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Declarative Learning Mechanisms Support Declarative but Not Probabilistic Feedback-Based Learning in Children with
Asiya Gul1, Lauren S Baron1, Kelsey B Black1
1MGH Institute of Health Professions, Boston, MA 02129, USA.
Abstract:
Declarative and probabilistic feedback-based learning was evaluated in 8-12-year-old school-age children with developmental language disorder (DLD; n = 14) and age-matched children with typical development (TD; n = 15). Children performed a visual two-choice word-learning task and a visual probabilistic classification task while their electroencephalogram (EEG) was recorded non-invasively from the scalp. Behavioral measures of accuracy and response to feedback, and electrophysiological responses to feedback were collected and compared between the two groups. While behavioral data indicated poorer performance by children with DLD in both learning paradigms, and similar response patterns to positive and negative feedback, electrophysiological data highlighted processing patterns in the DLD group that differed by task. More specifically, in this group, feedback processing in the context of declarative learning, which is known to be dominated by the medial temporal lobe (MTL), was associated with enhanced N170, an event-related brain potential (ERP) associated with MTL activation. The N170 amplitude was found to be correlated with declarative task performance in the DLD group. During probabilistic learning, known to be governed by the striatal-based learning system, the feedback-related negativity (FRN) ERP, which is the product of the cortico-striatal circuit dominated feedback processing. Within the context of probabilistic learning, enhanced N170 was associated with poor learning in the TD group, suggesting that MTL activation during probabilistic learning disrupts learning. These results are interpreted within the context of a proposed feedback parity hypothesis suggesting that in children with DLD, the system that dominates learning (i.e., MTL during declarative learning and the striatum during probabilistic learning) dominates and supports feedback processing.
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