title: Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments.
abstract: 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.
authors.affiliation: Department of Psychology, Stanford University, Stanford, CA 94305, USA. Electronic address: ycleong@stanford.edu.
authors.forename: Yuan Chang
authors.initials: YC
authors.lastname: Leong
authors.affiliation: Department of Psychology, Princeton University, Princeton, NJ 08544, USA. Electronic address: angelar@princeton.edu.
authors.forename: Angela
authors.initials: A
authors.lastname: Radulescu
authors.affiliation: Dstillery, New York, NY 10016, USA.
authors.forename: Reka
authors.initials: R
authors.lastname: Daniel
authors.affiliation: Trinity Partners, San Francisco, CA 94111, USA.
authors.forename: Vivian
authors.initials: V
authors.lastname: DeWoskin
authors.affiliation: Department of Psychology, Princeton University, Princeton, NJ 08544, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: yael@princeton.edu.
authors.forename: Yael
authors.initials: Y
authors.lastname: Niv
mesh_terms: D000293:Adolescent; D000328:Adult; D001288:Attention / Q000502:physiology*; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology; D001931:Brain Mapping; D002755:Choice Behavior; D003657:Decision Making / Q000502:physiology*; D004777:Environment; D053483:Eye Movement Measurements; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D008297:Male; D017072:Neostriatum / Q000000981:diagnostic imaging / Q000502:physiology*; D017397:Prefrontal Cortex / Q000000981:diagnostic imaging / Q000502:physiology*; D012054:Reinforcement, Psychology; D012201:Reward; D013647:Task Performance and Analysis; D055815:Young Adult
keywords: MVPA; attention; computational modeling; decision making; fMRI; prediction error; reinforcement learning; striatum; value; vmPFC