Poster No:
343
Submission Type:
Abstract Submission
Authors:
Yijun Chen1, Brylee Hawkins1, Hannah Puckett1, Katherine Sharp2, Andrea Lopez2, Dasa Zeithamova-Demircan3, Hua Xie2, Alyssa Verbalis2, Lauren Kenworthy2, Chandan Vaidya1
Institutions:
1Georgetown University, Washington DC, WA, 2Children’s National Medical Center, Washington DC, WA, 3University of Oregon, Eugene, OR
First Author:
Yijun Chen
Georgetown University
Washington DC, WA
Co-Author(s):
Andrea Lopez
Children’s National Medical Center
Washington DC, WA
Hua Xie
Children’s National Medical Center
Washington DC, WA
Introduction:
Learning concepts that generalize to novel information, such as categories, are important for adaptive functioning (Zeithamova et al., 2019). Generalization difficulties in autism spectrum disorders (ASD) are often attributed to a preference for detailed processing over integration or behavioral rigidity; whether this is due to atypical learning has received less attention (Happé & Frith, 2006; Harris et al., 2015; Mercado III et al., 2016). Although limited, studies of category learning in ASD reveal mixed results and provide little insight into why some children acquire generalizable category knowledge while others do not (Vanpaemel & Bayer, 2021). In this study, we examined individual variability in concept learning mechanisms in 43 adolescents with ASD (males=30, aged 14-18 years, M=16.07 ± 1.18) with normal intelligence (M=114 ± 12.1).
Methods:
Participants performed a perceptual category learning task (Bowman & Zeithamova, 2018) involving out-of-scanner feedback-based training followed by a no-feedback generalization phase coupled with fMRI. During training, participants categorized exemplars that differed in 2 features from a category prototype. During generalization, participants categorized new exemplars varying in similarity to the prototype, 0, 1, 2, and 3 features away. Two similarity-based computational models of hypothesized learning strategies were fit to individual generalization data, prototype, posited to rely on abstract category representations and exemplar, posited to rely on individual category members. The neural basis of these strategies was identified with two regressors that included modulation for each trial by prototype and exemplar model predictions.
Results:
Behavioral results revealed robust learning and generalization. Repeated measures ANOVA indicated linear increase in accuracy across training blocks (F (4, 164) = 47.4, p < 0.001) and generalization accuracy decreased linearly with reduced similarity to prototype (F (3, 123) = 29.4, p < 0.001). Model-based analyses showed that 86% of participants were best fit by the prototype model, indicating that most participants successfully relied on abstract category representations rather than individual exemplars.
ROI analyses of model-based regressors revealed significant prototype-related activation in the ventromedial prefrontal cortex and exemplar-related activation in the inferior parietal cortex. A whole-brain voxel-wise analysis identified prototype-related activation in a cluster in the right supramarginal gyrus (SMG, corrected p = 0.03), and activation in this cluster was positively associated with the mean reaction time during generalization (r = 0.42, p = 0.006), indicating that this region plays a role in engaging prototypical representations. Additionally, a cluster in the left primary sensorimotor region (corrected p = 0.02) showed activation linked to lower accuracy (r = -0.44, p = 0.01) and worse prototype model fit (r = 0.72, p < 0.001); furthermore, it exhibited higher activation in the low-accuracy group compared to high-accuracy groups (cutoff: median accuracy=0.8, p = 0.009).
Conclusions:
Results reveal heterogeneity in concept learning mechanisms in autistic adolescents. Neural correlates of prototype and exemplar mechanisms were similar to those noted in past studies with neurotypical adults, with the exception of SMG and sensorimotor involvement. These regions were associated with performance variability in speed and accuracy of generalization performance. Future research should examine whether these individual differences in learning mechanisms are associated with preference for local processing and behavioral rigidity.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Learning and Memory:
Implicit Memory
Learning and Memory Other
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Autism
Cognition
Computational Neuroscience
Cortex
Development
DISORDERS
FUNCTIONAL MRI
Learning
MRI
Other - concept learning
1|2Indicates the priority used for review
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Functional MRI
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Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
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Provide references using APA citation style.
Bowman, C. R., & Zeithamova, D. (2018). Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization. The Journal of Neuroscience, 38(10), 2605–2614. https://doi.org/10.1523/JNEUROSCI.2811-17.2018
Happé, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36, 5–25.
Harris, H., Israeli, D., Minshew, N., Bonneh, Y., Heeger, D. J., Behrmann, M., & Sagi, D. (2015). Perceptual learning in autism: Over-specificity and possible remedies. Nature Neuroscience, 18(11), 1574–1576.
Mercado III, E., Church, B. A., & Seccia, A. M. (2016). Commentary: Perceptual learning in autism: Over-specificity and possible remedies. Frontiers in Integrative Neuroscience, 10, 18.
Vanpaemel, W., & Bayer, J. (2021). Prototype-based category learning in autism: A review. Neuroscience & Biobehavioral Reviews, 127, 607–618. https://doi.org/10.1016/j.neubiorev.2021.05.016
Zeithamova, D., Mack, M. L., Braunlich, K., Davis, T., Seger, C. A., Van Kesteren, M. T., & Wutz, A. (2019). Brain mechanisms of concept learning. Journal of Neuroscience, 39(42), 8259–8266.
No