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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
Language: English
Authors: Yafit Gabay (ORCID 0000-0002-7899-3044), Lana Jacob, Atil Mansour (ORCID 0009-0002-3460-4788), Uri Hertz (ORCID 0000-0003-4852-3516)
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
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  Data: <searchLink fieldCode="AR" term="%22Yafit+Gabay%22">Yafit Gabay</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-7899-3044">0000-0002-7899-3044</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lana+Jacob%22">Lana Jacob</searchLink><br /><searchLink fieldCode="AR" term="%22Atil+Mansour%22">Atil Mansour</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0002-3460-4788">0009-0002-3460-4788</externalLink>)<br /><searchLink fieldCode="AR" term="%22Uri+Hertz%22">Uri Hertz</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4852-3516">0000-0003-4852-3516</externalLink>)
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  Data: 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/
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  Data: 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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