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Related Experiment Video

Updated: May 10, 2025

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

Inference from social evaluation.

Zachary J Davis¹, Kelsey R Allen², Max Kleiman-Weiner³

¹Department of Psychology, Stanford University.

Journal of Personality and Social Psychology

|April 24, 2025

Summary

This summary is machine-generated.

People use social evaluations like praise and blame to infer hidden causes of events. This study shows how these judgments reveal insights into actions, capabilities, and situational factors.

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Area of Science:

Cognitive Psychology
Social Psychology
Computational Social Science

Background:

Humans infer hidden causes from physical evidence.
Social evaluations, such as praise and blame, are common in daily interactions.
These evaluations offer clues to understanding events and actions.

Purpose of the Study:

Investigate how people use social evaluations as evidence to infer hidden causes.
Determine the systematic inferences drawn from praise and blame.
Model the generative process of praise and blame judgments.

Main Methods:

Conducted three experiments presenting scenarios with social evaluations.
Participants inferred situational factors, actions, capabilities, and social roles.
Developed computational models for praise/blame generation and used Bayesian inference.

Main Results:

People systematically infer situational factors, actions, capabilities, and social roles from social evaluations.
Computational models accurately predicted participant inferences.
Bayesian inversion of generative models explained observed inference patterns.

Conclusions:

Social evaluations are a rich source of information for causal inference.
Computational models can capture the reasoning behind praise and blame.
Bayesian inference provides a framework for understanding how social feedback informs causal judgments.