A neural network simulation of the outgroup homogeneity effect

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Social Psychology

Background:

• The out-group homogeneity effect (OHE) is a cognitive bias where individuals perceive members of out-groups as more similar to each other than members of their own in-group.
• Understanding the mechanisms underlying OHE is crucial for addressing intergroup biases and stereotypes.

Purpose of the Study:

• To present a neural network simulation of the out-group homogeneity effect (OHE).
• To demonstrate the efficacy of delta-rule learning in simulating OHE.
• To identify and analyze key parameters influencing the magnitude of OHE.

Main Methods:

• Utilized a feedback neural network model with delta-rule learning, previously applied to stereotype and causal learning.
• Simulated the OHE by manipulating parameters such as in-group/out-group size ratio, population size, learning rate, weight decay, and learning for extreme cases.

Main Results:

• The neural network model successfully simulated the OHE, attributed to the error-correcting properties of delta-rule learning.
• Five simulation aspects significantly influenced the size of the OHE: (a) in-group/out-group size ratio, (b) population size, (c) learning rate, (d) weight decay rate, and (e) learning for extreme cases.

Conclusions:

• The delta-rule learning model provides a robust framework for simulating OHE and other social cognitive phenomena.
• The identified parameters offer testable hypotheses for empirical research on OHE and intergroup cognition.
• The model's versatility highlights its potential for broad applications in studying social cognition.