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Learning and production of movement sequences: Behavioral, neurophysiological, and modeling perspectives
A wave of recent behavioral studies has generated a new wealth of parametric observations about serial order behavior. What was a trickle of neurophysiological studies has grown to a steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of...
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Published in: | Human movement science 2004-11, Vol.23 (5), p.699-746 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A wave of recent behavioral studies has generated a new wealth of parametric observations about serial order behavior. What was a trickle of neurophysiological studies has grown to a steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we review major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2
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N, discrete sequence production, and serial reaction time. These tasks populate a continuum from higher to lower degrees of internal control of sequential organization and probe important contemporary issues such as the nature of working-memory representations for sequential behavior, and the development and role of chunks in hierarchical control. The main movement classes reviewed are speech and keypressing, both involving small amplitude movements amenable to parametric study. A synopsis of serial order models, vis-à-vis major empirical findings leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been complemented by successful prediction of distinctively patterned electrophysiological recordings. In lateral prefrontal cortex, parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model – the N-STREAMS neural network model – exemplifies ongoing attempts to accommodate a broad range of both behavioral and neurobiological data within a CQ-consistent theory. |
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ISSN: | 0167-9457 1872-7646 |
DOI: | 10.1016/j.humov.2004.10.008 |