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Related Experiment Video

Updated: Mar 7, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

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Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment.

Timothy Doyeon Kim¹, Mohammad Kabir², Joshua I Gold³

¹Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, and.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|March 1, 2017

Summary

Prior expectations influence brain decision-making during saccadic eye movements. This study reveals how processes within and across saccades interact to optimize visual search in dynamic environments.

Keywords:

decision-making eye movement response times saccade

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Last Updated: Mar 7, 2026

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Published on: March 18, 2019

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Saccadic eye movements are crucial for visual exploration and decision-making.
Current models often study saccadic decisions in isolation, limiting understanding of complex, real-world scenarios.

Purpose of the Study:

To investigate how prior expectations influence reaction times (RTs) in decision-making.
To explore the interplay between within-saccade and across-saccade decision processes.
To elucidate the role of functional information-processing loops in optimizing saccade generation.

Main Methods:

Utilized a serial reaction time (RT) task with human subjects.
Analyzed saccadic eye movements and reaction times.
Modeled decision processes using a rise-to-threshold framework.

Main Results:

Prior expectations significantly affected RTs through interdependent decision processes.
Saccades were governed by a rise-to-threshold process with starting points reflecting expected transition probabilities.
Information accumulation across saccades was necessary for current state decisions, influencing subsequent saccadic processes.

Conclusions:

Demonstrated that decision processes operate both within and across saccades, guided by learned expectations.
Identified functional information-processing loops that link within- and across-saccade processes.
Showcased how these loops optimize saccade generation in dynamic environments for adaptive behavior.