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Physiology & behavior · 2026

Knowing when to stop: Interoceptive prediction of aversive outcomes guides intake regulation.

СтатьяНазвание и аннотация: Перевод в очереди
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Huang Shihui, Chowdhury Zaina, Sava Vivian D, Skinner Alex C, Cole Lena T, Tekin Dinara, Marrow Emily R, Clark Penelope A, Bekele Hasset W, Riley Anthony L

Аннотация

Adaptive behavior requires knowing when to stop. Internal bodily signals are known to influence intake, yet the mechanisms through which interoceptive signals help terminate intake before harm occurs remain poorly defined. Here, we developed a model of interoceptive prediction to test whether internal signals predicting that continued intake will produce an aversive outcome can regulate consumption below that threshold. Male and female Sprague-Dawley (n = 110) self-administered saccharin, and intake exceeding an individualized threshold, defined as 50% of the rat's baseline consumption, was followed by LiCl injection. In Experiment 1, rats receiving response-contingent LiCl progressively approximated their individualized thresholds and maintained intake within 15% above and below those thresholds more frequently than controls that did not receive this contingency. In Experiment 2, neither yoked controls receiving LiCl matched to the timing and frequency of paired titration rats nor partial reinforcement controls receiving LiCl on 50% of sessions showed comparable titration, indicating that titration was not explained by aversive exposure alone. Instead, accurate titration required a predictive contingency between the amount consumed and the aversive consequence. Females adopted a more conservative regulatory strategy than males, maintaining intake near but preferentially below the aversive threshold. These findings establish a behavioral model of interoceptive prediction in intake regulation and provide a framework for investigating how failures in internal-state-guided control may contribute to substance use disorders and other maladaptive behaviors.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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