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Active sensing in the categorization of visual patterns.

Yang SC, Lengyel M, Wolpert DM - Elife (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.

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.

No MeSH data available.


Performance in the no-rescanning control experiment.Categorization performance as a function of revealing number for participants in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 2C).DOI:http://dx.doi.org/10.7554/eLife.12215.005
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fig2s1: Performance in the no-rescanning control experiment.Categorization performance as a function of revealing number for participants in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 2C).DOI:http://dx.doi.org/10.7554/eLife.12215.005

Mentions: (A) Example stimuli for each of the three image types sampled from two-dimensional Gaussian processes. (B) Experimental design. Participants started each trial by fixating the center cross. In the free-scan condition, an aperture of the underlying image was revealed at each fixation location. In the passive condition, revealing locations were chosen by the computer. In both conditions, after a random number of revealings, participants were required to make a category choice (patchy, P, versus stripy, S) and were given feedback. (C) Categorization performance as a function of revealing number for each of the three participants (symbols and error bars: mean  SEM across trials), and their average, under the free-scan and passive conditions corresponding to different revealing strategies. Lines and shaded areas show across-trial mean  SEM for the ideal observer model. Figure 2—figure supplement 1 shows categorization performance in a control experiment in which no rescanning was allowed.


Active sensing in the categorization of visual patterns.

Yang SC, Lengyel M, Wolpert DM - Elife (2016)

Performance in the no-rescanning control experiment.Categorization performance as a function of revealing number for participants in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 2C).DOI:http://dx.doi.org/10.7554/eLife.12215.005
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4764587&req=5

fig2s1: Performance in the no-rescanning control experiment.Categorization performance as a function of revealing number for participants in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 2C).DOI:http://dx.doi.org/10.7554/eLife.12215.005
Mentions: (A) Example stimuli for each of the three image types sampled from two-dimensional Gaussian processes. (B) Experimental design. Participants started each trial by fixating the center cross. In the free-scan condition, an aperture of the underlying image was revealed at each fixation location. In the passive condition, revealing locations were chosen by the computer. In both conditions, after a random number of revealings, participants were required to make a category choice (patchy, P, versus stripy, S) and were given feedback. (C) Categorization performance as a function of revealing number for each of the three participants (symbols and error bars: mean  SEM across trials), and their average, under the free-scan and passive conditions corresponding to different revealing strategies. Lines and shaded areas show across-trial mean  SEM for the ideal observer model. Figure 2—figure supplement 1 shows categorization performance in a control experiment in which no rescanning was allowed.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.

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.

No MeSH data available.