Adapting cultural mixture modeling for continuous measures of knowledge and memory fluency.
Previous research (e.g., cultural consensus theory (Romney, Weller, & Batchelder, American Anthropologist, 88, 313-338, 1986); cultural mixture modeling (Mueller & Veinott, 2008)) has used overt response patterns (i.e., responses to questionnaires and surveys) to identify whether a group shares a single coherent attitude or belief set. Yet many domains in social science have focused on implicit attitudes that are not apparent in overt responses but still may be detected via response time patterns. We propose a method for modeling response times as a mixture of Gaussians, adapting the strong-consensus model of cultural mixture modeling to model this implicit measure of knowledge strength. We report the results of two behavioral experiments and one simulation experiment that establish the usefulness of the approach, as well as some of the boundary conditions under which distinct groups of shared agreement might be recovered, even when the group identity is not known. The results reveal that the ability to recover and identify shared-belief groups depends on (1) the level of noise in the measurement, (2) the differential signals for strong versus weak attitudes, and (3) the similarity between group attitudes. Consequently, the method shows promise for identifying latent groups among a population whose overt attitudes do not differ, but whose implicit or covert attitudes or knowledge may differ.
Department of Cognitive and Learning Sciences, Michigan Technological University, Houghton, MI, USA.
Yin-Yin Sarah
YY
Tan
Department of Cognitive and Learning Sciences, Michigan Technological University, Houghton, MI, USA. shanem@mtu.edu.
Shane T
ST
Mueller
D001290:Attitude; D003198:Computer Simulation; D003469:Culture; D005006:Ethnicity / Q000523:psychology*; D006801:Humans; D019359:Knowledge; D008568:Memory; D008960:Models, Psychological; D011930:Reaction Time; D011795:Surveys and Questionnaires
Cultural consensus theory; Finite mixture modeling; Implicit memory