A compositional neural code in high-level visual cortex can explain jumbled word reading

  1. Aakash Agrawal
  2. KVS Hari
  3. SP Arun  Is a corresponding author
  1. Indian Institute of Science, Bangalore, India

Abstract

We read jubmled wrods effortlessly, but the neural correlates of this remarkable ability remain poorly understood. We hypothesized that viewing a jumbled word activates a visual representation that is compared to known words. To test this hypothesis, we devised a purely visual model in which neurons tuned to letter shape respond to longer strings in a compositional manner by linearly summing letter responses. We found that dissimilarities between letter strings in this model can explain human performance on visual search, and responses to jumbled words in word reading tasks. Brain imaging revealed that viewing a string activates this letter-based code in the lateral occipital (LO) region and that subsequent comparisons to stored words are consistent with activations of the visual word form area (VWFA). Thus, a compositional neural code potentially contributes to efficient reading.

Data availability

Data and code necessary to reproduce the results are available in an Open Science Framework repository at https://osf.io/384zw/

The following data sets were generated
    1. VisionLabIISc
    (2020) jumbledwordsfMRI
    Open Science Framework, 384zw.

Article and author information

Author details

  1. Aakash Agrawal

    Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Bangalore, India
    Competing interests
    The authors declare that no competing interests exist.
  2. KVS Hari

    Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, Bangalore, India
    Competing interests
    The authors declare that no competing interests exist.
  3. SP Arun

    Centre for Neuroscience, Indian Institute of Science, Bangalore, Bangalore, India
    For correspondence
    sparun@iisc.ac.in
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9602-5066

Funding

Wellcome Trust/DBT India Alliance (IA/S/17/1/503081)

  • SP Arun

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 subjects gave informed consent to an experimental protocol approved by the Institutional Human Ethics Committee of the Indian Institute of Science (IHEC # 6-15092017).

Reviewing Editor

  1. Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States

Publication history

  1. Received: January 2, 2020
  2. Accepted: May 4, 2020
  3. Accepted Manuscript published: May 5, 2020 (version 1)
  4. Accepted Manuscript updated: May 7, 2020 (version 2)
  5. Version of Record published: June 4, 2020 (version 3)

Copyright

© 2020, Agrawal 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

  • 3,199
    Page views
  • 346
    Downloads
  • 5
    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)

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)

  1. Aakash Agrawal
  2. KVS Hari
  3. SP Arun
(2020)
A compositional neural code in high-level visual cortex can explain jumbled word reading
eLife 9:e54846.
https://doi.org/10.7554/eLife.54846

Further reading

    1. Neuroscience
    Arefeh Sherafati et al.
    Research Article Updated

    Cochlear implants are neuroprosthetic devices that can restore hearing in people with severe to profound hearing loss by electrically stimulating the auditory nerve. Because of physical limitations on the precision of this stimulation, the acoustic information delivered by a cochlear implant does not convey the same level of acoustic detail as that conveyed by normal hearing. As a result, speech understanding in listeners with cochlear implants is typically poorer and more effortful than in listeners with normal hearing. The brain networks supporting speech understanding in listeners with cochlear implants are not well understood, partly due to difficulties obtaining functional neuroimaging data in this population. In the current study, we assessed the brain regions supporting spoken word understanding in adult listeners with right unilateral cochlear implants (n=20) and matched controls (n=18) using high-density diffuse optical tomography (HD-DOT), a quiet and non-invasive imaging modality with spatial resolution comparable to that of functional MRI. We found that while listening to spoken words in quiet, listeners with cochlear implants showed greater activity in the left prefrontal cortex than listeners with normal hearing, specifically in a region engaged in a separate spatial working memory task. These results suggest that listeners with cochlear implants require greater cognitive processing during speech understanding than listeners with normal hearing, supported by compensatory recruitment of the left prefrontal cortex.

    1. Neuroscience
    Mohammad Ali Salehinejad et al.
    Research Article Updated

    Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is not well understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. The results suggest that sleep deprivation upscales cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Finally, we show that learning and memory formation, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are impaired during sleep deprivation. Our data indicate that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.