Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data.
Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data.
复制标题
使用经验采样数据对情绪与饮食选择之间的反馈机制进行倒数马尔可夫建模。
DOI：
10.1080/00273171.2015.1033510
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发表时间：
2015
影响因子：
3.8
通讯作者：
Ip,EdwardH
中科院分区：
文献类型：
--
作者：
Lu,Ji;Pan,Junhao;Zhang,Qiang;Dubé,Laurette;Ip,EdwardH
随着纵向数据的大量收集，经验抽样方法（ESM）的最新进展有利于社会科学实证研究，但也提出了重要的方法挑战。由于传统的统计模型通常不能很好地分析包含反馈回路的变量系统，因此本文提出了一种扩展的隐马尔可夫模型来模拟瞬间情绪和饮食行为之间的相互关系。本文重新研究了一个ESM数据集（Lu, Huet, & Dube），该数据集在10天内每天6次观察160名参与者的食物消耗和瞬间情绪。通过对情绪与饮食健康决策之间反馈回路的分析，提出的互易马尔可夫模型（RMM）可以同时适应隐藏状态（“一般”情绪状态：积极与消极状态）和观察状态（饮食：比平时更健康、与平时一样或不如平时健康），而无需假设观察与情绪或行为变化的平滑轨迹之间的独立性。RMM分析的结果说明了饮食消费和情绪之间的互惠链，以及调节饮食和情绪之间相互关系的情境因素的作用。仿真实验结果与实证研究结果一致，进一步证明了该方法在恢复参数方面是有希望的。
With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal the relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet, & Dube, ) that observed 160 participants' food consumption and momentary emotions 6 times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal-healthiness decision, the proposed reciprocal Markov model (RMM) can accommodate both hidden (“general” emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent with the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters.