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Online measurement of learning temporal statistical structure in categorization tasks
The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508059/ https://www.ncbi.nlm.nih.gov/pubmed/35377057 http://dx.doi.org/10.3758/s13421-022-01302-5 |
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author | Sáringer, Szabolcs Fehér, Ágnes Sáry, Gyula Kaposvári, Péter |
author_facet | Sáringer, Szabolcs Fehér, Ágnes Sáry, Gyula Kaposvári, Péter |
author_sort | Sáringer, Szabolcs |
collection | PubMed |
description | The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13421-022-01302-5. |
format | Online Article Text |
id | pubmed-9508059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95080592022-09-25 Online measurement of learning temporal statistical structure in categorization tasks Sáringer, Szabolcs Fehér, Ágnes Sáry, Gyula Kaposvári, Péter Mem Cognit Article The ability to grasp relevant patterns from a continuous stream of environmental information is called statistical learning. Although the representations that emerge during visual statistical learning (VSL) are well characterized, little is known about how they are formed. We developed a sensitive behavioral design to characterize the VSL trajectory during ongoing task performance. In sequential categorization tasks, we assessed two previously identified VSL markers: priming of the second predictable image in a pair manifested by a reduced reaction time (RT) and greater accuracy, and the anticipatory effect on the first image revealed by a longer RT. First, in Experiment 1A, we used an adapted paradigm and replicated these VSL markers; however, they appeared to be confounded by motor learning. Next, in Experiment 1B, we confirmed the confounding influence of motor learning. To assess VSL without motor learning, in Experiment 2 we (1) simplified the categorization task, (2) raised the number of subjects and image repetitions, and (3) increased the number of single unpaired images. Using linear mixed-effect modeling and estimated marginal means of linear trends, we found that the RT curves differed significantly between predictable paired and control single images. Further, the VSL curve fitted a logarithmic model, suggesting a rapid learning process. These results suggest that our paradigm in Experiment 2 seems to be a viable online tool to monitor the behavioral correlates of unsupervised implicit VSL. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13421-022-01302-5. Springer US 2022-04-04 2022 /pmc/articles/PMC9508059/ /pubmed/35377057 http://dx.doi.org/10.3758/s13421-022-01302-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sáringer, Szabolcs Fehér, Ágnes Sáry, Gyula Kaposvári, Péter Online measurement of learning temporal statistical structure in categorization tasks |
title | Online measurement of learning temporal statistical structure in categorization tasks |
title_full | Online measurement of learning temporal statistical structure in categorization tasks |
title_fullStr | Online measurement of learning temporal statistical structure in categorization tasks |
title_full_unstemmed | Online measurement of learning temporal statistical structure in categorization tasks |
title_short | Online measurement of learning temporal statistical structure in categorization tasks |
title_sort | online measurement of learning temporal statistical structure in categorization tasks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508059/ https://www.ncbi.nlm.nih.gov/pubmed/35377057 http://dx.doi.org/10.3758/s13421-022-01302-5 |
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