Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept Article Swipe
Marc J. Lanovaz
,
Varsovia Hernández
,
Alejandro León
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/jeab.70029
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/jeab.70029
Traditionally, the experimental analysis of behavior has relied on the single discrete response paradigm (e.g., key pecks, lever presses, screen clicks) to identify behavioral patterns. However, the development and availability of new technology allow researchers to move beyond this paradigm and use other features to detect schedules. Thus, our study used spatiotemporal data to compare the accuracy of four machine learning algorithms (i.e., logistic regression, support vector classifiers, random forests, and artificial neural networks) in detecting the presence and the components of time‐based schedules in 12 rats involved in a behavioral experiment. Using spatiotemporal data, the algorithms accurately identified the presence or absence of programmed schedules and correctly differentiated between fixed‐ and variable‐space schedules. That said, our analyses failed to identify an algorithm to discriminate fixed‐time from variable‐time schedules. Furthermore, none of the algorithms performed systematically better than the others. Our findings provide preliminary support for the utility of using spatiotemporal data with machine learning to detect stimulus schedules.
Related Topics
Concepts
Computer science
Artificial intelligence
Machine learning
Random forest
Support vector machine
Artificial neural network
Animal behavior
Lever
Behavioral pattern
Key (lock)
Variable (mathematics)
Mathematics
Software engineering
Biology
Mathematical analysis
Quantum mechanics
Computer security
Zoology
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/jeab.70029
- OA Status
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- References
- 29
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4411917615Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/jeab.70029Digital Object Identifier
- Title
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Machine learning to detect schedules using spatiotemporal data of behavior: A proof of conceptWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
- Publication date
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2025-06-30Full publication date if available
- Authors
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Marc J. Lanovaz, Varsovia Hernández, Alejandro LeónList of authors in order
- Landing page
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https://doi.org/10.1002/jeab.70029Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1002/jeab.70029Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Machine learning, Random forest, Support vector machine, Artificial neural network, Animal behavior, Lever, Behavioral pattern, Key (lock), Variable (mathematics), Mathematics, Software engineering, Biology, Mathematical analysis, Quantum mechanics, Computer security, Zoology, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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