In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems. We consider the nature of the interactions between these systems and show how these interactions can lead to either adaptive or maladaptive behavioral outcomes. We then review evidence that an additional system guides inference concerning the hidden states of other agents, such as their beliefs, preferences, and intentions, in a social context. We also describe emerging evidence for an arbitration mechanism between model-based and model-free reinforcement learning, placing such a mechanism within the broader context of the hierarchical control of behavior.

These results indicate that a relatively short-term intervention program can produce measurable improvements in the face recognition skills of children with autism. As a treatment for face processing deficits, the Let's Face It! program has advantages of being cost-free, adaptable to the specific learning needs of the individual child and suitable for home and school applications.

Summary Humans exhibit a preference for options they have freely chosen over equally valued options they have not; however, the neural mechanism that drives this bias and its functional significance have yet to be identified. Here, we propose a model in which choice biases arise due to amplified positive reward prediction errors associated with free choice. Using a novel variant of a probabilistic learning task, we show that choice biases are selective to options that are predominantly associated with positive outcomes. A polymorphism in DARPP-32, a gene linked to dopaminergic striatal plasticity and individual differences in reinforcement learning, was found to predict the effect of choice as a function of value. We propose that these choice biases are the behavioral byproduct of a credit assignment mechanism responsible for ensuring the effective delivery of dopaminergic reinforcement learning signals broadcast to the striatum.

Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience, and machine learning. Interactions between these fields, as promoted through the common hub of RL, has facilitated paradigm shifts relating multiple levels of analysis within a singular framework (e.g dopamine function). Recently, more sophisticated RL algorithms have been incorporated to better account for human learning, and in particular its oft documented reliance on two separable systems. However, along with many benefits, this dichotomous lens can distort questions, and may contribute to an unnecessarily narrow perspective on learning and decision making. Here we outline some of the consequences that come from over-confidently mapping algorithms, such as model-based vs. model-free RL, with putative cognitive processes. We argue that the field is well positioned to move beyond simplistic dichotomies, and we propose a means of re-focusing research questions toward the rich and complex components that comprise learning and decision making.