Department of Psychology, University of York, York, UK.
Department of Psychology, University of York, York, UK.
Cognition. 2023 Jan;230:105288. doi: 10.1016/j.cognition.2022.105288. Epub 2022 Sep 24.
When we encounter a stranger for the first time, we spontaneously attribute to them a wide variety of character traits based on their facial appearance. There is increasing consensus that learning plays a key role in these first impressions. According to the Trait Inference Mapping (TIM) model, first impressions are the products of mappings between 'face space' and 'trait space' acquired through domain-general associative processes. Drawing on the associative learning literature, TIM predicts that first-learned associations between facial appearance and character will be particularly influential: they will be difficult to unlearn and will be more likely to generalise to novel contexts than appearance-trait associations acquired subsequently. The study of face-trait learning de novo is complicated by the fact that participants, even young children, already have extensive experience with faces before they enter the lab. This renders the study of first-learned associations from faces intractable. Here, we overcome this problem by using Greebles - a class of novel synthetic objects about which participants had no previous knowledge or preconceptions - as a proxy for faces. In four experiments (total N = 640) with adult participants we adapt classic AB-A and AB-C renewal paradigms to study appearance-trait learning. Our results indicate that appearance-trait associations are subject to contextual control, and are resistant to counter-stereotypical experience.
当我们第一次遇到陌生人时，我们会根据他们的面部特征自发地赋予他们各种各样的性格特征。越来越多的共识认为，学习在这些第一印象中起着关键作用。根据特质推断映射（TIM）模型，第一印象是通过一般联想过程获得的“脸空间”和“特质空间”之间的映射产物。借鉴联想学习文献，TIM 预测，面部外观和性格之间的第一印象关联将特别有影响力：它们将难以消除，并且比随后获得的外观-特质关联更有可能推广到新的情境。从头开始研究面部特质学习很复杂，因为参与者，甚至是年幼的孩子，在进入实验室之前已经有了丰富的面部经验。这使得研究从面部开始的第一印象关联变得棘手。在这里，我们通过使用 Greebles（一类新的合成物体，参与者对此一无所知或没有先入为主的观念）作为面部的替代品来克服这个问题。在四项涉及成年参与者的实验（总 N=640）中，我们改编了经典的 AB-A 和 AB-C 更新范式来研究外貌-特质学习。我们的结果表明，外貌-特质关联受到上下文控制，并且不受反刻板印象经验的影响。
