Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators
Time prediction is pervasive, and it is unclear whether it is supra-modal or task-specific. This study aimed to investigate the role of motor temporal prediction in preparing to stop a movement following a sensory stimulus. Participants performed a straight-line movement with their finger until a ta...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811924004798 |
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| Summary: | Time prediction is pervasive, and it is unclear whether it is supra-modal or task-specific. This study aimed to investigate the role of motor temporal prediction in preparing to stop a movement following a sensory stimulus. Participants performed a straight-line movement with their finger until a target signal, which occurred after a short or long foreperiod. In one task, participants changed movement direction between trials (multidirectional task), while in the other, they always moved in the same direction (unidirectional task). The motor trajectory and EEG signals were continuously recorded. During the foreperiod, participants slowed down their movement, reflecting preparation to stop. To assess the influence of motor temporal prediction we examined how a given trial influences performance on the subsequent trial (sequential effect) when the movement changes or stays the same (multi- vs. unidirectional). In the unidirectional task, but not in the multidirectional task we found sequential effects on several behavioural parameters. In contrast, sequential effects were observed in both tasks on EEG results. This study revealed a temporal prediction related to motor movement (behavioural indicators), and a temporal preparation while waiting for the target (EEG indicator). These findings highlight the importance of considering various temporal prediction mechanisms. |
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| ISSN: | 1095-9572 |