The factors that contribute to prejudice and discrimination between different groups of individuals are unclear, even though such relations have long been studied within and amongst various societies.

To understand the influences that lead to intergroup bias, confounds like monetary self-interest and history of conflict can be stripped away by randomly assigning individuals to novel sets—what psychologists call minimal groups.

Thus, any consequences emerging from this arbitrary redistribution must result from identifying with a new group. Interestingly, such categorization induces strong favoritism towards fellow in-group members—separating the social world into “us” versus “them”.

Based on previous work by Tajfel and colleagues, this video demonstrates how to induce minimal groups in order to examine how social categorization biases decision-making.

In this experiment, participants are subjected to two phases—group categorization and decision-making—to examine whether discriminatory behavior can be arbitrarily generated.

In the first part—group classification—participants are asked to complete an estimation task, where they simply guesstimate how many dots are shown on the screen over a number of trials.

Their performance levels are used to separate them into two groups: neutral and value. Participants in the neutral condition are further divided and labeled as under- or over-estimators—in which they are told they provided the lowest and highest estimates, respectively. Whereas, for the value condition, participants are either told their estimates are more or less accurate than average.

Subsequently, in the second phase, they are given several matrices to make decisions that either monetarily reward or punish other participants. To accomplish this, every matrix consists of numbered terms ordered in two rows and 14 columns, and each line is labeled as supporting the participant’s members—the in-group—or the others, the out-group.

Combining these possibilities creates three types of choices: in- and out-groups—where both rows are within the same groups—and differential—one of each, setting up intergroup decisions

Furthermore, to vary trade-off amounts within and between groups, the matrices are designed to satisfy one of three criteria: A, the maximum penalties exceed the maximum rewards; B, there are no penalties and the payoff is nearly equal; or C, the maximum rewards exceed the maximum penalties.

Within each matrix type, the terms are organized based on fairness. That is, the end positions reflect the opposite extremes of punishment and reward, while the middle columns represent maximal fairness, as the payout is the most equal. The dependent variable then is the position of the chosen terms.

To average across all choice types, the positions are scored from 1 to 14, where 14 stands for the choice which gives the member of the in-group the maximum possible points on that matrix, and 1 gives the in-group member the minimum possible points. Thus, a 7.5—an average of columns 7 and 8—represents the maximally fair decision across all choice types.

Regardless of conditions, it is predicted that in-group favoritism will emerge, supporting intentional discriminatory behavior after social categorization.

Prior to the experiment, conduct a power analysis to recruit a sufficient number of participants.

To begin, greet each participant and escort them into the lab. Once they are seated, hand them a laptop.

Start the first task—the presentation of dot clusters. Notice that the inter-stimulus-interval, or ISI, varies from 125 to 500 ms to allow enough time for each participant to estimate the number of dots they observed. Also note that their responses are saved.

Following the dot estimation, inform all participants that you are interested in studying other types of judgments and, for convenience, you will be placing them into one of two groups.

Without them knowing the actual results, randomly divide the neutral condition into under- and over-estimators, and the value condition into those who gave either more or less accurate estimates.

Now, lead participants into separate cubicles and inform them that they will soon make real monetary decisions where they can reward and punish other participants.

Allow them to complete the matrices according to their group identification. Instruct them to indicate their choices by selecting one box per matrix.

Finally, after all choices have been made and turned in, fully debrief the participants.

To analyze the responses, individually score each matrix from 1 to 14, where 1 gives the in-group member the minimum possible points and 14 provides them the maximum possible on that matrix.

To visualize the data, plot the average scores across choice types for each condition. Use one-sample t-tests to determine whether the individual means scores in each group were significantly different from the point of fairness, 7.5.

Notice that regardless of condition, participants responded fairly when decisions involved members of their group—in-group choices—or those entirely outside of their group—out-group decisions.

However, when it came to making decisions between groups—the differential choices—the averages were significantly greater than 7.5. These results reflect that in-group favoritism—a form of discriminatory behavior—can emerge after arbitrary classifications.

Moreover, the differences cannot be attributed to general tendencies to make unfair decisions, since participants typically chose the maximally fair option when deciding between two in- or two out-group members.

Now that you are familiar with how individuals deliberately make decisions that reward their in-group at the expense of others, let’s look at how researchers use minimal group inductions to investigate social interactions like empathy, as well as to examine the underlying neural correlates of intergroup biases.

Researchers used the same initial estimation task to fictively divide participants, and then asked them to observe pictures of people in painful or non-painful situations.

They were then instructed to imagine themselves or members of two minimal groups—in-group vs. out-group—in the same situations and accordingly rate the level of perceived pain.

Individuals felt more empathy for someone in pain when that person was in their same social group, which suggests that in-group biases are also present in empathic situations.

In another study by Van Bavel and colleagues, White participants were randomly placed into mixed-race groups, and then scanned via fMRI to identify the neural substrates involved in processing faces from in- and out-group members.

In the first part, they were asked to rate each face on a scale ranging from dislike to like. Regardless of race, individuals gave more positive ratings for their own group compared to those in the out-group.

Moreover, activity within the orbitofrontal cortex mediated such biases. These results indicate that minimal group inductions can even override racial categorizations.

You’ve just watched JoVE’s video on creating the minimal group paradigm. Now you should have a good understanding of how to design and conduct an experiment that induces discrimination, as well as how to analyze data and make conclusions about intergroup behavior.

Thanks for watching!