■尽管人们对早期适应不良的模式越来越感兴趣,由于对它们的结构缺乏清晰的了解,在理解它们的影响方面的进展减速了。计算不同的综合分数时没有坚实的基础或明确的含义。在这里,我们解释了模式方差在理论上可以分解为三个组成部分:特定于模式的,由于未满足的核心需求,以及我们称之为一般敏感性的共同方差;每个变量都可以与其他实质性变量有区别地相关。使用这个框架,我们实证检验了图式的结构及其与面部情感识别的关系,可以广泛影响我们的社交互动的关键能力。
■一个成年人样本完成了情感识别任务和年轻模式问卷。使用不同的因素模型,分析了不同模式的具体和共有差异。然后,探讨了这些方差分量与面部情感识别的关系。
■一般因素解释了27%,40%,和项目总差异的64%,schemas,和域,分别。划分出公共方差,几乎没有保留特定领域的差异。关于面部情感识别,它们与特定的模式无关;然而,一般易感因素与愤怒认知相关。
■模式的方差分解方法,使用双因素模型,可能为探索模式的影响提供了更清晰的方法。虽然领域得分被广泛使用,其可靠性,有效性,意义值得怀疑。通用因素,这始终可以从经验数据中提取,需要进一步的关注。
UNASSIGNED: Despite the growing interest in the early maladaptive schemas, the progress in understanding their impacts is decelerated by a lack of clear understanding of their structure. Different composite scores are calculated without a solid ground or a clarified meaning. Here we explain that the schema variance can be theoretically decomposed into three components: schema-specific, domain-specific due to the unmet core needs, and the common variance we call general susceptibility; each can differentially correlate with other substantive variables. Using this framework, we empirically examine the structure of schemas and their relationships to facial emotion recognition, a crucial ability that can widely affect our social interactions.
UNASSIGNED: A sample of adults completed an emotion recognition task and the Young Schema Questionnaire. Using different factor models, the specific and shared variance across schemas was analyzed. Then, the relation of these variance components to facial emotion recognition was explored.
UNASSIGNED: A general factor explained 27%, 40%, and 64% of the total variance in items, schemas, and domains, respectively. Partialling out the common variance, there was little domain-specific variance remained. Regarding facial emotion recognition, they were not correlated with specific schemas; however, the general susceptibility factor was correlated with anger recognition.
UNASSIGNED: The variance decomposition approach to schemas, which uses the bifactor model, may offer a clearer way to explore the impacts of schemas. While domain scores are widely used, their reliability, validity, and meaning are questionable. The generic factor, which is consistently extractable from empirical data, requires further attention.