasured confounding. One class of methods designed to control confounding and measurement error is based on instrumental variables (IV). While useful in a variety of applications, the validity and interpretation of IV estimates depend on strong assumptions, the plausibility of which must be considered with regard to the causal relation in question.

In particular, IV analyses depend on the assumption that subjects were effectively randomized, even if the randomization was accidental (in the case of an administrative policy change or exposure to a natural disaster) and adherence to random assignment was low. IV methods can be used to control for confounding in observational studies, to control for confounding due to noncompliance, and to correct for misclassification.

IV analysis, however, can produce serious biases in effect estimates. It can also be difficult to identify the particular subpopulation to which the causal effect IV estimate applies. Moreover, IV analysis can add considerable imprecision to causal effect estimates. Small sample size poses an additional challenge in applying IV methods.

Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

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Experimental Design: Types, Examples & Methods

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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• Natural Experiment

Whilst oftentimes people tend to think of experiments occurring in laboratories and controlled settings, psychologists also consider real-world environments as opportunities to investigate phenomena. Behaviour changes depending on the setting, and investigating research areas in their natural settings can amplify the validity of the findings. Natural experiments offer researchers the opportunity to investigate human behaviour in everyday life. 

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• Cell Biology

What is a natural experiment?

What are the advantages of natural experiments?

What are the disadvantages of natural experiments?

What is a quasi-experiment?

What are the advantages of quasi-experiments?

What are the disadvantages of quasi-experiments?

When are natural experiments conducted?

Why can't researchers draw cause-and-effect conclusions from natural experiments and natural setting quasi-experiments?

When might you use a quasi-experiment?

Which of the following experiments does not involve the researcher manipulating the independent variable? 

Why may a researcher use a natural experiment in terms of ethical issues?

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• We are going to explore natural experiments used in psychological research.
• We will start by highlighting the natural experiment definition.
• We will then explore how natural experiments are used in psychology and cover natural experiment examples of research to demonstrate to help illustrate our points.
• Moving on, we will cover natural and field experiments to highlight the differences between the two types of investigations.
• And to finish, we will explore the natural experiment's advantages and disadvantages.

Natural Experiment Defintion

Natural experiments are essentially experiments that investigate naturally occurring phenomena. The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation by the researcher.

In experiments, changes in the independent variable (IV) are observed to identify if these changes affect the dependent variable (DV). However, in natural experiments, the researcher does not manipulate the IV. Instead, they observe the natural changes that occur.

Some examples of naturally occurring IVs are sex at birth, whether people have experienced a natural disaster, experienced a traumatic experience, or been diagnosed with a specific illness.

These examples show that it's next to impossible for the researcher to manipulate these.

Natural Experiment: Psychology

Why may researchers choose to use a natural experiment? As we have just discussed, sometimes researchers can't manipulate the IV. But, they may still wish to see how changes in the IV affect the DV, so use a natural experiment.

Sometimes a researcher can manipulate the IV, but it may be unethical or impractical to do so, so they conduct a natural experiment.

In natural experiments, the researcher can see how changes in the IV affect a DV, but unlike in lab experiments, the researcher has to identify how the IV is changing. In contrast, lab experiments pre-determine how the IV will be manipulated.

Natural Experiment: Examples

Natural experiments often take place in real-world settings. An example can be seen in examining the effect of female and male performance in an office environment and if gender plays a role in the retention of customers. Other examples include examining behaviours in schools, and the effect age has on behaviour.

Let's look at a hypothetical study that uses a natural experiment research method.

A research team was interested in investigating attitudes towards the community after experiencing a natural disaster.

The study collected data using interviews. The IV was naturally occurring as the researcher did not manipulate the IV; instead, they recruited participants who had recently experienced a natural disaster.

Natural Experiment vs Field Experiment

The table below summarises the key similarities and differences between natural experiments vs field experiments.

Natural Experiment: Advantages and Disadvantages

In the following section will present the natural experiment's advantages and disadvantages. We will discuss the new research possibilities, causal conclusions, rare opportunities, pre-existing sampling bias and ethical issues.

New Research Opportunities

Natural experiments provide opportunities for research that can't be done for ethical and practical reasons.

For example, it is impossible to manipulate a natural disaster or maternal deprivation on participants.

So, natural experiments are the only ethical way for researchers to investigate the causal relationship of the above topics. Thus, natural experiments open up practical research opportunities to study conditions that cannot be manipulated.

High Ecological Validity

Natural experiments have high ecological validity because natural experiments study real-world problems that occur naturally in real-life settings.

When research is found to use and apply real-life settings and techniques, it is considered to have high mundane realism.

And the advantage of this is that the results are more likely applicable and generalisable to real-life situations.

Rare Opportunities

There are scarce opportunities for researchers to conduct a natural experiment. Most natural events are ‘one-off’ situations. Because natural events are unique, the results have limited generalisability to similar situations.

In addition, it is next to impossible for researchers to replicate natural experiments; therefore, it is difficult to establish the reliability of findings.

Pre-Existing Sampling Bias

In natural experiments, pre-existing sampling bias can be a problem. In natural experiments, researchers cannot randomly assign participants to different conditions because naturally occurring events create them. Therefore, in natural experiments, participant differences may act as confounding variables .

As a result, sample bias in natural experiments can lead to low internal validity and generalisability of the research.

Ethical Issues

Although natural experiments are considered the only ethically acceptable method for studying conditions that can't be manipulated, ethical issues may still arise. Because natural experiments are often conducted after traumatic events, interviewing or observing people after the event could cause psychological harm to participants.

Researchers should prepare for potential ethical issues, such as psychological harm, usually dealt with by offering therapy. However, this can be pretty costly. And the ethical issue may lead participants to drop out of the research, which can also affect the quality of the research.

Natural Experiment - Key takeaways

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher.

The advantages of natural experiments are that they provide opportunities for research that researchers cannot do for ethical or practical reasons and have high ecological validity.

The disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

Flashcards in Natural Experiment 22

In a natural experiment, researchers take advantage of events that occur or have already occurred naturally. Therefore, the researchers cannot change or control the IV of the natural experiment.

Natural experiments provide opportunities for research that may not be conducted due to ethical and practical reasons. Also, natural experiments have good ecological validity.

There are scarce opportunities for researchers to conduct a natural experiment and pre-existing sampling bias can be an issue. Also, ethical issues can still arise in natural experiments.

A quasi-experiment is an experiment that examines pre-existing differences between people. There is an IV, but it already exists and is not manipulated by the researcher.

Lab-based quasi-experiments are conducted in a well-controlled setting, which implies good internal validity and reliability. Also, quasi-experiments allow comparisons between peoples according to their pre-existing differences.

Researchers can only tentatively draw causal conclusions in natural setting quasi-experiments and pre-existing sampling bias can be an issue.

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Frequently Asked Questions about Natural Experiment

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher. 

What is an example of natural experiment?

Beckett (2006) investigated the effects of deprivation on children’s IQ at age 11. They compared 128 Romanian children who UK families had adopted at various ages and 50 UK children who had been adopted before six months. They found that Romanian children who had been adopted before six months of age had similar IQs to the UK children; however, Romanian children adopted after six months of age had much worse scores. 

What are the characteristics of a natural experiment?

The characteristics of natural experiments are that they are carried out in a natural setting and the IV is not manipulated in this type of experiment. 

What are the advantages and disadvantages of natural experiments?

And the disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

What are natural experiments in research?

Natural experiments in psychology research are often used when manipulating a variable is unethical or impractical.

Test your knowledge with multiple choice flashcards

True or false: Similar to lab experiments, natural experiments are conducted in controlled settings.

True or false: Confounding/ extraneous variables can be an issue in natural experiments. 

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Do natural experiments have an important future in the study of mental disorders?

Anita thapar.

1 Child & Adolescent Psychiatry Section, Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK

Michael Rutter

2 MRC SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK

There is an enormous interest in identifying the causes of psychiatric disorders but there are considerable challenges in identifying which risks are genuinely causal. Traditionally risk factors have been inferred from observational designs. However, association with psychiatric outcome does not equate to causation. There are a number of threats that clinicians and researchers face in making causal inferences from traditional observational designs because adversities or exposures are not randomly allocated to individuals. Natural experiments provide an alternative strategy to randomized controlled trials as they take advantage of situations whereby links between exposure and other variables are separated by naturally occurring events or situations. In this review, we describe a growing range of different types of natural experiment and highlight that there is a greater confidence about findings where there is a convergence of findings across different designs. For example, exposure to hostile parenting is consistently found to be associated with conduct problems using different natural experiment designs providing support for this being a causal risk factor. Different genetically informative designs have repeatedly found that exposure to negative life events and being bullied are linked to later depression. However, for exposure to prenatal cigarette smoking, while findings from natural experiment designs are consistent with a causal effect on offspring lower birth weight, they do not support the hypothesis that intra-uterine cigarette smoking has a causal effect on attention-deficit/hyperactivity disorder and conduct problems and emerging findings highlight caution about inferring causal effects on bipolar disorder and schizophrenia.

Introduction

Psychiatric disorders have a complex etiology; influenced by multiple genetic as well as environmental risk factors. Although most are heritable, in the shorter term, environmental factors are more tractable to modification. If environmental exposures are causal, then their modification should lead to improvements in population health or the psychiatric disorder being treated (see Table 1 : meaning of key terms). Thus it is important to assess which of our selected environmental exposures are genuinely causal – but this is challenging.

Key terms and what they mean

Kraemer et al ., 1997 ; Sedgwick, 2013 ; Arnold and Ercumen, 2016 .

Traditionally many of the exposures that we believe to be risk factors for psychiatric disorder have been implicated through observational designs. These infer causation from observations of association. However, association is not causation. Threats to causal inference include reverse causation, confounding, and selection bias (Rutter, 2007 ; Thapar and Rutter, 2015 ). For example, has the supposed risk factor of family discord arisen as the result of the individual's psychiatric disorder – reverse causation? Has the common factor of social disadvantage contributed to both the outcome of psychiatric disorder and family discord – confounding? Does cannabis have a causal risk effect on schizophrenia or is it that those with a higher propensity to develop schizophrenia are more likely to use cannabis – selection bias?

These are important threats because if they lead to misleading and inconsistent conclusions, this confuses clinicians, researchers, the general public, and patients. At worst it leads to wasted resources. The challenges in inferring causality are not just restricted to psychiatry. For example, observational studies suggested vitamin E had a protective effect on cardiovascular disease until randomized controlled trials (RCTs) suggested that this was not the case (Eidelman et al ., 2004 ). RCTs are often considered as the ‘gold standard’ for assessing causal effects. However, given that RCTs of many environmental exposures relevant to psychopathology are not going to be feasible or ethical, what is the alternative?

‘Natural experiments’ provide an alternative strategy. We refer to designs that take advantage of situations whereby links between the exposure and other variables are separated by naturally occurring events or situations. Unlike RCTs, the manipulation is not undertaken by the researcher. Some involve the design and others the statistical methods.

In this review, we will consider some types of natural experiments and describe how they have been applied in the field of psychiatry. The aim is not to provide an exhaustive account of different methods but rather to focus on the principles, design, and limitations. There are a number of other methods that in the interests of space will not be covered in this review but are discussed elsewhere (Rutter and Thapar, 2018 ). Although there are other reviews (e.g. Pingault et al ., 2018 ), we aim to describe a broad range of designs and will provide examples of findings that would be relevant to a clinician.

There is a growing trend toward viewing causal inference as a single approach based on considering what would have occurred if an individual had not been exposed to the risk? [see Krieger and Davey Smith ( 2016 ) for an excellent discussion]. However, we agree with Krieger and Davey Smith ( 2016 ) for taking a broader view; one that emphasizes convergence or ‘triangulation’ of findings across diverse types of designs that have different types of biases and assumptions. When the same finding is observed using different approaches, it provides greater confidence in inferring causality especially when such studies are conducted in different populations.

Genetically informative designs that remove familial and genetic confounding

Many of the most important risk factors for psychopathology, such as life events and inter-personal discord, are person-dependent; they are not randomly allocated. Thus, it is unsurprising that decades of research have shown that many types of adversities run in families and are heritable (e.g. McGuffin et al ., 1988 ; Plomin, 2018 ). This raises the possibility that an association between exposure and psychiatric outcome could arise through familial or genetic confounding (Thapar and Rutter, 2009 , 2015 ).

It is for this reason that genetically informative designs such as twin studies have been invaluable for testing whether links between environmental exposures and psychopathology remain associated once genetic or familial confounds are taken into account.

