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Contingency Table

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Related Experiment Video

Updated: Jan 21, 2026

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

Learning mechanisms underlying accurate and biased contingency judgments.

Helena Matute¹, Fernando Blanco¹, Marcos Díaz-Lago¹

¹Department of Psychology.

Journal of Experimental Psychology. Animal Learning and Cognition

|August 6, 2019

Summary

Humans accurately learn event contingencies for survival, but sometimes overestimate null contingencies. This review explores if cue-outcome associations explain both accurate and biased contingency learning, and causal inference.

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Area of Science:

Cognitive Psychology
Learning and Memory
Behavioral Science

Background:

Accurate contingency detection is crucial for prediction and causal inference in humans and animals.
However, individuals sometimes exhibit biased contingency judgments, specifically overestimating null contingencies.

Purpose of the Study:

To evaluate cue-outcome associations as a unifying mechanism for both accurate and biased contingency learning.
To determine if associative principles can also explain causal learning.

Main Methods:

Review of existing literature on contingency learning and associative models.
Analysis of predictions derived from associative models and supporting empirical evidence.
Discussion of findings from diverse research areas that support the associative framework.

Main Results:

Associative models offer a potential explanation for accurate contingency judgments.
The associative framework may also account for biases in contingency learning, such as overestimation of null contingencies.
Evidence from various research domains supports the role of associations in learning and causal inference.

Conclusions:

Cue-outcome associations provide a promising common mechanism for understanding both accurate and biased contingency learning.
The associative view has implications for understanding causal learning.
Further research is needed to address limitations and explore alternative models in contingency and causal learning.