Humans Integrate Visual and Haptic Information in a Statistically Optimal Fashion.¶
Ernst, & Banks. (2002). Humans Integrate Visual and Haptic Information in a Statistically Optimal Fashion. Nature, 415(6870), 429-433.
Cited by¶
4 citations across 4 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Cue Integration
- Vision dominates audition for spatial localization because vision is spatially more precise; audition dominates vision for temporal-onset judgments because it is temporally more precise.
This sourceDemonstrates that human visual-haptic integration is a maximum-likelihood, inverse-variance-weighted combination yielding a lower-variance estimate, reweighting as cue reliability changes.
Supported in partVerified against the publisher's abstract
The abstract states a variance-minimizing maximum-likelihood combination of visual and haptic cues, but not the reciprocal-of-summed-precisions formula or the central-limit comparison.
“Here we propose that a general principle, which minimizes variance in the final estimate, determines the degree to which vision or haptics dominates.”
- Vision dominates audition for spatial localization because vision is spatially more precise; audition dominates vision for temporal-onset judgments because it is temporally more precise.
- Precision Weighting
- Computational cognitive science — predictive-coding models weight prediction errors by precision; attention is precision modulation of selected channels; multisensory integration follows precision-weighted Bayesian rules, as in visual-haptic integration.
This sourceDemonstrates that multisensory integration follows precision-weighted (inverse-variance) maximum-likelihood combination.
Supported in partVerified against the publisher's abstract
Ernst and Banks show visual-haptic cue combination resembles a variance-minimising maximum-likelihood integrator, supporting only the multisensory example.
“Thus, the nervous system seems to combine visual and haptic information in a fashion that is similar to a maximum-likelihood integrator.”
- Computational cognitive science — predictive-coding models weight prediction errors by precision; attention is precision modulation of selected channels; multisensory integration follows precision-weighted Bayesian rules, as in visual-haptic integration.
Mechanisms¶
- Bayesian Cue Integration Model
- Multimodal Fusion Tracker
- It earns its place wherever no single sense suffices and the channels fail at different times, so their combination covers each other's blind moments — the well-studied benefit of integrating cues weighted by their reliability
This sourceShows that visual and haptic cues are combined approximately as a maximum-likelihood estimate, with weights determined by each cue's reliability.
- It earns its place wherever no single sense suffices and the channels fail at different times, so their combination covers each other's blind moments — the well-studied benefit of integrating cues weighted by their reliability
Verification¶
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