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Related Experiment Video

Updated: Jul 26, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Probabilities and polarity biases in conditional inference.

M Oaksford¹, N Chater, J Larkin

¹School of Psychology, Cardiff University, Wales, United Kingdom. oaksford@cardiff.ac.uk

Journal of Experimental Psychology. Learning, Memory, and Cognition

|August 18, 2000

Summary

This study introduces a probabilistic model explaining polarity biases in conditional inference. It demonstrates that these biases arise from the higher probability of negated categories, a rational effect rather than a cognitive error.

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Area of Science:

Cognitive Psychology
Computational Modeling
Human Reasoning

Background:

Conditional inference tasks often reveal polarity biases.
Existing models struggle to fully explain these biases, particularly those observed with negation.

Purpose of the Study:

To propose a probabilistic computational level model of conditional inference.
To explain polarity biases using a novel assumption about negation probability.

Main Methods:

Developed a probabilistic model where negations represent higher probability categories.
Conducted three experiments manipulating category probabilities to test the model's predictions.

Main Results:

Confirmed that high-probability categories, like negations, elicit a high-probability conclusion effect.
The findings support the probabilistic account of polarity biases.

Conclusions:

Polarity biases in conditional inference are a rational consequence of category probabilities.
The proposed model offers a parsimonious explanation for these biases in human reasoning.