Computational Markers Show Specific Deficits for Dyslexia and ADHD in Complex Learning Settings
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| Title: | Computational Markers Show Specific Deficits for Dyslexia and ADHD in Complex Learning Settings |
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| Language: | English |
| Authors: | Yafit Gabay (ORCID |
| Source: | npj Science of Learning. 2025 10. |
| Availability: | Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/ |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Dyslexia, Attention Deficit Hyperactivity Disorder, Difficulty Level, Reinforcement, Learning, Foreign Countries, Inferences, Models, Computation |
| Geographic Terms: | Israel |
| Assessment and Survey Identifiers: | Raven Progressive Matrices |
| DOI: | 10.1038/s41539-025-00323-4 |
| ISSN: | 2056-7936 |
| Abstract: | The current study examined how individuals with neurodevelopmental disorders navigate the complexities of learning within multidimensional environments marked by uncertain dimension values and without explicit guidance. Participants engaged in a game-like complex reinforcement learning task in which the stimuli dimension determining reward remained undisclosed, necessitating that participants discover which dimension should be prioritized for detecting the maximum reward. For comparison, a control condition featuring a simple reinforcement learning task was included in which the predictive dimension was explicitly revealed. The findings showed that individuals with ADHD and dyslexia exhibited reduced performance across both tasks compared to their controls. Computational modeling revealed that relative to controls, participants with ADHD exhibited a markedly decreased ability to utilize demanding yet more optimal Bayesian inference strategies, whereas participants with dyslexia demonstrated heightened decay rates, indicating quicker discounting of recently learned associations. These findings illuminate different computational markers of neurodevelopmental disorders in naturalistic learning contexts. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/fq9um |
| Entry Date: | 2025 |
| Accession Number: | EJ1474299 |
| Database: | ERIC |
| Abstract: | The current study examined how individuals with neurodevelopmental disorders navigate the complexities of learning within multidimensional environments marked by uncertain dimension values and without explicit guidance. Participants engaged in a game-like complex reinforcement learning task in which the stimuli dimension determining reward remained undisclosed, necessitating that participants discover which dimension should be prioritized for detecting the maximum reward. For comparison, a control condition featuring a simple reinforcement learning task was included in which the predictive dimension was explicitly revealed. The findings showed that individuals with ADHD and dyslexia exhibited reduced performance across both tasks compared to their controls. Computational modeling revealed that relative to controls, participants with ADHD exhibited a markedly decreased ability to utilize demanding yet more optimal Bayesian inference strategies, whereas participants with dyslexia demonstrated heightened decay rates, indicating quicker discounting of recently learned associations. These findings illuminate different computational markers of neurodevelopmental disorders in naturalistic learning contexts. |
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| ISSN: | 2056-7936 |
| DOI: | 10.1038/s41539-025-00323-4 |
