Process and representation in multiple-cue judgment

This thesis investigates the cognitive processes and representations underlying human judgment in a multiple-cue judgment task. Several recent models assume that people have several qualitatively distinct and competing levels of knowledge representations (Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998; Nosofsky, Palmeri, & McKinley, 1994; Sloman, 1996). The most successful cognitive models in categorization and multiple-cue judgment are, respectively, exemplar-based models and rule-based models. Study 1 investigated if the different theoretical conclusions in categorization and multiple-cue judgment derive from genuine differences in the processes, or are accidental to the different research methods. The results revealed large individual differences and a shift from exemplar memory to cue abstraction when the criterion is changed from a binary to a continuous variable, and especially for a probabilistic criterion. People appear to switch between qualitatively distinct processes in the two tasks. In Study 2, we expected learning in dyads to promote explicit rule-based thinking as a consequence of verbalization (social abstraction effect) and performance to improve due to the larger joint exemplar knowledge base (exemplar pooling effect). Study 2 suggests that dyads perform better, making more accurate judgments than participants working alone, but we failed to detect any difference in the representation of knowledge. When working in dyads, we can store more exemplars in memory together that leads to more efficient exploitation of memory and exemplar retrieval dominates the judgments. In contrast to earlier research, dyads surpassed the combined base-line level defined by the aggregated performance by members of the dyad working alone. Taken together, the results of these studies indicate that the differences that characterize typical categorization and multiple- cue judgment tasks are conducive of qualitatively different cognitive processes