In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning.

SUMMARY Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants’ dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants’ choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants’ focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error.

Reinforcement learning enables organisms to adjust their behavior in order to maximize rewards. Electrophysiological recordings of dopaminergic midbrain neurons have shown that they code the difference between actual and predicted rewards, i.e., the reward prediction error, in many species. This error signal is conveyed to both the striatum and cortical areas and is thought to play a central role in learning to optimize behavior. However, in human daily life rewards are diverse and often only indirect feedback is available. Here we explore the range of rewards that are processed by the dopaminergic system in human participants, and examine whether it is also involved in learning in the absence of explicit rewards. While results from electrophysiological recordings in humans are sparse, evidence linking dopaminergic activity to the metabolic signal recorded from the midbrain and striatum with functional magnetic resonance imaging (fMRI) is available. Results from fMRI studies suggest that the human ventral striatum (VS) receives valuation information for a diverse set of rewarding stimuli. These range from simple primary reinforcers such as juice rewards over abstract social rewards to internally generated signals on perceived correctness, suggesting that the VS is involved in learning from trial-and-error irrespective of the specific nature of provided rewards. In addition, we summarize evidence that the VS can also be implicated when learning from observing others, and in tasks that go beyond simple stimulus-action-outcome learning, indicating that the reward system is also recruited in more complex learning tasks.

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