Cargando…

A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns

Implicit learning (IL) occurs unconsciously and without intention. Perceptual fluency is the ease of processing elicited by previous exposure to a stimulus. It has been assumed that perceptual fluency is associated with IL. However, the role of perceptual fluency following IL has not been investigat...

Descripción completa

Detalles Bibliográficos
Autores principales: Schultz, Benjamin G., Stevens, Catherine J., Keller, Peter E., Tillmann, Barbara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783451/
https://www.ncbi.nlm.nih.gov/pubmed/24086461
http://dx.doi.org/10.1371/journal.pone.0075163
_version_ 1782285668614930432
author Schultz, Benjamin G.
Stevens, Catherine J.
Keller, Peter E.
Tillmann, Barbara
author_facet Schultz, Benjamin G.
Stevens, Catherine J.
Keller, Peter E.
Tillmann, Barbara
author_sort Schultz, Benjamin G.
collection PubMed
description Implicit learning (IL) occurs unconsciously and without intention. Perceptual fluency is the ease of processing elicited by previous exposure to a stimulus. It has been assumed that perceptual fluency is associated with IL. However, the role of perceptual fluency following IL has not been investigated in temporal pattern learning. Two experiments by Schultz, Stevens, Keller, and Tillmann demonstrated the IL of auditory temporal patterns using a serial reaction-time task and a generation task based on the process dissociation procedure. The generation task demonstrated that learning was implicit in both experiments via motor fluency, that is, the inability to suppress learned information. With the aim to disentangle conscious and unconscious processes, we analyze unreported recognition data associated with the Schultz et al. experiments using the sequence identification measurement model. The model assumes that perceptual fluency reflects unconscious processes and IL. For Experiment 1, the model indicated that conscious and unconscious processes contributed to recognition of temporal patterns, but that unconscious processes had a greater influence on recognition than conscious processes. In the model implementation of Experiment 2, there was equal contribution of conscious and unconscious processes in the recognition of temporal patterns. As Schultz et al. demonstrated IL in both experiments using a generation task, and the conditions reported here in Experiments 1 and 2 were identical, two explanations are offered for the discrepancy in model and behavioral results based on the two tasks: 1) perceptual fluency may not be necessary to infer IL, or 2) conscious control over implicitly learned information may vary as a function of perceptual fluency and motor fluency.
format Online
Article
Text
id pubmed-3783451
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-37834512013-10-01 A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns Schultz, Benjamin G. Stevens, Catherine J. Keller, Peter E. Tillmann, Barbara PLoS One Research Article Implicit learning (IL) occurs unconsciously and without intention. Perceptual fluency is the ease of processing elicited by previous exposure to a stimulus. It has been assumed that perceptual fluency is associated with IL. However, the role of perceptual fluency following IL has not been investigated in temporal pattern learning. Two experiments by Schultz, Stevens, Keller, and Tillmann demonstrated the IL of auditory temporal patterns using a serial reaction-time task and a generation task based on the process dissociation procedure. The generation task demonstrated that learning was implicit in both experiments via motor fluency, that is, the inability to suppress learned information. With the aim to disentangle conscious and unconscious processes, we analyze unreported recognition data associated with the Schultz et al. experiments using the sequence identification measurement model. The model assumes that perceptual fluency reflects unconscious processes and IL. For Experiment 1, the model indicated that conscious and unconscious processes contributed to recognition of temporal patterns, but that unconscious processes had a greater influence on recognition than conscious processes. In the model implementation of Experiment 2, there was equal contribution of conscious and unconscious processes in the recognition of temporal patterns. As Schultz et al. demonstrated IL in both experiments using a generation task, and the conditions reported here in Experiments 1 and 2 were identical, two explanations are offered for the discrepancy in model and behavioral results based on the two tasks: 1) perceptual fluency may not be necessary to infer IL, or 2) conscious control over implicitly learned information may vary as a function of perceptual fluency and motor fluency. Public Library of Science 2013-09-25 /pmc/articles/PMC3783451/ /pubmed/24086461 http://dx.doi.org/10.1371/journal.pone.0075163 Text en © 2013 Schultz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Schultz, Benjamin G.
Stevens, Catherine J.
Keller, Peter E.
Tillmann, Barbara
A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title_full A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title_fullStr A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title_full_unstemmed A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title_short A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns
title_sort sequence identification measurement model to investigate the implicit learning of metrical temporal patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783451/
https://www.ncbi.nlm.nih.gov/pubmed/24086461
http://dx.doi.org/10.1371/journal.pone.0075163
work_keys_str_mv AT schultzbenjaming asequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT stevenscatherinej asequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT kellerpetere asequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT tillmannbarbara asequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT schultzbenjaming sequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT stevenscatherinej sequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT kellerpetere sequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns
AT tillmannbarbara sequenceidentificationmeasurementmodeltoinvestigatetheimplicitlearningofmetricaltemporalpatterns