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Related Experiment Video

Updated: Mar 30, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

How previous experience shapes perception in different sensory modalities.

Joel S Snyder¹, Caspar M Schwiedrzik², A Davi Vitela¹

¹Department of Psychology, University of Nevada Las Vegas, Las Vegas, NV, USA.

Frontiers in Human Neuroscience

|November 20, 2015

Summary

Temporal context significantly influences sensory perception, causing contrastive or attractive effects. These effects, observed across senses, suggest general computational principles in sensory systems, potentially explained by Bayesian models.

Keywords:

Bayesian modeling adaptation audition context effects perceptual memory vision

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Prior sensory experiences strongly shape current stimulus processing and perception.
Temporal context effects, including contrastive and attractive temporal context effects (TCEs), are well-documented, primarily with simple visual stimuli.
These context effects extend to other sensory modalities (audition, touch) and complex stimuli, suggesting fundamental computational principles in sensory systems.

Purpose of the Study:

To investigate the generalizability of temporal context effects across sensory modalities and stimulus complexity.
To explore the neural underpinnings of contrastive and attractive temporal context effects.
To provide a functional account of temporal context effects using computational models.

Main Methods:

Review of existing literature on temporal context effects in vision, audition, and touch.
Analysis of neuroimaging data (e.g., fMRI, EEG) to identify neural correlates of context effects.
Application of Bayesian models to explain the functional role of temporal context in perception.

Main Results:

Temporal context effects (contrastive and attractive) are not limited to simple visual stimuli but occur across various sensory modalities and stimulus complexities.
Neuroimaging studies reveal distinct neural processes in different cortical areas for contrastive and attractive context effects, indicating separate functional roles.
Bayesian models successfully account for both types of context effects, positing that prior experience optimizes sensory systems for future stimuli.

Conclusions:

Temporal context effects are a general principle of sensory processing, reflecting adaptive mechanisms of the brain.
Distinct neural pathways support contrastive and attractive temporal context effects, highlighting specialized computational operations.
Bayesian frameworks offer a powerful tool for understanding how prior experience shapes perception through temporal context.