How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task
Sequence learning in the serial response time task (SRTT) is one of few learning phenomena where researchers agree that such learning may proceed in the absence of awareness, while it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning...
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Ubiquity Press
2025-04-01
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| Series: | Journal of Cognition |
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| Online Access: | https://account.journalofcognition.org/index.php/up-j-jc/article/view/439 |
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| author | Marius Barth Christoph Stahl Hilde Haider |
| author_facet | Marius Barth Christoph Stahl Hilde Haider |
| author_sort | Marius Barth |
| collection | DOAJ |
| description | Sequence learning in the serial response time task (SRTT) is one of few learning phenomena where researchers agree that such learning may proceed in the absence of awareness, while it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning largely focused on the type of representation that may underlie implicit sequence learning, and whether or not two independent learning systems are necessary to explain qualitative differences between implicit and explicit learning. Using the drift-diffusion model, here we take a cognitive-processes perspective on sequence learning and investigate the cognitive operations that benefit from implicit and explicit sequence learning (e.g., stimulus detection and encoding, response selection, and response execution). To separate the processes involved in expressing implicit versus explicit knowledge, we manipulated explicit sequence knowledge independently of the opportunity to express such knowledge, and analyzed the resulting performance data with a drift-diffusion model to disentangle the contributions of these sub-processes. Results revealed that implicit sequence learning does not affect stimulus processing, but benefits response selection. Moreover, beyond response selection, response execution was affected. Explicit sequence knowledge did not change this pattern if participants worked on probabilistic materials, where it is difficult to anticipate the next response. However, if materials were deterministic, explicit knowledge enabled participants to switch from stimulus-based to plan-based action control, which was reflected in ample changes in the cognitive processes involved in performing the task. First implications for theories of sequence learning, and how the diffusion model may be helpful in future research, are dicussed. |
| format | Article |
| id | doaj-art-243f18ebfab1463b96066f2ccb6f882a |
| institution | DOAJ |
| issn | 2514-4820 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Journal of Cognition |
| spelling | doaj-art-243f18ebfab1463b96066f2ccb6f882a2025-08-20T03:07:44ZengUbiquity PressJournal of Cognition2514-48202025-04-0181303010.5334/joc.439438How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time TaskMarius Barth0https://orcid.org/0000-0002-3421-6665Christoph Stahl1https://orcid.org/0000-0002-9033-894XHilde Haider2https://orcid.org/0000-0001-7293-3166Department of Psychology, University of CologneDepartment of Psychology, University of CologneDepartment of Psychology, University of CologneSequence learning in the serial response time task (SRTT) is one of few learning phenomena where researchers agree that such learning may proceed in the absence of awareness, while it is also possible to explicitly learn a sequence of events. In the past few decades, research into sequence learning largely focused on the type of representation that may underlie implicit sequence learning, and whether or not two independent learning systems are necessary to explain qualitative differences between implicit and explicit learning. Using the drift-diffusion model, here we take a cognitive-processes perspective on sequence learning and investigate the cognitive operations that benefit from implicit and explicit sequence learning (e.g., stimulus detection and encoding, response selection, and response execution). To separate the processes involved in expressing implicit versus explicit knowledge, we manipulated explicit sequence knowledge independently of the opportunity to express such knowledge, and analyzed the resulting performance data with a drift-diffusion model to disentangle the contributions of these sub-processes. Results revealed that implicit sequence learning does not affect stimulus processing, but benefits response selection. Moreover, beyond response selection, response execution was affected. Explicit sequence knowledge did not change this pattern if participants worked on probabilistic materials, where it is difficult to anticipate the next response. However, if materials were deterministic, explicit knowledge enabled participants to switch from stimulus-based to plan-based action control, which was reflected in ample changes in the cognitive processes involved in performing the task. First implications for theories of sequence learning, and how the diffusion model may be helpful in future research, are dicussed.https://account.journalofcognition.org/index.php/up-j-jc/article/view/439implicit learningsequence learningdrift-diffusion model |
| spellingShingle | Marius Barth Christoph Stahl Hilde Haider How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task Journal of Cognition implicit learning sequence learning drift-diffusion model |
| title | How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task |
| title_full | How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task |
| title_fullStr | How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task |
| title_full_unstemmed | How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task |
| title_short | How Implicit Sequence Learning and Explicit Sequence Knowledge Are Expressed in a Serial Response Time Task |
| title_sort | how implicit sequence learning and explicit sequence knowledge are expressed in a serial response time task |
| topic | implicit learning sequence learning drift-diffusion model |
| url | https://account.journalofcognition.org/index.php/up-j-jc/article/view/439 |
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