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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who are...
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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...
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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Related Experiment Video

Updated: Jun 5, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

On Kleinberg's Stochastic Discrimination Procedure.

Albrecht Irle, Jonas Kauschke

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 22, 2010
    PubMed
    Summary
    This summary is machine-generated.

    A new condition explains the high accuracy and generalization of stochastic discrimination (SD) pattern recognition. This method is robust even when resampling distribution assumptions are relaxed.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Pattern Recognition

    Background:

    • Stochastic Discrimination (SD) is a pattern recognition method developed by Kleinberg.
    • Observed good generalization and overtraining-resistance properties of SD require further theoretical explanation.
    • The method's reliance on uniform distribution assumptions for resampling may limit its applicability.

    Purpose of the Study:

    • To establish a new condition for high accuracy on test sets for the stochastic discrimination method.
    • To provide a theoretical explanation for the generalization and overtraining-resistance of SD.
    • To investigate the validity of SD when the uniform distribution assumption for resampling is relaxed.

    Main Methods:

    • Derivation of a novel mathematical condition for high accuracy in stochastic discrimination.
    • Theoretical analysis of generalization properties and overtraining resistance based on the new condition.
    • Examination of stochastic discrimination's performance under relaxed resampling distribution assumptions.

    Main Results:

    • A new, simple condition for achieving high accuracy on test sets using stochastic discrimination is identified.
    • This condition elucidates the inherent generalization capabilities and resistance to overtraining in SD.
    • The stochastic discrimination method is demonstrated to be valid even without the assumption of uniform distribution for resampling.

    Conclusions:

    • The newly identified condition offers a clear explanation for SD's effectiveness in pattern recognition.
    • Stochastic discrimination exhibits robust performance and generalization, even when its underlying assumptions are modified.
    • The findings expand the potential applicability of the stochastic discrimination method in machine learning.