Please use this identifier to cite or link to this item: http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4032
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dc.contributor.authorMatthias, Deliano-
dc.contributor.authorKarsten, Tabelow-
dc.contributor.authorReinhard, König-
dc.date.accessioned2018-09-10T02:55:07Z-
dc.date.available2018-09-10T02:55:07Z-
dc.date.issued2016-06-15-
dc.identifier.issn1932-6203 (Online)-
dc.identifier.urihttp://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4032-
dc.description.abstractEstimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.en_US
dc.language.isoenen_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.subjectMedicineen_US
dc.subjectScienceen_US
dc.titleImproving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysisen_US
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