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.