Evidence for a general performance-monitoring system in the human brain

Ivan Zubarev*, Lauri Parkkonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
159 Downloads (Pure)

Abstract

Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action-monitoring system has not been tested directly. Here, we use concurrent EEG-MEG measurements and a probabilistic learning task to demonstrate that event-related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high- versus low-surprise negative feedback, and (d) erroneous versus correct brain-computer-interface output. We further show that these patterns originate from highly-overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.

Original languageEnglish
Pages (from-to)4322-4333
Number of pages12
JournalHuman Brain Mapping
Volume39
Issue number11
DOIs
Publication statusPublished - Nov 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Brain-computer interface
  • Electroencephalography
  • Error processing
  • Error-related negativity
  • Feedback-related negativity
  • Machine learning
  • Magnetoencephalography
  • Performance monitoring
  • Reward processing

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