Some designs, such as the discordant sib pair and in vitro fertilization (IVF) design (Thapar et al ., 2007 ), enable removal of genetic or familial confounds for prenatal exposures. For example, prenatal exposure to cigarette smoke has been linked with later risk for offspring attention-deficit/hyperactivity disorder (ADHD), conduct disorder, bipolar disorder, and schizophrenia. The effects could potentially be causal; for example, mediated by effects of nicotine on the developing brain. However, unmeasured confounds and selection biases are a concern, meaning that natural experiment designs have proved very useful here (Quinn et al ., 2017 ; Rice et al ., 2018 ).

Twin and adoption studies are not able to separate genetic confounds for prenatal exposures. That is because twins share their prenatal exposures and varying degrees of genetic liability and for adopted offspring, it is their biological mother who provides both the prenatal environmental and half of their genetic makeup. However, such designs are well-suited for assessing post-natal exposures. Some designs such as the children-of-twins design (D'Onofrio et al ., 2003 ) and adoption designs are especially well-suited for examining cross-generational environmental as well as genetic transmission (see Table 2 ).

Genetically informative designs and what they can be used to assess

Maternal v. paternal exposure during pregnancy

One method that has been used to disaggregate intra-uterine and genetic or house-hold/familial-level influences involves testing associations between maternal v. paternal exposures during pregnancy and offspring outcomes ( Fig. 1 ). If the link is mediated by an intra-uterine effect, a stronger association would be expected for the maternal exposure. For example, in a UK population-birth cohort ALSPAC, strong associations were observed between maternal smoking in pregnancy and shorter birth length (Howe et al ., 2016 ) and lower birth weight in offspring (Langley et al ., 2012 ) that were not observed when exposure to paternal smoking was examined (see Table 1 ; this is an example of a negative control exposure). However, in this same cohort, associations between exposure to smoking in pregnancy and ADHD were as strong for maternal exposures as they were for paternal exposures even in the case of mothers who did not smoke. These results held when the contribution of additional passive smoking was considered. There are limitations to this design including the fact that parents will show similarities in exposures due to genetic (assortative mating) and social reasons and it is restricted to the sorts of exposures that both parents could feasibly experience in pregnancy.

Maternal v. paternal exposure.

Discordant sibling pair design

Full biologic siblings share on average 50% of their genome. Thus differences between them can be used to assess family-level confounds that include genetic and shared environmental contributions.

(i) Prenatal exposures. As they share the same mother, they become of special interest when they have been differentially exposed to prenatal factors. For example, taking the example of maternal smoking in pregnancy and ADHD, eight studies of discordant sibling pairs have now found that the siblings who were unexposed to smoking in utero showed elevated levels of ADHD (Rice et al ., 2018 ). Similar findings were observed for conduct problems. Birth weight provided the negative control as the studies that examined this outcome found that the association with cigarette smoking remained strong. A recent, large discordant sibling study also failed to find support for a causal effect of exposure to prenatal smoking on severe mental illness (bipolar disorder and schizophrenia) suggesting the contribution of family-level confounders to previously observed associations (Quinn et al ., 2017 ). There are many limitations to this design that have been described elsewhere. These include the issue of selection as mothers are behaving differently in different pregnancies. For example, the sample consists of a group of mothers who are able to quit smoking in one pregnancy but not the other. Also, there is the problem that siblings will be born at different times and thus will be exposed to different family-level and population-level risks.

(ii) Assessing later adversities using a sibling pair design and its extension, the co-relative study. The discordant sibling pair design and its extension involving pairs of relatives from the same generation such as half-siblings and cousins have also been used to assess causal links between adolescent and adult exposures and psychiatric disorders. For example, the observed association between cannabis use and schizophrenia has been well-established. However, the causal relationship could be subject to question given that those who are at elevated familial or genetic liability or with prodromal symptoms could be more likely to use cannabis (confounding, selection bias, and reverse causation). In one large, Swedish study, the authors used an extended sibling pair design to investigate the causal relationship between cannabis and schizophrenia (Giordano et al ., 2015 ). The association was much attenuated once familial confounding was taken into account; the effect size also was diminished when potential prodromal effects were considered that was assessed by increasing the temporal delay between cannabis abuse and admission for schizophrenia (odds ratio 1.67). The findings suggested that there is a likely causal link between cannabis use and schizophrenia for some but that the effect size is not as strong as previously reported because of the contribution of familial confounding and reverse causation.

An alternative design that enables separation of prenatal exposures from genetic ones is based on individuals who have been conceived through assisted reproductive technologies. Some of these individuals are genetically related to the woman who undergoes the pregnancy and others are genetically unrelated (see Fig. 2 ). If a prenatal exposure has causal effects, then association with the offspring outcome should be observed regardless of whether mother–offspring dyads are genetically related or unrelated. That was the case for maternal smoking in pregnancy and lower birth weight (Thapar et al ., 2009 ) and also for associations between maternal reports of stress in pregnancy and lower birth weight and preterm birth (Rice et al ., 2010 ).

In-vitro fertilisation design.

However, for association between maternal smoking in pregnancy and a trait measure of ADHD in offspring (Thapar et al ., 2009 ) as well as conduct problems (Rice et al ., 2009 ), association was only observed in genetically related mother–offspring dyads not in the unrelated pairs, suggesting genetic confounding. The finding converges with those from the maternal v. paternal exposure and discordant sibling pair designs. Interestingly, the magnitude of association in the related pairs was similar to that observed in other observational studies and including measured confounders of the sort including in observational designs, such as parental psychopathology, social class did not remove the genetic confound.

That is, findings from this and other studies suggest that residual confounds remain a problem for observational studies and that including multiple confounders is not a substitute for an informative design.

The IVF design (Thapar et al ., 2007 ) has also been used to assess inter-generational transmission of psychopathology and to examine post-natal adversity. For example, using this approach, depression symptoms were found to be environmentally transmitted and environmental links were observed between hostile parenting and antisocial behavior in offspring (Harold et al ., 2011 ).

The IVF design does have a number of limitations however. These include the representativeness of the families who have undergone IVF treatment and the low prevalence of certain types of risk factors (e.g. maternal smoking in pregnancy).

Twin designs

Twin designs utilize the fact that monozygotic (MZ) twins share on average 100% of their genes (DNA sequence) and dizygotic (DZ) twins share on average 50% of their genome.

The twin design allows variation in any given measure to be partitioned into genetic and environmental variance. Where both exposure and psychiatric outcome are assessed, ideally longitudinally to avoid the problem of reverse causation, the association between exposure (e.g. life events) and outcome (e.g. depression) can be decomposed into genetic and environmental components. As the genetic covariance between exposure and outcome is explicitly modelled, essentially the genetic confound is removed. Here, the investigator is interested in whether there is an environmental link that remains between the exposure and outcome. This design has been invaluable in demonstrating a number of potentially causal environmental risk factors for psychopathology.

For example, family and twin studies of depression in childhood, adolescence, and adult life have observed a familial and genetic contribution to life events, mainly those that are person-dependent (e.g. losing a job) rather than ones that are independent (death of a relative), as well as to depression (McGuffin et al ., 1988 ; Plomin, 2018 ).

Twin studies that have investigated the link between life events and depression suggest that the association between independent life events and depression appears to be mainly or entirely environmental; that is consistent with a causal explanation (Kendler et al ., 1999 ). For dependent life events, there is a stronger genetic contribution to the link with depression. This seems to be partly explained by self-selection into risk exposure by those predisposed to depression (Kendler et al ., 1999 ) and becomes more prominent from adolescence onwards (Rice et al ., 2003 ).

Another example is the link between harsh parenting and antisocial behavior in children. One twin study found that the association with corporal punishment was primarily explained by genetic factors (Jaffee et al ., 2004 ). This could arise, for example, through parental response to the child's behavior which is genetically influenced. However, the findings for physical abuse were different. Here, the link with antisocial behavior was environmentally mediated, and consistent with a causal explanation.

Twin designs, their uses, strengths, and limitations have been described in detail elsewhere (State and Thapar, 2015 ). When genetic contributions are identified through bivariate twin analyses that we have described, it can index selection bias and potential threats to causal inference. However even with longitudinal twin designs, an environmentally mediated link does not prove a causal link between an exposure and outcome as there could be alternative pathways that explain the association including measurement artifacts.

Discordant MZ twin pairs

This design utilizes the fact that MZ twins are considered to share 100% of their genes and means that differences in their phenotype are attributed to non-genetic contributions that include non-shared environment as well as measurement error and stochastic effects. The approach involves assessing whether MZ twins who are differentially exposed to a stressor or adversity (e.g. discordant for victimization) show differences in a given outcome (e.g. depression).

For example, in the UK E-risk twin study of 7–10 years old, 110 MZ twin pairs who were discordant for bullying victimization were assessed (Arseneault et al ., 2008 ). The co-twins who were bullied showed higher internalizing (anxiety/depression) symptom scores than those who were not exposed to bullying. A more recent US longitudinal twin study also investigated 145 MZ twins who were discordant for bullying victimization in childhood (Silberg et al ., 2016 ). Although being bullied showed a genetic link with social anxiety; there were also environmental links with social anxiety, separation anxiety, and young adult suicidal ideation. The findings from both of these studies are consistent with a causal effect of bullying victimization on emotional/anxiety symptoms and are important given the interest in reports from longitudinal observational designs.

In another longitudinal MZ discordant twin study (Caspi et al ., 2004 ), Caspi et al . assessed maternal hostility and warmth. This was achieved by conducting independent ratings from a recorded 5 min speech sample from the mother when talking about the child (expressed emotion EE). Maternal expressed emotion was found to be environmentally associated with later teacher-reported behavioral problems.

Although the MZ discordant pair design is useful because it controls for genetic confounding there are some drawbacks. For example, we now know that MZ twins are not per se 100% genetically identical, for example, through non-inherited genetic differences. Also discordant MZ twin pairs could be considered as atypical and rare especially for very highly heritable disorders such as autism or ADHD or schizophrenia. The exposure could be behaving as a proxy for some other risk factor that impacted on one twin and not the other.

Children of Twins design and extensions

The Children of Twins (CoT) design allow investigation of cross-generational links between parent and offspring psychopathology or parentally provided exposures and offspring outcomes. It takes advantage of the fact that the offspring of MZ and DZ twins are socially cousins (DZ twins are also genetically cousins) but the MZ twin offspring are genetically half siblings.

This type of design, for example, has been used to assess the cross-generational transmission of depression. In an Australian study of twins, their spouse, and offspring, environmental factors were found to explain the link between parents and offspring depression even when accounting for depression in spouses (Singh et al ., 2011 ). Similar findings had been found in an earlier US study (Silberg et al ., 2010 ). Another CoT study from Sweden found that depression symptoms in parents showed concurrent environmental but not genetic links with offspring internalizing symptoms (McAdams et al ., 2015 ). The findings accord with those from the IVF study (Harold et al ., 2011 ). A more recent Swedish CoT design observed only environmental transmission between parents and offspring for anxiety and neuroticism; again with no genetic contribution (Eley et al ., 2015 ).

These findings might appear puzzling in that while it is important to observe environmental transmission of depression and anxiety, there are no genetic contributions observed for either and this is inconsistent with twin studies (Sullivan et al ., 2000 ). Twin studies observe modest heritability for depression. One difficulty for cross-generational investigations is the assumption that the same genetic influences contribute across development when that is unlikely (e.g. Power et al ., 2017 ; Riglin et al ., 2017 ). Another issue is that twin study heritability estimates capture passive gene–environment correlation effects that would be reduced in CoT studies and eliminated in the IVF design.

The CoT design has also been used to assess postnatal adversities. One such study (Lynch et al ., 2006 ) found that harsh physical punishment remained associated with childhood behavioral problems even when genetic factors had been allowed for. These findings are in keeping with the twin study findings and taken together are consistent with harsh parenting having a causal effect on childhood antisocial behavior.

Adoption studies

Adoption studies allow genetic and prenatal influences to be separated from post-adoption experiences. They provide a powerful method for assessing the contribution of rearing influences because these are known to be affected by with genetically influenced parental attributes. Ordinarily these biological parental characteristics would in turn be correlated with child characteristics including psychopathology thereby introducing a potential genetic confound. The advantage of adoption studies is that they remove this confound, the so-called passive gene–environment correlation because the genotypes of the parents who are rearing the children are independent of the child's genotypes.

There are several examples where adoption studies have been able to demonstrate the contribution of the rearing environment. For example, a study of adopted away children showed that negative parenting provided by the adoptive parent was associated with their adoptive child's antisocial behavior (Ge et al ., 1996 ). The adoptive parent's negative parenting was also associated with substance abuse/dependency or antisocial personality in the child's biological parents; that association appeared to be mediated via the child's behavior. Overall the findings suggested causal effects of negative parenting on children's antisocial behavior but also showed that the children's genetically influenced antisocial behavior in turn affected the parenting of the adoptive parents. The observation that negative parenting has a causal effect on offspring antisocial behavior converges with the findings from twin studies showing a convergence of findings from different designs.

