Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception

品味 感知 结合属性 心理学 认知心理学 联想学习 领域(数学分析) 认知科学 神经科学 数学 数学分析 纯数学
作者
Yili Zhao,In‐Seon Lee,Margaret Rose-McCandlish,Qingbao Yu,Dominik Mischkowski,Jason A. Avery,John E. Ingeholm,Richard C. Reynolds,Gang Chen,Lauren Y. Atlas
标识
DOI:10.1101/2025.04.10.648205
摘要

Abstract Predictive cues significantly influence perception through associative learning. However, it is unknown whether circuits are conserved across domains. We investigated how associative learning influences perceived intensity and valence of pain and hedonic taste and whether the mechanisms that support expectancy-based modulation vary as a function of aversiveness and modality. Sixty participants were randomly assigned to receive either painful heat, unpleasant liquid saline, or pleasant liquid sucrose during fMRI scanning. Following conditioning, cues that were initially associated with low or high intensity outcomes were intermittently followed by stimuli calibrated to elicit medium intensity ratings. Learned cues modulated expectations and subjective outcomes similarly across domains. Consistent with this, the orbitofrontal cortex exhibited domain-general anticipatory activation. The left anterior insula mediated domain-general cue effects on subjective intensity, whereas the thalamus mediated cue effects on subjective valence. Notably, these two regions were nearly identical to those previously implicated in mediating cue effects on pain (nearly identical to those previously implicated in cue effects on mediating pain (Atlas et al., 2010). Pain specificity was evident when we measured variations in stimulus intensity, whether we used univariate or multivariate approaches, but there was minimal evidence of specificity by modality or aversiveness when we examined cue effects on medium trials. These findings suggest that shared neural circuits mediate the effects of learned expectations on perception, linking pain with other areas of affective processing and perception across domains.

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