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Research Article

Role of the striatum in incidental learning of sound categories

View ORCID ProfileSung-Joo Lim, View ORCID ProfileJulie A. Fiez, and View ORCID ProfileLori L. Holt
  1. aDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213;
  2. bCenter for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213;
  3. cCenter for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213;
  4. dCenter for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260;
  5. eDepartment of Psychology, University of Pittsburgh, PA 15260

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PNAS March 5, 2019 116 (10) 4671-4680; first published February 19, 2019; https://doi.org/10.1073/pnas.1811992116
Sung-Joo Lim
aDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213;
bCenter for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213;
cCenter for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213;
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  • ORCID record for Sung-Joo Lim
  • For correspondence: sungj.m.lim@gmail.com
Julie A. Fiez
bCenter for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213;
cCenter for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213;
dCenter for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260;
eDepartment of Psychology, University of Pittsburgh, PA 15260
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  • ORCID record for Julie A. Fiez
Lori L. Holt
aDepartment of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213;
bCenter for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213;
cCenter for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213;
dCenter for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260;
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  • ORCID record for Lori L. Holt
  1. Edited by Richard N. Aslin, Haskins Laboratories, New Haven, CT, and approved January 18, 2019 (received for review July 17, 2018)

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    Fig. 1.

    Overview of the experimental approach. (A) Schematic illustration of the sound categories. Each alien was paired with four sound exemplars. Each exemplar was composed of an invariant low-frequency spectral peak (P1; dashed line) and one of the four possible higher-frequency spectral peaks (P2; either a gray or black solid line). (A, Upper) The four categories learned by the experimental group. (A, Lower) The categories learned by the control group. Each group experienced an identical pair of offset sound categories, defined by a single acoustic dimension (i.e., decreasing vs. increasing in frequency across time). Only the onset-sweep categories differed across groups. For the experimental group, onset categories were organized into two categories potentially linearly separable in a higher-dimensional perceptual space defined by the integration of multiple acoustic dimensions [i.e., steady-state (SS) frequency and P2 onset-frequency locus]. For the control group, onset-category exemplars were randomly drawn from this space (Methods). The scatterplots show the higher-dimensional relationships of the onset-sweep categories for each group, with color indicating the categories. (B) Illustration of the videogame. (B, Left) A game screenshot. Each alien creature appears from a consistent quadrant. (B, Right) A schematic depiction of a game trial structure with multiple events. On each trial, one alien appears and a single exemplar from the associated sound category is presented repeatedly. Participants must navigate the videogame to center the alien and take the correct gaming action (23). The trial length (appearance of each alien) depends upon how well participants play the game; thus, it is highly variable within and across participants.

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    Fig. 2.

    Behavioral performance. (A) Proportion correct categorization of offset- and onset-category exemplars in the posttest. Hashed bars indicate mean performance across the trained exemplars experienced in the videogame. Solid bars indicate mean performance for novel exemplars withheld from training (i.e., generalization) in the experimental (blue) and control (red) groups. (B) Group average in-scanner videogame performance, as measured by the highest game level achieved. (C) Correlation of the videogame performance (highest game level achieved) and categorization accuracy for the novel offset- and onset-category exemplars. Pearson’s correlation r values are shown. Dashed lines in A and C indicate chance level (0.25) performance. Error bars indicate ±1 SEM. See SI Appendix, Fig. S1 for further analysis of behavioral error pattern differences across the experimental and control groups.

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    Fig. 3.

    Activation of the striatum across the experimental and control groups during videogame training. (A) Regions within the striatum exhibiting a significant Group × Time Course effect in response to game audiovisual stimulus events. The highlighted regions are a union of active striatal clusters across the three functional runs (caudate body and putamen; SI Appendix, Table S1). (B) The average activation time course of the striatal regions active in videogame play. Shaded regions indicate ±1 SEM. Post hoc tests revealed greater BOLD activation in the experimental than control group (t25s > 2.23, Ps < 0.035 from 4.5 to 10.5 s from the start of each game trial).

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    Fig. 4.

    Relationship between behavioral measures and striatal activation. (A) Correlation between the activation of striatal clusters during game play and generalization of category learning to novel onset-category exemplars in an explicit posttraining categorization test (Left), and in-scanner videogame performance (Right). Data points and regression lines of the experimental and control groups are indicated by blue and red, respectively. β-Coefficient estimates indicate the peak activation of the striatum during game play shown in Fig. 3B. (B) Summary of the relationships among behavioral game performance, posttraining categorization, and striatal activation during game play. Pearson’s correlation r values are derived from an individual, post hoc correlation analysis following the significant Group × Striatal Activation interaction effect from a multiple linear regression analysis predicting corresponding behavioral performance measure.

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    Fig. 5.

    Functional connectivity between the posterior striatum and speech-selective left superior temporal sulcus tissue. (Left) l-STS tissue was identified as exhibiting greater BOLD activation for hearing speech compared with nonspeech sounds (i.e., speech: English words and syllables vs. nonspeech: semantically matched environmental sounds and sound exemplars from the videogame) in a separate localizer task before videogame training. The group-based speech > nonspeech contrast mask is shown in orange, and individually defined speech-selective ROIs are shown as blue spheres. (Right) Correlations between generalization of onset-category learning in the posttest and the extent of functional connectivity between the striatum to the localized l-STS ROIs on a single-subject basis. Behavioral categorization performance (y axis) represents generalization performance for categorizing novel onset-category exemplars. The bar graph (Inset) shows average connectivity for each group from the striatal seeds to individually defined speech-selective ROIs. The functional connectivity measure is expressed in Fisher’s z-correlation coefficient. The correlation r values are derived from post hoc correlation analyses, following upon a significant Group × Striatal Connectivity interaction effect from a multiple linear regression analysis. Error bars indicate ±1 SEM.

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Role of the striatum in incidental learning of sound categories
Sung-Joo Lim, Julie A. Fiez, Lori L. Holt
Proceedings of the National Academy of Sciences Mar 2019, 116 (10) 4671-4680; DOI: 10.1073/pnas.1811992116

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Role of the striatum in incidental learning of sound categories
Sung-Joo Lim, Julie A. Fiez, Lori L. Holt
Proceedings of the National Academy of Sciences Mar 2019, 116 (10) 4671-4680; DOI: 10.1073/pnas.1811992116
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