Figure - available from: Complexity
This content is subject to copyright. Terms and conditions apply.
Structure of the chromosomes. A face is constructed by placing on the base face the features indicated by genes 1, 3, 6, 8, and 10, at the positions indicated by genes 2, 4, 5, 7, and 9.
Source publication
Human faces play a central role in our lives. Thanks to our behavioural capacity to perceive faces, how a face looks in a painting, a movie, or an advertisement can dramatically influence what we feel about them and what emotions are elicited. Facial information is processed by our brain in such a way that we immediately make judgements like attrac...
Citations
... When we see a face for the first time, we infer the personality traits of that person by matching the visual input to our mental prototypes of faces with different attributes. From the result of this match, we infer the personality traits of the owner of 2 Complexity the face [38], making attributions such as trustworthiness or dominance [41][42][43][44][45][46]. NBRC methods produce relevant CIs displaying the image that the participants use as a referent to evaluate the required judgement, referred to as "prototypical image" [33]. ...
Consumer behavior knowledge is essential to designing successful products. However, measuring subjective perceptions affecting this behavior is a complex issue that depends on many factors. Identifying visual cues elicited by the product’s appearance is key in many cases. Marketing research on this topic has produced different approaches to the question. This paper proposes the use of Noise-Based Reverse Correlation techniques in the identification of product form features carrying a particular semantic message. This technique has been successfully utilized in social sciences to obtain prototypical images of faces representing social stereotypes from different judgements. In this work, an exploratory study on subcompact cars is performed by applying Noise-Based Reverse Correlation to identify relevant form features conveying a sports car image. The results provide meaningful information about the car attributes involved in communicating this idea, thus validating the use of the technique in this particular case. More research is needed to generalize and adapt Noise-Based Reverse Correlation procedures to different product scenarios and semantic concepts.
... Yet, identifying what specific face features drive these judgments is challenging because the face is a highly complex information space of multivariate 3D shape and complexion [8]. Most work is limited to only on a few specific features such as face width e.g., [4] and existing modeling solutions are often not suited to explore the high-dimensional space of the face [6,8]. This in turn has restricted the generative capacity of virtual agents to display realistic and psychologically impactful face signals, thus necessitating the costly hand-crafting of faces (e.g., [2]) that have low variability [10], which ultimately impacts the effectiveness of human-agent interactions. ...
First impressions of key social traits such as trustworthiness and competence are often based on rapid judgments of facial appearance, with substantial downstream consequences for individuals. A challenge remains to understand the specific face features that drive social perception. Here, we address this by showing that two major models of social perception (warmth/competence, Fiske et al., 2006; trustworthiness/dominance, Oosterhof & Todorov, 2008) are structured by a set of latent features that are shared across social traits, plus a set of trait-specific features that distinguish them. Specifically, we used a novel 3D face generator (Zhan, Garrod, van Rijsbergen, & Schyns, 2019), reverse correlation, social trait perception, and a data reduction technique to model these shared and unique features. Thirty participants (15 women, white, Western, 18-35 years) each viewed 2400 randomly generated 3D face identities and rated each on the four bipolar social trait dimensions (e.g., ‘very submissive’ to ‘very dominant’) in separate tasks. To identify the specific 3D face features that elicit these perceptions, we linearly regressed the 3D face information presented on each trial and the participant’s responses, producing 360 3D face models per trait. Next, to identify their features, we reduced all 3D face models with non-negative matrix factorization and mapped the resulting feature combinations that characterize each trait. Dominance and competence share an inwards change of the eyebrow region, also shared with cold and untrustworthy. Thus, a single feature can be shared across traits, including those thought to be unrelated with each other. Trustworthiness and warmth similarly share many features, predominantly around the mouth (i.e., upturned mouth corners). Our results reveal a compositionality of social trait perception, driven by shared 3D shape features plus unique accents, which have the generative capacity of designing digital social avatars and robots that convey first impressions of key social traits.
