Historical representations of social groups across 200 years of word embeddings from Google Books.

Using word embeddings from 850 billion words in English-language Google Books, we provide an extensive analysis of historical change and stability in social group representations (stereotypes) across a long timeframe (from 1800 to 1999), for a large number of social group targets (Black, White, Asian, Irish, Hispanic, Native American, Man, Woman, Old, Young, Fat, Thin, Rich, Poor), and their emergent, bottom-up associations with 14,000 words and a subset of 600 traits. The results provide a nuanced picture of change and persistence in stereotypes across 200 y. Change was observed in the top-associated words and traits: Whether analyzing the top 10 or 50 associates, at least 50% of top associates changed across successive decades. Despite this changing content of top-associated words, the average valence (positivity/negativity) of these top stereotypes was generally persistent. Ultimately, through advances in the availability of historical word embeddings, this study offers a comprehensive characterization of both change and persistence in social group representations as revealed through books of the English-speaking world from 1800 to 1999.

The project implicit international dataset: Measuring implicit and explicit social group attitudes and stereotypes across 34 countries (2009-2019).

For decades, researchers across the social sciences have sought to document and explain the worldwide variation in social group attitudes (evaluative representations, e.g., young-good/old-bad) and stereotypes (attribute representations, e.g., male-science/female-arts). Indeed, uncovering such country-level variation can provide key insights into questions ranging from how attitudes and stereotypes are clustered across places to why places vary in attitudes and stereotypes (including ecological and social correlates). Here, we introduce the Project Implicit:International (PI:International) dataset that has the potential to propel such research by offering the first cross-country dataset of both implicit (indirectly measured) and explicit (directly measured) attitudes and stereotypes across multiple topics and years. PI:International comprises 2.3 million tests for seven topics (race, sexual orientation, age, body weight, nationality, and skin-tone attitudes, as well as men/women-science/arts stereotypes) using both indirect (Implicit Association Test; IAT) and direct (self-report) measures collected continuously from 2009 to 2019 from 34 countries in each country's native language(s). We show that the IAT data from PI:International have adequate internal consistency (split-half reliability), convergent validity (implicit-explicit correlations), and known groups validity. Given such reliability and validity, we summarize basic descriptive statistics on the overall strength and variability of implicit and explicit attitudes and stereotypes around the world. The PI:International dataset, including both summary data and trial-level data from the IAT, is provided openly to facilitate wide access and novel discoveries on the global nature of implicit and explicit attitudes and stereotypes.

Patterns of Implicit and Explicit Attitudes: IV. Change and Stability From 2007 to 2020.

Using more than 7.1 million implicit and explicit attitude tests drawn from U.S. participants to the Project Implicit website, we examined long-term trends across 14 years (2007-2020). Despite tumultuous sociopolitical events, trends from 2017 to 2020 persisted largely as forecasted from past data (2007-2016). Since 2007, all explicit attitudes decreased in bias between 22% (age attitudes) and 98% (race attitudes). Implicit sexuality, race, and skin-tone attitudes also continued to decrease in bias, by 65%, 26%, and 25%, respectively. Implicit age, disability, and body-weight attitudes, however, continued to show little to no long-term change. Patterns of change and stability were generally consistent across demographic groups (e.g., men and women), indicating widespread, macrolevel change. Ultimately, the data magnify evidence that (some) implicit attitudes reveal persistent, long-term change toward neutrality. The data also newly reveal the potential for short-term influence from sociopolitical events that temporarily disrupt progress toward neutrality, although attitudes eventually return to long-term homeostasis in trends.

The relationship between implicit intergroup attitudes and beliefs.

