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The tight coupling between category and causal learning
The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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Springer-Verlag
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2860093/ https://www.ncbi.nlm.nih.gov/pubmed/19562395 http://dx.doi.org/10.1007/s10339-009-0267-x |
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author | Waldmann, Michael R. Meder, Björn von Sydow, Momme Hagmayer, York |
author_facet | Waldmann, Michael R. Meder, Björn von Sydow, Momme Hagmayer, York |
author_sort | Waldmann, Michael R. |
collection | PubMed |
description | The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation. |
format | Text |
id | pubmed-2860093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Springer-Verlag |
record_format | MEDLINE/PubMed |
spelling | pubmed-28600932010-05-21 The tight coupling between category and causal learning Waldmann, Michael R. Meder, Björn von Sydow, Momme Hagmayer, York Cogn Process Research Report The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation. Springer-Verlag 2009-06-27 2010 /pmc/articles/PMC2860093/ /pubmed/19562395 http://dx.doi.org/10.1007/s10339-009-0267-x Text en © The Author(s) 2009 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. |
spellingShingle | Research Report Waldmann, Michael R. Meder, Björn von Sydow, Momme Hagmayer, York The tight coupling between category and causal learning |
title | The tight coupling between category and causal learning |
title_full | The tight coupling between category and causal learning |
title_fullStr | The tight coupling between category and causal learning |
title_full_unstemmed | The tight coupling between category and causal learning |
title_short | The tight coupling between category and causal learning |
title_sort | tight coupling between category and causal learning |
topic | Research Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2860093/ https://www.ncbi.nlm.nih.gov/pubmed/19562395 http://dx.doi.org/10.1007/s10339-009-0267-x |
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