A Generative Model of Cognitive State from Task and Eye Movements
W Joseph MacInnes1, Amelia R Hunt2, Alasdair D F Clarke3
1School of Psychology, National Research University Higher School of Economics, Moscow, Russian Federation.
Cognitive Computation
|February 12, 2019
Summary
Researchers developed a computational model to predict eye movements based on tasks and internal cognitive states. This model accurately simulates saccade behavior, advancing our understanding of visual attention and decision-making.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Vision Science
Background:
- Early eye tracking studies indicated task influences eye movement patterns.
- Inferring observer intentions from saccade behavior is a key challenge.
- Current models often focus on bottom-up salience, with limited top-down integration.
Purpose of the Study:
- To create a computational cognitive model of saccade selection.
- To investigate the predictive value of task and eye movement properties.
- To link low-level visual salience with higher-level cognitive goals.
Main Methods:
- Utilized a Dynamic Bayesian Network (DBN) for modeling.
- Analyzed saccadic features for task diagnosticity.
- Incorporated internal cognitive state and temporal transitions.
Main Results:
- The model accurately predicts saccadic sequence results in search paradigms.
- Generated saccadic vectors closely match human observer behavior.
- Demonstrated self-transitioning attentional sets based on visual information.
Conclusions:
- Internal cognitive state influences saccadic behavior beyond instructed tasks.
- The generative model simulates saccade generation in real-time.
- The model provides a Bayesian, cognitive approach to attentional set transitions.
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