Intergroup attitudes (evaluations) are generalized valence attributions to social groups (e.g., white-bad/Asian-good), whereas intergroup beliefs (stereotypes) are specific trait attributions to social groups (e.g., white-dumb/Asian-smart). When explicit (self-report) measures are used, attitudes toward and beliefs about the same social group are often related to each other but can also be dissociated. The present work used three approaches (correlational, experimental, and archival) to conduct a systematic investigation of the relationship between implicit (indirectly revealed) intergroup attitudes and beliefs. In study 1 (n = 1,942), we found significant correlations and, in some cases, evidence for redundancy, between Implicit Association Tests (IATs) measuring attitudes toward and beliefs about the same social groups (mean r = 0.31, 95% confidence interval: [0.24; 0.39]). In study 2 (n = 383), manipulating attitudes via evaluative conditioning produced parallel changes in belief IATs, demonstrating that implicit attitudes can causally drive implicit beliefs when information about the specific semantic trait is absent. In study 3, we used word embeddings derived from a large corpus of online text to show that the relative distance of 22 social groups from positive vs. negative words (reflecting generalized attitudes) was highly correlated with their distance from warm vs. cold, and even competent vs. incompetent, words (reflecting specific beliefs). Overall, these studies provide convergent evidence for tight connections between implicit attitudes and beliefs, suggesting that the dissociations observed using explicit measures may arise uniquely from deliberate judgment processes.

Gender Stereotypes in Natural Language: Word Embeddings Show Robust Consistency Across Child and Adult Language Corpora of More Than 65 Million Words.

Stereotypes are associations between social groups and semantic attributes that are widely shared within societies. The spoken and written language of a society affords a unique way to measure the magnitude and prevalence of these widely shared collective representations. Here, we used word embeddings to systematically quantify gender stereotypes in language corpora that are unprecedented in size (65+ million words) and scope (child and adult conversations, books, movies, TV). Across corpora, gender stereotypes emerged consistently and robustly for both theoretically selected stereotypes (e.g., work-home) and comprehensive lists of more than 600 personality traits and more than 300 occupations. Despite underlying differences across language corpora (e.g., time periods, formats, age groups), results revealed the pervasiveness of gender stereotypes in every corpus. Using gender stereotypes as the focal issue, we unite 19th-century theories of collective representations and 21st-century evidence on implicit social cognition to understand the subtle yet persistent presence of collective representations in language.

Gender in Science, Technology, Engineering, and Mathematics: Issues, Causes, Solutions.

The landscape of gender in education and the workforce has shifted over the past decades: women have made gains in representation, equitable pay, and recognition through awards, grants, and publications. Despite overall change, differences persist in the fields of science, technology, engineering, and mathematics (STEM). This Viewpoints article on gender disparities in STEM offers an overarching perspective by addressing what the issues are, why the issues may emerge, and how the issues may be solved. In Part 1, recent data on gaps in representation, compensation, and recognition (awards, grants, publications) are reviewed, highlighting differences across subfields (e.g., computer science vs biology) and across career trajectories (e.g., bachelor's degrees vs senior faculty). In Part 2, evidence on leading explanations for these gaps, including explanations centered on abilities, preferences, and explicit and implicit bias, is presented. Particular attention is paid to implicit bias: mental processes that exist largely outside of conscious awareness and control in both male and female perceivers and female targets themselves. Given its prevalence and persistence, implicit bias warrants a central focus for research and application. Finally, in Part 3, the current knowledge is presented on interventions to change individuals' beliefs and behaviors, as well as organizational culture and practices. The moral issues surrounding equal access aside, understanding and addressing the complex issues surrounding gender in STEM are important because of the possible benefits to STEM and society that will be realized only when full participation of all capable and qualified individuals is guaranteed.

Children's implicit attitude acquisition: Evaluative statements succeed, repeated pairings fail.

From the earliest ages tested, children and adults show similar overall magnitudes of implicit attitudes toward various social groups. However, such consistency in attitude magnitude may obscure meaningful age-related change in the ways that children (vs. adults) acquire implicit attitudes. This experiment investigated children's implicit attitude acquisition by comparing the separate and joint effects of two learning interventions, previously shown to form implicit attitudes in adults. Children (N = 280, ages 7-11 years) were taught about novel social groups through either evaluative statements (ES; auditorily presented verbal statements such as 'Longfaces are bad, Squarefaces are good'), repeated evaluative pairings (REP; visual pairings of Longface/Squareface group members with valenced images such as a puppy or snake), or a combination of ES+REP. Results showed that children acquired implicit attitudes following ES and ES+REP, with REP providing no additional learning beyond ES alone. Moreover, children did not acquire implicit attitudes in four variations of REP, each designed to facilitate learning by systematically increasing verbal scaffolding to specify (a) the learning goal, (b) the valence of the unconditioned stimuli, and (c) the group categories of the conditioned stimuli. These findings underscore the early-emerging role of verbal statements in children's implicit attitude acquisition, as well as a possible age-related limit in children's acquisition of novel implicit attitudes from repeated pairings.

