Yu-Chin Chiu, Jiefeng Jiang, Tobias Egner

A longstanding dichotomy in cognitive psychology and neuroscience pits controlled, top-down driven against associative, bottom-up driven behavior, where cognitive control processes allow us to override well-learned stimulus-response (S-R) associations. By contrast, some recent studies have raised the intriguing possibility of an integration between associative and controlled processing in the form of stimulus-control state (S-C) associations, the learned linkage of specific stimuli to particular control states, such as high attentional selectivity. The neural machinery mediating S-C learning remains poorly understood, however. Here, we combined functional neuroimaging (fMRI) with a recently developed Stroop protocol that allowed us to dissociate reductions in Stroop interference based on S-R learning from those based on S-C learning in humans. We modeled subjects' acquisition of S-C and S-R associations using an associative learning model and then employed trial-by-trial S-C and S-R prediction error (PE) estimates in model-based behavioral and fMRI analyses. We found that PE estimates derived from S-C and S-R associations accounted for the reductions in behavioral Stroop interference effects in the S-C and S-R learning conditions, respectively. Moreover, model-based fMRI analyses identified the caudate nucleus as the key structure involved in selectively updating stimulus-control state associations. Complementary analyses also revealed a greater reliance on parietal cortex when utilizing the learned S-R versus S-C associations to minimize Stroop interference. These results support the emerging view that generalizable control states can become associated with specific bottom-up cues, and they place the caudate nucleus of the dorsal striatum at the center of the neural stimulus-control learning machinery. Recent behavioral studies have demonstrated that control states, for instance, heightened attentional selectivity, can become directly associated with - and subsequently retrieved by - particular stimuli, thus breaking down the traditional dichotomy between top-down and bottom-up driven behavior. However, the neural mechanisms underlying this type of stimulus-control learning remain poorly understood. We therefore combined non-invasive human neuroimaging with a task that allowed us to dissociate the acquisition of stimulus-control associations from that of stimulus-response associations. The results revealed the caudate nucleus as the key brain structure involved in selectively driving stimulus-control learning. These data represent the first identification of the neural mechanisms of stimulus-specific control associations, and they significantly extend current conceptions of the type of learning processes mediated by the caudate.