Shared neural underpinnings of multisensory integration and trial-by-trial perceptual recalibration in humans
Abstract
Perception adapts to mismatching multisensory information, both when different cues appear simultaneously and when they appear sequentially. While both multisensory integration and adaptive trial-by-trial recalibration are central for behavior, it remains unknown whether they are mechanistically linked and arise from a common neural substrate. To relate the neural underpinnings of sensory integration and recalibration, we measured whole-brain magnetoencephalography while human participants performed an audio-visual ventriloquist task. Using single-trial multivariate analysis, we localized the perceptually-relevant encoding of multisensory information within and between trials. While we found neural signatures of multisensory integration within temporal and parietal regions, only medial superior parietal activity encoded past and current sensory information and mediated the perceptual recalibration within and between trials. These results highlight a common neural substrate of sensory integration and perceptual recalibration, and reveal a role of medial parietal regions in linking present and previous multisensory evidence to guide adaptive behavior.
Data availability
The behavioral data presented in Figure 1 and LDA performance data and source regression data used to calculate the t-values in Figures 2-5, as well as data for Figure 4-figure supplement 1 have been deposited on Dryad (https://dx.doi.org/10.5061/dryad.t0p9c93).
-
Data from: Shared neural underpinnings of multisensory integration and trial-by-trial perceptual recalibration in humansDryad Digital Repository, doi:10.5061/dryad.t0p9c93.
Article and author information
Author details
Funding
H2020 European Research Council (ERC-2014-CoG No 646657)
- Christoph Kayser
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All participants submitted written informed consent. The study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee. Ethics Application No: 300140078 (College of Science and Engineering, University of Glasgow).
Copyright
© 2019, Park & Kayser
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,350
- views
-
- 400
- downloads
-
- 44
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.
-
- Neuroscience
Mice can generate a cognitive map of an environment based on self-motion signals when there is a fixed association between their starting point and the location of their goal.