Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm.
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| Title: | Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm. |
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| Authors: | Lenfesty, Brendan1 (AUTHOR) lenfesty-b@ulster.ac.uk, Bhattacharyya, Saugat1 (AUTHOR) s.bhattacharyya@ulster.ac.uk, Wong-Lin, KongFatt1 (AUTHOR) k.wong-lin@ulster.ac.uk |
| Source: | Neural Computation. Mar2025, Vol. 37 Issue 3, p569-587. 19p. |
| Subjects: | Temporal integration, Signal-to-noise ratio, Stochastic models, Decision making, Data modeling |
| Abstract: | Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computation is the property of MIT Press 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lenfesty%2C+Brendan%22">Lenfesty, Brendan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lenfesty-b@ulster.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Bhattacharyya%2C+Saugat%22">Bhattacharyya, Saugat</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> s.bhattacharyya@ulster.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Wong-Lin%2C+KongFatt%22">Wong-Lin, KongFatt</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> k.wong-lin@ulster.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Mar2025, Vol. 37 Issue 3, p569-587. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Temporal+integration%22">Temporal integration</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Decision formation in perceptual decision making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable toward some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioral data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models and assuming the noise coefficient term is known beforehand, SINDy, enhanced with approaches using multiple trials, could readily estimate the deterministic terms in the dynamical equations, choice accuracy, and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory-intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modeling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision making and, more generally, for first-passage time problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computation is the property of MIT Press 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: 10.1162/neco_a_01736 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 569 Subjects: – SubjectFull: Temporal integration Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Stochastic models Type: general – SubjectFull: Decision making Type: general – SubjectFull: Data modeling Type: general Titles: – TitleFull: Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lenfesty, Brendan – PersonEntity: Name: NameFull: Bhattacharyya, Saugat – PersonEntity: Name: NameFull: Wong-Lin, KongFatt IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08997667 Numbering: – Type: volume Value: 37 – Type: issue Value: 3 Titles: – TitleFull: Neural Computation Type: main |
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