Humans can learn bimodal priors in complex sensorimotor behaviour.
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| Title: | Humans can learn bimodal priors in complex sensorimotor behaviour. |
|---|---|
| Authors: | Zahno, Stephan1 (AUTHOR) stephan.zahno@unibe.ch, Beck, Damian1 (AUTHOR) damian.beck@unibe.ch, Hossner, Ernst-Joachim1 (AUTHOR) ernst.hossner@unibe.ch, Kording, Konrad2 (AUTHOR) kording@upenn.edu |
| Source: | Proceedings of the Royal Society B: Biological Sciences. 3/1/2026, Vol. 293 Issue 2066, p1-11. 11p. |
| Subjects: | Sensorimotor integration, Implicit learning, Virtual reality, Bayesian analysis, Fine motor ability, Visual perception, Cognition |
| Abstract: | Bayesian integration offers a powerful unifying framework for perception, cognition and motor control. Extensive laboratory-based research shows that humans integrate sensory inputs and prior expectations in a Bayesian manner to guide action. However, it remains unclear whether this extends to naturalistic tasks involving more complex movements and environmental statistics. Here, we examine whether humans can learn bimodal priors in a complex sensorimotor task: returning tennis serves. Participants returned serves in an extended reality set-up with task demands closely matching real tennis. The opponent's serve locations followed a bimodal (i.e. two-peaked) distribution. We manipulated visual uncertainty through three ball speeds. After extensive exposure to the opponent's serves, participants' movements were systematically biased by the bimodal prior distribution. Consistent with Bayesian theory, the magnitude of the bias scaled with uncertainty. In addition to prior expectations, our data show that participants' movements were also biased by biomechanical constraints and motor costs. Intriguingly, explicit knowledge tests revealed that participants incorporated prior knowledge of the opponent's serve distribution into their behaviour without explicit awareness of the pattern. These findings demonstrate that humans can implicitly acquire and exploit bimodal priors in complex, naturalistic sensorimotor behaviour. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the Royal Society B: Biological Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192086124 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Humans can learn bimodal priors in complex sensorimotor behaviour. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zahno%2C+Stephan%22">Zahno, Stephan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> stephan.zahno@unibe.ch</i><br /><searchLink fieldCode="AR" term="%22Beck%2C+Damian%22">Beck, Damian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> damian.beck@unibe.ch</i><br /><searchLink fieldCode="AR" term="%22Hossner%2C+Ernst-Joachim%22">Hossner, Ernst-Joachim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ernst.hossner@unibe.ch</i><br /><searchLink fieldCode="AR" term="%22Kording%2C+Konrad%22">Kording, Konrad</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> kording@upenn.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+Royal+Society+B%3A+Biological+Sciences%22">Proceedings of the Royal Society B: Biological Sciences</searchLink>. 3/1/2026, Vol. 293 Issue 2066, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sensorimotor+integration%22">Sensorimotor integration</searchLink><br /><searchLink fieldCode="DE" term="%22Implicit+learning%22">Implicit learning</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Fine+motor+ability%22">Fine motor ability</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition%22">Cognition</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Bayesian integration offers a powerful unifying framework for perception, cognition and motor control. Extensive laboratory-based research shows that humans integrate sensory inputs and prior expectations in a Bayesian manner to guide action. However, it remains unclear whether this extends to naturalistic tasks involving more complex movements and environmental statistics. Here, we examine whether humans can learn bimodal priors in a complex sensorimotor task: returning tennis serves. Participants returned serves in an extended reality set-up with task demands closely matching real tennis. The opponent's serve locations followed a bimodal (i.e. two-peaked) distribution. We manipulated visual uncertainty through three ball speeds. After extensive exposure to the opponent's serves, participants' movements were systematically biased by the bimodal prior distribution. Consistent with Bayesian theory, the magnitude of the bias scaled with uncertainty. In addition to prior expectations, our data show that participants' movements were also biased by biomechanical constraints and motor costs. Intriguingly, explicit knowledge tests revealed that participants incorporated prior knowledge of the opponent's serve distribution into their behaviour without explicit awareness of the pattern. These findings demonstrate that humans can implicitly acquire and exploit bimodal priors in complex, naturalistic sensorimotor behaviour. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the Royal Society B: Biological Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1098/rspb.2025.2296 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Sensorimotor integration Type: general – SubjectFull: Implicit learning Type: general – SubjectFull: Virtual reality Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Fine motor ability Type: general – SubjectFull: Visual perception Type: general – SubjectFull: Cognition Type: general Titles: – TitleFull: Humans can learn bimodal priors in complex sensorimotor behaviour. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zahno, Stephan – PersonEntity: Name: NameFull: Beck, Damian – PersonEntity: Name: NameFull: Hossner, Ernst-Joachim – PersonEntity: Name: NameFull: Kording, Konrad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 3/1/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09628452 Numbering: – Type: volume Value: 293 – Type: issue Value: 2066 Titles: – TitleFull: Proceedings of the Royal Society B: Biological Sciences Type: main |
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