故事回忆是一种情景记忆范式,在对衰老影响感兴趣的研究人员中很受欢迎,疾病,和/或对记忆功能的损害;在研究神经典型的年轻人的个体差异研究人员中,它不那么受欢迎。差异心理学家可能偏爱其他情景记忆范式的一个原因是,对故事回忆进行评分的前景令人生畏,因为它通常需要手动评分数百或数千个自由回忆的叙述。在这项研究中,我调查了两个与得分故事回忆有关的问题,以进行个体差异研究。首先,通过对故事回忆进行评分以记忆中心和外围细节是否有任何收获,或者一个分数就足够了。第二,我调查了是否可以使用计算方法自动评分-即,BERTSCore和GPT-4。共有235人参与了这项研究。在潜在变量级别,中枢和外周因素高度相关(r=.99),这两个因素与外部因素相关(即,流体智能,结晶的智力,和工作记忆容量)类似。关于自动评分,BERTSCore和GPT-4衍生得分均与手动衍生得分密切相关(r≥.97);此外,从各种评分方法估计的因素都显示出与外部因素相关的相似模式。因此,差异心理学家可以通过忽略细节类型和使用自动化方法来简化评分。需要进一步的研究,特别是自动化方法,由于BERTSCore和GPT-4得出的评分偶尔都是leptokurtic,而手动评分则不是.
Story recall is an episodic memory paradigm that is popular among researchers interested in the effects of aging, disease, and/or injury on memory functioning; it is less popular among individual-differences researchers studying neurotypical young adults. One reason differential psychologists may favor other episodic memory paradigms is that the prospect of scoring story recall is daunting, as it typically requires manually scoring hundreds or thousands of freely recalled narratives. In this study, I investigated two questions related to scoring story recall for individual differences research. First, whether there is anything to gain by scoring story recall for memory of central and peripheral details or if a single score is sufficient. Second, I investigated whether scoring can be automated using computational methods - namely, BERTScore and GPT-4. A total of 235 individuals participated in this study. At the latent variable level, central and peripheral factors were highly correlated (r = .99), and the two factors correlated with external factors (viz., fluid intelligence, crystallized intelligence, and working memory capacity) similarly. Regarding automated scoring, both BERTScore and GPT-4 derived scores were strongly correlated with manually derived scores (r ≥ .97); additionally, factors estimated from the various scoring methods all showed a similar pattern of correlations with the external factors. Thus, differential psychologists may be able to streamline scoring by disregarding detail type and by using automated approaches. Further research is needed, particularly of the automated approaches, as both BERTScore and GPT-4 derived scores were occasionally leptokurtic while manual scores were not.