1. Neuroscience
Download icon

Tracking prototype and exemplar representations in the brain across learning

  1. Caitlin R Bowman  Is a corresponding author
  2. Takako Iwashita
  3. Dasa Zeithamova  Is a corresponding author
  1. University of Oregon, United States
Research Article
  • Cited 1
  • Views 1,759
  • Annotations
Cite this article as: eLife 2020;9:e59360 doi: 10.7554/eLife.59360


There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Data availability

Raw MRI data have been deposited at openneuro.org/datasets/ds002813. Source data have been provided for Figures 3-6.

Article and author information

Author details

  1. Caitlin R Bowman

    Psychology, University of Oregon, Eugene, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5833-3591
  2. Takako Iwashita

    Psychology, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Dasa Zeithamova

    Psychology, University of Oregon, Eugene, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.


National Institute on Aging (F32-AG-054204)

  • Caitlin R Bowman

National Institute of Neurological Disorders and Stroke (R01-NS112366)

  • Dasa Zeithamova

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


Human subjects: All participants provided written informed consent, and Research Compliance Services at the University of Oregon approved all experimental procedures (approval code 10162014.010).

Reviewing Editor

  1. Morgan Barense, University of Toronto, Canada

Publication history

  1. Received: May 27, 2020
  2. Accepted: November 26, 2020
  3. Accepted Manuscript published: November 26, 2020 (version 1)
  4. Version of Record published: December 17, 2020 (version 2)


© 2020, Bowman 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.


  • 1,759
    Page views
  • 176
  • 1

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
    Casey Paquola et al.
    Tools and Resources Updated

    Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is ‘BigBrain’. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, ’BigBrainWarp’, that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

    1. Neuroscience
    Gabriella R Sterne et al.
    Tools and Resources Updated

    Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult Drosophila melanogaster, comprising approximately one third of all SEZ neurons. We characterize the single-cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.