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Category structure guides the formation of neural representations

Abstract

Perceptual variability is often viewed as having multiple benefits in object learning and categorization. Despite the abundant results demonstrating benefits such as increased transfer of knowledge, the neural mechanisms underlying variability as well as the developmental trajectories of how variability precipitates changes to category boundaries are unknown. By manipulating an individual’s exposure to variability of novel, metrically organized categories during an fMRI-adaptation paradigm, we were able to assess the functional differences between similarity and variability in category learning and generalization across two time-points in development: adulthood (n = 14) and late childhood (n = 13). During this study, participants were repeatedly exposed to category members from different distributions. After a period of adaptation, a deviant stimulus that differed from the expected distribution was then presented. This deviant differed in either an invariant dimension (a feature that remained consistent throughout presentation was altered) or a similarity dimension (a feature that changed throughout exposure was changed in a new dimension). Our results can be summarized in three main findings: (1) Variability during exposure recruited the right fusiform gyrus to a greater extent than tight exposure. (2) Deviant items were generalized based on the exemplar distributions during exposure, although children only generalized items if provided variable exposure. (3) Variability influenced release to a greater extent in children than adults. These results are discussed in relation to the variability and category learning literature more broadly.

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Data availability

Data, stimuli, and other materials are available upon reasonable request from the authors. Interested parties can e-mail requests to the corresponding author.

Code availability

Code pertaining to the experimental protocol is also available upon reasonable request from the authors.

Notes

  1. 1.

    Of the included children, two children only contributed data from two functional runs due to excessive motion. For all children, this useable data was for separate categories and category structures, thus every participant contributed at least 1 useable run in each exposure condition. This resulted in 48 (24 tight and 24 variable) useable runs for children compared to 56 (28 tight and 28 variable) useable runs for adults.

  2. 2.

    To keep an equal number of variable and tight runs and an equal number of runs for each category, runs were grouped into pairs (Tight Flower and Far Alien; Tight Alien and Far Flower). Both runs in a pair were excluded if one of the runs had more than five spikes or they had a combined total of more than eight spikes. No adults had no motion spikes. Children had an average of 1.9 spikes on variable runs and an average of 1.6 spikes on tight runs.

  3. 3.

    Note that in many studies using similar category designs, the invariant feature is referred to as the rule (see Deng and Sloutsky, 2015 and 2016). However, because the categories were not explicitly taught, and because the design is ultimately agnostic to whether invariant deviants do or do not belong within the category, we believe “invariant deviants” to be a more accurate description of these items in this study.

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Acknowledgements

We thank members of the Imaging Research Facility at Indiana University for conversations and suggestions concerning study procedures and the CAN Lab research assistants involved in this project. This project was supported by National Institute of Health 2 T32 Grant # HD 007475-21 and by the Indiana University Office of the Vice President for Research Emerging Area of Research Initiative, Learning: Brains, Machines, and Children. No funding sources were involved in the study design, analysis, or interpretation of the data, in the writing of this article, or in the decision to submit this article for publication.

Funding

This project was supported by National Institute of Health 2 T32 Grant # HD 007475-21 and by the Indiana University Office of the Vice President for Research Emerging Area of Research Initiative, Learning: Brains, Machines, and Children.

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All authors contributed to the study conception, design, data analysis, and writing. Material preparation and data collection were performed by DP. KJ is the PI of the laboratory, supervised the other author and funded the project. Both authors read and approved the final manuscript.

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Correspondence to Karin H. James.

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Communicated by Winston D Byblow.

Appendix 1

Appendix 1

The metric changes were as follows: Within the alien similarity space, the metric changes were as follows: the eyestalk changed 0.08 inches per step, the surface area changed by adjusting the width of the body approximately 0.08 inches and the height approximately 0.07 inches, the arms changed 5° per step. Within the flower similarity space, the stems changed the zigzag function in adobe illustrator 5%, the petals were rounded 5°, the gradient of the center changed 5%. The metric nature of our categories resulted in numerical differences in our exposure types. The tight variability consisted of a similarity space that could only vary a maximum of one value per each of the three dimensions in the similarity space. Thus, potential values included only values 8 through 10 on each of the 3 dimensions. The tight exposure included a distribution of stimuli that varied from the central stimulus (stimulus 9-9-9) an average of 2.05 steps, with the average of each dimension in the similarity space being (9.01, 8.89, 9.06). For the variable space, the similarity space could vary a maximum of five values per each of the three dimensions in the similarity space. Therefore, potential values included 4–14 on each dimension. This distribution varied from the central stimulus an average of 8.25 steps, and the average of the stimulus space was (8.17, 8.52, 8.96).

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Plebanek, D.J., James, K.H. Category structure guides the formation of neural representations. Exp Brain Res 239, 1667–1684 (2021). https://doi.org/10.1007/s00221-021-06088-7

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Keywords

  • Variability
  • Category learning
  • Development
  • Neural adaptation
  • FMRI