Nina Rouhani, Cooper D. Grossman, Jamie Feusner, Anita Tusche
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引用次数: 0
Abstract
Food seeking and avoidance engage primary reward systems to drive behavior. It is nevertheless unclear whether innate or learned food biases interact with general reward processing to interfere with goal-directed choice. To this end, we recruited a large non-clinical sample of females with high eating-disorder symptoms (‘HED’) and a matched sample of females with low eating-disorder symptoms (‘LED’) to complete a reward-learning task where the calorie content of food stimuli was incidental to the goal of maximizing monetary reward. We find and replicate a low-calorie food bias in HED and a high-calorie food bias in LED, reflecting the strength of pre-experimental food-reward associations. An emotional arousal manipulation shifts this group-dependent bias across individual differences, with interoceptive awareness predicting this change. Reinforcement-learning models further identify distinct cognitive components supporting these group-specific food biases. Our results highlight the influence of reinforcement-based mechanisms and emotional arousal in eliciting potentially maladaptive food-reward associations.
寻求食物和回避食物都需要初级奖赏系统来驱动行为。然而,先天或后天的食物偏向是否会与一般奖赏处理相互作用,从而干扰目标导向的选择,目前尚不清楚。为此,我们招募了大量非临床样本的高进食障碍症状女性("HED")和与之匹配的低进食障碍症状女性("LED"),让她们完成一项奖励学习任务,在这项任务中,食物刺激的卡路里含量与金钱奖励最大化的目标无关。我们发现并复制了 HED 的低热量食物偏向和 LED 的高热量食物偏向,这反映了实验前食物奖励关联的强度。情绪唤醒操纵改变了这种依赖于群体的偏向,跨越了个体差异,而感知间意识则预测了这种变化。强化学习模型进一步确定了支持这些特定群体食物偏向的独特认知成分。我们的研究结果凸显了基于强化的机制和情绪唤醒在诱发潜在不良食物奖励联想方面的影响。
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.