Patterns of Implicit and Explicit Attitudes: I. Long-Term Change and Stability From 2007 to 2016.

Using 4.4 million tests of implicit and explicit attitudes measured continuously from an Internet population of U.S. respondents over 13 years, we conducted the first comparative analysis using time-series models to examine patterns of long-term change in six social-group attitudes: sexual orientation, race, skin tone, age, disability, and body weight. Even within just a decade, all explicit responses showed change toward attitude neutrality. Parallel implicit responses also showed change toward neutrality for sexual orientation, race, and skin-tone attitudes but revealed stability over time for age and disability attitudes and change away from neutrality for body-weight attitudes. These data provide previously unavailable evidence for long-term implicit attitude change and stability across multiple social groups; the data can be used to generate and test theoretical predictions as well as construct forecasts of future attitudes.

Mechanisms upholding the persistence of stigma across 100 years of historical text.

Today, many social groups face negative stereotypes. Is such negativity a stable feature of society and, if so, what mechanisms maintain stability both within and across group targets? Answering these theoretically and practically important questions requires data on dozens of group stereotypes examined simultaneously over historical and societal scales, which is only possible through recent advances in Natural Language Processing. Across two studies, we use word embeddings from millions of English-language books over 100 years (1900-2000) and extract stereotypes for 58 stigmatized groups. Study 1 examines aggregate, societal-level trends in stereotype negativity by averaging across these groups. Results reveal striking persistence in aggregate negativity (no meaningful slope), suggesting that society maintains a stable level of negative stereotypes. Study 2 introduces and tests a new framework identifying potential mechanisms upholding stereotype negativity over time. We find evidence of two key sources of this aggregate persistence: within-group "reproducibility" (e.g., stereotype negativity can be maintained by using different traits with the same underlying meaning) and across-group "replacement" (e.g., negativity from one group is transferred to other related groups). These findings provide novel historical evidence of mechanisms upholding stigmatization in society and raise new questions regarding the possibility of future stigma change.

Extracting intersectional stereotypes from embeddings: Developing and validating the Flexible Intersectional Stereotype Extraction procedure.

Social group-based identities intersect. The meaning of "woman" is modulated by adding social class as in "rich woman" or "poor woman." How does such intersectionality operate at-scale in everyday language? Which intersections dominate (are most frequent)? What qualities (positivity, competence, warmth) are ascribed to each intersection? In this study, we make it possible to address such questions by developing a stepwise procedure, Flexible Intersectional Stereotype Extraction (FISE), applied to word embeddings (GloVe; BERT) trained on billions of words of English Internet text, revealing insights into intersectional stereotypes. First, applying FISE to occupation stereotypes across intersections of gender, race, and class showed alignment with ground-truth data on occupation demographics, providing initial validation. Second, applying FISE to trait adjectives showed strong androcentrism (Men) and ethnocentrism (White) in dominating everyday English language (e.g. White + Men are associated with 59% of traits; Black + Women with 5%). Associated traits also revealed intersectional differences: advantaged intersectional groups, especially intersections involving Rich, had more common, positive, warm, competent, and dominant trait associates. Together, the empirical insights from FISE illustrate its utility for transparently and efficiently quantifying intersectional stereotypes in existing large text corpora, with potential to expand intersectionality research across unprecedented time and place. This project further sets up the infrastructure necessary to pursue new research on the emergent properties of intersectional identities.

A 3D framework of implicit attitude change.

According to early theories, implicit (automatic) social attitudes are difficult if not impossible to change. Although this view has recently been challenged by research relying on experimental, developmental, and cultural approaches, relevant work remains siloed across research communities. As such, the time is ripe to systematize and integrate disparate (and seemingly contradictory) findings and to identify gaps in existing knowledge. To this end, we introduce a 3D framework classifying research on implicit attitude change by levels of analysis (individual vs. collective), sources of change (experimental, ontogenetic, and cultural), and timescales (short term vs. long term). This 3D framework highlights where evidence for implicit attitude change is more versus less well established and pinpoints directions for future research, including at the intersection of fields.

