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Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Components of Language01:24

Components of Language

Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs. “eh”). Phonemes combine to...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Related Experiment Video

Updated: Jun 14, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Semantics derived automatically from language corpora contain human-like biases.

Aylin Caliskan1, Joanna J Bryson1,2, Arvind Narayanan1

  • 1Center for Information Technology Policy, Princeton University, Princeton, NJ, USA. aylinc@princeton.edu jjb@alum.mit.edu arvindn@cs.princeton.edu.

Science (New York, N.Y.)
|April 15, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning models trained on web text replicate human semantic biases found in Implicit Association Tests. This reveals how historical biases are embedded in language data, offering methods to identify and address cultural biases in technology.

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Decoding Natural Behavior from Neuroethological Embedding
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Decoding Natural Behavior from Neuroethological Embedding

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Related Experiment Videos

Last Updated: Jun 14, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Social Science

Background:

  • Machine learning (ML) derives artificial intelligence by identifying patterns in data.
  • Human language corpora contain implicit societal biases.
  • The Implicit Association Test (IAT) measures the strength of automatic associations between concepts.

Purpose of the Study:

  • To investigate whether machine learning models trained on human language exhibit human-like semantic biases.
  • To determine if ML models can replicate biases measured by the IAT.
  • To explore the potential of ML for identifying and mitigating cultural biases.

Main Methods:

  • Applied a statistical machine learning model to a large text corpus from the World Wide Web.
  • Trained the model on standard text data.
  • Evaluated the model's semantic associations against known human biases, including those measured by the IAT.

Main Results:

  • The ML model replicated a spectrum of human semantic biases, mirroring IAT results.
  • Biases were observed across various domains, including morally neutral (insects, flowers), problematic (race, gender), and veridical (gender and careers/names).
  • Text corpora accurately imprint historical human biases, which are recoverable through ML analysis.

Conclusions:

  • Machine learning models trained on real-world text data inherit and reflect human semantic biases.
  • Text data serves as a repository for historical biases, which can be quantified using ML.
  • The developed methods offer a promising approach for detecting and addressing cultural and technological biases.