Decision-making dynamics are predicted by arousal and uninstructed movements
Summary: During sensory-guided behavior, an animal’s decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Cell Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211124724000378 |
| _version_ | 1797291760512139264 |
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| author | Daniel Hulsey Kevin Zumwalt Luca Mazzucato David A. McCormick Santiago Jaramillo |
| author_facet | Daniel Hulsey Kevin Zumwalt Luca Mazzucato David A. McCormick Santiago Jaramillo |
| author_sort | Daniel Hulsey |
| collection | DOAJ |
| description | Summary: During sensory-guided behavior, an animal’s decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal’s arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance. |
| first_indexed | 2024-03-07T19:42:22Z |
| format | Article |
| id | doaj.art-40b062e91c56478a91849586b54a0927 |
| institution | Directory Open Access Journal |
| issn | 2211-1247 |
| language | English |
| last_indexed | 2024-03-07T19:42:22Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports |
| spelling | doaj.art-40b062e91c56478a91849586b54a09272024-02-29T05:18:43ZengElsevierCell Reports2211-12472024-02-01432113709Decision-making dynamics are predicted by arousal and uninstructed movementsDaniel Hulsey0Kevin Zumwalt1Luca Mazzucato2David A. McCormick3Santiago Jaramillo4Institute of Neuroscience, University of Oregon, Eugene, OR 97405, USAInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USAInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA; Departments of Physics and Mathematics, University of Oregon, Eugene, OR 97405, USA; Corresponding authorInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA; Corresponding authorInstitute of Neuroscience, University of Oregon, Eugene, OR 97405, USA; Department of Biology, University of Oregon, Eugene, OR 97405, USA; Corresponding authorSummary: During sensory-guided behavior, an animal’s decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal’s arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.http://www.sciencedirect.com/science/article/pii/S2211124724000378CP: Neuroscience |
| spellingShingle | Daniel Hulsey Kevin Zumwalt Luca Mazzucato David A. McCormick Santiago Jaramillo Decision-making dynamics are predicted by arousal and uninstructed movements Cell Reports CP: Neuroscience |
| title | Decision-making dynamics are predicted by arousal and uninstructed movements |
| title_full | Decision-making dynamics are predicted by arousal and uninstructed movements |
| title_fullStr | Decision-making dynamics are predicted by arousal and uninstructed movements |
| title_full_unstemmed | Decision-making dynamics are predicted by arousal and uninstructed movements |
| title_short | Decision-making dynamics are predicted by arousal and uninstructed movements |
| title_sort | decision making dynamics are predicted by arousal and uninstructed movements |
| topic | CP: Neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2211124724000378 |
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