Abstract
When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory–motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions.SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics.
Highlights
Making predictions about future events challenges us to extract structure from streams of sensory signals that initially appearReceived Jan. 17, 2017; revised May 18, 2017; accepted May 26, 2017
We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context versus matching the exact sequence statistics
We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits that support learning of the exact temporal statistics
Summary
Making predictions about future events challenges us to extract structure from streams of sensory signals that initially appearReceived Jan. 17, 2017; revised May 18, 2017; accepted May 26, 2017. Making predictions about future events challenges us to extract structure from streams of sensory signals that initially appear. P.T.), ZK from the Biotechnology and Biological Sciences Research Council (Grant H012508 to Z.K.), the Leverhulme Trust (Grant RF-2011-378 to Z.K.) and the European Community’s Seventh Framework Programme (Grant FP7/ 2007–2013 under agreement PITN-GA 2011-290011 to Z.K.), and the Wellcome Trust (Grant 095183/Z/10/Z to A.E.W.). We thank Caroline di Bernardi Luft for help with data collection and Matthew Dexter for help with software development. Wang et al Brain Mechanisms for Learning Temporal Structure Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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