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Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach

Even after a long time of research on dual-tasking, the question whether the two tasks are always processed serially (response selection bottleneck models, RSB) or also in parallel (capacity-sharing models) is still going on. The first models postulate that the central processing stages of two tasks...

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Autores principales: Mattes, André, Tavera, Felice, Ophey, Anja, Roheger, Mandy, Gaschler, Robert, Haider, Hilde
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211618/
https://www.ncbi.nlm.nih.gov/pubmed/32335762
http://dx.doi.org/10.1007/s00426-020-01337-w
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author Mattes, André
Tavera, Felice
Ophey, Anja
Roheger, Mandy
Gaschler, Robert
Haider, Hilde
author_facet Mattes, André
Tavera, Felice
Ophey, Anja
Roheger, Mandy
Gaschler, Robert
Haider, Hilde
author_sort Mattes, André
collection PubMed
description Even after a long time of research on dual-tasking, the question whether the two tasks are always processed serially (response selection bottleneck models, RSB) or also in parallel (capacity-sharing models) is still going on. The first models postulate that the central processing stages of two tasks cannot overlap, producing a central processing bottleneck in Task 2. The second class of models posits that cognitive resources are shared between the central processing stages of two tasks, allowing for parallel processing. In a series of three experiments, we aimed at inducing parallel vs. serial processing by manipulating the relative frequency of short vs. long SOAs (Experiments 1 and 2) and including no-go trials in Task 2 (Experiment 3). Beyond the conventional response time (RT) analyses, we employed drift–diffusion model analyses to differentiate between parallel and serial processing. Even though our findings were rather consistent across the three experiments, they neither support unambiguously the assumptions derived from the RSB model nor those derived from capacity-sharing models. SOA frequency might lead to an adaptation to frequent time patterns. Overall, our diffusion model results and mean RTs seem to be better explained by participant’s time expectancies.
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spelling pubmed-82116182021-07-01 Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach Mattes, André Tavera, Felice Ophey, Anja Roheger, Mandy Gaschler, Robert Haider, Hilde Psychol Res Original Article Even after a long time of research on dual-tasking, the question whether the two tasks are always processed serially (response selection bottleneck models, RSB) or also in parallel (capacity-sharing models) is still going on. The first models postulate that the central processing stages of two tasks cannot overlap, producing a central processing bottleneck in Task 2. The second class of models posits that cognitive resources are shared between the central processing stages of two tasks, allowing for parallel processing. In a series of three experiments, we aimed at inducing parallel vs. serial processing by manipulating the relative frequency of short vs. long SOAs (Experiments 1 and 2) and including no-go trials in Task 2 (Experiment 3). Beyond the conventional response time (RT) analyses, we employed drift–diffusion model analyses to differentiate between parallel and serial processing. Even though our findings were rather consistent across the three experiments, they neither support unambiguously the assumptions derived from the RSB model nor those derived from capacity-sharing models. SOA frequency might lead to an adaptation to frequent time patterns. Overall, our diffusion model results and mean RTs seem to be better explained by participant’s time expectancies. Springer Berlin Heidelberg 2020-04-25 2021 /pmc/articles/PMC8211618/ /pubmed/32335762 http://dx.doi.org/10.1007/s00426-020-01337-w Text en © The Author(s) 2020 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
Mattes, André
Tavera, Felice
Ophey, Anja
Roheger, Mandy
Gaschler, Robert
Haider, Hilde
Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title_full Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title_fullStr Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title_full_unstemmed Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title_short Parallel and serial task processing in the PRP paradigm: a drift–diffusion model approach
title_sort parallel and serial task processing in the prp paradigm: a drift–diffusion model approach
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211618/
https://www.ncbi.nlm.nih.gov/pubmed/32335762
http://dx.doi.org/10.1007/s00426-020-01337-w
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