A more recent example is provided by a Swedish large-scale adoption study cross-generational study (Kendler et al ., 2018 ). The authors were able to assess the contribution of genetic and rearing influences to parent–offspring resemblance for treated major depressive disorder. They found that both genetic and rearing influences contributed equally to parent–offspring resemblance in major depressive disorder. The adoptive families enabled the authors to further show that genetic and rearing influences acted additively rather than having an interactive effect. The authors highlighted that there had been four previous adoption studies of depression; although genetic contributions had previously been observed, only one had observed an environmental contribution to depression. However, now there have been two adoption studies that have showed an environmental contribution to inter-generational transmission of depression. Also the same findings have been observed in three children of twin designs and in the IVF design, although here some of these find environmental contributions only with no genetic transmission.

Overall the findings from different genetically informative studies of depression are converging on the suggestion that environmental/social factors contribute to the cross-generational transmission of depression. That of course has important clinical treatment and prevention implications.

Designs involving the introduction or removal of risks to a population: potentially removing selection or allocation bias

Given a serious challenge to causal inference is selection or allocation bias, a number of studies have taken advantage of situations where risks have been introduced to or removed from an entire population.

Universal introduction of risk

Here, the best known studies are the Dutch Hunger Winter (Susser et al ., 1996 ) and Chinese famine studies (St Clair et al ., 2005 ) that examined the consequences of intra-uterine exposure to famine. These studies focused on populations that were exposed to universal time-limited famines that affected some individuals during the intra-uterine period. Exposed individuals in both studies showed around a twofold elevated risk of schizophrenia as well as congenital anomalies of the central nervous system. As there was no evidence for selection for exposure to either of these famines, the findings suggest that extreme nutritional deficiency in early pregnancy likely has a causal risk effect for schizophrenia. However, the conditions in both of these studies was extreme and atypical so whether the findings have relevance for the etiology of schizophrenia as a whole is unknown.

Universal removal of risk

In this design, the strength is that it again removes selection or allocation bias whereby the person or some external agent influences the removal of risk. One good example is provided by the Great Smoky Mountains Study that is a longitudinal epidemiological study. During the course of this study of over 1000 children, a casino opened on a Native American reserve and provided a substantial increase to the family income for around a quarter of the original sample. The investigators were able to examine data before and after this happened. They showed that the relief of poverty led to decreased levels of oppositional defiant disorder and conduct disorder but not anxiety or depression (Costello et al ., 2003 ). The effects appeared to be mediated via altered parenting that included increased levels of supervision and parental time. Later follow-up showed that family income supplementation provided in childhood continued to be associated with lower rates of psychiatric problems including alcohol and cannabis abuse, lower rates of convictions for minor offenses, and higher levels of education. There were no links with later behavioral disorders or depression or other drug use (Costello et al ., 2010 ).

Interrupted time series

This design takes advantage of multiple waves of data that have been collected before and after the introduction or removal of the putative causal variable. This could be used to assess the impact of a policy or a naturally occurring event.

For example, after the introduction of UK legislation to reduce paracetamol package sizes, there was an observed drop in deaths from paracetamol overdoses (Hawton et al ., 2013 ).

Another example comes from a study of gang membership that is known to be associated with higher rates of delinquency (Thornberry et al ., 1993 ). However, it is not known whether that is due to selection effects with those having a propensity to be delinquent choosing to be in a gang or whether it is the causal social effects of being in a gang. Thornberry et al . ( 2002 ) found as might be expected important selection effects; boys who joined gangs were more delinquent than those who did not. However, they also showed that once boys left the gang, their rates of delinquency dropped off though not back to the level they were prior to joining the gang. This observation suggested that gang membership had additional social influences on delinquency. However, reverse causation and unmeasured confounders are possible contributors because we do not know what affected the boys’ decisions to leave the gang.

Changes in policy

If these are applied to a whole nation and data are available before and after the introduction of the policy, then this can provide a useful natural experiment situation. One study in Sweden (Nilsson, 2008 ) focused on the effects of prenatal alcohol exposure in two regions that were subjected to an experimental policy change in alcohol sales. The intention was to shift the population away from drinking spirits to consuming drinks with a lower alcohol content. However, it inadvertently resulted in very marked increases in the consumption of strong beer especially amongst teenagers. The experimental policy started in 1967 but was terminated abruptly in mid-1968 once it was realized that alcohol consumption had increased. Using registry data, the researchers were able to assess a cohort of children who had been in utero during the exposed period. As the policy was time and geographically limited, the exposed cohort could be compared with unexposed cohorts in adjacent geographic regions and in adjacent time-unexposed cohorts. At around 30 years of age, the exposed group showed greatly reduced educational achievements, lower earnings, and greater welfare dependency than those born to the unexposed cohorts. The effects were strongest in males, those exposed for the longest in intrauterine life and those born in younger mothers. The results suggest that prenatal exposure to alcohol likely had intrauterine risk effects on offspring. However, the problem with this sort of policy study is that the results are obtained from analyses at a group rather than individual level.

Radical change in environment: adoption following profound institutional deprivation

One good example of a natural experiment was provided by the English and Romanian Adoptees Study that involved a very radical change in early environment. This is a longitudinal study of individuals who were exposed to institutional care and extreme privation from early infancy. The possibilities of selection bias and reverse causation were essentially removed because the children were admitted very early and virtually no children left care until the government regime fell in 1989. These children subsequently were exposed to a radical change in rearing environment after they were adopted into relatively advantaged homes in the UK. The findings from this study showed that although there was some recovery, early institutional care of the type experienced by these children for more than 6 months resulted in difficulties that persisted to adulthood including autistic-type symptoms, ADHD-like problems, disinhibited social engagement, and emotional symptoms but not cognitive impairment (Sonuga-Barke et al ., 2017 ).

As is the case for some of the other natural experiments, such as the famine studies, although selection bias is removed, the question is whether the findings apply to less severe and more common forms of deprivation.

Using instrumental variables as a statistical method to deal with unmeasured confounding

An instrumental variable is a measured variable that is associated with the exposure of interest but that is not associated with the same selection effects and confounds. If the exposure has a genuinely causal risk effect on the outcome, then we would expect the instrumental variable also to be associated with the outcome. Early use and misuse of alcohol have been considered as potential causal risks or exposures for the later outcomes of alcohol dependence and misuse in adult life. Early puberty has been used as an instrumental variable for early use and misuse of alcohol because it is strongly associated with these exposures yet is not subject to the same selection biases or confounds.

Three studies have found that while early alcohol use and misuse in adolescence is associated with later alcohol problems, early puberty does not predict alcohol problems (Stattin and Magnusson, 1990 ; Caspi and Moffitt, 1991 ; Pulkkinen et al ., 2006 ).

These findings suggest that early alcohol use is likely an early manifestation of later alcohol problems rather than a cause of it.

Mendelian randomization: a special type of instrumental variable

Mendelian randomization (MR) utilizes the random assortment of parental genotypes to offspring during meiosis. Here a genetic variant that is robustly associated with the exposure is used as the instrumental variable and provided certain assumptions are met should provide a means of controlling for confounding and reverse causation (see Fig. 3 ). As more genetic variants are being identified through genome-wide association studies, there is a growing interest in using MR to test causal hypotheses and many methodological extensions of this approach (Davey Smith and Hemani, 2014 ). One approach called two-sample MR takes advantage of already published large genome-wide association studies. It uses genetic variants for exposures [e.g. C reactive protein (CRP)] as instrumental variables and another set of genetic variants from another independent GWAS for the outcomes variants (e.g. cardiovascular disease). MR has been used most successfully in relation to cardiovascular disease. For example, MR has been used to show that CRP does not have a causal risk effect on cardiovascular disease (C Reactive Protein Coronary Heart Disease Genetics Collaboration (CCGC) et al ., 2011 ). More recently, MR has started to be used in psychiatry; for example, a recent study observed body mass index effects on depression but not the reverse (Nagel et al ., 2018 ). MR is challenging because of its assumptions. For example, there is a need for genetic variants that have a strong and robust association with the exposure in question, although there are methods that allow for combining multiple genome-wide significant variants. Also if the genetic variant (instrument) has pleiotropic effects, and that is often the case, or influences a confounder or affects the outcome via another mechanism other than via the exposure, then that poses problems. There are methods for assessing pleiotropy and again, like all the methods we have discussed, MR findings on their own need to be interpreted with caution. However, when findings converge with other designs, they can be helpful in inferring causation. They are also a helpful alternative to RCTs.

Mendelian randomization. ( a ) The instrument is associated with the outcome only through the exposure. ( b ) Limitations – if the instrument is associated with a confounder or there is a horizontal pleiotropy.

Conclusions

It is crucial that genuinely causal influences on psychopathology are identified if interventions and policies are going to be effective. In recent years, findings relevant to psychiatry have emerged from different natural experiment designs and some are consistent across different designs; this strengthens causal inference. For example, hostile parenting affects antisocial behavior and RCTs uphold this causal inference. Genetically informative studies converge in favor of life events and victimization being environmentally linked with depression and environmental cross-generational transmission for depression. However, although smoking cessation programs for pregnant women are clearly a priority as cigarette smoke is detrimental to offspring physical health, the natural experiment designs suggest these will not be a useful means for preventing ADHD or antisocial behavior. So do natural experiments have an important future in the study of mental disorders? The answer is a firm yes.

Acknowledgements

AT receives grant funding from the Wellcome Trust and MRC.

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Classic Psychology Experiments

The history of psychology is filled with fascinating studies and classic psychology experiments that helped change the way we think about ourselves and human behavior. Sometimes the results of these experiments were so surprising they challenged conventional wisdom about the human mind and actions. In other cases, these experiments were also quite controversial.

Some of the most famous examples include Milgram's obedience experiment and Zimbardo's prison experiment. Explore some of these classic psychology experiments to learn more about some of the best-known research in psychology history.

Harlow’s Rhesus Monkey Experiments

In a series of controversial experiments conducted in the late 1950s and early 1960s, psychologist Harry Harlow demonstrated the powerful effects of love on normal development. By showing the devastating effects of deprivation on young rhesus monkeys , Harlow revealed the importance of love for healthy childhood development.

His experiments were often unethical and shockingly cruel, yet they uncovered fundamental truths that have heavily influenced our understanding of child development.

In one famous version of the experiments, infant monkeys were separated from their mothers immediately after birth and placed in an environment where they had access to either a wire monkey "mother" or a version of the faux-mother covered in a soft-terry cloth. While the wire mother provided food, the cloth mother provided only softness and comfort.

Harlow found that while the infant monkeys would go to the wire mother for food, they vastly preferred the company of the soft and comforting cloth mother. The study demonstrated that maternal bonds   were about much more than simply providing nourishment and that comfort and security played a major role in the formation of attachments .

Pavlov’s Classical Conditioning Experiments

The concept of classical conditioning is studied by every entry-level psychology student, so it may be surprising to learn that the man who first noted this phenomenon was not a psychologist at all. Pavlov was actually studying the digestive systems of dogs when he noticed that his subjects began to salivate whenever they saw his lab assistant.

What he soon discovered through his experiments was that certain responses (drooling) could be conditioned by associating a previously neutral stimulus (metronome or buzzer) with a stimulus that naturally and automatically triggers a response (food). Pavlov's experiments with dogs established classical conditioning.

The Asch Conformity Experiments

Researchers have long been interested in the degree to which people follow or rebel against social norms. During the 1950s, psychologist Solomon Asch conducted a series of experiments designed to demonstrate the powers of conformity in groups.  

The study revealed that people are surprisingly susceptible to going along with the group, even when they know the group is wrong.​ In Asch's studies, students were told that they were taking a vision test and were asked to identify which of three lines was the same length as a target line.

When asked alone, the students were highly accurate in their assessments. In other trials, confederate participants intentionally picked the incorrect line. As a result, many of the real participants gave the same answer as the other students, demonstrating how conformity could be both a powerful and subtle influence on human behavior.

Skinner's Operant Conditioning Experiments

Skinner studied how behavior can be reinforced to be repeated or weakened to be extinguished. He designed the Skinner Box where an animal, often a rodent, would be given a food pellet or an electric shock. A rat would learn that pressing a level delivered a food pellet. Or the rat would learn to press the lever in order to halt electric shocks.

Then, the animal may learn to associate a light or sound with being able to get the reward or halt negative stimuli by pressing the lever. Furthermore, he studied whether continuous, fixed ratio, fixed interval , variable ratio, and variable interval reinforcement led to faster response or learning.

Milgram’s Obedience Experiments

In Milgram's experiment , participants were asked to deliver electrical shocks to a "learner" whenever an incorrect answer was given. In reality, the learner was actually a confederate in the experiment who pretended to be shocked. The purpose of the experiment was to determine how far people were willing to go in order to obey the commands of an authority figure.

