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Concurrent visual sequence learning
Many researchers in the field of implicit statistical learning agree that there does not exist one general implicit learning mechanism, but rather, that implicit learning takes place in highly specialized encapsulated modules. However, the exact representational content of these modules is still und...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457409/ https://www.ncbi.nlm.nih.gov/pubmed/36947194 http://dx.doi.org/10.1007/s00426-023-01810-2 |
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author | Wilts, Sarah Haider, Hilde |
author_facet | Wilts, Sarah Haider, Hilde |
author_sort | Wilts, Sarah |
collection | PubMed |
description | Many researchers in the field of implicit statistical learning agree that there does not exist one general implicit learning mechanism, but rather, that implicit learning takes place in highly specialized encapsulated modules. However, the exact representational content of these modules is still under debate. While there is ample evidence for a distinction between modalities (e.g., visual, auditory perception), the representational content of the modules might even be distinguished by features within the same modalities (e.g., location, color, and shape within the visual modality). In implicit sequence learning, there is evidence for the latter hypothesis, as a stimulus-color sequence can be learned concurrently with a stimulus-location sequence. Our aim was to test whether this also holds true for non-spatial features within the visual modality. This has been shown in artificial grammar learning, but not yet in implicit sequence learning. Hence, in Experiment 1, we replicated an artificial grammar learning experiment of Conway and Christiansen (2006) in which participants were supposed to learn color and shape grammars concurrently. In Experiment 2, we investigated concurrent learning of sequences with an implicit sequence learning paradigm: the serial reaction time task. Here, we found evidence for concurrent learning of two sequences, a color and shape sequence. Overall, the findings converge to the assumption that implicit learning might be based on features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00426-023-01810-2. |
format | Online Article Text |
id | pubmed-10457409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-104574092023-08-27 Concurrent visual sequence learning Wilts, Sarah Haider, Hilde Psychol Res Original Article Many researchers in the field of implicit statistical learning agree that there does not exist one general implicit learning mechanism, but rather, that implicit learning takes place in highly specialized encapsulated modules. However, the exact representational content of these modules is still under debate. While there is ample evidence for a distinction between modalities (e.g., visual, auditory perception), the representational content of the modules might even be distinguished by features within the same modalities (e.g., location, color, and shape within the visual modality). In implicit sequence learning, there is evidence for the latter hypothesis, as a stimulus-color sequence can be learned concurrently with a stimulus-location sequence. Our aim was to test whether this also holds true for non-spatial features within the visual modality. This has been shown in artificial grammar learning, but not yet in implicit sequence learning. Hence, in Experiment 1, we replicated an artificial grammar learning experiment of Conway and Christiansen (2006) in which participants were supposed to learn color and shape grammars concurrently. In Experiment 2, we investigated concurrent learning of sequences with an implicit sequence learning paradigm: the serial reaction time task. Here, we found evidence for concurrent learning of two sequences, a color and shape sequence. Overall, the findings converge to the assumption that implicit learning might be based on features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00426-023-01810-2. Springer Berlin Heidelberg 2023-03-22 2023 /pmc/articles/PMC10457409/ /pubmed/36947194 http://dx.doi.org/10.1007/s00426-023-01810-2 Text en © The Author(s) 2023 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 | Original Article Wilts, Sarah Haider, Hilde Concurrent visual sequence learning |
title | Concurrent visual sequence learning |
title_full | Concurrent visual sequence learning |
title_fullStr | Concurrent visual sequence learning |
title_full_unstemmed | Concurrent visual sequence learning |
title_short | Concurrent visual sequence learning |
title_sort | concurrent visual sequence learning |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457409/ https://www.ncbi.nlm.nih.gov/pubmed/36947194 http://dx.doi.org/10.1007/s00426-023-01810-2 |
work_keys_str_mv | AT wiltssarah concurrentvisualsequencelearning AT haiderhilde concurrentvisualsequencelearning |