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Related Experiment Video

Updated: Jun 1, 2026

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Attentional episodes in visual perception.

Brad Wyble¹, Mary C Potter, Howard Bowman

¹Department of Psychology, Syracuse University, Syracuse, NY 13210, USA. bwyble@gmail.com

Journal of Experimental Psychology. General

|May 25, 2011

Summary

This summary is machine-generated.

Visual attention samples information in temporal episodes, with breaks causing the attentional blink. Dense target clusters improve report accuracy but impair temporal order perception, challenging seamless temporal experience.

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Area of Science:

Cognitive Psychology
Neuroscience
Computational Modeling

Background:

Human temporal perception appears seamless, but is influenced by attentional processes.
The attentional blink (AB) demonstrates suppressed attention between targets.
The episodic simultaneous type/serial token model proposes attention samples temporal information in discrete episodes.

Purpose of the Study:

To test predictions of the episodic model of visual attention.
To investigate the trade-off between report accuracy and temporal order perception.
To explore the dynamics and boundary conditions of attentional episodes.

Main Methods:

Participants reported targets from dense temporal clusters versus interleaved sequences.
Experiments manipulated target density and temporal spacing.
Analysis focused on report accuracy and temporal order judgments.

Main Results:

Higher report accuracy for targets in dense temporal clusters compared to interleaved targets.
Impaired temporal order perception for targets presented in dense clusters.
Evidence for episodic breaks in attention under specific temporal conditions.

Conclusions:

Visual attention operates in discrete temporal episodes, not a seamless flow.
The attentional blink arises from suppressed attention between these episodes.
Trade-offs exist between perceptual capacity and temporal resolution within attentional models.