Jian Li

The authors employ the computational learning approach that is widely used in the striatum to examine the contributions of the amygdala, and find that these two structures have complementary roles in aversive learning. Although the human amygdala and striatum have both been implicated in associative learning, only the striatum's contribution has been consistently computationally characterized. Using a reversal learning task, we found that amygdala blood oxygen level–dependent activity tracked associability as estimated by a computational model, and dissociated it from the striatal representation of reinforcement prediction error. These results extend the computational learning approach from striatum to amygdala, demonstrating their complementary roles in aversive learning.

Influential reinforcement learning theories propose that prediction error signals in the brain's nigrostriatal system guide learning for trial-and-error decision-making. However, since different decision variables can be learned from quantitatively similar error signals, a critical question is: what is the content of decision representations trained by the error signals? We used fMRI to monitor neural activity in a two-armed bandit counterfactual decision task that provided human subjects with information about forgone and obtained monetary outcomes so as to dissociate teaching signals that update expected values for each action, versus signals that train relative preferences between actions (a policy). The reward probabilities of both choices varied independently from each other. This specific design allowed us to test whether subjects' choice behavior was guided by policy-based methods, which directly map states to advantageous actions, or value-based methods such as Q-learning, where choice policies are instead generated by learning an intermediate representation (reward expectancy). Behaviorally, we found human participants' choices were significantly influenced by obtained as well as forgone rewards from the previous trial. We also found subjects' blood oxygen level-dependent responses in striatum were modulated in opposite directions by the experienced and forgone rewards but not by reward expectancy. This neural pattern, as well as subjects' choice behavior, is consistent with a teaching signal for developing habits or relative action preferences, rather than prediction errors for updating separate action values.