lecture13a-08 - Control of Attention and Gaze in Natural...

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Control of Attention and Gaze in Natural Environments
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Humans must select a limited subset of the available information in the environment. Fundamental Constraints Attention is limited. Visual Working Memory is limited. Only a limited amount of information can be retained. Evidence for this?
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Selecting information from visual scenes What controls the selection process?
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Image properties eg contrast, edges, chromatic saliency can account for some fixations when viewing images of scenes. Saliency - bottom-up
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Limitations of Saliency Models Important information may not be salient eg Stop signs in a cluttered environment. Salient information may not be important - eg retinal image transients from eye/body movements. Doesn’t account for many observed fixations, especially in natural behavior (eg Land etc).
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Natural vision is not the same as viewing pictures. Behavioral goals determine what information is needed. Task structure (often) allows interpretation of role of fixations. Need to Study Natural Behavior
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Foot placement Obstacle avoidance Heading Viewing pictures of scenes is different from acting within scenes. Top-down factors
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To what extent is the selection of information from scenes determined by cognitive goals (ie top-down) and how much by the stimulus itself (ie salient regions - bottom-up effects)?
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Modeling Top Down Control Virtual Humanoid has a small library of simple visual behaviors: Sidewalk Following Picking Up Blocks Avoiding Obstacles Each behavior uses a limited, task-relevant selection of visual information from scene. This is computationally efficient. Walter the Virtual Humanoid QuickTimeª and a YUV420 codec decompressor are needed to see this picture.
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Walter learns where/when to direct gaze using reinforcement learning algorithm. Walter’s sequence of fixations obstacles sidewalk litter
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Walter the Virtual Humanoid What about unexpected events?
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Dynamic Environments QuickTimeª and a YUV420 codec decompressor are needed to see this picture.
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How to deal with unexpected events? Efficient Top-down Can handle unexpected salient events Expensive Bottom-up Unexpected events Computational load
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Driving Simulator
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Gaze distribution is very different for different tasks Follow Follow +Stop Total Fixation Duration(%) 7.02 5.89 77.1 42 0.779 6.44 14.9 45.4 0.191 0.243  120 m   30 m  ROAD CAR SIDE INT BACK Time fixating Intersection.
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Any selective perceptual system must choose the right visual computations, and when to carry them out. How do we deal with the unpredictability of the natural world? Answer - it’s not all that unpredictable and we’re really good
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This note was uploaded on 08/12/2008 for the course PSY 341K taught by Professor Gilden during the Spring '08 term at University of Texas.

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lecture13a-08 - Control of Attention and Gaze in Natural...

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