Milgram  found that 65% of participants were willing to deliver the maximum level of shocks   despite the fact that the learner seemed to be in serious distress or even unconscious.

Why This Experiment Is Notable

Milgram's experiment is one of the most controversial in psychology history. Many participants experienced considerable distress as a result of their participation and in many cases were never debriefed after the conclusion of the experiment. The experiment played a role in the development of ethical guidelines for the use of human participants in psychology experiments.

The Stanford Prison Experiment

Philip Zimbardo's famous experiment cast regular students in the roles of prisoners and prison guards. While the study was originally slated to last 2 weeks, it had to be halted after just 6 days because the guards became abusive and the prisoners began to show signs of extreme stress and anxiety.

Zimbardo's famous study was referred to after the abuses in Abu Ghraib came to light. Many experts believe that such group behaviors are heavily influenced by the power of the situation and the behavioral expectations placed on people cast in different roles.

It is worth noting criticisms of Zimbardo's experiment, however. While the general recollection of the experiment is that the guards became excessively abusive on their own as a natural response to their role, the reality is that they were explicitly instructed to mistreat the prisoners, potentially detracting from the conclusions of the study.

Van rosmalen L, Van der veer R, Van der horst FCP. The nature of love: Harlow, Bowlby and Bettelheim on affectionless mothers. Hist Psychiatry. 2020. doi:10.1177/0957154X19898997

Gantt WH . Ivan Pavlov . Encyclopaedia Brittanica .

Jeon, HL. The environmental factor within the Solomon Asch Line Test . International Journal of Social Science and Humanity. 2014;4(4):264-268. doi:10.7763/IJSSH.2014.V4.360 

Koren M. B.F. Skinner: The man who taught pigeons to play ping-pong and rats to pull levers . Smithsonian Magazine .

B.F. Skinner Foundation. A brief survey of operant behavior .

Gonzalez-franco M, Slater M, Birney ME, Swapp D, Haslam SA, Reicher SD. Participant concerns for the Learner in a Virtual Reality replication of the Milgram obedience study. PLoS ONE. 2018;13(12):e0209704. doi:10.1371/journal.pone.0209704

Zimbardo PG. Philip G. Zimbardo on his career and the Stanford Prison Experiment's 40th anniversary. Interview by Scott Drury, Scott A. Hutchens, Duane E. Shuttlesworth, and Carole L. White. Hist Psychol. 2012;15(2):161-170. doi:10.1037/a0025884

Le texier T. Debunking the Stanford Prison Experiment. Am Psychol. 2019;74(7):823-839. doi:10.1037/amp0000401

Perry G. Deception and illusion in Milgram's accounts of the Obedience Experiments . Theoretical & Applied Ethics . 2013;2(2):79-92.

Specter M. Drool: How Everyone Gets Pavlov Wrong . The New Yorker. 2014; November 24.

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

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True, Natural and Field Experiments An easy lesson idea for learning about experiments.

Travis Dixon September 29, 2016 Research Methodology

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There is a difference between a “true experiment” a “field experiment” and  a “natural experiment”. These separate experimental methods are commonly used in psychological research and they each have their strengths and limitations.

True Experiments

Berry’s classic study compared two cultures in order to understand how economics, parenting and cultural values can influence behaviour. But what type of method would we call this?

A true experiment is one where:

• have randomly assigned participants to a condition (if using independent samples)

Repeated measures designs don’t need random allocation because there is no allocation as all participants do both conditions.

One potential issue in laboratory experiments is that they are conducted in environments that are not natural for the participants, so the behaviour might not reflect what happens in real life.

Field Experiments

A field experiment is one where:

• the researcher conducts an experiment by manipulating an IV,
• …and measuring the effects on the DV in a natural environment.

They still try to minimize the effects of other variables and to control for these, but it’s just happening in a natural environment: the field.

• Natural Experiment

A natural experiment is one where:

• the independent variable is naturally occurring. i.e. it hasn’t been manipulated by the researcher.

There are many instances where naturally occurring events or phenomenon may interest researchers. The issue with natural experiments is that it can’t be guaranteed that it is the independent variable that is having an effect on the dependent variable.

• Quantitative Research Methods Glossary
• Let’s STOP the research methods madness!
• What makes an experiment “quasi”?

Activity Idea

Students can work with a partner to decide if the following are true, field or natural experiments.

If you cant’ decide, what other information do you need?

• Berry’s cross-cultural study on conformity ( Key Study: Conformity Across Cultures (Berry, 1967)
• Bandura’s bobo doll study ( Key Study: Bandura’s Bobo Doll (1963)
• Rosenzweig’s rat study ( Key Study: Animal research on neuroplasticity (Rosenzweig and Bennett, 1961)

Let’s make it a bit trickier:

• Key Study: London Taxi Drivers vs. Bus Drivers (Maguire, 2006)
• Key Study: Evolution of Gender Differences in Sexual Behaviour (Clark and Hatfield, 1989)
• Key Study: Serotonin, tryptophan and the brain (Passamonti et al., 2012)
• Saint Helena Study : television was introduced on the island of Saint Helena in the Atlantic ocean and the researchers measured the behaviour of the kids before and after TV was introduced.
• Light Therapy : the researchers randomly assigned patients with depression into three different groups. The three groups received different forms of light therapy to treat depression (red light, bright light, soft light). The lights were installed in the participants’ bedrooms and were timed to come on naturally. The effects on depression were measured via interviews.

What are the strengths and limitations of:

• True Experiment 
• Field Experiment 

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Annual Review of Public Health

Volume 38, 2017, review article, open access, natural experiments: an overview of methods, approaches, and contributions to public health intervention research.

• Peter Craig 1 , Srinivasa Vittal Katikireddi 1 , Alastair Leyland 1 , and Frank Popham 1
• View Affiliations Hide Affiliations Affiliations: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G2 3QB, United Kingdom; email: [email protected] , [email protected] , [email protected] , [email protected]
• Vol. 38:39-56 (Volume publication date March 2017) https://doi.org/10.1146/annurev-publhealth-031816-044327
• First published as a Review in Advance on January 11, 2017
• Copyright © 2017 Annual Reviews. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA) International License, which permits unrestricted use, distribution, and reproduction in any medium and any derivative work is made available under the same, similar, or a compatible license. See credit lines of images or other third-party material in this article for license information.

Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.

