Understanding How People Make Trait Attributions from Faces
Author: Lin, Chujun
Year: 2019
Degree: Dissertation (Ph.D.)
Advisor: Adolphs, Ralph
Committee Members: Alvarez, R. Michael; Adolphs, Ralph; Camerer, Colin F.; Mobbs, Dean
Option: Social Science
DOI: 10.7907/CEMB-6R23
Abstract
This thesis is motivated by the fascinating question of how people make inferences about others from their faces. How do we infer somebody’s intent or personality merely from looking at them? I studied this question by investigating how people make trait attributions in two specific contexts -- political election (Chapter 2) and political corruption (Chapter 3) -- as well as how people make a large variety of trait attributions from faces in general (Chapter 4). I employed novel methods to representatively sample the words used to rate faces, and to select the facial stimuli themselves (e.g., using artificial neural networks), to test the reproducibility and generalizability of my results (e.g., pre-registration, generalization across participants from different cultures), and to elucidate the underlying mechanisms (e.g., mediation modeling, digital manipulation of facial stimuli). The results demonstrated that trait attributions from politician’s faces were associated with real election outcomes in different cultures, and that culture shaped trait attributions relevant to a given context (Chapter 2); trait attributions from politician’s faces were also associated with real corruption/violation records of the politicians, and perceived corruptibility was associated with the width of the face (Chapter 3). Trait attributions from faces in general (Chapter 4) were well-described by four novel dimensions that I discovered: critical/condescending, leadership/competence, female-stereotype, and youth-stereotype. Taken together, the findings provide a new psychological framework for trait attributions, demonstrate cross- cultural generalizability, and link trait attributions to real-world behaviors.
Files
- Thesis_20190522_Final.pdf (application/pdf)