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
the research question(s),
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?)
state the hypothesis(es),
describe the design and procedures you expect to find in the next section.
what the introduction says, what kind of sample would you select?
you design a true experiment to the
test the hypotheses, what do you expect the independent and dependent variables
many conditions will there be and what are they?
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
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
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.
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).
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.
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.
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.
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