Neural encoding of task-dependent errors during adaptive learning
Abstract
Effective learning requires using errors in a task-dependent manner, for example adjusting to errors that result from unpredicted environmental changes but ignoring errors that result from environmental stochasticity. Where and how the brain represents errors in a task-dependent manner and uses them to guide behavior are not well understood. We imaged the brains of human participants performing a predictive-inference task with two conditions that had different sources of errors. Their performance was sensitive to this difference, including more choice switches after fundamental changes versus stochastic fluctuations in reward contingencies. Using multi-voxel pattern classification, we identified task-dependent representations of error magnitude and past errors in posterior parietal cortex. These representations were distinct from representations of the resulting behavioral adjustments in dorsomedial frontal, anterior cingulate, and orbitofrontal cortex. The results provide new insights into how the human brain represents errors in a task-dependent manner and guides subsequent adaptive behavior.
Data availability
The fMRI dataset has been made available at OpenNeuro under the accession ds003170. Codes and behavioral dataset are available at Github (https://github.com/changhaokao/mvpa_changepoint_fmri).
Article and author information
Author details
Funding
National Institute of Mental Health (R01-MH098899)
- Joshua I Gold
- Joseph W Kable
National Science Foundation (1533623)
- Joshua I Gold
- Joseph W Kable
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All procedures were approved by University of Pennsylvania Internal Review Board. All participants provided informed consent before the experiment. (IRB Protocol # 816727).
Copyright
© 2020, Kao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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