A language of movements : grammar learning in a serial reaction time test
(2023) The Brain Conference on Structuring of Knowledge for Flexible Behaviour- Abstract
- Implicit sequence learning (ISL) has been studied using a variety of tasks two of which have been particularly influential: the serial reaction time task (SRTT) and the artificial grammar learning (AGL) task, stemming from the motor learning domain and psycholinguistics, respectively. SRTTs measure the difference in response times between a repeatedly presented and random sequences of keyboard presses. Contrarily, AGL tasks measure the proficiency at judging if strings of letters are from the same probabilistic system as those presented in a preceding learning phase, disguised as a working memory test. While the SRTT provides a motor output that allows for following the progression of learning, it typically lacks important features from... (More)
- Implicit sequence learning (ISL) has been studied using a variety of tasks two of which have been particularly influential: the serial reaction time task (SRTT) and the artificial grammar learning (AGL) task, stemming from the motor learning domain and psycholinguistics, respectively. SRTTs measure the difference in response times between a repeatedly presented and random sequences of keyboard presses. Contrarily, AGL tasks measure the proficiency at judging if strings of letters are from the same probabilistic system as those presented in a preceding learning phase, disguised as a working memory test. While the SRTT provides a motor output that allows for following the progression of learning, it typically lacks important features from AGL, namely: An active recall task and the use of probabilistic systems (i.e. grammars) abundant in more natural ISL situations. We present a novel SRTT design in which sequences of prompted keyboard presses are drawn from probabilistic systems (grammars) governing which keyboard transitions are allowed in a learning phase and tested using a sequence generation task. Participant either got sequences from a grammar in which allowed transition were either 80% or 20% probable (HiLo), or from one in which each transition had a 50/50 chance (EqProb). Results from the learning task shows differentiation in learning patterns between the groups, indicative of the grammars’ effect on the SRTT performance. In the sequence generation task, the HiLo group generated more accurate sequences in the grammatical than the ungrammatical sequence generation task than the EqProb group. We show that participants can learn a probabilistic system of keyboard transition probabilities in an SRTT and utilize it for generating sequences that are consistent with the system. Moreover, the sequence production task is inspired by the AGL design and offers an opportunity to compare findings from ISL research in the linguistic and motor domains. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/53247a1f-f734-4975-8ced-57180e0513f3
- author
- Novén, Mikael LU ; Sidenius, Isak and Karabanov, Anke Ninija
- publishing date
- 2023
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- The Brain Conference on Structuring of Knowledge for Flexible Behaviour
- conference location
- Rungsted Kyst, Denmark
- conference dates
- 2023-10-18 - 2023-10-21
- language
- English
- LU publication?
- no
- id
- 53247a1f-f734-4975-8ced-57180e0513f3
- date added to LUP
- 2024-11-12 08:36:10
- date last changed
- 2025-04-04 14:18:22
@misc{53247a1f-f734-4975-8ced-57180e0513f3,
abstract = {{Implicit sequence learning (ISL) has been studied using a variety of tasks two of which have been particularly influential: the serial reaction time task (SRTT) and the artificial grammar learning (AGL) task, stemming from the motor learning domain and psycholinguistics, respectively. SRTTs measure the difference in response times between a repeatedly presented and random sequences of keyboard presses. Contrarily, AGL tasks measure the proficiency at judging if strings of letters are from the same probabilistic system as those presented in a preceding learning phase, disguised as a working memory test. While the SRTT provides a motor output that allows for following the progression of learning, it typically lacks important features from AGL, namely: An active recall task and the use of probabilistic systems (i.e. grammars) abundant in more natural ISL situations. We present a novel SRTT design in which sequences of prompted keyboard presses are drawn from probabilistic systems (grammars) governing which keyboard transitions are allowed in a learning phase and tested using a sequence generation task. Participant either got sequences from a grammar in which allowed transition were either 80% or 20% probable (HiLo), or from one in which each transition had a 50/50 chance (EqProb). Results from the learning task shows differentiation in learning patterns between the groups, indicative of the grammars’ effect on the SRTT performance. In the sequence generation task, the HiLo group generated more accurate sequences in the grammatical than the ungrammatical sequence generation task than the EqProb group. We show that participants can learn a probabilistic system of keyboard transition probabilities in an SRTT and utilize it for generating sequences that are consistent with the system. Moreover, the sequence production task is inspired by the AGL design and offers an opportunity to compare findings from ISL research in the linguistic and motor domains.}},
author = {{Novén, Mikael and Sidenius, Isak and Karabanov, Anke Ninija}},
language = {{eng}},
title = {{A language of movements : grammar learning in a serial reaction time test}},
year = {{2023}},
}