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Literature Cited

• Abadie A , Diamond A , Hainmueller J . 1.  2010 . Synthetic control methods for comparative case studies: estimating the effect of California's Tobacco Control Program. J. Am. Stat. Assoc. 105 : 493– 505 [Google Scholar]
• Abadie A , Diamond A , Hainmueller J . 2.  2011 . Synth: an R package for synthetic control methods in comparative case studies. J. Stat. Softw. 42 : 1– 17 [Google Scholar]
• Abadie A , Diamond A , Hainmueller J . 3.  2015 . Comparative politics and the synthetic control method. Am. J. Polit. Sci. 50 : 495– 510 [Google Scholar]
• Abadie A , Gardeazabal J . 4.  2003 . The economic costs of conflict: a case study of the Basque Country. Am. Econ. Rev. 93 : 113– 32 [Google Scholar]
• 5.  Acad. Med. Sci. 2007 . Identifying the Environmental Causes of Disease: How Should We Decide What to Believe and When to Take Action. London: Acad. Med. Sci. [Google Scholar]
• Andalon M . 6.  2011 . Impact of Oportunidades in overweight and obesity in Mexico. Health Econ 20 : Suppl. 1 1– 18 [Google Scholar]
• Austin PC . 7.  2011 . An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. 46 : 399– 424 [Google Scholar]
• Basu S , Rehkopf DH , Siddiqi A , Glymour MM , Kawachi I . 8.  2016 . Health behaviors, mental health, and health care utilization among single mothers after welfare reforms in the 1990s. Am. J. Epidemiol. 83 : 531– 38 [Google Scholar]
• Bauhoff S . 9.  2014 . The effect of school district nutrition policies on dietary intake and overweight: a synthetic control approach. Econ. Hum. Biol. 12 : 45– 55 [Google Scholar]
• Bonell C , Fletcher A , Morton M , Lorenc T , Moore L . 10.  2012 . Realist randomised controlled trials: a new approach to evaluating complex public health interventions. Soc. Sci. Med. 75 : 2299– 306 [Google Scholar]
• Bor J , Moscoe E , Mutevedzi P , Newell M-L , Barnighausen T . 11.  2014 . Regression discontinuity designs in epidemiology: causal inference without randomized trials. Epidemiology 25 : 729– 37 [Google Scholar]
• Boyd J , Ferrante AM , O'Keefe C , Bass AJ , Randall AM . 12.  et al. 2012 . Data linkage infrastructure for cross-jurisdictional health-related research in Australia. BMC Health Serv. Res. 2 : 480 [Google Scholar]
• Brown J , Neary J , Katikireddi SV , Thomson H , McQuaid RW . 13.  et al. 2015 . Protocol for a mixed-methods longitudinal study to identify factors influencing return to work in the over 50s participating in the UK Work Programme: Supporting Older People into Employment (SOPIE). BMJ Open 5 : e010525 [Google Scholar]
• Chattopadhyay R , Duflo E . 14.  2004 . Women as policy makers: evidence from a randomised policy experiment in India. Econometrica 72 : 1409– 43 [Google Scholar]
• 15.  Comm. Soc. Determinants Health. 2008 . Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. Final Report of the Commission on Social Determinants of Health. Geneva: World Health Organ. [Google Scholar]
• Craig P , Cooper C , Gunnell D , Macintyre S , Petticrew M . 16.  et al. 2012 . Using natural experiments to evaluate population health interventions: new Medical Research Council guidance. J. Epidemiol. Community Health 66 : 1182– 86 [Google Scholar]
• Crifasi CK , Meyers JS , Vernick JS , Webster DW . 17.  2015 . Effects of changes in permit-to-purchase handgun laws in Connecticut and Missouri on suicide rates. Prev. Med. 79 : 43– 49 [Google Scholar]
• D'Agostino RB . 18.  1998 . Tutorial in biostatistics. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med. 17 : 2265– 81 [Google Scholar]
• De Angelo G , Hansen B . 19.  2014 . Life and death in the fast lane: police enforcement and traffic fatalities. Am. Econ. J. Econ. Policy 6 : 231– 57 [Google Scholar]
• Deaton A . 20.  2010 . Instruments, randomisation and learning about development. J. Econ. Lit. 48 : 424– 55 [Google Scholar]
• Dundas R , Ouédraogo S , Bond L , Briggs AH , Chalmers J . 21.  et al. 2014 . Evaluation of health in pregnancy grants in Scotland: a protocol for a natural experiment. BMJ Open 4 : e006547 [Google Scholar]
• Dunning T . 22.  2012 . Natural Experiments in the Social Sciences: A Design-Based Approach Cambridge, UK: Cambridge Univ. Press [Google Scholar]
• Dusheiko M , Gravelle H , Jacobs R , Smith P . 23.  2006 . The effect of financial incentives on gatekeeping doctors: evidence from a natural experiment. J. Health Econ. 25 : 449– 78 [Google Scholar]
• Eadie D , Heim D , MacAskill S , Ross A , Hastings G , Davies J . 24.  2008 . A qualitative analysis of compliance with smoke-free legislation in community bars in Scotland: implications for public health. Addiction 103 : 1019– 26 [Google Scholar]
• Fall T , Hägg S , Mägi R , Ploner A , Fischer K . 25.  et al. 2013 . The role of adiposity in cardiometabolic traits: a Mendelian randomization analysis. PLOS Med 10 : e1001474 [Google Scholar]
• 26.  Farr Inst. Health Inf. Res. 2016 . Environmental and public health research. Farr Inst. Health Inf. Res. Dundee, UK: http://www.farrinstitute.org/research-education/research/environmental-and-public-health [Google Scholar]
• 27.  Foresight 2007 . Tackling Obesities: Future Choices. Challenges for Research and Research Management. London: Gov. Off. Sci. [Google Scholar]
• Fuller T , Peters J , Pearson M , Anderson R . 28.  2014 . Impact of the transparent reporting of evaluations with nonrandomized designs reporting guideline: ten years on. Am. J. Public Health 104 : e110– 17 [Google Scholar]
• Goodman A , van Sluijs EMF , Ogilvie D . 29.  2016 . Impact of offering cycle training in schools upon cycling behaviour: a natural experimental study. Int. J. Behav. Nutr. Phys. Act. 13 : 34 [Google Scholar]
• Green CP , Heywood JS , Navarro M . 30.  2014 . Did liberalising bar hours decrease traffic accidents?. J. Health Econ. 35 : 189– 98 [Google Scholar]
• Grundy C , Steinbach R , Edwards P , Green J , Armstrong B . 31.  et al. 2009 . Effect of 20 mph traffic speed zones on road injuries in London, 1986–2006: controlled interrupted time series analysis. BMJ 339 : b4469 [Google Scholar]
• Gunnell D , Fernando R , Hewagama M , Priyangika W , Konradsen F , Eddleston M . 32.  2007 . The impact of pesticide regulations on suicide in Sri Lanka. Int. J. Epidemiol. 36 : 1235– 42 [Google Scholar]
• Heckman JJ . 33.  1995 . Randomization as an instrumental variable. Rev. Econ. Stat. 78 : 336– 41 [Google Scholar]
• Hernán M , Robins J . 34.  2017 . Causal Inference Boca Raton, FL: Chapman Hall/CRC In press [Google Scholar]
• Hernán M , Robins JM . 35.  2006 . Instruments for causal inference. An epidemiologist's dream. Epidemiology 17 : 360– 72 [Google Scholar]
• Holmes MV , Dale CE , Zuccolo L , Silverwood RJ , Guo Y . 36.  et al. 2014 . Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ 349 g4164 [Google Scholar]
• 37.  House Commons Sci. Technol. Comm 2016 . The Big Data Dilemma. Fourth Report of Session 2015–16. HC 468 London: Station. Off. Ltd. [Google Scholar]
• Humphreys DK , Panter J , Sahlqvist S , Goodman A , Ogilvie D . 38.  2016 . Changing the environment to improve population health: a framework for considering exposure in natural experimental studies. J. Epidemiol. Community Health. doi: 10.1136/jech-2015-206381 [Google Scholar]
• Ichida Y , Hirai H , Kondo K , Kawachi I , Takeda T , Endo H . 39.  2013 . Does social participation improve self-rated health in the older population? A quasi-experimental intervention study. Soc. Sci. Med. 94 : 83– 90 [Google Scholar]
• 40.  IOM (Inst. Med.) 2010 . Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making Washington, DC: Natl. Acad. Press [Google Scholar]
• Jones A , Rice N . 41.  2009 . Econometric evaluation of health policies HEDG Work. Pap. 09/09 Univ. York [Google Scholar]
• Katikireddi SV , Bond L , Hilton S . 42.  2014 . Changing policy framing as a deliberate strategy for public health advocacy: a qualitative policy case study of minimum unit pricing of alcohol. Milbank Q 92 : 250– 83 [Google Scholar]
• Katikireddi SV , Der G , Roberts C , Haw S . 43.  2016 . Has childhood smoking reduced following smoke-free public places legislation? A segmented regression analysis of cross-sectional UK school-based surveys. Nicotine Tob. Res. 18 : 1670– 74 [Google Scholar]
• Kontopantelis E , Doran T , Springate DA , Buchan I , Reeves D . 44.  2015 . Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ 350 : h2750 [Google Scholar]
• Kreif N , Grieve R , Hangartner D , Nikolova S , Turner AJ , Sutton M . 45.  2015 . Examination of the synthetic control method for evaluating health policies with multiple treated units. Health Econ doi: 10.1002/hec.3258 [Google Scholar]
• Labrecque JA , Kaufman JS . 46.  2016 . Can a quasi-experimental design be a better idea than an experimental one?. Epidemiology 27 : 500– 2 [Google Scholar]
• Lee DS , Lemieux T . 47.  2010 . Regression discontinuity designs in economics. J. Econ. Lit. 48 : 281– 355 [Google Scholar]
• Lewis SJ , Araya R , Davey Smith G , Freathy R , Gunnell D . 48.  et al. 2011 . Smoking is associated with, but does not cause, depressed mood in pregnancy—a Mendelian randomization study. PLOS ONE 6 : e21689 [Google Scholar]
• Linden A , Adams JL . 49.  2011 . Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluation. J. Eval. Clin. Pract. 17 : 1231– 38 [Google Scholar]
• Linden A , Adams JL . 50.  2012 . Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation. J. Eval. Clin. Pract. 18 : 317– 25 [Google Scholar]
• Little RJ , Rubin DB . 51.  2000 . Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu. Rev. Public Health 21 : 121– 45 [Google Scholar]
• Ludwig J , Miller D . 52.  2007 . Does Head Start improve children's life chances? Evidence from an RD design. Q. J. Econ. 122 : 159– 208 [Google Scholar]
• Mcleod AI , Vingilis ER . 53.  2008 . Power computations in time series analyses for traffic safety interventions. Accid. Anal. Prev. 40 : 1244– 48 [Google Scholar]
• Melhuish E , Belsky J , Leyland AH , Barnes J . 54.  Natl. Eval. Sure Start Res. Team. 2008 . Effects of fully-established Sure Start Local Programmes on 3-year-old children and their families living in England: a quasi-experimental observational study. Lancet 372 : 1641– 47 [Google Scholar]
• Messer LC , Oakes JM , Mason S . 55.  2010 . Effects of socioeconomic and racial residential segregation on preterm birth: a cautionary tale of structural confounding. Am. J. Epidemiol. 171 : 664– 73 [Google Scholar]
• Moore G , Audrey S , Barker M , Bond L , Bonell C . 56.  et al. 2015 . MRC process evaluation of complex intervention. Medical Research Council guidance. BMJ 350 : h1258 [Google Scholar]
• Moscoe E , Bor J , Barnighausen T . 57.  2015 . Regression discontinuity designs are under-used in medicine, epidemiology and public health: a review of current and best practice. J. Clin. Epidemiol. 68 : 132– 43 [Google Scholar]
• Nandi A , Hajizadeh M , Harper S , Koski A , Strumpf EC , Heymann J . 58.  2016 . Increased duration of paid maternity leave lowers infant mortality in low and middle-income countries: a quasi-experimental study. PLOS Med. 13 : e1001985 [Google Scholar]
• Pega F , Blakely T , Glymour MM , Carter KN , Kawachi I . 59.  2016 . Using marginal structural modelling to estimate the cumulative impact of an unconditional tax credit on self-rated health. Am. J. Epidemiol. 183 : 315– 24 [Google Scholar]
• Phillips R , Amos A , Ritchie D , Cunningham-Burley S , Martin C . 60.  2007 . Smoking in the home after the smoke-free legislation in Scotland: qualitative study. BMJ 335 : 553 [Google Scholar]
• Ramsay CR , Matowe L , Grilli R , Grimshaw JM , Thomas RE . 61.  2005 . Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behaviour change strategies. Int. J. Technol. Assess. Health Care 19 : 613– 23 [Google Scholar]
• Restrepo BJ , Rieger M . 62.  2016 . Denmark's policy on artificial trans fat and cardiovascular disease. Am. J. Prev. Med. 50 : 69– 76 [Google Scholar]
• Rihoux B , Ragin C . 63.  2009 . Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques London: Sage [Google Scholar]
• Robinson M , Geue C , Lewsey J , Mackay D , McCartney G . 64.  et al. 2014 . Evaluating the impact of the Alcohol Act on off-trade alcohol sales: a natural experiment in Scotland. Addiction 109 : 2035– 43 [Google Scholar]
• Rosenbaum PR , Rubin DB . 65.  1983 . The central role of the propensity score in observational studies for causal effects. Biometrika 70 : 41– 55 [Google Scholar]
• Rubin DB . 66.  2008 . For objective causal inference, design trumps analysis. Ann. Appl. Stat. 2 : 808– 40 [Google Scholar]
• Ryan AM , Krinsky S , Kontopantelis E , Doran T . 67.  2016 . Long-term evidence for the effect of pay-for-performance in primary care on mortality in the UK: a population study. Lancet 388 : 268– 74 [Google Scholar]
• Sanson-Fisher RW , D'Este CS , Carey ML , Noble N , Paul CL . 68.  2014 . Evaluation of systems-oriented public health interventions: alternative research designs. Annu. Rev. Public Health 35 : 9– 27 [Google Scholar]
• Shadish WR , Cook TD , Campbell DT . 69.  2002 . Experimental and Quasi-Experimental Designs for Generalized Causal Inference New York: Houghton Mifflin [Google Scholar]
• Shah BR , Laupacis A , Hux JE , Austin PC . 70.  2005 . Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J. Clin. Epidemiol. 58 : 550– 59 [Google Scholar]
• Swanson SA , Hernán MA . 71.  2013 . Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology 24 : 370– 74 [Google Scholar]
• Thomas J , O'Mara-Eves A , Brunton G . 72.  2014 . Using qualitative comparative analysis (QCA) in systematic reviews of complex interventions: a worked example. Syst. Rev. 3 : 67 [Google Scholar]
• van Leeuwen N , Lingsma HF , de Craen T , Nieboer D , Mooijaart S . 73.  et al. 2016 . Regression discontinuity design: simulation and application in two cardiovascular trials with continuous outcomes. Epidemiology 27 : 503– 11 [Google Scholar]
• von Elm E , Altman DG , Egger M , Pocock SJ , Gøtzsche PC . 74.  et al. 2008 . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J. Clin. Epidemiol. 61 : 344– 49 [Google Scholar]
• Wagner AK , Soumerai SB , Zhang F , Ross-Degnan D . 75.  2002 . Segmented regression analysis of interrupted time series studies in medication use research. J. Clin. Pharm. Ther. 27 : 299– 309 [Google Scholar]
• Wanless D . 76.  2004 . Securing Good Health for the Whole Population London: HM Treas. [Google Scholar]
• Warren J , Wistow J , Bambra C . 77.  2013 . Applying qualitative comparative analysis (QCA) to evaluate a public health policy initiative in the North East of England. Policy Soc 32 : 289– 301 [Google Scholar]
• Yen ST , Andrews M , Chen Z , Eastwood DB . 78.  2008 . Food stamp program participation and food insecurity: an instrumental variables approach. Am. J. Agric. Econ. 90 : 117– 32 [Google Scholar]

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Natural experiments are carried out in natural conditions, however the research is unable to manipulate the IV and therefore examines the effect of a naturally occurring variable on the dependent variable (DV).

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11+ Psychology Experiment Ideas (Goals + Methods)

Have you ever wondered why some days you remember things easily, while on others you keep forgetting? Or why certain songs make you super happy and others just…meh?

Our minds are like big, mysterious puzzles, and every day we're finding new pieces to fit. One of the coolest ways to explore our brains and the way they work is through psychology experiments.

A psychology experiment is a special kind of test or activity researchers use to learn more about how our minds work and why we behave the way we do.

It's like a detective game where scientists ask questions and try out different clues to find answers about our feelings, thoughts, and actions. These experiments aren't just for scientists in white coats but can be fun activities we all try to discover more about ourselves and others.

Some of these experiments have become so famous, they’re like the celebrities of the science world! Like the Marshmallow Test, where kids had to wait to eat a yummy marshmallow, or Pavlov's Dogs, where dogs learned to drool just hearing a bell.

Let's look at a few examples of psychology experiments you can do at home.

What Are Some Classic Experiments?

Imagine a time when the mysteries of the mind were being uncovered in groundbreaking ways. During these moments, a few experiments became legendary, capturing the world's attention with their intriguing results.

The Marshmallow Test

One of the most talked-about experiments of the 20th century was the Marshmallow Test , conducted by Walter Mischel in the late 1960s at Stanford University.

The goal was simple but profound: to understand a child's ability to delay gratification and exercise self-control.

Children were placed in a room with a marshmallow and given a choice: eat the marshmallow now or wait 15 minutes and receive two as a reward. Many kids struggled with the wait, some devouring the treat immediately, while others demonstrated remarkable patience.

But the experiment didn’t end there. Years later, Mischel discovered something astonishing. The children who had waited for the second marshmallow were generally more successful in several areas of life, from school achievements to job satisfaction!

While this experiment highlighted the importance of teaching patience and self-control from a young age, it wasn't without its criticisms. Some argued that a child's background, upbringing, or immediate surroundings might play a significant role in their choices.

Moreover, there were concerns about the ethics of judging a child's potential success based on a brief interaction with a marshmallow.

Pavlov's Dogs

Traveling further back in time and over to Russia, another classic experiment took the world by storm. Ivan Pavlov , in the early 1900s, wasn't initially studying learning or behavior. He was exploring the digestive systems of dogs.

But during his research, Pavlov stumbled upon a fascinating discovery. He noticed that by ringing a bell every time he fed his dogs, they eventually began to associate the bell's sound with mealtime. So much so, that merely ringing the bell, even without presenting food, made the dogs drool in anticipation!

This reaction demonstrated the concept of "conditioning" - where behaviors can be learned by linking two unrelated stimuli. Pavlov's work revolutionized the world's understanding of learning and had ripple effects in various areas like animal training and therapy techniques.

Pavlov came up with the term classical conditioning , which is still used today. Other psychologists have developed more nuanced types of conditioning that help us understand how people learn to perform different behaviours.

