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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.


Relative efficiency across free-scan sessions.Relative efficiency computed as the ratio of the scale parameter as (see Materials and methods) of each session to that of the last session. Circles and lines show mean and SD obtained by bootstrapping trials within a session 50 times. The revealings on day 2-3 (free-scan sessions) were 0.90–1.04 (across participants) times as efficient compared to day 1 revealings (free-scan familiarization sessions). This suggests that participants were already choosing revealing locations on day 1 with near-asymptotic efficiency.DOI:http://dx.doi.org/10.7554/eLife.12215.015
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fig5s1: Relative efficiency across free-scan sessions.Relative efficiency computed as the ratio of the scale parameter as (see Materials and methods) of each session to that of the last session. Circles and lines show mean and SD obtained by bootstrapping trials within a session 50 times. The revealings on day 2-3 (free-scan sessions) were 0.90–1.04 (across participants) times as efficient compared to day 1 revealings (free-scan familiarization sessions). This suggests that participants were already choosing revealing locations on day 1 with near-asymptotic efficiency.DOI:http://dx.doi.org/10.7554/eLife.12215.015

Mentions: (A) Cumulative information gain of an ideal observer (matched to participants’ prior bias and perceptual noise) with different revealing strategies (black, green, and blue) and participants’ own revealings (red). Data are represented as mean SEM across trials. Figure 5—figure supplement 1 shows a measure of efficiency extracted from these information curves across sessions. (B) Information gains for three heuristic strategies (See text for details, and Materials and methods): posterior-independent & order-dependent fixations (orange), posterior-dependent & order-independent fixations (purple), and posterior- & order-dependent fixations (brown). The information gain curves for the three heuristics overlap in all cases. Participants’ active revealings (red lines, as in A) were 1.81 (95% CI, 1.68–1.94), 1.85 (95% CI, 1.72–1.99), and 1.92 (95% CI, 1.74–2.04) times more efficient in gathering information than these heuristics, respectively. Data are represented as mean SEM across trials.


Active sensing in the categorization of visual patterns.

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

Relative efficiency across free-scan sessions.Relative efficiency computed as the ratio of the scale parameter as (see Materials and methods) of each session to that of the last session. Circles and lines show mean and SD obtained by bootstrapping trials within a session 50 times. The revealings on day 2-3 (free-scan sessions) were 0.90–1.04 (across participants) times as efficient compared to day 1 revealings (free-scan familiarization sessions). This suggests that participants were already choosing revealing locations on day 1 with near-asymptotic efficiency.DOI:http://dx.doi.org/10.7554/eLife.12215.015
© Copyright Policy
Related In: Results  -  Collection

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

fig5s1: Relative efficiency across free-scan sessions.Relative efficiency computed as the ratio of the scale parameter as (see Materials and methods) of each session to that of the last session. Circles and lines show mean and SD obtained by bootstrapping trials within a session 50 times. The revealings on day 2-3 (free-scan sessions) were 0.90–1.04 (across participants) times as efficient compared to day 1 revealings (free-scan familiarization sessions). This suggests that participants were already choosing revealing locations on day 1 with near-asymptotic efficiency.DOI:http://dx.doi.org/10.7554/eLife.12215.015
Mentions: (A) Cumulative information gain of an ideal observer (matched to participants’ prior bias and perceptual noise) with different revealing strategies (black, green, and blue) and participants’ own revealings (red). Data are represented as mean SEM across trials. Figure 5—figure supplement 1 shows a measure of efficiency extracted from these information curves across sessions. (B) Information gains for three heuristic strategies (See text for details, and Materials and methods): posterior-independent & order-dependent fixations (orange), posterior-dependent & order-independent fixations (purple), and posterior- & order-dependent fixations (brown). The information gain curves for the three heuristics overlap in all cases. Participants’ active revealings (red lines, as in A) were 1.81 (95% CI, 1.68–1.94), 1.85 (95% CI, 1.72–1.99), and 1.92 (95% CI, 1.74–2.04) times more efficient in gathering information than these heuristics, respectively. Data are represented as mean SEM across trials.

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.