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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.


Related in: MedlinePlus

Revealing density maps in the no-rescanning control experiment.Revealing density maps in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 3A).DOI:http://dx.doi.org/10.7554/eLife.12215.007
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fig3s1: Revealing density maps in the no-rescanning control experiment.Revealing density maps in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 3A).DOI:http://dx.doi.org/10.7554/eLife.12215.007

Mentions: (A) Revealing density maps for participants and BAS. Last three columns show mean-corrected revealing densities for each of the three underlying image types (removing the mean density across image types, first column). Bottom: color scales used for all mean densities (left), and for all mean-corrected densities (right). All density maps use the same scale, such that a density of 1 corresponds to the peak mean density across all maps. Figure 3—figure supplement 1 shows revealing density maps obtained for participants in a control experiment in which no rescanning was allowed. Figure 3—figure supplement 2 shows the measured saccadic noise that was incorporated into the BAS simulations. Figure 3—figure supplement 3 shows density maps separately for correct and incorrect trials. (B) The curves are correlations for individual participants as a function of revealing number with their own maps (left) and the maps generated by BAS (right). The bars are correlations at 25 revealing (see Materials and methods). Orange shows within image type correlation, ie. correlation between revealing densities obtained for images of the same type, and purple shows across image type correlation. Data are represented as mean SD for the curves and mean 95% confidence intervals for the bars.


Active sensing in the categorization of visual patterns.

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

Revealing density maps in the no-rescanning control experiment.Revealing density maps in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 3A).DOI:http://dx.doi.org/10.7554/eLife.12215.007
© Copyright Policy
Related In: Results  -  Collection

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

fig3s1: Revealing density maps in the no-rescanning control experiment.Revealing density maps in the control (no rescanning) and the average participant in the main experiment (rescanning) in which rescanning was allowed (cf. Figure 3A).DOI:http://dx.doi.org/10.7554/eLife.12215.007
Mentions: (A) Revealing density maps for participants and BAS. Last three columns show mean-corrected revealing densities for each of the three underlying image types (removing the mean density across image types, first column). Bottom: color scales used for all mean densities (left), and for all mean-corrected densities (right). All density maps use the same scale, such that a density of 1 corresponds to the peak mean density across all maps. Figure 3—figure supplement 1 shows revealing density maps obtained for participants in a control experiment in which no rescanning was allowed. Figure 3—figure supplement 2 shows the measured saccadic noise that was incorporated into the BAS simulations. Figure 3—figure supplement 3 shows density maps separately for correct and incorrect trials. (B) The curves are correlations for individual participants as a function of revealing number with their own maps (left) and the maps generated by BAS (right). The bars are correlations at 25 revealing (see Materials and methods). Orange shows within image type correlation, ie. correlation between revealing densities obtained for images of the same type, and purple shows across image type correlation. Data are represented as mean SD for the curves and mean 95% confidence intervals for the bars.

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.


Related in: MedlinePlus