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Hunger enhances consistent economic choices in non-human primates
Hunger and thirst are fundamental biological processes that drive consumption behavior in humans and non-human animals. While the existing literature in neuroscience suggests that these satiety states change how consumable rewards are represented in the brain, it remains unclear as to how they chang...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5443835/ https://www.ncbi.nlm.nih.gov/pubmed/28539584 http://dx.doi.org/10.1038/s41598-017-02417-5 |
Sumario: | Hunger and thirst are fundamental biological processes that drive consumption behavior in humans and non-human animals. While the existing literature in neuroscience suggests that these satiety states change how consumable rewards are represented in the brain, it remains unclear as to how they change animal choice behavior and the underlying economic preferences. Here, I used combined techniques from experimental economics, psychology, and neuroscience to measure food preferences of marmoset monkeys (Callithrix jacchus), a recently developed primate model for neuroscience. Hunger states of animals were manipulated by scheduling feeding intervals, resulting in three different conditions: sated, non-sated, and hungry. During these hunger states, animals performed pairwise choices of food items, which included all possible pairwise combinations of five different food items except for same-food pairs. Results showed that hunger enhanced economic rationality, evident as a decrease of transitivity violations (item A was preferred to item B, and B to C, but C was preferred to A). Further analysis demonstrated that hungry monkeys chose more-preferred items over less-preferred items in a more deterministic manner, while the individual food preferences appeared to remain stable across hunger states. These results suggest that hunger enhances consistent choice behavior and shifts animals towards efficient outcome maximization. |
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