Heterogeneity in affective complexity among men and women.
Heterogeneity in affective complexity among men and women.
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DOI：
10.1037/emo0000956
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发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Beltz AM
中科院分区：
文献类型：
--
作者：
Foster KT;Beltz AM
情感现象具有显著的复杂性和异质性——共同的经历和情感在个体之间会引发不同的反应以及情感问题的风险（例如，女性的发生率高于男性）。然而，通过对个体进行平均，情感科学研究传统上把情感视为同质的。直接对情感复杂性（AC）中个体特有的异质性进行建模——比如情感体验的粒度和协变——对于识别AC的共享（即共同的；一般规律的）和/或非共享（即个人的；特殊规律的）特征至关重要。本研究应用一种个体特异性技术来捕捉男性和女性日常情感及情感问题风险的异质性，并利用个性化结果来提高对AC的总体理解。
在一项密集纵向研究中，年轻成年人（n = 56；25名女性）在75天中的每一天都报告自己的情感。使用p技术（即个体特异性因素分析）对AC进行建模，并将其与传统的个体间情感模型（即二元正负情感模型）在预测女性与男性情感问题风险方面的效用进行比较。然后应用一种社区检测网络算法来估计个体特异性AC，以建立一个基于特殊规律信息的AC一般规律模型。
个体特异性分析发现个体之间的AC存在很大差异（即2 - 8个因素的范围）。相对于传统的二元模型，特殊规律模型在区分性别相关的情感问题风险方面具有递增的效用。对特殊规律结果的一般规律审视（通过社区检测）揭示了正负情感网络中的不同动态。
个体特异性科学在描绘AC的异质性以及揭示情感问题的风险路径方面具有特别的前景。
Affective phenomena have noteworthy complexity and heterogeneity – shared experiences and emotions evoke distinct responses and risk for affective problems across individuals (e.g., higher rates in women than men). Yet, by averaging across individuals, affective science research traditionally treats affect as homogenous. Directly modeling person-specific heterogeneity in affective complexity (AC) – like the granularity and covariation of affective experiences – is paramount for identifying shared (i.e., common; nomothetic) and/or unshared (i.e., personal; idiographic) features of AC. The present study applied a person-specific technique to capture heterogeneity in daily affect and risk for affective problems in men and women and leveraged personalized results to improve general understanding of AC. Young adults (n=56; 25 female) reported affect on each of 75-days of an intensive longitudinal study. AC was modeled using p-technique (i.e., person-specific factor analysis) and its utility over traditional, between-person models of affect (i.e., bivariate positive and negative affect) was compared for prediction of risk for affective problems in women compared to men. A community detection network algorithm was then applied to estimate person-specific AC to develop an idiographically-informed nomothetic model of AC. Person-specific analyses detected wide variation in AC across individuals (i.e., range of 2–8 factors). Relative to the traditional bivariate model, idiographic models had incremental utility for differentiating risk for affective problems by gender. Nomothetic review of idiographic results (via community detection) revealed distinct dynamics in positive and negative affect networks. Person-specific science holds particular promise for mapping heterogeneity in AC and uncovering risk pathways for affective problems.
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影响因子：
17.7
作者：
Hettema, JM;Neale, MC;Kendler, KS
通讯作者：
Kendler, KS
影响因子：
4.1
作者：
Fisher, Aaron J.;Bosley, Hannah G.
通讯作者：
Bosley, Hannah G.
影响因子：
7.6
作者：
Green, DP;Salovey, P;Truax, KM
通讯作者：
Truax, KM
影响因子：
2.6
作者：
Barrett LF
通讯作者：
Barrett LF
影响因子：
3.8
作者：
Beltz AM;Wright AG;Sprague BN;Molenaar PC
通讯作者：
Molenaar PC