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Full Access What the temporal dynamics of unimodal sensory estimation can tell us about statistically optimal multimodal integration

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What the temporal dynamics of unimodal sensory estimation can tell us about statistically optimal multimodal integration

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The brain combines noisy, redundant stimulus estimates (e.g., position of an object provided by vision and haptics) in proportion to their reliabilities. The mechanisms used by the brain to assess reliability (inverse of noise) of its estimates are, however, not fully understood. Most models make the untested assumption that internal estimates of a property respond rapidly to fluctuations arising from noise. We tested this assumption by employing a visual stimulus consisting of an array of flickering vertical lines distributed to produce an area of higher density which the subject had to track. This stimulus was similar to one that has been shown (Byrne and Henriques, 2013) to combine optimally with haptic estimates of position. The area of higher density was inconspicuously jumped left or right at varying time intervals before the subject indicated its location relative to a reference line. By looking for whether the jump affected subjects’ judgements, we could assess the time required for updating internal position estimates. Subjects’ responses showed that large jumps had little effect on position estimates until the post-jump stimulus was present for over 300 ms. At this time the estimate suddenly transitioned to a new location. Updating for smaller jumps proceeded more smoothly. These results imply that position estimates are maintained in an ‘attractor’, which takes time to shift when sensory information changes. This is inconsistent with current models of how reliability is computed by the brain because such models, which rely on population coding or temporal sampling, require estimates to fluctuate in lock-step with noise.

Affiliations: 1: Centre for Vision Research, York University, Canada

The brain combines noisy, redundant stimulus estimates (e.g., position of an object provided by vision and haptics) in proportion to their reliabilities. The mechanisms used by the brain to assess reliability (inverse of noise) of its estimates are, however, not fully understood. Most models make the untested assumption that internal estimates of a property respond rapidly to fluctuations arising from noise. We tested this assumption by employing a visual stimulus consisting of an array of flickering vertical lines distributed to produce an area of higher density which the subject had to track. This stimulus was similar to one that has been shown (Byrne and Henriques, 2013) to combine optimally with haptic estimates of position. The area of higher density was inconspicuously jumped left or right at varying time intervals before the subject indicated its location relative to a reference line. By looking for whether the jump affected subjects’ judgements, we could assess the time required for updating internal position estimates. Subjects’ responses showed that large jumps had little effect on position estimates until the post-jump stimulus was present for over 300 ms. At this time the estimate suddenly transitioned to a new location. Updating for smaller jumps proceeded more smoothly. These results imply that position estimates are maintained in an ‘attractor’, which takes time to shift when sensory information changes. This is inconsistent with current models of how reliability is computed by the brain because such models, which rely on population coding or temporal sampling, require estimates to fluctuate in lock-step with noise.

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/content/journals/10.1163/22134808-000s0090
2013-05-16
2016-12-04

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