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Bayesian Alternation during Tactile Augmentation
A large number of studies suggest that the integration of multisensory signals by humans is well-described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are ab...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054009/ https://www.ncbi.nlm.nih.gov/pubmed/27774057 http://dx.doi.org/10.3389/fnbeh.2016.00187 |
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author | Goeke, Caspar M. Planera, Serena Finger, Holger König, Peter |
author_facet | Goeke, Caspar M. Planera, Serena Finger, Holger König, Peter |
author_sort | Goeke, Caspar M. |
collection | PubMed |
description | A large number of studies suggest that the integration of multisensory signals by humans is well-described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study, we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC) task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition), rotation only (native condition), and both augmented and native information (bimodal condition). Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants' responses with a probit model and calculated the just notable difference (JND). Then, we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χ(red)(2) = 1.67) than the Bayesian integration model (χ(red)(2) = 4.34). Slightly higher accuracy showed a non-Bayesian winner takes all (WTA) model (χ(red)(2) = 1.64), which either used only native or only augmented values per subject for prediction. However, the performance of the Bayesian alternation model could be substantially improved (χ(red)(2) = 1.09) utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in untrained humans is combined via a subjective Bayesian alternation process. Therefore, we conclude that behavior in our bimodal condition is explained better by top down-subjective weighting than by bottom-up weighting based upon objective cue reliability. |
format | Online Article Text |
id | pubmed-5054009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50540092016-10-21 Bayesian Alternation during Tactile Augmentation Goeke, Caspar M. Planera, Serena Finger, Holger König, Peter Front Behav Neurosci Neuroscience A large number of studies suggest that the integration of multisensory signals by humans is well-described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study, we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC) task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition), rotation only (native condition), and both augmented and native information (bimodal condition). Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants' responses with a probit model and calculated the just notable difference (JND). Then, we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χ(red)(2) = 1.67) than the Bayesian integration model (χ(red)(2) = 4.34). Slightly higher accuracy showed a non-Bayesian winner takes all (WTA) model (χ(red)(2) = 1.64), which either used only native or only augmented values per subject for prediction. However, the performance of the Bayesian alternation model could be substantially improved (χ(red)(2) = 1.09) utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in untrained humans is combined via a subjective Bayesian alternation process. Therefore, we conclude that behavior in our bimodal condition is explained better by top down-subjective weighting than by bottom-up weighting based upon objective cue reliability. Frontiers Media S.A. 2016-10-07 /pmc/articles/PMC5054009/ /pubmed/27774057 http://dx.doi.org/10.3389/fnbeh.2016.00187 Text en Copyright © 2016 Goeke, Planera, Finger and König. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Goeke, Caspar M. Planera, Serena Finger, Holger König, Peter Bayesian Alternation during Tactile Augmentation |
title | Bayesian Alternation during Tactile Augmentation |
title_full | Bayesian Alternation during Tactile Augmentation |
title_fullStr | Bayesian Alternation during Tactile Augmentation |
title_full_unstemmed | Bayesian Alternation during Tactile Augmentation |
title_short | Bayesian Alternation during Tactile Augmentation |
title_sort | bayesian alternation during tactile augmentation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054009/ https://www.ncbi.nlm.nih.gov/pubmed/27774057 http://dx.doi.org/10.3389/fnbeh.2016.00187 |
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