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dc.contributor.advisorFiske, Susan T.-
dc.contributor.advisorTodorov, Alexander-
dc.contributor.authorNicolas Ferreira, Gandalf-
dc.contributor.otherPsychology Department-
dc.description.abstractCategorizing and stereotyping others are unavoidable features of human life. However, despite decades of research, there is still no complete consensus on the dimensions that perceivers use to make sense of others. Various models have proposed dimensions such as warmth, competence, socioeconomic status, and progressive-conservative beliefs, but how to integrate these models and whether other dimensions should also be modeled remains controversial. A limitation of current models is their reliance on predetermined numerical ratings on dimensions that are explicitly queried. Here I develop and introduce free response measures of stereotype content in order to study more spontaneous impressions. This approach has several advantages over traditional metrics: (a) it circumvents researcher biases in the selection of evaluative dimensions, (b) it provides information about the salience of evaluative dimensions, and (c) it allows for an examination of additional relevant cognitive processes (e.g., reaction times). Across three chapters, I describe the process of developing text analysis instruments, their application to studying information-gathering processes (in an “adversarial” collaboration), and a spontaneous stereotype content model, a taxonomy of free-response stereotypes. These chapters provide evidence for the role of spontaneous stereotypes in an integrative and generative framework, uncovering moderators of stereotype dimension priority, dimensional usage rates and intercorrelations, as well as stereotype processes and properties that improve our understanding of person perception. This research has implications for the measurement of psychological dimensions in text (e.g., hateful stereotypes in social media), the integration of adversary models of social cognition, and the discovery of novel constructs that may better model the complexity of an increasingly diverse social world.-
dc.publisherPrinceton, NJ : Princeton University-
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=> </a>-
dc.subjectnatural language processing-
dc.subjectsocial categorization-
dc.subjecttext analysis-
dc.subject.classificationComputer science-
dc.typeAcademic dissertations (Ph.D.)-
Appears in Collections:Psychology

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