Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis
Estimation 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 violati...
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Public Library of Science (PLoS)
2018
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oai:localhost:DHQB_123456789-40322018-10-22T08:44:24Z Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis Matthias, Deliano Karsten, Tabelow Reinhard, König Medicine Science Estimation 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. 2018-09-10T02:55:07Z 2018-09-10T02:55:07Z 2016-06-15 1932-6203 (Online) http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4032 en Public Library of Science (PLoS) |
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Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
| collection |
Trung tâm Học liệu Đại học Quảng Bình (Dspace) |
| language |
English |
| topic |
Medicine Science |
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Medicine Science Matthias, Deliano Karsten, Tabelow Reinhard, König Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| description |
Estimation 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. |
| author |
Matthias, Deliano Karsten, Tabelow Reinhard, König |
| author_facet |
Matthias, Deliano Karsten, Tabelow Reinhard, König |
| author_sort |
Matthias, Deliano |
| title |
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| title_short |
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| title_full |
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| title_fullStr |
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| title_full_unstemmed |
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis |
| title_sort |
improving accuracy and temporal resolution of learning curve estimation for within- and across-session analysis |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2018 |
| url |
http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4032 |
| _version_ |
1717292467945996288 |
| score |
9,463379 |