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Action selection is important for species survival. The basal ganglia, a subcortical structure, has long been thought to play a crucial role in action selection and movement initiation. Classical theories suggest that an important role of the striatum, the input region of the basal ganglia, is to select actions to be performed based on cortical projections carrying action information. However, thanks to recent progress in neural recording techniques, new experimental evidence suggests that the striatum does not perform action selection. Rather, the striatum plays an advisory role. Thus the classical theories of the basal ganglia need to be revisited and revised. As a rst step, in this work we hypothesize a new computational role for the striatum. We present a network-level theory in which the striatum transforms cortical action bids into action evaluations. Based on the region’s neural circuitry, we theorize that the role of the striatum is to transform bids to action values that are normalized, contrast-enhanced, orthogonalized, and encoded as continuous values through the use of two separate neuron populations with bipolar tuning and both feedforward and collateral inhibitory mechanisms. We simulate our network and investigate the role of the network components in its dynamics. Finally, we compare the behavior of our network to previous literature on decision-making behavior in rodents and primates. 

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty. 

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.