CUED Publications database

Active sensing in the categorization of visual patterns.

Yang, SC-H and Lengyel, M and Wolpert, DM (2015) Active sensing in the categorization of visual patterns. Elife, 5.

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Abstract

Interpreting visual scenes typically requires us to accumulate information from multiple locations in a scene. Using a novel gaze-contingent paradigm in a visual categorization task, we show that participants' scan paths follow an active sensing strategy that incorporates information already acquired about the scene and knowledge of the statistical structure of patterns. Intriguingly, categorization performance was markedly improved when locations were revealed to participants by an optimal Bayesian active sensor algorithm. By using a combination of a Bayesian ideal observer and the active sensor algorithm, we estimate that a major portion of this apparent suboptimality of fixation locations arises from prior biases, perceptual noise and inaccuracies in eye movements, and the central process of selecting fixation locations is around 70% efficient in our task. Our results suggest that participants select eye movements with the goal of maximizing information about abstract categories that require the integration of information from multiple locations.

Item Type: Article
Uncontrolled Keywords: Bayesian analysis active sensing eye movements human ideal observer neuroscience visual categorization Adult Eye Movements Humans Models, Neurological Pattern Recognition, Visual Psychomotor Performance Visual Perception
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:54
Last Modified: 21 Sep 2017 01:35
DOI: