Proceedings of the Annual Meeting of the Cognitive Science Society

The current study aimed to elucidate the contributions of thesubcortical basal ganglia to human language by adopting theview that these structures engage in a basic neurocomputationthat may account for its involvement across a wide range of lin-guistic phenomena. Specifically, we tested the hypothesis thatbasal ganglia reinforcement learning mechanisms may accountfor variability in semantic selection processes necessary forambiguity resolution. To test this, we used a biased homographlexical ambiguity priming task that allowed us to measure au-tomatic processes for resolving ambiguity towards high fre-quency word meanings. Individual differences in task perfor-mance were then related to indices of basal ganglia function-ing and reinforcement learning, which were used to group sub-jects by learning style: primarily from choosing positive feed-back (Choosers), primarily from avoiding negative feedback(Avoiders), and balanced participants who learned equally wellfrom both (Balanced). The pattern of results suggests that bal-anced individuals, whom learn from both positive and negativereward equally well, had significantly lower access to the sub-ordinate homograph word meaning. Choosers and Avoiders,on the other hand, had higher access to the subordinate wordmeaning even after a long delay between prime and target. Ex-perimental findings were then tested using an ACT-R compu-tational model of reinforcement learning that learns from bothpositive and negative feedback. Results from the computa-tional model confirm and extend the pattern of behavioral find-ings, and provide a reinforcement learning account of lexicalpriming processes in human linguistic abilities, where a dual-path reinforcement learning system is necessary for preciselymapping out word co-occurrence probabilities.