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Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow.

Layton OW, Fajen BR - PLoS Comput. Biol. (2016)

Bottom Line: Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments.Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field.Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

ABSTRACT
Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model's heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

No MeSH data available.


Related in: MedlinePlus

Simulations of the differential motion and motion pooling models of self-motion in the presence of an object that moves independently of the observer over 1.5 sec.In (a-b), the object approaches the observer at 15° and 70°, respectively. In panel c, the object moves to the right while maintaining a fixed depth relative to the observer, and in panel d, the object retreats at a 56° angle. Heading estimates produced by the differential motion and motion pooling models are indicated by blue and gold, respectively. Colored bands surrounding each curve indicate ±1 standard error of the mean (SEM). In all conditions, the object started to the left of the observer’s heading. Error is defined as negative and positive for estimates that deviated from the heading direction in the direction opposite object motion or in the same direction as object motion, respectively. The orange, red, and green bars at the top of each subplot indicate the periods of time before, while, and after the object crosses the observer’s future path. Magenta horizontal dashed lines indicate mean human heading judgments from psychophysical data in similar circumstances, where heading is judged at the end of the trial (1.5 sec; error bars indicate ±1 SEM). The positions of the object FoE (orange dashed line) are 7.5° (a) and 35° (b).
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pcbi.1004942.g002: Simulations of the differential motion and motion pooling models of self-motion in the presence of an object that moves independently of the observer over 1.5 sec.In (a-b), the object approaches the observer at 15° and 70°, respectively. In panel c, the object moves to the right while maintaining a fixed depth relative to the observer, and in panel d, the object retreats at a 56° angle. Heading estimates produced by the differential motion and motion pooling models are indicated by blue and gold, respectively. Colored bands surrounding each curve indicate ±1 standard error of the mean (SEM). In all conditions, the object started to the left of the observer’s heading. Error is defined as negative and positive for estimates that deviated from the heading direction in the direction opposite object motion or in the same direction as object motion, respectively. The orange, red, and green bars at the top of each subplot indicate the periods of time before, while, and after the object crosses the observer’s future path. Magenta horizontal dashed lines indicate mean human heading judgments from psychophysical data in similar circumstances, where heading is judged at the end of the trial (1.5 sec; error bars indicate ±1 SEM). The positions of the object FoE (orange dashed line) are 7.5° (a) and 35° (b).

Mentions: In this section, we examine model estimates of heading during self-motion in the presence of moving objects that cross the observer’s future path while approaching (Fig 2A and 2B), maintaining a fixed-depth (Fig 2C), or retreating (Fig 2D). The blue and gold curves in each plot show the mean heading error over time for the differential motion and motion pooling models, respectively, with lighter shaded regions indicating ±1 SE. The color of the horizontal bar at the top of each subplot in Fig 2 indicates when the moving object is crossing the observer’s future path (red), as well as the portions of the trial before (orange) and after (green) crossing.


Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow.

Layton OW, Fajen BR - PLoS Comput. Biol. (2016)

Simulations of the differential motion and motion pooling models of self-motion in the presence of an object that moves independently of the observer over 1.5 sec.In (a-b), the object approaches the observer at 15° and 70°, respectively. In panel c, the object moves to the right while maintaining a fixed depth relative to the observer, and in panel d, the object retreats at a 56° angle. Heading estimates produced by the differential motion and motion pooling models are indicated by blue and gold, respectively. Colored bands surrounding each curve indicate ±1 standard error of the mean (SEM). In all conditions, the object started to the left of the observer’s heading. Error is defined as negative and positive for estimates that deviated from the heading direction in the direction opposite object motion or in the same direction as object motion, respectively. The orange, red, and green bars at the top of each subplot indicate the periods of time before, while, and after the object crosses the observer’s future path. Magenta horizontal dashed lines indicate mean human heading judgments from psychophysical data in similar circumstances, where heading is judged at the end of the trial (1.5 sec; error bars indicate ±1 SEM). The positions of the object FoE (orange dashed line) are 7.5° (a) and 35° (b).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4920404&req=5

pcbi.1004942.g002: Simulations of the differential motion and motion pooling models of self-motion in the presence of an object that moves independently of the observer over 1.5 sec.In (a-b), the object approaches the observer at 15° and 70°, respectively. In panel c, the object moves to the right while maintaining a fixed depth relative to the observer, and in panel d, the object retreats at a 56° angle. Heading estimates produced by the differential motion and motion pooling models are indicated by blue and gold, respectively. Colored bands surrounding each curve indicate ±1 standard error of the mean (SEM). In all conditions, the object started to the left of the observer’s heading. Error is defined as negative and positive for estimates that deviated from the heading direction in the direction opposite object motion or in the same direction as object motion, respectively. The orange, red, and green bars at the top of each subplot indicate the periods of time before, while, and after the object crosses the observer’s future path. Magenta horizontal dashed lines indicate mean human heading judgments from psychophysical data in similar circumstances, where heading is judged at the end of the trial (1.5 sec; error bars indicate ±1 SEM). The positions of the object FoE (orange dashed line) are 7.5° (a) and 35° (b).
Mentions: In this section, we examine model estimates of heading during self-motion in the presence of moving objects that cross the observer’s future path while approaching (Fig 2A and 2B), maintaining a fixed-depth (Fig 2C), or retreating (Fig 2D). The blue and gold curves in each plot show the mean heading error over time for the differential motion and motion pooling models, respectively, with lighter shaded regions indicating ±1 SE. The color of the horizontal bar at the top of each subplot in Fig 2 indicates when the moving object is crossing the observer’s future path (red), as well as the portions of the trial before (orange) and after (green) crossing.

Bottom Line: Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments.Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field.Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

ABSTRACT
Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model's heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

No MeSH data available.


Related in: MedlinePlus