What determines the speed of our decisions? Various models of decision-making have focused on perceptual evidence, past experience, and task complexity as important factors determining the degree of deliberation needed for a decision. Here, we build on a sequential sampling decision-making framework to develop a new model that captures a range of reaction time (RT) effects by accounting for both working memory and instrumental learning processes. The model captures choices and RTs at various stages of learning, and in learning environments with varying complexity. Moreover, the model generalizes from tasks with deterministic reward contingencies to probabilistic ones. The model succeeds in part by incorporating prior uncertainty over actions when modeling RT. This straightforward process model provides a parsimonious account of decision dynamics during instrumental learning and makes unique predictions about internal representations of action values.

Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning takes place in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning.

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments.. Instead, these aspects of instrumental behavior are assumed to be supported by the brain's executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

People often learn from the outcomes of their actions, even when these outcomes do not involve material rewards or punishments. How does our brain provide this flexibility? We combined behavior, computational modeling, and functional neuroimaging to probe whether learning from abstract novel outcomes harnesses the same circuitry that supports learning from familiar secondary reinforcers. Behavior and neuroimaging revealed that novel images can act as a substitute for rewards during instrumental learning, producing reliable reward-like signals in dopaminergic circuits. Moreover, we found evidence that prefrontal correlates of executive control may play a role in shaping flexible responses in reward circuits. These results suggest that learning from novel outcomes is supported by an interplay between high-level representations in prefrontal cortex and low-level responses in subcortical reward circuits. This interaction may allow for human reinforcement learning over arbitrarily abstract reward functions.

Traditional associative learning tasks focus on the formation of associations between salient events and arbitrary stimuli that predict those events. This is exemplified in cerebellar-dependent delay eyeblink conditioning, where arbitrary cues such as a tone or light act as conditioned stimuli (CSs) that predict aversive sensations at the cornea (unconditioned stimulus [US]). Here, we ask if a similar framework could be applied to another type of cerebellar-dependent sensorimotor learning - sensorimotor adaptation. Models of sensorimotor adaptation posit that the introduction of an environmental perturbation results in an error signal that is used to update an internal model of a sensorimotor map for motor planning. Here, we take a step toward an integrative account of these two forms of cerebellar-dependent learning, examining the relevance of core concepts from associative learning for sensorimotor adaptation. Using a visuomotor adaptation reaching task, we paired movement-related feedback (US) with neutral auditory or visual contextual cues that served as CSs. Trial-by-trial changes in feedforward movement kinematics exhibited three key signatures of associative learning: differential conditioning, sensitivity to the CS-US interval, and compound conditioning. Moreover, after compound conditioning, a robust negative correlation was observed between responses to the two elemental CSs of the compound (i.e. overshadowing), consistent with the additivity principle posited by theories of associative learning. The existence of associative learning effects in sensorimotor adaptation provides a proof-of-concept for linking cerebellar-dependent learning paradigms within a common theoretical framework.