Anterograde interference emerges along a gradient as a function of task similarity: A behavioural study

Autor: Raphaël Hamel, Jean‐François Lepage, Pierre‐Michel Bernier
Rok vydání: 2021
Předmět:
Zdroj: European Journal of Neuroscience. 55:49-66
ISSN: 1460-9568
0953-816X
Popis: Anterograde interference emerges when two opposite (B → A) or identical tasks (A → A) are learned in close temporal succession, suggesting that interference cannot be fully accounted for by competing memories. Informed by neurobiological evidence, this work tested the hypothesis that interference depends upon the degree of overlap between the neural networks involved in the learning of two tasks. In a fully within-subject and counterbalanced design, participants (n = 24) took part in two learning sessions where the putative overlap between learning-specific neural networks was behaviourally manipulated across four conditions by modifying reach direction and the effector used during gradual visuomotor adaptation. The results showed that anterograde interference emerged regardless of memory competition-that is, to a similar extent in the B → A and A → A conditions-and along a gradient as a function of the tasks' similarity. Specifically, learning under similar reaching conditions generated more anterograde interference than learning under dissimilar reaching conditions, suggesting that putatively overlapping neural networks are required to generate interference. Overall, these results indicate that competing memories are not the sole contributor to anterograde interference and suggest that overlapping neural networks between two learning sessions are required to trigger interference. One discussed possibility is that initial learning modifies the properties of its neural networks to constrain further plasticity induction and learning capabilities, therefore causing anterograde interference in a network-dependent manner. One implication is that learning-specific neural networks must be maximally dissociated to minimize the interfering influences of previous learning on subsequent learning.
Databáze: OpenAIRE
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