Similarity and dissimilarity as evidence in perceptual categorization

In exemplar models the similarities between a new stimulus and each category exemplar constitute positive evidence for category membership. In contrast, other models assume that, if the new stimulus is sufficiently dissimilar to a category member, then that dissimilarity constitutes evidence against category membership. We propose a new similarity–dissimilarity exemplar model that provides a framework for integrating these two types of accounts. The evidence for a category is assumed to be the summed similarity to members of that category plus the summed dissimilarity to members of competing categories. The similarity–dissimilarity exemplar model is shown to mimic the standard exemplar model very closely in the unidimensional domain

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization

Is the Short Rate Drift Actually Nonlinear?

Virtually all existing continuous-time, single-factor term structure models are based on a short rate process that has a linear drift function. However, there is no strong a priori argument in favor of linearity, and Stanton (1997) and Ait-Sahalia (1996) employ nonparametric estimation techniques to conclude that the drift function of the short rate contains important nonlinearities. Comparatively little is known about the finite-sample properties of these estimators, particularly when they are applied to frequent sampling of a very persistent process, like short term interest rates. In this paper, we apply these estimators to simulated sample paths of a square-root diffusion. Although the drift function is linear, both estimators suggest nonlinearities of the type and magnitude reported in by Stanton (1997) and Ait-Sahalia (1996). These results, along with the results of a simple GMM estimation procedure applied to the Stanton and Ait-Sahalia data sets, imply that nonlinearity of the short rate drift is not a robust stylized fact.term structure, continuous-time