Exploring the development of face recognition across childhood via logistic mixed-effects modelling of the standardised Cambridge Face Memory Test.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Louise Ewing, Nadja Althaus, Emily K Farran, Michael Papasavva, Inês Mares, Marie L Smith
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Abstract

Individual differences in face identity recognition abilities are present across the lifespan but require developmentally differentiated methods of assessment. Here, we examine the empirical validity of a widely used face identity recognition measure, the Cambridge Face Memory Test for Children (CFMT-C). Logistic mixed-effects modelling of a large data set (607 children, 5-12 years) replicates and extends the findings of the only previous normative study of the CFMT-C (Croydon et al., Neuropsychologia, 62, 60-67, 2014). This novel, analytical approach enables us to take into account sources of variability typically overlooked in a classical analysis. We consider variability introduced by the task, alongside variability across children, to provide the first comprehensive characterisation of the interactive effects of factors inherent to participants (e.g. age, gender, and ethnicity), and the test (stage: face learning, simple recognition, harder recognition) on face memory performance. In line with past findings, we clearly observed age-related improvement in the task. Additionally, and for the first time, we report that this developmental effect is significantly more pronounced in the later, harder stages of the task; that there is an effect of gender, with females having better performance; and that consideration of participant ethnicity or testing context did not alter the best fitting model of these data. These results highlight the value of applying multilevel statistical models to characterise the factors driving performance variability, providing evidence of the divergence in recognition abilities across genders and confirming the stability of the CFMT-C in assessing face recognition abilities across variable experimental contexts and with diverse participant groups.

通过标准剑桥面部记忆测试的logistic混合效应模型探索儿童面部识别的发展。
面部识别能力的个体差异贯穿整个生命周期,但需要发展分化的评估方法。在这里,我们检验了广泛使用的面部身份识别测量,剑桥儿童面部记忆测试(CFMT-C)的经验有效性。大型数据集(607名儿童,5-12岁)的Logistic混合效应模型重复并扩展了之前唯一的CFMT-C规范研究的结果(Croydon等人,神经心理学,62,60-67,2014)。这种新颖的分析方法使我们能够考虑到在经典分析中通常被忽视的变异性的来源。我们考虑了任务引入的可变性,以及儿童之间的可变性,以首次全面描述参与者固有因素(如年龄、性别和种族)和测试(阶段:面部学习、简单识别、更难识别)对面部记忆表现的互动影响。与过去的研究结果一致,我们清楚地观察到与年龄相关的任务改善。此外,我们第一次报告说,这种发展效应在任务的后期、更困难的阶段更为明显;这是性别的影响,女性表现更好;考虑到参与者的种族或测试背景并没有改变这些数据的最佳拟合模型。这些结果突出了应用多层次统计模型来描述驱动性能变化的因素的价值,提供了性别识别能力差异的证据,并证实了CFMT-C在评估不同实验背景和不同参与者群体的人脸识别能力方面的稳定性。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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