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Article: Reliable detection for implicit waveform-specific learning in continuous tracking task paradigm

TitleReliable detection for implicit waveform-specific learning in continuous tracking task paradigm
Authors
Issue Date2017
PublisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2017, v. 7, p. 12333 How to Cite?
AbstractImplicit waveform-specific (IWS) learning during a typical continuous tracking task paradigm has been reported for decades, as evidenced by better tracking improvement on the repeated segment of a specific target waveform than random segments. However, the occurrence of the IWS learning in such a task paradigm has been challenged by several unsuccessful results in recent literature. This research concerns reliable detection of the induced IWS learning and to this end, proposes to use the similarity between the cursor and the target along the direction corresponding to the waveform pattern as the performance measure. A 3-day experiment designed with full examination on IWS learning including a practice phase, an immediate test phase and a consolidation test phase after 24 hours was conducted to validate the feasibility and sensitivity of the Pearson’s correlation coefficient on the vertical movement r v in this study. Experiment results indicate that r v is more sensitive in detecting the IWS learning in all phases compared to the conventional root mean square error (RMSE) performance measure. The findings confirm the importance of the performance measure in implicit learning research and the similarity measure in accordance with the waveform could be promising for waveform-specific learning detection in this paradigm.
Persistent Identifierhttp://hdl.handle.net/10722/248480
ISSN
2017 Impact Factor: 4.122
2015 SCImago Journal Rankings: 2.073
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYang, L-
dc.contributor.authorWan, F-
dc.contributor.authorNan, W-
dc.contributor.authorZhu, FF-
dc.contributor.authorHu, Y-
dc.date.accessioned2017-10-18T08:43:51Z-
dc.date.available2017-10-18T08:43:51Z-
dc.date.issued2017-
dc.identifier.citationScientific Reports, 2017, v. 7, p. 12333-
dc.identifier.issn2045-2322-
dc.identifier.urihttp://hdl.handle.net/10722/248480-
dc.description.abstractImplicit waveform-specific (IWS) learning during a typical continuous tracking task paradigm has been reported for decades, as evidenced by better tracking improvement on the repeated segment of a specific target waveform than random segments. However, the occurrence of the IWS learning in such a task paradigm has been challenged by several unsuccessful results in recent literature. This research concerns reliable detection of the induced IWS learning and to this end, proposes to use the similarity between the cursor and the target along the direction corresponding to the waveform pattern as the performance measure. A 3-day experiment designed with full examination on IWS learning including a practice phase, an immediate test phase and a consolidation test phase after 24 hours was conducted to validate the feasibility and sensitivity of the Pearson’s correlation coefficient on the vertical movement r v in this study. Experiment results indicate that r v is more sensitive in detecting the IWS learning in all phases compared to the conventional root mean square error (RMSE) performance measure. The findings confirm the importance of the performance measure in implicit learning research and the similarity measure in accordance with the waveform could be promising for waveform-specific learning detection in this paradigm.-
dc.languageeng-
dc.publisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reports-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleReliable detection for implicit waveform-specific learning in continuous tracking task paradigm-
dc.typeArticle-
dc.identifier.emailZhu, FF: ffzhu@hku.hk-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityZhu, FF=rp02104-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41598-017-11977-5-
dc.identifier.hkuros282435-
dc.identifier.volume7-
dc.identifier.spage12333-
dc.identifier.epage12333-
dc.identifier.isiWOS:000411678500005-
dc.publisher.placeUnited Kingdom-

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