Humans integrate visual and haptic information in a statistically optimal fashion

By Marc O. Ernst, M. Banks. Nature, 2002

4,833 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/246b9ed99e5b412051e50cfcd0c01bea5d183f59

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Bayesian just-so stories in psychology and neuroscience. (2012, background): “Various behavioral studies have demonstrated that human performance in such conditions is extremely similar to a model that optimally combines information from different modalities (e.g., Alais & Burr, 2004; Ernst & Banks, 2002).”
How Do Expectations Shape Perception? (2018, background): “Interestingly, human observers often approach such an ‘ideal observer’ ([102], but see [103]), inspiring the idea that the human perceptual system may implement (approximate) Bayesian inference [104].”
Cognitive computational neuroscience (2018, background): “A third and critically important class of cognitive model is that of Bayesian models (Box 3).(33,35,36,34,143,144,145,32) Bayesian inference provides an essential normative perspective on cognition.”
A computational perspective on the neural basis of multisensory spatial representations (2002, background): “For best performance, the statistical inference must assign greater weights to the most reliable cues, and adjust these weights according to the contex”
Bayesian integration in sensorimotor learning (2004, methodology): “On each trial one of four types of feedback (j0, jM, jL, j1) was displayed; the selection of the feedback was random, with the relative frequencies of the four types being (3, 1, 1, 1) respectively.”
Prospective Optimization (2014, background): “For example, in studies of visual depth perception, information was shown to combine according to its relative precision [24], [25], and likewise for vision and touch [26] or vision and hearing [27].”
Sources of uncertainty in intuitive physics (2012, methodology): “We could then calculate the mean and standard deviation of the posterior distribution using Bayesian cue combination (e.g., Ernst & Banks, 2002): !”
The Brain’s Tendency to Bind Audiovisual Signals Is Stable but Not General (2016, neutral): “The fact that differences in unisensory processing influence integration is already well established (Alais & Burr, 2004; Ernst & Banks, 2002; Rohe & Noppeney, 2015b); on the other hand, the binding tendency, which provides a true One possible source of between-subjects variability in cross-modal…”
Multisensory integration: current issues from the perspective of the single neuron (2008, neutral): “Often this is accomplished by weighting the various cues based on how much information they are likely to provide about a given even”
Bayesian inference with probabilistic population codes (2006, positive): “The goal of these simulations was to assess whether the mean and variance of the distributions encoded in the output layer are consistent with optimal Bayesian inference (equations (2) and (3)).”
5G-Enabled Tactile Internet (2016, background): “In addition to being important requirement for non-haptic control, the auditory and visual feedbacks play a critical role in increasing the perceptual performance as the human brain naturally integrates different sensory modalities [29].”
Multisensory integration: flexible use of general operations (2014, neutral): “In the framework of the brain as a ‘‘Bayesian’’ estimator of the environment (Ernst and Banks, 2002; Fetsch et al., 2013; Knill and Pouget, 2004), uncertainty of sensory estimates will be minimized by combining multiple, independent measurements.”