title: Multifaceted aspects of chunking enable robust algorithms.
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 multimodal 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.
authors: Rehabilitation Institute of Chicago and Northwestern University, Chicago, Illinois; daniel.acuna@northwestern.edu.
authors: Daniel E
authors: DE
authors: Acuna
authors: University of California, Santa Barbara, California;
authors: Nicholas F
authors: NF
authors: Wymbs
authors: Center for Neuroscience and the Center for the Neural Basis of Cognition, University of Pittsburg, Pittsburgh, Pennsylvania; and.
authors: Chelsea A
authors: CA
authors: Reynolds
authors: University of Pittsburgh, Pittsburgh, Pennsylvania.
authors: Nathalie
authors: N
authors: Picard
authors: University of Pittsburgh, Pittsburgh, Pennsylvania.
authors: Robert S
authors: RS
authors: Turner
authors: University of Pittsburgh, Pittsburgh, Pennsylvania.
authors: Peter L
authors: PL
authors: Strick
authors: University of California, Santa Barbara, California;
authors: Scott T
authors: ST
authors: Grafton
authors: Rehabilitation Institute of Chicago and Northwestern University, Chicago, Illinois;
authors: Konrad P
authors: KP
authors: Kording
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D009068:Movement; D011214:Practice, Psychological; D011930:Reaction Time
keywords: discrete sequence production; learning; memory