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 DongWon Oh, PhD


Postdoctoral Associate
New York University
6 Washington Place
New York, NY 10003 USA



Bio


DongWon Oh studies how people recognize and form impressions from other individuals’ faces – a foundation for human interaction. To address this question, he uses behavioral experiments, data-driven modeling, face morphing, reverse correlation, and more recently, neuroimaging.

DongWon is a postdoc in Social Cognitive and Neural Sciences Lab at New York University (advisor: Jonathan B. Freeman). Before his current position, DongWon completed a Psychology Ph.D. at Princeton University (advisor: Alexander Todorov).

Data-Driven
Modeling of
Face Evalution




People make intuitive judgments of other people’s traits (e.g., intelligence, kindness) from their appearances with ease, irrespective of the accuracy of the judgments. Many of these judgments affect important social outcomes, like voting, and hiring (Todorov et al. Ann Rev Psychol 2015).
Data-driven modeling of social impressions can identify the visual information responsible for these impressions (e.g., an impression of competence) with little prior assumptions as to what facial features matter. Some features that matter reflect stereotypes prevalent in society. For instnace, masculinity is one of the main ingredients of impressions of competence (Oh, Buck, & Todorov, Psych Sci 2019). This gender stereotype was not apparent because competence impressions are strongly correlated with facial attractiveness, which tends to be negatively correlated with masculinity. Taking advantage of the computational nature of the models of impressions, we built a model of competence that can manipulate perceived competence while controlling for attractiveness. This model removes the strong halo effect on competence impressions and shows that competent-looking faces are more masculine. This line of research not only identifies the visual information that affects social impressions but also uncovers hidden stereotype biases in these impressions.

Controlling for facial attractiveness (a natural confound of competence impressions) more competent faces look more masculine.
x = the manipulation level of the competence impression models; y = to what extent people categorized faces as male; arrays of faces = two models used to manipulate faces on their perceived competence).


The multidimensional nature of a data-driven computatoinal model allows a separate analysis of the effects the facial shape and reflectance on various social judgments (e.g., attractiveness), two main sources of facial social judgments.

Using mixed-effects models, we found that (1) the effect of face shape and face reflectance information on social judgments is largely linear and additive, (2) which kind of information (shape vs. reflectance) is weighted more heavily in judgments depends on the judged dimension (i.e., shape is more important for trustworthienss, dominance, and extroversion judgments; reflectance for competence; both for attractiveness), and (3) the relative amount of contribution of shape and reflectance is stable irrespective of the amount of visual information available (Oh, Dotsch, & Todorov, Vis Res, accepted).

References


Oh D, Buck EA, Todorov A (2018) Revealing hidden gender biases in competence impressions from faces. Psychological Science, 30, 65-79.

Oh D, Dotsch R, Todorov A (accepted) Contribution of shape and reflectance information to social judgments from faces. Vision Research. 

Todorov A, Olivola CY, Dotsch R, Mende-Siedlecki P (2015) Social attributions from faces: Determinants, consequences, accuracy, and functional significance. Annual Review of Psychology, 66, 519–545.


Gender Biases in
Social Face Perception





People make intuitive judgments of other people’s traits (e.g., intelligence, kindness) from faces. Such intuitive judgments  reveal biases people have about sexes (e.g., “men are competitive”, “women are emotional“).
Perceivers’ expectations about gender (e.g., gender stereotypes) affect social judgments of faces of different genders. Dimensionality reduction  and data-driven face models found that human raters’ impressions of female faces are more simplified (e.g., higher correlations among the impressions of warmth, dominance, attractiveness, happiness, emotional stability, and so on) and are more highly tied to overall positivity-negativity than male faces (Oh, Dotsch, Porter, & Todorov, J Exp Psychol: Gen 2019). Further, raters who are more willing to endorse gender stereotypes (e.g., men are competitive, women are emotional), they show more simplified impressions of both men and women.

People have more simplified and valence-laden impressions of women than of men.
Left: y = the level of correlations among impressions; each dot = the level of correlations between a pair of impression ratings).
Right: PC1 = the component representing the dependency of impressions on overall positivity-negativity in male and female impressions; PC2 = the component explaining the second largest amount of variance in male and female impressions.


Masculine facial characteristics are at the basis of people’s face-based judgments of competence. (Oh, Buck, & Todorov, Psych Sci 2018). It was revealed via data-driven models describing facial characteristics that elicit competence impressions (in the absence of attractiveness – which covaries with competence impressions under normal circumstances).

This line of research informs us about the importance of how people’s preconceptions manifests in how we form first impressions based on faces.

References

Oh D, Buck EA, Todorov A (2018) Revealing hidden gender biases in competence impressions from faces. Psychological Science, 30, 65-79.


