Abstract
Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.
Original language | English |
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Article number | e2312898121 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 121 |
Issue number | 5 |
Early online date | 24 Jan 2024 |
DOIs | |
Publication status | Published - 30 Jan 2024 |
Funding
ACKNOWLEDGMENTS. This research was supported by an ERC Starting Grant from the H2020 European Research Council (ERC STG 715605 to S.v.G) and a Research Talent Grant from the Dutch Research Council (NWO; 406.17.531 to L.B. and S.v.G.).
Funders | Funder number |
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European Research Council |
Keywords
- computational modeling
- decision-making
- spontaneous arousal
- task performance