Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors

E.G. Rowe, N. Tsuchiya, M.I. Garrido
doi: https://doi.org/10.1101/832386
E.G. Rowe
Queensland Brain Institute, University of QueenslandCentre for Advanced Imaging, University of QueenslandSchool of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash UniversityTurner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: elise.rowe@monash.edu
N. Tsuchiya
School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash UniversityTurner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, AustraliaCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Suita, Osaka 565-0871, JapanAdvanced Telecommunications Research Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
M.I. Garrido
Queensland Brain Institute, University of QueenslandCentre for Advanced Imaging, University of QueenslandARC Centre of Excellence for Integrative Brain FunctionMelbourne School of Psychological Sciences, The University of Melbourne
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Detecting changes in the environment is fundamental for survival, as these may indicate potential rewards or threats. According to predictive coding theory, detecting these irregularities relies on both incoming sensory information and our prior beliefs; with incongruity between the two manifesting as a prediction error (PE) response. Many changes occurring in our environment do not pose any threat and may go unnoticed. Such subtle changes can nevertheless be detected by the brain without ever emerging into consciousness. What remains unclear is how such sensory changes are processed in the brain at the network-level. Here, we developed a visual oddball paradigm, where participants engaged in a central letter task during electroencephalographic (EEG) recordings while presented with task-irrelevant (and unreported) high- or low-coherence background random dot motion. Critically, once in a while, the direction of the dots changed. After the EEG session, we behaviourally confirmed that changes in motion direction at high- and low-coherence were visible and invisible, respectively. ERP analyses revealed that changes in motion direction elicited PE regardless of visibility of such changes but with distinct spatiotemporal patterns. To better understand these responses at the network-level, we applied Dynamic Causal Modelling (DCM) to the EEG data. Bayesian Model Averaging analysis of the DCMs showed that both visible and invisible PE relied on a release from adaptation (or repetition suppression) within sensory areas of V1 and MT. Furthermore, while feedforward upregulation was present for invisible PE, results for the visible change PE also included downregulation of feedback connections between right MT to V1. Overall, our findings reveal a complex interplay of modulation in causal network underlying visible and invisible motion changes.

Footnotes

  • Conflict of Interest: The authors declare no competing financial interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted November 07, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors
E.G. Rowe, N. Tsuchiya, M.I. Garrido
bioRxiv 832386; doi: https://doi.org/10.1101/832386
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors
E.G. Rowe, N. Tsuchiya, M.I. Garrido
bioRxiv 832386; doi: https://doi.org/10.1101/832386

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (1641)
  • Biochemistry (2721)
  • Bioengineering (1902)
  • Bioinformatics (10200)
  • Biophysics (4173)
  • Cancer Biology (3202)
  • Cell Biology (4522)
  • Clinical Trials (135)
  • Developmental Biology (2831)
  • Ecology (4444)
  • Epidemiology (2040)
  • Evolutionary Biology (7213)
  • Genetics (5462)
  • Genomics (6792)
  • Immunology (2377)
  • Microbiology (7462)
  • Molecular Biology (2977)
  • Neuroscience (18524)
  • Paleontology (135)
  • Pathology (472)
  • Pharmacology and Toxicology (776)
  • Physiology (1147)
  • Plant Biology (2692)
  • Scientific Communication and Education (679)
  • Synthetic Biology (885)
  • Systems Biology (2839)
  • Zoology (465)