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Related Experiment Video

Updated: Apr 25, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Motivated perception of probabilistic information.

Heather C Lench¹, Rachel Smallman¹, Kathleen E Darbor¹

¹Department of Psychology, Texas A&M University, United States.

|August 25, 2014

Summary

When motivated, people perceive probabilities as more variable, increasing desirability bias. This bias affects judgments about outcomes, especially personal ones, despite objective probabilities.

Keywords:

Automatic optimism Desirability bias Risk judgments Social judgments Unrealistic optimism

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Area of Science:

Cognitive Psychology
Decision Science
Behavioral Economics

Background:

Desirability bias describes the tendency to overestimate the likelihood of positive outcomes.
Probabilistic information is usually seen as an objective anchor for judgments.
Motivation can alter the perception of probabilistic information, potentially enhancing desirability bias.

Purpose of the Study:

To investigate how motivation influences the perception and use of probabilistic information.
To explore the mechanisms through which motivation contributes to desirability bias.
To examine the role of perceived probability variance in judgment bias.

Main Methods:

Studies involved a game of chance where participants judged outcome likelihoods.
Probabilistic information was presented with varying ranges and subjective uncertainty.
Judgments were compared between self-relevant and other-relevant outcomes, and motivated vs. unmotivated conditions.

Main Results:

Desirability bias was observed for self-relevant outcomes but not for unaffiliated others, even with equal probabilities.
Motivation led to the perception of greater subjective and objective variance in probabilities.
Increased perceived probability variance correlated with greater desirability bias.

Conclusions:

Motivation increases perceived variance in probabilistic information.
This heightened perceived variance contributes to desirability bias in judgment.
Understanding this mechanism is crucial for interpreting decisions under uncertainty.