Classical conditioning is the process by which a neutral stimulus becomes associated with a meaningful stimulus , leading to the same response. In Pavlov's case, the neutral stimulus (bell) became associated with the meaningful stimulus (food), leading the dogs to salivate just by hearing the bell.

Modern thinkers often critique Pavlov's methods from an ethical standpoint. The dogs, crucial to his discovery, may not have been treated with today's standards of care and respect in research.

Both these experiments, while enlightening, also underline the importance of conducting research with empathy and consideration, especially when it involves living beings.

What is Ethical Experimentation?

The tales of Pavlov's bells and Mischel's marshmallows offer us not just insights into the human mind and behavior but also raise a significant question: At what cost do these discoveries come?

Ethical experimentation isn't just a fancy term; it's the backbone of good science. When we talk about ethics, we're referring to the moral principles that guide a researcher's decisions and actions. But why does it matter so much in the realm of psychological experimentation?

An example of an experiment that had major ethical issues is an experiment called the Monster Study . This study was conducted in 1936 and was interested in why children develop a stutter.

The major issue with it is that the psychologists treated some of the children poorly over a period of five months, telling them things like “You must try to stop yourself immediately. Don’t ever speak unless you can do it right.”

You can imagine how that made the children feel!

This study helped create guidelines for ethical treatment in experiments. The guidelines include:

Respect for Individuals: Whether it's a dog in Pavlov's lab or a child in Mischel's study room, every participant—human or animal—deserves respect. They should never be subjected to harm or undue stress. For humans, informed consent (knowing what they're signing up for) is a must. This means that if a child is participating, they, along with their guardians, should understand what the experiment entails and agree to it without being pressured.

Honesty is the Best Policy: Researchers have a responsibility to be truthful. This means not only being honest with participants about the study but also reporting findings truthfully, even if the results aren't what they hoped for. There can be exceptions if an experiment will only succeed if the participants aren't fully aware, but it has to be approved by an ethics committee .

Safety First: No discovery, no matter how groundbreaking, is worth harming a participant. The well-being and mental, emotional, and physical safety of participants is paramount. Experiments should be designed to minimize risks and discomfort.

Considering the Long-Term: Some experiments might have effects that aren't immediately obvious. For example, while a child might seem fine after participating in an experiment, they could feel stressed or anxious later on. Ethical researchers consider and plan for these possibilities, offering support and follow-up if needed.

The Rights of Animals: Just because animals can't voice their rights doesn't mean they don't have any. They should be treated with care, dignity, and respect. This means providing them with appropriate living conditions, not subjecting them to undue harm, and considering alternatives to animal testing when possible.

While the world of psychological experiments offers fascinating insights into behavior and the mind, it's essential to tread with care and compassion. The golden rule? Treat every participant, human or animal, as you'd wish to be treated. After all, the true mark of a groundbreaking experiment isn't just its findings but the ethical integrity with which it's conducted.

So, even if you're experimenting at home, please keep in mind the impact your experiments could have on the people and beings around you!

Let's get into some ideas for experiments.

1) Testing Conformity

Our primary aim with this experiment is to explore the intriguing world of social influences, specifically focusing on how much sway a group has over an individual's decisions. This social influence is called groupthink .

Humans, as social creatures, often find solace in numbers, seeking the approval and acceptance of those around them. But how deep does this need run? Does the desire to "fit in" overpower our trust in our own judgments?

This experiment not only provides insights into these questions but also touches upon the broader themes of peer pressure, societal norms, and individuality. Understanding this could shed light on various real-world situations, from why fashion trends catch on to more critical scenarios like how misinformation can spread.

Method: This idea is inspired by the classic Asch Conformity Experiments . Here's a simple way to try it:

• Assemble a group of people (about 7-8). Only one person will be the real participant; the others will be in on the experiment.
• Show the group a picture of three lines of different lengths and another line labeled "Test Line."
• Ask each person to say out loud which of the three lines matches the length of the "Test Line."
• Unknown to the real participant, the other members will intentionally choose the wrong line. This is to see if the participant goes along with the group's incorrect choice, even if they can see it's wrong.

Real-World Impacts of Groupthink

Groupthink is more than just a science term; we see it in our daily lives:

Decisions at Work or School: Imagine being in a group where everyone wants to do one thing, even if it's not the best idea. People might not speak up because they're worried about standing out or being the only one with a different opinion.

Wrong Information: Ever heard a rumor that turned out to be untrue? Sometimes, if many people believe and share something, others might believe it too, even if it's not correct. This happens a lot on the internet.

Peer Pressure: Sometimes, friends might all want to do something that's not safe or right. People might join in just because they don't want to feel left out.

Missing Out on New Ideas: When everyone thinks the same way and agrees all the time, cool new ideas might never get heard. It's like always coloring with the same crayon and missing out on all the other bright colors!

2) Testing Color and Mood

We all have favorite colors, right? But did you ever wonder if colors can make you feel a certain way? Color psychology is the study of how colors can influence our feelings and actions.

For instance, does blue always calm us down? Does red make us feel excited or even a bit angry? By exploring this, we can learn how colors play a role in our daily lives, from the clothes we wear to the color of our bedroom walls.

• Find a quiet room and set up different colored lights or large sheets of colored paper: blue, red, yellow, and green.
• Invite some friends over and let each person spend a few minutes under each colored light or in front of each colored paper.
• After each color, ask your friends to write down or talk about how they feel. Are they relaxed? Energized? Happy? Sad?

Researchers have always been curious about this. Some studies have shown that colors like blue and green can make people feel calm, while colors like red might make them feel more alert or even hungry!

Real-World Impacts of Color Psychology

Ever noticed how different places use colors?

Hospitals and doctors' clinics often use soft blues and greens. This might be to help patients feel more relaxed and calm.

Many fast food restaurants use bright reds and yellows. These colors might make us feel hungry or want to eat quickly and leave.

Classrooms might use a mix of colors to help students feel both calm and energized.

3) Testing Music and Brainpower

Think about your favorite song. Do you feel smarter or more focused when you listen to it? This experiment seeks to understand the relationship between music and our brain's ability to remember things. Some people believe that certain types of music, like classical tunes, can help us study or work better. Let's find out if it's true!

• Prepare a list of 10-15 things to remember, like a grocery list or names of places.
• Invite some friends over. First, let them try to memorize the list in a quiet room.
• After a short break, play some music (try different types like pop, classical, or even nature sounds) and ask them to memorize the list again.
• Compare the results. Was there a difference in how much they remembered with and without music?

The " Mozart Effect " is a popular idea. Some studies in the past suggested that listening to Mozart's music might make people smarter, at least for a little while. But other researchers think the effect might not be specific to Mozart; it could be that any music we enjoy boosts our mood and helps our brain work better.

Real-World Impacts of Music and Memory

Think about how we use music:

• Study Sessions: Many students listen to music while studying, believing it helps them concentrate better.
• Workout Playlists: Gyms play energetic music to keep people motivated and help them push through tough workouts.
• Meditation and Relaxation: Calm, soothing sounds are often used to help people relax or meditate.

4) Testing Dreams and Food

Ever had a really wild dream and wondered where it came from? Some say that eating certain foods before bedtime can make our dreams more vivid or even a bit strange.

This experiment is all about diving into the dreamy world of sleep to see if what we eat can really change our nighttime adventures. Can a piece of chocolate or a slice of cheese transport us to a land of wacky dreams? Let's find out!

• Ask a group of friends to keep a "dream diary" for a week. Every morning, they should write down what they remember about their dreams.
• For the next week, ask them to eat a small snack before bed, like cheese, chocolate, or even spicy foods.
• They should continue writing in their "dream diary" every morning.
• At the end of the two weeks, compare the dream notes. Do the dreams seem different during the snack week?

The link between food and dreams isn't super clear, but some people have shared personal stories. For example, some say that spicy food can lead to bizarre dreams. Scientists aren't completely sure why, but it could be related to how food affects our body temperature or brain activity during sleep.

A cool idea related to this experiment is that of vivid dreams , which are very clear, detailed, and easy to remember dreams. Some people are even able to control their vivid dreams, or say that they feel as real as daily, waking life !

Real-World Impacts of Food and Dreams

Our discoveries might shed light on:

• Bedtime Routines: Knowing which foods might affect our dreams can help us choose better snacks before bedtime, especially if we want calmer sleep.
• Understanding Our Brain: Dreams can be mysterious, but studying them can give us clues about how our brains work at night.
• Cultural Beliefs: Many cultures have myths or stories about foods and dreams. Our findings might add a fun twist to these age-old tales!

5) Testing Mirrors and Self-image

Stand in front of a mirror. How do you feel? Proud? Shy? Curious? Mirrors reflect more than just our appearance; they might influence how we think about ourselves.

This experiment delves into the mystery of self-perception. Do we feel more confident when we see our reflection? Or do we become more self-conscious? Let's take a closer look.

• Set up two rooms: one with mirrors on all walls and another with no mirrors at all.
• Invite friends over and ask them to spend some time in each room doing normal activities, like reading or talking.
• After their time in both rooms, ask them questions like: "Did you think about how you looked more in one room? Did you feel more confident or shy?"
• Compare the responses to see if the presence of mirrors changes how they feel about themselves.

Studies have shown that when people are in rooms with mirrors, they can become more aware of themselves. Some might stand straighter, fix their hair, or even change how they behave. The mirror acts like an audience, making us more conscious of our actions.

Real-World Impacts of Mirrors and Self-perception

Mirrors aren't just for checking our hair. Ever wonder why clothing stores have so many mirrors? They might help shoppers visualize themselves in new outfits, encouraging them to buy.

Mirrors in gyms can motivate people to work out with correct form and posture. They also help us see progress in real-time!

And sometimes, looking in a mirror can be a reminder to take care of ourselves, both inside and out.

But remember, what we look like isn't as important as how we act in the world or how healthy we are. Some people claim that having too many mirrors around can actually make us more self conscious and distract us from the good parts of ourselves.

Some studies are showing that mirrors can actually increase self-compassion , amongst other things. As any tool, it seems like mirrors can be both good and bad, depending on how we use them!

6) Testing Plants and Talking

Have you ever seen someone talking to their plants? It might sound silly, but some people believe that plants can "feel" our vibes and that talking to them might even help them grow better.

In this experiment, we'll explore whether plants can indeed react to our voices and if they might grow taller, faster, or healthier when we chat with them.

• Get three similar plants, placing each one in a separate room.
• Talk to the first plant, saying positive things like "You're doing great!" or singing to it.
• Say negative things to the second plant, like "You're not growing fast enough!"
• Don't talk to the third plant at all; let it be your "silent" control group .
• Water all plants equally and make sure they all get the same amount of light.
• At the end of the month, measure the growth of each plant and note any differences in their health or size.

The idea isn't brand new. Some experiments from the past suggest plants might respond to sounds or vibrations. Some growers play music for their crops, thinking it helps them flourish.

Even if talking to our plants doesn't have an impact on their growth, it can make us feel better! Sometimes, if we are lonely, talking to our plants can help us feel less alone. Remember, they are living too!

Real-World Impacts of Talking to Plants

If plants do react to our voices, gardeners and farmers might adopt new techniques, like playing music in greenhouses or regularly talking to plants.

Taking care of plants and talking to them could become a recommended activity for reducing stress and boosting mood.

And if plants react to sound, it gives us a whole new perspective on how connected all living things might be .

7) Testing Virtual Reality and Senses

Virtual reality (VR) seems like magic, doesn't it? You put on a headset and suddenly, you're in a different world! But how does this "new world" affect our senses? This experiment wants to find out how our brains react to VR compared to the real world. Do we feel, see, or hear things differently? Let's get to the bottom of this digital mystery!

• You'll need a VR headset and a game or experience that can be replicated in real life (like walking through a forest). If you don't have a headset yourself, there are virtual reality arcades now!
• Invite friends to first experience the scenario in VR.
• Afterwards, replicate the experience in the real world, like taking a walk in an actual forest.
• Ask them questions about both experiences: Did one seem more real than the other? Which sounds were more clear? Which colors were brighter? Did they feel different emotions?

As VR becomes more popular, scientists have been curious about its effects. Some studies show that our brains can sometimes struggle to tell the difference between VR and reality. That's why some people might feel like they're really "falling" in a VR game even though they're standing still.

Real-World Impacts of VR on Our Senses

Schools might use VR to teach lessons, like taking students on a virtual trip to ancient Egypt. Understanding how our senses react in VR can also help game designers create even more exciting and realistic games.

Doctors could use VR to help patients overcome fears or to provide relaxation exercises. This is actually already a method therapists can use for helping patients who have serious phobias. This is called exposure therapy , which basically means slowly exposing someone (or yourself) to the thing you fear, starting from very far away to becoming closer.

