English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Using the past to estimate sensory uncertainty

Beierholm, U., Rohe, T., Ferrari, A., Stegle, O., & Noppeney, U. (2020). Using the past to estimate sensory uncertainty. eLife, 1-42. doi:10.7554/eLife.54172.

Item is

Files

show Files

Locators

show
hide
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Beierholm, U, Author
Rohe, T1, Author           
Ferrari, A, Author
Stegle, O, Author           
Noppeney, U1, Author           
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: To form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.

In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discounting

Our results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

Details

show
hide
Language(s):
 Dates: 2019-082020-12
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.54172
eDoc: e54172
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: eLife
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 42 Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X