PDF(Pubmed)
Abstract：
Self-reports remain affective science\'s only direct measure of subjective affective experiences. Yet, little research has sought to understand the psychological process that transforms subjective experience into self-reports. Here, we propose that by framing these self-reports as dynamic affective decisions, affective scientists may leverage the computational tools of decision-making research, sequential sampling models specifically, to better disentangle affective experience from the noisy decision processes that constitute self-report. We further outline how such an approach could help affective scientists better probe the specific mechanisms that underlie important moderators of affective experience (e.g., contextual differences, individual differences, and emotion regulation) and discuss how adopting this decision-making framework could generate insight into affective processes more broadly and facilitate reciprocal collaborations between affective and decision scientists towards a more comprehensive and integrative psychological science.
摘要：
自我报告仍然是情感科学对主观情感体验的唯一直接衡量标准。然而，很少有研究试图了解将主观经验转化为自我报告的心理过程。这里,我们建议通过将这些自我报告构建为动态情感决策，情感科学家可以利用决策研究的计算工具，特别是顺序抽样模型，更好地将情感体验与构成自我报告的嘈杂决策过程分开。我们进一步概述了这种方法如何帮助情感科学家更好地探索情感体验的重要主持人的具体机制(例如，上下文差异，个体差异,和情绪调节），并讨论采用这种决策框架如何更广泛地对情感过程产生洞察力，并促进情感和决策科学家之间的互惠合作，以建立更全面和综合的心理科学。
