Fish and chips: Using machine learning to estimate the effects of basal cortisol on fish foraging behavior

Foraging is an essential behavior for animal survival and requires both learning and decision-making skills. However, despite its relevance and ubiquity, there is still no effective mathematical framework to adequately estimate foraging performance that also takes interindividual variability into ac...

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Bibliographic Details
Published in:Frontiers in behavioral neuroscience 2023-02, Vol.17, p.1028190-1028190
Main Authors: Bessa, Wallace M, Cadengue, Lucas S, Luchiari, Ana C
Format: Article
Language:English
Subjects:
Algorithms
Animal cognition
Behavioral Neuroscience
Cognition
Comparative analysis
Cortisol
Decision making
epsilon-greedy
Exploitation
Fish
foraging
Foraging behavior
Hormones
Learning algorithms
Machine learning
multi-armed bandit
Physiology
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Summary:Foraging is an essential behavior for animal survival and requires both learning and decision-making skills. However, despite its relevance and ubiquity, there is still no effective mathematical framework to adequately estimate foraging performance that also takes interindividual variability into account. In this work, foraging performance is evaluated in the context of multi-armed bandit (MAB) problems by means of a biological model and a machine learning algorithm. Siamese fighting fish ( ) were used as a biological model and their ability to forage was assessed in a four-arm cross-maze over 21 trials. It was observed that fish performance varies according to their basal cortisol levels, i.e., a reduced average reward is associated with low and high levels of basal cortisol, while the optimal level maximizes foraging performance. In addition, we suggest the adoption of the epsilon-greedy algorithm to deal with the exploration-exploitation tradeoff and simulate foraging decisions. The algorithm provided results closely related to the biological model and allowed the normalized basal cortisol levels to be correlated with a corresponding tuning parameter. The obtained results indicate that machine learning, by helping to shed light on the intrinsic relationships between physiological parameters and animal behavior, can be a powerful tool for studying animal cognition and behavioral sciences.
ISSN:1662-5153
1662-5153
DOI:10.3389/fnbeh.2023.1028190