ChatGPT as Research Scientist: Probing GPT's capabilities as a Research Librarian, Research Ethicist, Data Generator, and Data Predictor.

How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor, using psychological science as a testing field. In Study 1 (Research Librarian), unlike human researchers, GPT-3.

Eliciting cognitive consistency increases acceptance of implicit bias.

Resistance to knowledge about implicit bias jeopardizes the ability to learn, understand, and act to outsmart bias. Across three experiments and five independent samples ( > 3,500), conditions that increase cognitive consistency were created alongside control conditions. In Experiment 1, using a race (Black-White) Implicit Association Test (IAT), cognitive consistency was enhanced when participants evaluated the validity and utility of the test before, rather than after, receiving the test result, leading to greater acceptance of bias.

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 (; ) trained on billions of words of English Internet text, revealing insights into intersectional stereotypes.

(when) do counterattitudinal exemplars shift implicit racial evaluations? Replications and extensions of Dasgupta and Greenwald (2001).

Dasgupta and Greenwald (2001) demonstrated that exposure to positive Black exemplars (e.g., Colin Powell) and negative White exemplars (e.

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.

All human social groups are human, but some are more human than others: A comprehensive investigation of the implicit association of "Human" to US racial/ethnic groups.

All human groups are equally human, but are they automatically represented as such? Harnessing data from 61,377 participants across 13 experiments (six primary and seven supplemental), a sharp dissociation between implicit and explicit measures emerged. Despite explicitly affirming the equal humanity of all racial/ethnic groups, White participants consistently associated Human (relative to Animal) more with White than Black, Hispanic, and Asian groups on Implicit Association Tests (IATs; experiments 1-4). This effect emerged across diverse representations of Animal that varied in valence (, , , and ; experiments 1-2).

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.

Two thousand years after Archimedes, psychologist finds three topics that will simply not yield to the experimental method.

Cesario argues that experiments cannot illuminate real group disparities because they leave out factors that operate in ordinary life. But what Cesario calls flaws are, in fact, the point of the experimental method. Of all the topics in science, we have to wonder why racial discrimination would be uniquely unsuited for investigating with experiments.

Systemic racism: individuals and interactions, institutions and society.

Systemic racism is a scientifically tractable phenomenon, urgent for cognitive scientists to address. This tutorial reviews the built-in systems that undermine life opportunities and outcomes by racial category, with a focus on challenges to Black Americans. From American colonial history, explicit practices and policies reinforced disadvantage across all domains of life, beginning with slavery, and continuing with vastly subordinated status.

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.

Specificity and incremental predictive validity of implicit attitudes: studies of a race-based phenotype.

Four studies involving 2552 White American participants were conducted to investigate bias based on the race-based phenotype of hair texture. Specifically, we probed the existence and magnitude of bias in favor of Eurocentric (straight) over Afrocentric (curly) hair and its specificity in predicting responses to a legal decision involving the phenotype. Study 1 revealed an implicit preference, measured by an Implicit Association Test (IAT), favoring Eurocentric over Afrocentric hair texture among White Americans.

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 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).

An implicit gender sex-science association in the general population and STEM faculty.

We investigated implicit associations between social categories or and the attributes or . In six experiments, Implicit Association Tests (IATs) showed associations. The bias was observed (a) in both men and women; (b) in participants who reported sexual attraction to both females and males (greater for the former); (c) in members of the general population as well as among STEM faculty from the highest ranked U.

Meta-Analytic Use of Balanced Identity Theory to Validate the Implicit Association Test.

This meta-analysis evaluated theoretical predictions from balanced identity theory (BIT) and evaluated the validity of zero points of Implicit Association Test (IAT) and self-report measures used to test these predictions. Twenty-one researchers contributed individual subject data from 36 experiments (total = 12,773) that used both explicit and implicit measures of the social-cognitive constructs. The meta-analysis confirmed predictions of BIT's balance-congruity principle and simultaneously validated interpretation of the IAT's zero point as indicating absence of preference between two attitude objects.

Pupillometric decoding of high-level musical imagery.

Humans report imagining sound where no physical sound is present: we replay conversations, practice speeches, and "hear" music all within the confines of our minds. Research has identified neural substrates underlying auditory imagery; yet deciphering its explicit contents has been elusive. Here we present a novel pupillometric method for decoding what individuals hear "inside their heads".

Inferring an unobservable population size from observable samples.

Success in the physical and social worlds often requires knowledge of population size. However, many populations cannot be observed in their entirety, making direct assessment of their size difficult, if not impossible. Nevertheless, an unobservable population size can be inferred from observable samples.

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.

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.

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.

Model-free and model-based learning processes in the updating of explicit and implicit evaluations.

Evaluating stimuli along a good-bad dimension is a fundamental computation performed by the human mind. In recent decades, research has documented dissociations and associations between explicit (i.e.

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.

Attitude change via repeated evaluative pairings versus evaluative statements: Shared and unique features.

When tested immediately, evaluative statements (ES; verbal information about upcoming categories and their positive/negative attributes) surprisingly shift implicit (IAT) attitudes more effectively than repeated evaluative pairings (REP; actual pairing of category members with positive/negative attributes). The present project (total N = 5,317) explored the shared and unique features of these two attitude change modalities by probing (a) commonalities visible in the extent to which propositional inferences created by ES infiltrate REP learning and (b) differences visible in performance of ES and REP learning over time. In REP, the number of stimulus pairings (varied parametrically from 4 to 24) produced no effect (Study 1), but verbally describing stimulus pairings as diagnostic versus nondiagnostic did modulate learning (Study 2), suggesting that even REP give rise to some form of propositional representation.

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.