Research

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Although modern science, if not folk psychology (3), has largely

discarded such notions, trait evaluations from faces predict important

social outcomes ranging from electoral success (4–6) to

sentencing decisions (7, 8).

Studies show that people rapidly evaluate

faces on multiple trait dimensions such as trustworthiness and

aggressiveness (9, 10). For example, trait judgments can be formed

after as little as 38-ms exposure to an emotionally neutral face (10).

Studies also show that the amygdala, a subcortical brain region

critical for fear conditioning and consolidation of emotional memories

(11), plays a key role in the assessment of face trustworthiness

(12–15). – Instinctual human behavior. Demonstrates that it benefits our survival and reproduction.

*Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.

*What does Valence mean in psychology?

Valence, or hedonic tone, is the affective quality referring to the intrinsic attractiveness/”good”-ness (positive valence) or averseness/”bad”-ness (negative valence) of an event, object, or situation. The term also characterizes and categorizes specific emotions.

Methods –Although

people evaluate faces on multiple trait dimensions, these evaluations

are highly correlated with each other [supporting information

(SI) Fig. S1]. To identify the underlying dimensions of face evaluation,

we (i) identified traits that are spontaneously inferred from

emotionally neutral faces, (ii) collected judgments on these trait

dimensions, and (iii) submitted these judgments to a principal

components analysis (PCA). At the first stage of the project, we

asked 55 participants to generate unconstrained descriptions of

faces (study 1). These descriptions were then classified into trait

dimensions. Fourteen dimensions accounted for 68% of the 1,100

descriptions and were selected for subsequent analyses. Participants

(total n327; studies 2.1 to 2.15) were then asked to judge the same

neutral faces on these trait dimensions and dominance (Table S1).

Dominance was included because of the central importance of this

trait in models of interpersonal perception (19). For two of the

traits, the interrater agreement was very low, and they were not

included in the subsequent analyses. The judgments for the remaining

traits were highly reliable (Cronbach’s  0.90; Table S2).

• They had people describe the faces in whatever way they wanted and then classified the descriptions into different trait dimensions. 14 of the traits that most accounted for the descriptions were then selected. 

The participants were then asked to rate the faces using these 14 traits plus dominance. 2 of the traits sucked so they removed them and used the rest.

All positive judgments (e.g., attractive,

responsible) had positive loadings, and all negative judgments (e.g.,

aggressive) had negative loadings on the first PC (Table S3),

suggesting that it can be interpreted as valence evaluation (20, 21).

Judgments of dominance, aggressiveness, and confidence had the

highest loading on the second PC, suggesting that it can be

interpreted as dominance evaluation (19). This 2D structure of face

evaluation is consistent with well established dimensional models of

social perception (19, 22, 23). For example, Wiggins et al. (19, 23),

starting with a large set of traits describing interpersonal relationships,

have shown that interpersonal perception can be described by

two orthogonal dimensions, affiliation and dominance, that are

similar to the dimensions identified here.

*The loadings with the same sign contribute within the component in the same way, while those with opposite sign still contribute to the component but in an opposed way. I.e. think about a component which captures athletes performance in short distance running, with three significant eigenvectors: 1. number of hours of training/week (hrs) 2. amount of energy available to burn/hour (kJ/hr) 3. last race time recorded (seconds/100 m). Our intuition is that all this three variables may be related to “race performance”, but while the 1st and the 2nd variable importance increases with larger values – the more training hours and energy available, the better -, for the 3rd variable the lower values are important, since performance in race is related to lower times /distance. C.

Using the face model, we randomly generated 300 emotionally

neutral faces (Fig. S5) and asked participants to judge them on

trustworthiness (study 3, n  29) and dominance (study 4, n  25).

Consistent with the prior findings, the correlation between the

mean trustworthiness and dominance judgments was low (0.17;

Table S5).

They made their own faces and predicted how people would rate them. Their predictions held true. They found that people were more sensitive at judging if someone was very untrustworthy-looking and weren’t as good at distinguishing someone who should look very trustworthy.

as shown in Fig. 1A, moving from the negative

(8 SD) to the positive (8 SD) extreme of the trustworthiness

dimension, faces seemed to change from expressing anger to

expressing happiness (Movie S1). Moving from the negative to

the positive extreme of the dominance dimension, faces seemed

to change from feminine and baby-faced to masculine and

mature-faced (Movie S2).

In contrast to the findings for the trustworthiness dimension,

dominance evaluation was weakly related to facial features

resembling emotional expressions (Fig. 2A). The only categorization

responses that were significantly higher than chance

(Table S7) were for neutral (4, 0, 4 SD), Fquadratic(1,18)

185.82, P  0.001, and fearful (8 SD). Extremely submissive

faces were classified as fearful.

They don’t talk about the eyebrows with dominance despite how obvious it seems.

it should be noted that the dimensional model is most

applicable to implicit face evaluation where no specific evaluative

context is provided (14). When a context makes a specific evaluative

dimension relevant (e.g., competence), decisions would be most

likely influenced by evaluations on this dimension. For example, in

electoral decisions, voters believe that competence is the most

important attribute for a politician and evaluations of competence

but not trustworthiness predict electoral success (4). Similarly, in

mating decisions, physical attractiveness could trump evaluations

on other dimensions, including trustworthiness (37). In other words,

in specific contexts, other dimensions of face evaluation may be

critical for decisions.