For instance, if someone is afraid of snakes. You might show them images of snakes first. Once they are comfortable with the picture, they can know there is one in the next room. Once they are okay with that, they might use a VR headset to see the snake in the same room with them, though of course there is not an actual snake there.

8) Testing Sleep and Learning

We all know that feeling of trying to study or work when we're super tired. Our brains feel foggy, and it's hard to remember stuff. But how exactly does sleep (or lack of it) influence our ability to learn and remember things?

With this experiment, we'll uncover the mysteries of sleep and see how it can be our secret weapon for better learning.

• Split participants into two groups.
• Ask both groups to study the same material in the evening.
• One group goes to bed early, while the other stays up late.
• The next morning, give both groups a quiz on what they studied.
• Compare the results to see which group remembered more.

Sleep and its relation to learning have been explored a lot. Scientists believe that during sleep, especially deep sleep, our brains sort and store new information. This is why sometimes, after a good night's rest, we might understand something better or remember more.

Real-World Impacts of Sleep and Learning

Understanding the power of sleep can help:

• Students: If they know the importance of sleep, students might plan better, mixing study sessions with rest, especially before big exams.
• Workplaces: Employers might consider more flexible hours, understanding that well-rested employees learn faster and make fewer mistakes.
• Health: Regularly missing out on sleep can have other bad effects on our health. So, promoting good sleep is about more than just better learning.

9) Testing Social Media and Mood

Have you ever felt different after spending time on social media? Maybe happy after seeing a friend's fun photos, or a bit sad after reading someone's tough news.

Social media is a big part of our lives, but how does it really affect our mood? This experiment aims to shine a light on the emotional roller-coaster of likes, shares, and comments.

• Ask participants to note down how they're feeling - are they happy, sad, excited, or bored?
• Have them spend a set amount of time (like 30 minutes) on their favorite social media platforms.
• After the session, ask them again about their mood. Did it change? Why?
• Discuss what they saw or read that made them feel that way.

Previous research has shown mixed results. Some studies suggest that seeing positive posts can make us feel good, while others say that too much time on social media can make us feel lonely or left out.

Real-World Impacts of Social Media on Mood

Understanding the emotional impact of social media can help users understand their feelings and take breaks if needed. Knowing is half the battle! Additionally, teachers and parents can guide young users on healthy social media habits, like limiting time or following positive accounts.

And if it's shown that social media does impact mood, social media companies can design friendlier, less stressful user experiences.

But even if the social media companies don't change things, we can still change our social media habits to make ourselves feel better.

10) Testing Handwriting or Typing

Think about the last time you took notes. Did you grab a pen and paper or did you type them out on a computer or tablet?

Both ways are popular, but there's a big question: which method helps us remember and understand better? In this experiment, we'll find out if the classic art of handwriting has an edge over speedy typing.

• Divide participants into two groups.
• Present a short lesson or story to both groups.
• One group will take notes by hand, while the other will type them out.
• After some time, quiz both groups on the content of the lesson or story.
• Compare the results to see which note-taking method led to better recall and understanding.

Studies have shown some interesting results. While typing can be faster and allows for more notes, handwriting might boost memory and comprehension because it engages the brain differently, making us process the information as we write.

Importantly, each person might find one or the other works better for them. This could be useful in understanding our learning habits and what instructional style would be best for us.

Real-World Impacts of Handwriting vs. Typing

Knowing the pros and cons of each method can:

• Boost Study Habits: Students can pick the method that helps them learn best, especially during important study sessions or lectures.
• Work Efficiency: In jobs where information retention is crucial, understanding the best method can increase efficiency and accuracy.
• Tech Design: If we find out more about how handwriting benefits us, tech companies might design gadgets that mimic the feel of writing while combining the advantages of digital tools.

11) Testing Money and Happiness

We often hear the saying, "Money can't buy happiness," but is that really true? Many dream of winning the lottery or getting a big raise, believing it would solve all problems.

In this experiment, we dig deep to see if there's a real connection between wealth and well-being.

• Survey a range of participants, from those who earn a little to those who earn a lot, about their overall happiness. You can keep it to your friends and family, but that might not be as accurate as surveying a wider group of people.
• Ask them to rank things that bring them joy and note if they believe more money would boost their happiness. You could try different methods, one where you include some things that they have to rank, such as gardening, spending time with friends, reading books, learning, etc. Or you could just leave a blank list that they can fill in with their own ideas.
• Study the data to find patterns or trends about income and happiness.

Some studies have found money can boost happiness, especially when it helps people out of tough financial spots. But after reaching a certain income, extra dollars usually do not add much extra joy.

In fact, psychologists just realized that once people have an income that can comfortably support their needs (and some of their wants), they stop getting happier with more . That number is roughly $75,000, but of course that depends on the cost of living and how many members are in the family.

Real-World Impacts of Money and Happiness

If we can understand the link between money and joy, it might help folks choose jobs they love over jobs that just pay well. And instead of buying things, people might spend on experiences, like trips or classes, that make lasting memories.

Most importantly, we all might spend more time on hobbies, friends, and family, knowing they're big parts of what makes life great.

Some people are hoping that with Artificial Intelligence being able to do a lot of the less well-paying jobs, people might be able to do work they enjoy more, all while making more money and having more time to do the things that make them happy.

12) Testing Temperature and Productivity

Have you ever noticed how a cold classroom or office makes it harder to focus? Or how on hot days, all you want to do is relax? In this experiment, we're going to find out if the temperature around us really does change how well we work.

• Find a group of participants and a room where you can change the temperature.
• Set the room to a chilly temperature and give the participants a set of tasks to do.
• Measure how well and quickly they do these tasks.
• The next day, make the room comfortably warm and have them do similar tasks.
• Compare the results to see if the warmer or cooler temperature made them work better.

Some studies have shown that people can work better when they're in a room that feels just right, not too cold or hot. Being too chilly can make fingers slow, and being too warm can make minds wander.

What temperature is "just right"? It won't be the same for everyone, but most people find it's between 70-73 degrees Fahrenheit (21-23 Celsius).

Real-World Implications of Temperature and Productivity

If we can learn more about how temperature affects our work, teachers might set classroom temperatures to help students focus and learn better, offices might adjust temperatures to get the best work out of their teams, and at home, we might find the best temperature for doing homework or chores quickly and well.

Interestingly, temperature also has an impact on our sleep quality. Most people find slightly cooler rooms to be better for good sleep. While the daytime temperature between 70-73F is good for productivity, a nighttime temperature around 65F (18C) is ideal for most people's sleep.

Psychology is like a treasure hunt, where the prize is understanding ourselves better. With every experiment, we learn a little more about why we think, feel, and act the way we do. Some of these experiments might seem simple, like seeing if colors change our mood or if being warm helps us work better. But even the simple questions can have big answers that help us in everyday life.

Remember, while doing experiments is fun, it's also important to always be kind and think about how others feel. We should never make someone uncomfortable just for a test. Instead, let's use these experiments to learn and grow, helping to make the world a brighter, more understanding place for everyone.

Related posts:

• 150+ Flirty Goodnight Texts For Him (Sweet and Naughty Examples)
• Dream Interpreter & Dictionary (270+ Meanings)
• Sleep Stages (Light, Deep, REM)
• What Part of the Brain Regulates Body Temperature?
• Why Do We Dream? (6 Theories and Psychological Reasons)

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While Some Unethical, These 4 Social Experiments Helped Explain Human Behavior

How have we learned about human behavior some studies caused a baby to fear animals — and other experiments helped us explore human nature..

From the CIA’s secret mind control program, MK Ultra, to the stuttering “Monster” study, American researchers have a long history of engaging in human experiments. The studies have helped us better understand ourselves and why we do certain things.

These four experiments did just this and helped us better understand human behavior. However, some of them would be considered unethical today due to either lack of informed consent or the mental and/or emotional damage they caused.

1. Cognitive Dissonance Experiment

After proposing the concept of cognitive dissonance , psychologist Leon Festinger created an experiment to test his theory that was also known as the boring experiment. 

Participants were paid either $1 or $20 to engage in mundane tasks, including turning pegs on a board and moving spools on and off a tray. Despite the boring nature of the activities, they were asked to tell the next participant that it was interesting and fun.

The people who were paid $20 felt more justified lying to others because they were better compensated — and they experienced less cognitive dissonance . Participants who were paid $1 felt greater cognitive dissonance due to their inability to rationalize lying.

In an attempt to reconcile their dissonance, they convinced themselves that the tasks were actually enjoyable.

2. The Little Albert Experiment  

In 1920, psychologist John. B. Watson and graduate student (and future wife) Rosalie Rayner wanted to see if they could produce a response in humans using classical conditioning — the way Pavlov did with dogs.  

They decided to expose a 9-month-old baby, whom they called Albert, to a white rat. At first, the baby displayed no fear and played with the rat. To startle Albert, Watson and Rayner would then make a loud noise by hitting a steel bar with a hammer. 

Each time they made the loud sound while Albert was playing with the rat, he became frightened, started crying, and crawled away from the rat. He had become classically conditioned to fear the rat because he associated it with something negative. He then developed stimulus generalization, where he feared other furry white objects — including a rabbit, white coat, and a Santa mask. 

3. Stanford Prison Experiment

In 1971, Stanford psychologist Philip Zimbardo designed a study to examine societal roles and situational power — through an experiment that recreated prison conditions. 

Zimbardo created a mock prison in a building on Stanford’s campus. He assigned study participants to be either guards or prisoners. Prisoners were given numbers instead of names, had a chain attached to one leg, and were dressed in smocks and stocking caps.

Those assigned to the role of a guard quickly conformed to their new position of power. They became hostile and aggressive toward the prisoners, subjecting them to psychological and verbal abuse — despite never having previously demonstrated such attitudes or behavior. The experiment was slated to last two weeks but needed to be ended after only six days. 

4. The Facial Expression Experiment

In 1924, psychology graduate student Carney Landis wanted to study how people’s emotions were reflected in their facial expressions, exploring whether certain emotions caused the same facial expressions in everyone.

Landis marked participants’ faces with black lines to study the movement of their facial muscles as they reacted. At first, he had them do innocuous tasks, such as listening to jazz music or smelling ammonia. 

As Landis grew frustrated that their responses weren’t strong enough, he had participants engage in increasingly shocking acts, such as sticking their hands into a bucket with live frogs in it. Eventually, Landis instructed participants to decapitate a live mouse. If they refused, he decapitated the mouse himself to elicit a strong reaction from them.

Read More: 5 Unethical Medical Experiments Brought Out of the Shadows of History

Article Sources

Our writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:

Advance Research Journal of Social Science . Cognitive dissonance: its role in decision making

New Scientist. How a baby was trained to fear

Stanford Prison Experiment. Philip G. Zimbardo

Incarceration . The dirty work of the Stanford Prison Experiment: Re-reading the dramaturgy of coercion

Journal of Experimental Psychology. Studies of emotional reactions. I. 'A preliminary study of facial expression."

The American Journal of Psychology. Carney Landis: 1897-1962

Allison Futterman is a Charlotte, N.C.-based writer whose science, history, and medical/health writing has appeared on a variety of platforms and in regional and national publications. These include Charlotte, People, Our State, and Philanthropy magazines, among others. She has a BA in communications and a MS in criminal justice.

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Artificial Intelligence

Our technology-powered thought laboratory, ai and neurotech are reinventing the very nature of the thought experiment..

Posted August 8, 2024 | Reviewed by Jessica Schrader

• From Plato to Einstein, thought experiments have driven major breakthroughs in science and philosophy.
• AI, brain interfaces, and thought-to-text tech are poised to supercharge mental exploration.
• Human-AI symbiosis promises to solve complex problems rapidly, sparking a new age of discovery.

Think about it. There may be no greater engine of transformation than the human brain. And throughout history, thought experiments have been the silent engines of intellectual progress, driving paradigm shifts in science, philosophy , and human understanding. From Plato's Allegory of the Cave to Einstein's elevator, these mental exercises have allowed thinkers to transcend the limitations of their physical world and explore the realm of pure ideas.

The power of thought experiments lies in their ability to distill complex concepts into digestible narratives, making the abstract tangible and the impossible imaginable. In ancient Greece, Zeno's paradoxes challenged our understanding of motion and infinity, laying the groundwork for calculus two millennia before its formal development. During the Scientific Revolution, Galileo's thought experiment of dropping balls from the Leaning Tower of Pisa (which he likely never actually performed) helped overturn Aristotelian physics and pave the way for Newton's laws of motion.

Revolutionizing Science and Philosophy

Perhaps no field has benefited more from thought experiments than physics. Einstein's famous musings about riding alongside a beam of light led to the development of special relativity, revolutionizing our understanding of space and time. Schrödinger's cat, a paradoxical feline both alive and dead, illuminated the bizarre implications of quantum superposition. These mental exercises have pushed the boundaries of human knowledge, allowing us to probe realms far beyond the reach of contemporary technology or experimentation.

