Objectives: According to the Procedural Deficit Hypothesis, abnormalities in corticostriatal pathways could account for the language-related deficits observed in developmental dyslexia. The same neural network has also been implicated in the ability to learn contingencies based on trial and error (i.e., reinforcement learning [RL]). On this basis, the present study tested the assumption that dyslexic individuals would be impaired in RL compared with neurotypicals in two different tasks. Methods: In a probabilistic selection task, participants were required to learn reinforcement contingencies based on probabilistic feedback. In an implicit transitive inference task, participants were also required to base their decisions on reinforcement histories, but feedback was deterministic and stimulus pairs were partially overlapping, such that participants were required to learn hierarchical relations. Results: Across tasks, results revealed that although the ability to learn from positive/negative feedback did not differ between the two groups, the learning of reinforcement contingencies was poorer in the dyslexia group compared with the neurotypicals group. Furthermore, in novel test pairs where previously learned information was presented in new combinations, dyslexic individuals performed similarly to neurotypicals. Conclusions: Taken together, these results suggest that learning of reinforcement contingencies occurs less robustly in individuals with developmental dyslexia. Inferences for the neuro-cognitive mechanisms of developmental dyslexia are discussed.
|Number of pages||11|
|Journal||Journal of the International Neuropsychological Society|
|State||Published - 7 Mar 2022|
Bibliographical noteFunding Information:
This research was supported by a grant from the National Institute of Psychobiology in Israel to YG (2111819). We would like to thank Prof. Michael Frank for providing the PS and TI tasks. The authors declare no conflict of interest.
Copyright © INS.
- Developmental dyslexia
- Implicit transitive inference task
- Probabilistic selection task
- Procedural learning
- Reinforcement learning