This thesis investigates the cognitive processes and representations underlying human judgment in a multiple-cue judgment task. Several recent models as-sume 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 cue abstraction models. The models are different in the computations and processes implied, but the structure of the task is similar. 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. In Study 2, we expected learning in dyads to promote explicit cue abstraction as a consequence of verbalization (a social abstraction effect) and performance to improve due to the larger joint exemplar knowledge base (an exemplar pooling effect). In Study 3 we used the generalized model Sigma to illustrate how change in task environments (linear vs. nonlinear) can shape the knowledge representation that is used. We expected that people are not able to use cue ab-straction when judging objects with a non-linear structure between the visual cues (features) of the objects and the criterion, and therefore they are forced to use exemplar-based processes. Taken together, the results of these studies indicate that differences that characterize typical categorization and multiple-cue judgment tasks are conducive of qualitatively different cognitive processes, and that the task environment plays an important role for which cognitive processes are used in multiple cue judgments.

<p>The inverse base-rate effect is the observation that on certain occasions people classify new objects as belonging to rare base-rate categories rather than common ones (e.g., D. L. Medin & S. M. Edelson, 1988). This finding is inconsistent with normative prescriptions of rationality, and provides an anomaly for current theories of human knowledge representation, such as the exemplar-based models of categorization, which predict a consistent use of base-rates (e.g., D. L. Medin & M. M. Schaffer, 1978). This thesis presents a novel explanation of the inverse base-rate effect. The proposal is that participants sometimes eliminate category options that are inconsistent with well-supported inference rules. These assumptions contrast with those by attentional theory (J. K. Kruschke, in press), according to which the inverse base-rate effect is the outcome of rapid attention shifts operating on cue-category associations. Study I, II, and III verified seven qualitative predictions derived from the eliminative inference idea. None of these phenomena can be explained by attentional theory. The most important of these findings were that elimination of well-known, common categories mediate the inverse base-rate effect rather than the strongest cue-category associations (Study I), that only participants with a rule-based mode of generalization exhibit the inverse base-rate effect (Study II), and that rapid attentional shifts per se do not accelerate learning, but rather decelerate it (Study III). In addition, Study I provided a quantitative implementation of the eliminative inference idea, ELMO, that demonstrated that this high-level reasoning process can produce the basic pattern of base-rate effects in the inverse base-rate design. Taken together, as an account of the inverse base-rate effect the empirical evidence of this thesis suggest that rule-based elimination is a powerful component of the inverse base-rate effect. But previous studies have indicated that attentional shifts affect the inverse base-rate effect, too. Therefore, a complete account of the inverse base-rate effect needs to integrate inductive and eliminative inferences operating on rule-based representations with attentional shifts. The Discussion of this thesis propose a number of suggestions for such integrative work. </p>

The inverse base-rate effect is the observation that on certain occasions people classify new objects as belonging to rare base-rate categories rather than common ones (e.g., D. L. Medin &amp; S. M. Edelson, 1988). This finding is inconsistent with normative prescriptions of rationality, and provides an anomaly for current theories of human knowledge representation, such as the exemplar-based models of categorization, which predict a consistent use of base-rates (e.g., D. L. Medin &amp; M. M. Schaffer, 1978). This thesis presents a novel explanation of the inverse base-rate effect. The proposal is that participants sometimes eliminate category options that are inconsistent with well-supported inference rules. These assumptions contrast with those by attentional theory (J. K. Kruschke, in press), according to which the inverse base-rate effect is the outcome of rapid attention shifts operating on cue-category associations. Study I, II, and III verified seven qualitative predictions derived from the eliminative inference idea. None of these phenomena can be explained by attentional theory. The most important of these findings were that elimination of well-known, common categories mediate the inverse base-rate effect rather than the strongest cue-category associations (Study I), that only participants with a rule-based mode of generalization exhibit the inverse base-rate effect (Study II), and that rapid attentional shifts per se do not accelerate learning, but rather decelerate it (Study III). In addition, Study I provided a quantitative implementation of the eliminative inference idea, ELMO, that demonstrated that this high-level reasoning process can produce the basic pattern of base-rate effects in the inverse base-rate design. Taken together, as an account of the inverse base-rate effect the empirical evidence of this thesis suggest that rule-based elimination is a powerful component of the inverse base-rate effect. But previous studies have indicated that attentional shifts affect the inverse base-rate effect, too. Therefore, a complete account of the inverse base-rate effect needs to integrate inductive and eliminative inferences operating on rule-based representations with attentional shifts. The Discussion of this thesis propose a number of suggestions for such integrative work.