The Dawn of Neuro-Enhanced Thinking

Now, as we stand on the brink of a new technological revolution, the humble thought experiment is poised for a dramatic evolution. The convergence of brain-computer interfaces ( BCIs ) like Neuralink, "thought-to-text" technologies, and large language models (LLMs) promises to transform the landscape of intellectual exploration in ways our predecessors could scarcely have imagined.

Imagine a world where the barriers between mind and machine dissolve, where thoughts can be transmitted at the speed of neurons firing. This is the promise of advanced BCIs and thought-to-text technologies. No longer constrained by the relatively slow process of typing or speaking, this new technology could potentially transfer complex ideas, hypotheses, and entire thought experiments directly from their brains to computer systems in ways that make "the blink of an eye" seem antiquated and sluggish.

AI as the Cognitive Amplifier

Enter large language models, the artificial intelligences that have already demonstrated remarkable capabilities in processing and generating human-like text. When coupled with the rapid influx of direct "pre-language thought" input, these LLMs could serve as cognitive amplifiers of unprecedented power.

As thoughts flow directly from the mind mind into the AI system, the LLM could instantaneously expand on the initial concepts, drawing connections across vast databases of scientific knowledge. It could generate multiple variations and permutations of the original thought experiment, identify potential flaws or inconsistencies in the logic, and suggest novel approaches or angles that the human mind might not have considered.

The Expanded Laboratory of the Mind

This symbiosis of human creativity and AI processing power could create a feedback loop of innovation, with each iteration refining and elevating the original concept. In this new and startling reality, a modern-day Einstein could conceive of a thought experiment akin to riding a beam of light, and within seconds have the concept fully articulated and modeled by the AI, receive instant feedback on its implications across multiple fields of physics, see visualizations of how this thought experiment interacts with our current understanding of the universe, and explore dozens of variations and extensions of the original idea.

The potential for scientific breakthroughs in this environment is staggering. Complex problems that might have taken years of contemplation and collaboration could potentially be unraveled in days or even hours. We're not just enhancing thought experiments—we're creating an entirely new domain of intellectual exploration, a "laboratory of the mind" where the boundaries between imagination and computation blur, where abstract ideas can be manipulated, tested, and evolved with unprecedented speed and precision.

Thinking About Ethics

However, this brave new world of thought experimentation is not without its challenges and ethical considerations. Privacy concerns are paramount—how do we ensure the sanctity of one's innermost thoughts when they can be so easily externalized? There's also the question of cognitive equality—would such technology create an insurmountable gap between those with access to these advanced systems and those without?

It's also critical to consider the potential for cognitive dependency. If we rely too heavily on AI-augmented thought processes, do we risk atrophying our natural cognitive abilities? How do we maintain the uniquely human aspects of creativity and intuition in this new landscape?

Future Thought

Despite these challenges, the potential benefits of this technology are too profound to ignore. We stand at the threshold of a new Cognitive Age , one in which the constraints on human thought and creativity are dramatically loosened. This fusion of direct brain-computer interfaces, thought-to-text technology, and advanced AI could spawn a new generation of thinkers—modern-day Einsteins equipped with cognitive tools beyond anything we've seen before.

The future of thought experimentation is no longer confined to the limits of individual human cognition . Instead, it expands into a vast, collaborative space where human creativity and artificial intelligence dance in a symphony of ideas, pushing the boundaries of what's possible and redefining the very nature of thinking itself. As we stand on the shoulders of giants like Plato, Galileo, and Einstein, we prepare to take a leap into a future where the power of thought knows no bounds.

John Nosta is an innovation theorist and founder of NostaLab.

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August 9, 2024

Experiments Prepare to Test Whether Consciousness Arises from Quantum Weirdness

Researchers wish to probe whether consciousness has a basis in quantum mechanical phenomena

By Hartmut Neven & Christof Koch

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The brain is a mere piece of furniture in the vastness of the cosmos, subject to the same physical laws as asteroids, electrons or photons. On the surface, its three pounds of neural tissue seem to have little to do with quantum mechanics , the textbook theory that underlies all physical systems, since quantum effects are most pronounced on microscopic scales. Newly proposed experiments, however, promise to bridge this gap between microscopic and macroscopic systems, like the brain, and offer answers to the mystery of consciousness.

Quantum mechanics explains a range of phenomena that cannot be understood using the intuitions formed by everyday experience. Recall the Schrödinger’s cat thought experiment , in which a cat exists in a superposition of states, both dead and alive. In our daily lives there seems to be no such uncertainty—a cat is either dead or alive. But the equations of quantum mechanics tell us that at any moment the world is composed of many such coexisting states, a tension that has long troubled physicists.

Taking the bull by its horns, the cosmologist Roger Penrose in 1989 made the radical suggestion that a conscious moment occurs whenever a superimposed quantum state collapses. The idea that two fundamental scientific mysteries—the origin of consciousness and the collapse of what is called the wave function in quantum mechanics—are related, triggered enormous excitement.

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Penrose’s theory can be grounded in the intricacies of quantum computation . Consider a quantum bit, a qubit, the unit of information in quantum information theory that exists in a superposition of a logical 0 with a logical 1. According to Penrose, when this system collapses into either 0 or 1, a flicker of conscious experience is created, described by a single classical bit.

Penrose, together with anesthesiologist Stuart Hameroff, suggested that such collapse takes place in microtubules , tubelike, elongated structural proteins that form part of the cytoskeleton of cells, such as those making up the central nervous system.

These ideas have never been taken up by the scientific community as brains are wet and warm, inimical to the formation of superpositions, at least compared to existing quantum computers that operate at temperatures 10,000 times colder than room temperature to avoid destroying superposition states.

Penrose’s proposal suffers from a flaw when applied to two or more entangled qubits. Measuring one of these entangled qubits instantaneously reveals the state of the other one, no matter how far away. Their states are correlated, but correlation is not causation, and, according to standard quantum mechanics, entanglement cannot be employed to achieve faster-than-light communication. However, per Penrose’s proposal, qubits participating in an entangled state share a conscious experience. When one of them assumes a definite state, we could use this to establish a communication channel capable of transmitting information faster than the speed of light, a violation of special relativity.

In our view, the entanglement of hundreds of qubits, if not thousands or more, is essential to adequately describe the phenomenal richness of any one subjective experience: the colors, motions, textures, smells, sounds, bodily sensations, emotions, thoughts, shards of memories and so on that constitute the feeling of life itself.

In an article published in the open-access journal Entropy , we and our colleagues turned the Penrose hypothesis on its head, suggesting that an experience is created whenever a system goes into a quantum superposition rather than when it collapses. According to our proposal, any system entering a state with one or more entangled superimposed qubits will experience a moment of consciousness.

You, the astute reader, must by now be saying to yourself: But wait a minute here—I don’t ever consciously experience a superposition of states. Any one experience has a definitive quality; it is one thing and not the other. I see a particular shade of red, feel a toothache. I don’t simultaneously experience red and not-red, pain and not-pain.

The definitiveness of any conscious experience naturally arises within the many-worlds interpretation of quantum mechanics . A metaphysical position first put forward by physicist Hugh Everett in 1957, the many-worlds view, posits time’s evolution as an enormously branched tree, with every possible outcome of a quantum event splitting off its own universe. A single qubit entering a superposition gives birth to two universes, in one of which the qubit’s state is 0 while in a twin universe everything is identical except that the qubit’s state is 1.

Entanglement potentially offers something else for brain scientists by providing a natural solution to what is called the binding problem, the subjective unity of every experience that has long posed a key challenge to the study of consciousness. Consider seeing the Statue of Liberty: her face, the crown on her head, the torch in her raised right hand, and so on. All these distinctions and relationships are bound together into a single perception whose substrate might be numerous qubits, all entangled with each other.

To make these esoteric ideas concrete, we propose three experiments that would increasingly shape our thinking on these matters. The first experiment, progressing right now thanks to funding from the Santa Monica–based Tiny Blue Dot Foundation, seeks to provide evidence of the relevance of quantum mechanics to neuroscience in two very accessible test beds: tiny fruit flies and cerebral organoids, the latter lentil-sized assemblies of thousands of neurons grown from human-induced pluripotent stem cells. It is known that the inert noble gas xenon can act as anesthetic in animals and people. Remarkably, an earlier experiment claimed that its anesthetic potency, measured as the concentration of the gas that induces immobility, depends on the specific isotopes of xenon. Two isotopes of an element contain the same number of positively charged protons but different numbers of noncharged neutrons in their nuclei. The chemical properties of isotopes—that is, what they interact with—are similar, by and large, even though their masses and magnetic properties differ slightly.

If fruit flies and organoids can be used to detect different xenon isotopes, the hunt will be on for the exact mechanisms by which a gas that is inert and that remains aloof from binding to proteins or other molecules achieves this. Is it the tiny difference in the mass of these isotopes (131 versus 132 nucleons) that makes the difference? Or is it their nuclear spin, a quantum mechanical property of the nucleus? These xenon isotopes differ substantially in their nuclear spin; some have zero spin and others 1 / 2 or 3 / 2 .

These xenon experiments will inform a second follow-on experiment in which we will attempt to couple qubits to brain organoids in a way that allows entanglement to spread between biological and technical qubits. The final experiment, which at this stage is still a purely conceptual one, aims to enhance consciousness by coupling engineered quantum states to a human brain in an entangled manner. The person may then experience an expanded state of consciousness like those accessed under the influence of ayahuasca or psilocybin.

Both quantum engineering and the design of brain-machine interfaces are progressing rapidly. It may not be beyond human ingenuity to directly probe and expand our conscious mind by making use of quantum science and technology.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

Center for Experimental Social Science

The center for experimental social science is an inter-disciplinary research center at new york university engaged in laboratory experimental work in the social sciences that combines high quality economic theory, psychology, political science, and neuroscience., weekly seminar: svetlana pevnitskaya, “information aggregation in social networks”, thursday, december 9, 2021, weekly seminar: paul healy, “incentive compatibility in experiments: an overview”, thursday, december 2, 2021.

Paul J. Healy is a Professor of Economics at Ohio State University. He earned his PhD in Social Science from Caltech in 2005, after completing his bachelor’s degree at Purdue University in 2000. Before Ohio State, Dr. Healy was an Assistant Professor of Economics at Carnegie Mellon’s Tepper School of Business. His research focus is wide-ranging, with papers on mechanism design and implementation, behavioral game theory, overconfidence, public goods, Bayesian decision theory, individual decision-making under uncertainty, and experimental methodology. He has published in the American Economic Review, the Journal of Political Economy, Psychological Review, Management Science, and several other field and general-interest journals. In 2009 Dr. Healy received an NSF CAREER award for his work on behavioral mechanism design. He is currently an associate editor of American Economic Review: Insights, is on the executive committee of the Economic Science Association, and previously served as associate editor of European Economic Review.

Weekly Seminar: Amanda Friedenberg, “Is Bounded Reasoning about Rationality Driven by Limited Ability?”, Thursday, November, 18, 2021

Rationality and common belief of rationality (RCBR) is a standard benchmark in game theory. Yet, a body of experimental research points to departures from RCBR. These RCBR departures are typically viewed as an artifact of limits in the ability to engage in interactive reasoning, i.e., to reason through sentences of the form “I think, you think, I think, etc …” We develop a conceptual and practicable framework to test the hypothesis that departures from RCBR are determined by limits in interactive reasoning. The identification strategy benefits from not relying on auxiliary measures of “ability” or “sophistication” that can capture concepts distinct from limited ability to engage in interactive reasoning. We conduct an experiment based on this identification strategy and show that at least 60% of subjects have RCBR departures that are not an artifact of limited ability to engage in interactive reasoning. Moreover, the experiment provides insight into how players’ reason when they depart from RCBR. It suggests that players’ reasoning depends on certain natural heuristics. 

Amanda Friedenberg is a Professor of Economics at the University of Arizona. Her work includes game theory, political economy, and (newly) experiments.

Weekly Seminar: Tianzan Pang, “Persistently Ignoring Others’ Information: A Laboratory Experiment on Retail Investors”, Thursday, November, 11, 2021

Weekly Seminar: Joshua Knobe, “Ordinary Judgements of Causation”, Thursday, November, 4, 2021

Weekly Seminar: Kirby Nielsen, “When Choices are Mistakes”, Thursday, October 21, 2021

Weekly Seminar: Gary Charness, “Improving Children’s Food Choices: Experimental Evidence From the Field”, Thursday, October 7, 2021

Weekly Seminar: Dorothea Kübler, “Repugnant Transactions: The Role of Agency and Extreme Consequences”, Thursday, September 23, 2021

Weekly Seminar: Georg Weizsäker, “Coaudience Neglect”, Thursday, September 9, 2021