Oh D, Dotsch R, Porter JM, Todorov A (2019) Gender biases in impressions from faces: Empirical studies and computational models. Journal of Experimental Psychology: General.

Non-Facial Biases in
Social Face Perception



People make intuitive judgments of other people’s traits (e.g., intelligence, kindness) from faces. Such intuitive judgments reveal biases people have about other individuals (e.g., “economically successful individuals are competent”).
By asking participnats to rate faces paired with upper-body clothes suggesting that the individual is either richer or poorer on competence,  we found that subtle economic status cues from clothes affect perceived competence from faces (Oh, Shafir, & Todorov, Nat Hum Beh accepted). The effect persisted even when perceivers are warned that such cues are non-informative or instructed and incentivized to ignore them. This bias puts low-income individuals at a disadvantage. 

People rated faces paired with “richer”-appearing clothes as more competent than the same faces paired with “poorer”-appearing clothes.
a: Human subjects were given various measures that discouraged them to rely on the clothes while judging competence of other individuals. x = the mean  competence rating of a face with it is paird with “poorer” clothes; y = the mean competence rating of a face with it is paird with “richer” clothes; each dot = each face.
b: Subjects were promised 100 USD to accurately judge how competent each face looked (that is, completely ignore the effect of the clothes). Strikingly, this did not change the biases induced by economic status cues in the clothes. x = each face; y = mean comeptence rating; each color = economic status of  clothes paired with the face.
References

Oh D, Shafir E, Todorov A (accepted) Economic status cues from clothes affect perceived competence from faces. Nature Human Behaviour. 


Journal Articles


2019


Oh D, Shafir E, Todorov A (in press) Economic status cues from clothes affect perceived competence from faces. Nature Human Behaviour. [Preprint] [Data] [Stimuli]

Oh D, Dotsch R, Todorov A (2019) Contribution of shape and reflectance information to social judgments from faces. Vision Research, 165, 131–142. doi: 10.1016/j.visres.2019.10.010 [Paper - free access until 1/3/2020] [Preprint] [Data/Stimuli/Code]

Jones BC, DeBruine LM, Flake JK, …, Oh D, Freeman JB, …, Coles NA (accepted). To which world regions does the valence-dominance model of social perception apply? (Registered Report Stage 2). Nature Human Behaviour. [Preprint] [Stage-1 Proposal] 
Oh D, Dotsch R, Porter JM, Todorov A (2019) Gender biases in impressions from faces: Empirical studies and computational models. Journal of Experimental Psychology: General. [Paper] [Preprint] [Data] [Stimuli]


2018


Oh D, Buck EA, Todorov A (2018) Revealing hidden gender biases in competence impressions from faces. Psychological Science, 30, 65-79. [Paper] [Preprint] [Data] [Stimuli]
Oh, D, Wang S, Todorov A (2018). 300 random Asian faces. figshare. Fileset. doi: 10.6084/m9.figshare.7361270 [Stimulus Set]


In Progress


Oh D, Freeman JB (under review) Person knowledge shapes face identity perception.
Ferrari C, Oh D, Labbree B, Todorov A (resubmitted) Learning the affective value of people: more than affect-based mechanisms.
Avery JJ, Cooper J, Oh D, Feldman L (resubmitted) Criminal stereotypes of Muslim and Arab Americans and the impact on evaluations of ambiguous criminal evidence.
Martinez JE, Oh D, Todorov A (in prep) Immigrant face-ism: documentation statuses evoke distinct visual stereotypes.


Conference
Abstracts
and Posters


Oh D (accepted, Feb 2020) Gender biases in face-based impressions. 2020 Society of Personality and Social Psychology Annual Convention. New Orleans, La. [Symposium]
Oh D, Dotsch R, Porter JM, Buck EA, Todorov A (Mar 2018) Revealing gender biases in first impressions. 2018 University of West Australia, ARC Centre of Excellence in Cognition and its Disorders, Person Perception Workshop. Perth, Australia. [Abstract]
Oh D, Dotsch R, Porter JM, Buck EA, Todorov A (Mar 2018) Revealing gender biases in first impressions. 2018 Society of Personality and Social Psychology Annual Convention. Atlanta, GA. [Poster]
Carr EW, Oh D, Brady TF, Winkielman P, Todorov A (Aug 2017) First impressions of happiness are biased by how average people appear. 2018 Academy of Management Symposium. Atlanta, GA. [Abstract]
Oh D, Dotsch R, Porter JM, Todorov A (Oct 2016) The structure of female face evaluation is simpler than male face evaluation: Empirical studies and computational models. 2016 NY Social and Affective Neuroscience Meet-Up. New York, NY.