Multifaceted aspects of chunking enable robust algorithms

Daniel E. Acuna*, Nicholas F. Wymbs, Chelsea A. Reynolds, Nathalie Picard, Robert S. Turner, Peter L. Strick, Scott T. Grafton, Konrad P. Kording

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Sequence production tasks are a standard tool to analyze motor learning, consolidation, and habituation. As sequences are learned, movements are typically grouped into subsets or chunks. For example, most Americans memorize telephone numbers in two chunks of three digits, and one chunk of four. Studies generally use response times or error rates to estimate how subjects chunk, and these estimates are often related to physiological data. Here we show that chunking is simultaneously reflected in reaction times, errors, and their correlations. This multi-modal structure enables us to propose a Bayesian algorithm that better estimates chunks while avoiding overfitting. Our algorithm reveals previously unknown behavioral structure, such as an increased error correlations with training, and promises a useful tool for the characterization of many forms of sequential motor behavior.

Original languageEnglish (US)
Pages (from-to)1849-1856
Number of pages8
JournalJournal of neurophysiology
Volume112
Issue number8
DOIs
StatePublished - Oct 15 2014

Keywords

  • Discrete sequence production
  • Learning
  • Memory

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology

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