1. Neuroscience
Download icon

Neural mechanisms underlying expectation-dependent inhibition of distracting information

  1. Dirk van Moorselaar  Is a corresponding author
  2. Eline Lampers
  3. Elisa Cordesius
  4. Heleen A Slagter
  1. Vrije Universiteit Amsterdam, Netherlands
  2. University of Amsterdam, Netherlands
Research Article
  • Cited 6
  • Views 1,022
  • Annotations
Cite this article as: eLife 2020;9:e61048 doi: 10.7554/eLife.61048

Abstract

Predictions based on learned statistical regularities in the visual worldhave been shown to facilitate attention and goal-directed behavior by sharpening the sensory representation of goal-relevant stimuli in advance. Yet, how the brain learns to ignore predictable goal-irrelevant or distracting information is unclear.Here, we used EEG anda visual search task in which the predictability of a distractor’s location and/or spatial frequency was manipulated to determine how spatial and feature distractor expectations are neurally implemented and reduce distractor interference. We find that expected distractor features could not only be decoded pre-stimulus, but their representation differed from the representation of that same feature when part of the target. Spatial distractor expectations did not induce changes in preparatory neural activity, but a strongly reduced Pd, an ERP index of inhibition. These results demonstrate that neural effects of statistical learning critically depend on the task relevance and dimension (spatial, feature) of predictions

Data availability

All data are publicly available on OSF . Analysis scripts can be downloaded via GitHub

Article and author information

Author details

  1. Dirk van Moorselaar

    Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    For correspondence
    dirkvanmoorselaar@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0491-1317
  2. Eline Lampers

    Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  3. Elisa Cordesius

    Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  4. Heleen A Slagter

    Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4180-1483

Funding

H2020 European Research Council (679399)

  • Heleen A Slagter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The ethical committee of the Department of Psychology of the University of Amsterdam approved the study (2018-BC-9051), which was conformed to the Declaration of Helsinki.

Reviewing Editor

  1. Joy Geng

Publication history

  1. Received: July 14, 2020
  2. Accepted: December 14, 2020
  3. Accepted Manuscript published: December 15, 2020 (version 1)
  4. Version of Record published: December 23, 2020 (version 2)

Copyright

© 2020, van Moorselaar et al.

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

  • 1,022
    Page views
  • 138
    Downloads
  • 6
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    2. Structural Biology and Molecular Biophysics
    Johannes Elferich et al.
    Research Article Updated

    Mechanosensory transduction (MT), the conversion of mechanical stimuli into electrical signals, underpins hearing and balance and is carried out within hair cells in the inner ear. Hair cells harbor actin-filled stereocilia, arranged in rows of descending heights, where the tips of stereocilia are connected to their taller neighbors by a filament composed of protocadherin 15 (PCDH15) and cadherin 23 (CDH23), deemed the ‘tip link.’ Tension exerted on the tip link opens an ion channel at the tip of the shorter stereocilia, thus converting mechanical force into an electrical signal. While biochemical and structural studies have provided insights into the molecular composition and structure of isolated portions of the tip link, the architecture, location, and conformational states of intact tip links, on stereocilia, remains unknown. Here, we report in situ cryo-electron microscopy imaging of the tip link in mouse stereocilia. We observe individual PCDH15 molecules at the tip and shaft of stereocilia and determine their stoichiometry, conformational heterogeneity, and their complexes with other filamentous proteins, perhaps including CDH23. The PCDH15 complexes occur in clusters, frequently with more than one copy of PCDH15 at the tip of stereocilia, suggesting that tip links might consist of more than one copy of PCDH15 complexes and, by extension, might include multiple MT complexes.

    1. Neuroscience
    Thomas Akam et al.
    Research Article

    Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system’s design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour.