LE 16: Stereotypes, Implicit Bias, and Behavioral Discrimination
Stereotype Transmission and Formation
Transmission Mechanisms:
Stereotypes can form through the collective knowledge of a large group of people.
Under certain conditions, collective knowledge can lead to accuracy.
Stereotypes also form through cultural transmission.
They can evolve through rational means based on available information.
Cognitive Biases in Transmission:
The mind biases information based on what it pays attention to, what it remembers, what it chooses to pass on, and what it wants to learn from others.
Because of these biases, stereotypes can end up having very little correspondence with reality.
The use of stereotypes often transitions from attitudes into active discrimination.
The Stereotype Content Model: Competence and Warmth
The Two Primary Dimensions: Research suggests that humans simplify social perceptions into two main dimensions: competence and warmth. * Competence: This dimension measures how society views a group in terms of being confident, independent, and capable of getting things done in the world. It involves whether a person can honor their promises. * Warmth: This refers to approachability and trust. It measures how society views a group's tolerance, good-naturedness, and sincerity.
Social Utility: These dimensions are critical because individuals interact with their social worlds to achieve personal interests. * Competence asks: "What can you do that might be useful for me?" * Warmth asks: "When you are doing things, do you wish me well or do you wish me ill?"
Historical Origins: In , these were referred to as the "good intellectual" (competence) and "good social" (warmth) dimensions.
Social and Occupational Group Clustering
Mapping Groups: When rating groups on a scale of competence and warmth, researchers find four natural groupings or clusters.
United States Data (Susan Fiske and Cindy Dupree): * Source: Data from approximately years ago published in the Proceedings of the National Academy of Sciences. * High Warmth, High Competence: Doctors, teachers, nurses, farmers, childcare workers, and professors. * Low Warmth, High Competence: Engineers, researchers, scientists, accountants, CEOs, and lawyers. * The Middling Cluster (Moderate Warmth and Competence): Writers, actors, bus drivers, mechanics, workers, plumbers, and salespeople. Politicians are notably low in this group. * Low Warmth, Low Competence: Sex workers, garbage collectors, fast food workers, customer service workers, laborers, and food service industry workers.
Mainland China Data: * High Warmth, High Competence: Firemen, soldiers, air hostesses, undergraduates, professors, and yoga instructors. * Low Warmth, High Competence: Entrepreneurs, sports stars, business people, the nouveau riche, government workers, and private entrepreneurs (often referred to as "strong women"). * High Warmth, Moderate Competence: Farmers, migrant workers, cleaning workers, "left-behind" children, the disabled, welfare recipients, laid-off workers, and homosexuals. * Low Warmth, Low Competence: Drug addicts, terrorists, criminals, beggars, the unemployed, sex workers, and urban management officers (Chengguan).
The Implicit Association Test (IAT)
Function: The IAT measures "System 1" processing—intuitive, fast, automatic associations based on habit and cultural learning rather than deliberate thought.
Mechanism of the Classic IAT: * Participants categorize faces (e.g., black or white) and words (e.g., good or bad). * Good Words: Love, joy, honest. * Bad Words: Poison, agony, detest. * Key Linking: The test maps two categories to a single key. For example, Key might be assigned to "Black person" and "Good word," while Key is assigned to "White person" and "Bad word." Successive rounds switch these associations (e.g., "Black person" and "Bad word").
Logic of the Measurement: * Co-activation: If an individual holds an implicit bias, they will be faster when the two concepts share a schema (e.g., white and good co-activating each other). * Interference: If the pairing contradicts an implicit association (e.g., black and good), reaction times increase because the concepts interfere with identification. * Order Effects: Researchers use counterbalancing (switching which pairing comes first) to ensure results are not skewed by the order of the tasks.
Global Findings on IAT Scores: * Sexuality (Straight vs. Gay): Argentina shows the highest implicit negative association; Japan, China, France, and Taiwan show milder associations. The USA is in the middle. * Race: Romania, Czech Republic, China, Japan, and Italy have relatively high implicit negative attitudes toward racial outgroups. The USA score is approximately , placing it relatively low compared to other countries, though still above (indicating active bias). * Body Weight: The USA is in the middle. High negative judgment is found in Denmark, Czech Republic, Germany, Canada, and Sweden. Low bias is found in Russia, Taiwan, India, and China. * Age: This is one of the highest implicit biases globally. However, the USA has among the lowest implicit negative attitudes toward aging compared to Germany, France, or the Czech Republic. * Skin Tone: The USA is at the bottom of the pack (showing the weakest implicit association of dark skin with badness). However, almost every country studied shows some degree of assigning negativity to darker skin tones.
Behavioral Tasks: Weapons and Shooter Bias
Weapons Identification Task (Keith Payne): * Trial Sequence: A mask (visual noise) $\rightarrow$ a prime face (black or white) for milliseconds $\rightarrow$ a target (tool or weapon) $\rightarrow$ another mask. * One-Second Trials (Reaction Time focus): When primed with a black face, participants are significantly slower to recognize a tool as a tool and faster to recognize a weapon as a weapon. No significant difference is found when primed with a white face. * Half-Second Trials (Error focus): Under time pressure, priming with a black face leads to more errors in misidentifying tools as weapons. This suggests a "lower criterion" for calling a stimulus a gun if associated with a black face.
Shooter Bias Test (Josh Correll): * The Game Model: Participants act as police officers deciding whether to shoot "armed" or "unarmed" targets appearing on screen. * Points System: Correctly shooting an armed target or correctly not shooting an unarmed target earns points. Incorrect decisions represent the cost of survival vs. the cost of killing innocent people. * Stimuli: Targets are black or white men holding either a lethal threat (pistol) or a harmless object (wallet, iPad). * Results: 1. Participants are faster to choose NOT to shoot unarmed white targets than unarmed black targets. 2. Participants are faster to choose TO shoot armed black targets than armed white targets. * Consistency: These effects replicate across at least experiments over years and are found globally (e.g., in France, comparing native populations to Middle Eastern immigrants).
Discrimination in Hiring: Callback Experiments
Methodology: Researchers send identical resumes (CVs) to employers, changing only the names to reflect stereotypical ethnic or racial identities.
The Discrimination Ratio (): * * A value of indicates no discrimination. * Values above indicate favoring the in-group.
Study Scope: Nine countries studied as of (USA, Belgium, Canada, France, Germany, Great Britain, Netherlands, Norway, Sweden).
Results in the United States: * African Americans: White-associated names are more likely to receive a callback than black-associated names. * Hispanics: White-associated names have a higher probability of a callback. * Asians: Lower callback rates are observed, though data points are fewer.
International Comparison: * France: The only country with significantly higher hiring bias than the USA. * Germany: The only country with a numerically lower (though not statistically lower) discrimination ratio than the USA. * Other Countries: Belgium, Canada, Netherlands, Norway, and Sweden all show discrimination ratios consistently above .
Questions & Discussion
Clarification on Urban Management Officers: A student from China explained that urban management officers (Chengguan) patrol streets to fine informal food sellers without permits. The professor noted this likely explains their position in the low warmth/low competence cluster.
Clustering Math: A student asked about how groups in the middle are categorized. The professor clarified that statistical "math magic" or classifiers find central points to determine natural groupings, distinguishing one cluster from another even when they are physically close on a graph.
IAT Counterbalancing: A student asked if the order of tests matters. The professor explained that trials are counterbalanced to prevent "order effects," and the results (bias detection) remain consistent regardless of which pairing is tested first.