Due by Oct. 3rd, 2018

Read an introduction for a research paper provided here, entitled, “What Is Typical Is Good: The Influence of Face Typicality on PerceivedTrustworthiness”. In about two pages, summarize the questions being addressed and the rationale for the study. More specifically, please

(1)   state the research question(s),

(2)   briefly summarize the rationale behind the study (what is the status of current research literature on this topic – what have done and what have yet to do? What characters make this study unique?)

(3)   clearly state the hypothesis(es),

(4)   and describe the design and procedures you expect to find in the next section.

a.     Given what the introduction says, what kind of sample would you select?

b.     If you design a true experiment to the test the hypotheses, what do you expect the independent and dependent variables to be?

c.       How many conditions will there be and what are they?

d.     What controls will need to be in place to address potential confounding variables?

Remember, here, your goal is try to think yourself as the researcher, and design a well-controlled true experiment study that fits the overview the researcher provides in the introduction section and addresses the question the researcher is investigating.  

Please use APA format when write your response paper, including a cover page and the body of the writing.

What Is Typical Is Good: The Influence of Face Typicality on Perceived Trustworthiness

Face typicality is important for face recognition (Bartlett, Hurry, & Thorley, 1984; Rhodes, Brennan, & Carey, 1987) and for the mind’s representation of face identity (Valentine, 1991). The highly studied norm-based face- space model (Valentine, 1991) posits that the typical, or average, face maintains a special status because it is extracted from faces previously seen and because it serves as a standard against which all faces are evaluated; in this model, all faces are represented as vectors originating from the typical face.

However, whether face typicality is important for face evaluation is unclear. Prior studies have focused primarily on the relationship between face typicality and attractiveness (e.g., DeBruine, Jones, Unger, Little, & Feinberg, 2007; Langlois, Roggman, & Musselman, 1994; Perrett, May, & Yoshikawa, 1994; Said & Todorov, 2011). In a pioneering study, Langlois and Roggman (1990) found that the digital average of 32 faces was perceived as more attractive than subsets of these faces and almost all the individual constituent faces. They interpreted this as indicating that, in general, an average face is the most attractive face. A meta-analysis subsequently confirmed a medium to large effect of face typicality on attractiveness judgments (Rhodes, 2006).

Other findings, however, cast doubt on the importance of typicality for attractiveness. Perrett et al. (1994) found that the digital average of a set of 60 female faces (the typical face) was judged as less attractive than the average of the 15 most attractive faces from the same set. Similarly, DeBruine et al. (2007) found that the judged attractiveness of face composites varying on a typicality- attractiveness dimension with the typical face located at the midpoint increased from the unattractive face to the typical face and then continued to increase as faces became more like the attractive face. Recently, Said and Todorov (2011) developed a model that predicts a face’s attractiveness from its position in a multidimensional face space. They found that the most attractive faces were close to the typical face on some dimensions, but far from the typical face on others.

Seemingly, these findings indicate that the value of face typicality for face evaluation may be smaller than previously thought. However, we argue that face typicality is an important determinant of face evaluation and affects trustworthiness judgments. We focus on trustworthiness judgments because they approximate general face evaluations. For example, in a principal component analysis of social judgments of faces, trustworthiness judgments were extremely highly correlated with the first principal component, which typically accounts for 60% of the variance and models evaluation (Oosterhof& Todorov, 2008). Given the relationships among typicality, familiarity, and positive affect, we expect that typicality affects trustworthiness judgments.

Typicality predicts the familiarity of objects from non- face categories (e.g., birds, automobiles; Halberstadt& Rhodes, 2003), and familiarity enhances positive affect toward objects (Lee, 2001). Face processing is no different. Bartlett et al. (1984) found that for never-before-seen faces, the perceived familiarity of typical faces was greater than that of atypical faces. In a study complementing these findings, Zebrowitz, Bronstad, and Lee (2007) found that familiar faces were liked more and were judged to be safer (i.e., more trustworthy and less hos- tile) than unfamiliar faces. Taken together, these findings suggest that perceived trustworthiness is influenced by face typicality. Recently, Todorov, Olivola, Dotsch, and Mende-Siedlecki (in press) found that perceived trust- worthiness decreased as the distance of computer- generated faces from the typical face increased, even though the faces’ dimensions (cues) were designed to be orthogonal (in the statistical face space) to the trustworthiness dimension. Interestingly, Galton (1883), who invented composite photography (the predecessor of modern morphing techniques), argued that every nation has its own typical face, which can be derived from aver- aging enough representative faces, and that this typical face represents the ideal face of the nation. Galton’s insight suggests that this “ideal” (typical) face, perhaps the most consensually familiar face in a population, can serve as an important standard for the evaluation of novel faces. Presumably, atypical faces in a population—those that are distant from the ideal face—would be evaluated more negatively than the ideal—typical—face.

In this study, we tested the influence of a face’s distance from the typical face (DFT) on observers’ perception of the face’s trustworthiness and attractiveness. In Experiment 1, we used a typical face and an attractive composite to create a range of face transforms. We expected that trustworthiness and attractiveness judgments would follow different trends, although ordinarily they are aligned. As the faces became more like the typical face, we anticipated trustworthiness judgments to follow a positive trend but attractiveness judgments to follow a negative trend.