Identifying and predicting stereotype change in large language corpora: 72 groups, 115 years (1900-2015), and four text sources.

The social world is carved into a complex variety of groups each associated with unique stereotypes that persist and shift over time. Innovations in natural language processing (word embeddings) enabled this comprehensive study on variability and correlates of change/stability in both manifest and latent stereotypes for 72 diverse groups tracked across 115 years of four English-language text corpora. Results showed, first, that group stereotypes changed by a moderate-to-large degree in manifest content (i.e., top traits associated with groups) but remained relatively more stable in latent structure (i.e., average cosine similarity of top traits' embeddings and vectors of valence, warmth, or competence). This dissociation suggests new insights into how stereotypes and their consequences may endure despite documented changes in other aspects of group representations. Second, results showed substantial variability of change/stability across the 72 groups, with some groups revealing large shifts in manifest and latent content, but others showing near-stability. Third, groups also varied in how consistently they were stereotyped across texts, with some groups showing divergent content, but others showing near-identical representations. Fourth, this variability in change/stability across groups was predicted from a combination of linguistic (e.g., frequency of mentioning the group; consistency of group stereotypes across texts) and social (e.g., the type of group) correlates. Groups that were more frequently mentioned in text changed more than those rarely mentioned; sociodemographic groups changed more than other group types (e.g., body-related stigmas, mental illnesses, occupations), providing the first quantitative evidence of specific group features that may support historical stereotype change. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Patterns of implicit and explicit attitudes II. Long-term change and stability, regardless of group membership.

Over the past decade, implicit attitudes about sexual orientation, race, and age have revealed both change toward neutrality (sexuality and race attitudes) and stability (age attitudes). But how consistently have such patterns of change and stability unfolded across U.S. society? Are the trends widespread, with most demographic groups changing or remaining stable in parallel, at the same rate and in the same direction? Or are the trends more idiosyncratic, with groups moving at different rates and/or directions, revealing nonparallel change? The answer can reveal whether the sources of change are unfolding at the collective, macrolevel of society, or at the mezzo-level of demographic group memberships. Results from over 2.5 million tests of sexuality, race, and age attitudes, collected continuously in the United States over 10 years (2007-2016) show that attitude trends are largely parallel across most demographic groups (e.g., respondents' gender, race, education). Parallel trends are more strongly evident in implicit social group attitudes, with explicit attitudes showing relatively more nonparallel trends. Two demographics, respondent age and political orientation, are exceptions: younger and politically liberal groups are generally changing faster toward implicit attitude neutrality than older and conservative groups. Nevertheless, the surprising consistency in trends across demographic groups points to the role of macrolevel societal variables as the most likely sources of widespread reductions in implicit and explicit social group attitudes over the past decade. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Children use targets' facial appearance to guide and predict social behavior.

Humans possess a tendency to rapidly and consistently make character evaluations from mere facial appearance. Recent work shows that this tendency emerges surprisingly early: children as young as 3-years-old provide adult-like assessments of others on character attributes such as "nice," "strong," and "smart" based only on subtle variations in targets' face shape and physiognomy (i.e., latent face-traits). The present research examined the behavioral consequences of children's face-trait judgments by asking whether, and if so when in development, the appearance of face-traits also (a) shapes children's judgments of targets' behaviors and (b) guides children's behavior toward targets. Experiments 1 and 2 showed that, by 3 years of age, children used facial features in character evaluations but not in judgments of targets' behavior, whereas by 5 years of age, children reliably made both character and behavior judgments from face-traits. Age-related change in behavior judgments was also observed in children's own behaviors toward targets: Experiments 3 and 4 showed that, by age 5 (but not earlier), children were more likely to give gifts to targets with trustworthy and submissive-looking faces (Experiment 3) and showed concordance between their character evaluations and gift-giving behaviors (Experiment 4). These findings newly suggest that, although children may rapidly make character evaluations from face-trait appearance, predicting and performing social behaviors based on face-traits may require more developed and specific understanding of traits and their relationships to behaviors. Nevertheless, by kindergarten, even relatively arbitrary and subtle face-traits appear to have meaningful consequences in shaping children's social judgments and interactions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).