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55
A neuropsychological theory of multiple systems in category learning
 PSYCHOLOGICAL REVIEW
, 1998
"... A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior ci ..."
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Cited by 229 (24 self)
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A neuropsychological theory is proposed that assumes category learning is a competition between separate verbal and implicit (i.e., procedurallearningbased) categorization systems. The theory assumes that the caudate nucleus is an important component of the implicit system and that the anterior cingulate and prefrontal cortices are critical to the verbal system. In addition to making predictions for normal human adults, the theory makes specific predictions for children, elderly people, and patients suffering from Parkinson's disease, Huntington's disease, major depression, amnesia, or lesions of the prefrontal cortex. Two separate formal descriptions of the theory are also provided. One describes trialbytrial learning, and the other describes global dynamics. The theory is tested on published neuropsychological data and on category learning data with normal adults.
Theorybased Bayesian models of inductive learning and reasoning
 Trends in Cognitive Sciences
, 2006
"... Theorybased Bayesian models of inductive reasoning 2 Theorybased Bayesian models of inductive reasoning ..."
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Cited by 83 (19 self)
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Theorybased Bayesian models of inductive reasoning 2 Theorybased Bayesian models of inductive reasoning
On the danger of averaging across observers when comparing decision bound and generalized context models of categorization
 Perception & Psychophysics
, 1999
"... Averaging across observers is common in psychological research. Often averaging reduces the measurement error, and thus does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorre ..."
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Cited by 59 (40 self)
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Averaging across observers is common in psychological research. Often averaging reduces the measurement error, and thus does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorrect inference about the behavior of individuals. This research investigated the influence of averaging across observers on the fits of decision bound models (F.G. Ashby, 1992a) and generalized context models (GCM; R.M. Nosofsky, 1986) through Monte Carlo simulation of a variety of categorization conditions, perceptual representations, and individual difference assumptions, and in an experiment. The results suggest that (a) averaging has little effect when the GCM is the correct model, (b) averaging often improves the fit of the GCM and worsens the fit of the decision bound model when the decision bound model is the correct model, (c) the GCM is quite flexible, and under many conditions can mimic the predictions of the decision bound model; the decision bound model, on the other hand, is generally unable to mimic the predictions of the GCM, (d) the validity of the decision bound model’s perceptual representation assumption can have a large effect on the inference drawn about the form of the decision bound, and (e) the experiment supported the claim that averaging improves the fit of the GCM. These results underscore the importance of performing single observer analysis if one is interested in understanding the categorization performance of individuals. The ability to categorize quickly and accurately is fundamental to survival. Everyday, we make hundreds of categorization judgments. Several detailed theories and quantitative models have been proposed to account for the perceptual and cognitive processes involved in categorization; the goal being to understand the categorization performance of individual behaving organisms.
Striatal Contributions to Category Learning: Quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease
, 2001
"... The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracybased analyses, as ..."
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Cited by 56 (39 self)
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The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracybased analyses, as well as quantitative modelbased analyses were performed. Unlike accuracybased analyses, the modelbased analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trialbytrial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed. (JINS, 2001, 7, 710 727.) Keywords: Categorization, Parkinson's disease, Striatum, Memory, Learning
Generalization, Similarity, and Bayesian Inference
"... this article we outline the foundations of such a theory, working in the general framework of Bayesian inference. Much of our proposal for extending Shepard's theory to the cases of multiple examples and arbitrary stimulus structures has already been introduced in other papers (Griffiths & Tenenbaum ..."
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Cited by 48 (10 self)
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this article we outline the foundations of such a theory, working in the general framework of Bayesian inference. Much of our proposal for extending Shepard's theory to the cases of multiple examples and arbitrary stimulus structures has already been introduced in other papers (Griffiths & Tenenbaum, 2000; Tenenbaum, 1997, 1999a, 1999b; Tenenbaum & Xu, 2000). Our goal here is to make explicit the link to Shepard's work and to use our framework to make connections between his work and other models of learning (Feldman, 1997; Gluck & Shanks, 1994; Haussler, Kearns & Schapire, 1994; Kruschke, 1992; Mitchell, 1997), generalization (Nosofsky, 1986; Heit, 1998), and similarity (Chater & Hahn, 1997; Medin, Goldstone & Gentner, 1993; Tversky, 1977). In particular, we will have a lot to say about how our generalization of Shepard's theory relates to Tversky's (1977) wellknown settheoretic models of similarity. Tversky's settheoretic approach and Shepard's metric space approach are often considered the two classic  and classically opposed  theories of similarity and generalization. By demonstrating close parallels between Tversky's approach and our Bayesian generalization of Shepard's approach, we hope to go some way towards unifying these two theoretical approaches and advancing the explanatory power of each. The plan of our article is as follows. In Section 2, we recast Shepard's analysis of generalization in a more general Bayesian framework, preserving the basic principles of his approach in a form that allows us to apply the theory to situations with multiple examples and arbitrary (nonspatially represented) stimulus structures. Sections 3 and 4 describe those extensions, and Section 5 concludes by discussing some implications of our theory for the internalization of...
A more rational model of categorization
 Proceedings of the 28th Annual Conference of the Cognitive Science Society
, 2006
"... The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by clustering similar stimuli together using Bayesian inference. As computing the posterior distribution over all assignments of stimuli to clusters is intractable, an approximation algorithm is used. The ..."
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Cited by 39 (16 self)
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The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by clustering similar stimuli together using Bayesian inference. As computing the posterior distribution over all assignments of stimuli to clusters is intractable, an approximation algorithm is used. The original algorithm used in the RMC was an incremental procedure that had no guarantees for the quality of the resulting approximation. Drawing on connections between the RMC and models used in nonparametric Bayesian density estimation, we present two alternative approximation algorithms that are asymptotically correct. Using these algorithms allows the effects of the assumptions of the RMC and the particular inference algorithm to be explored
Attention in learning
 Current Directions in Psychological Science
, 2003
"... explaining many phenomena in learning. The mechanism of selective attention in learning is also well motivated by its ability to minimize proactive interference and enhance generalization, thereby accelerating learning. Therefore, not only does the mechanism help explain behavioral phenomena, it mak ..."
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Cited by 37 (9 self)
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explaining many phenomena in learning. The mechanism of selective attention in learning is also well motivated by its ability to minimize proactive interference and enhance generalization, thereby accelerating learning. Therefore, not only does the mechanism help explain behavioral phenomena, it makes sense that it should have evolved (Kruschke & Hullinger, 2010). The phrase “learned selective attention ” denotes three qualities. First, “attention ” means the amplification or attenuation of the processing of stimuli. Second, “selective” refers to differentially amplifying and/or attenuating a subset of the components of the stimulus. This selectivity within a stimulus is different from attenuating or amplifying all aspects of a stimulus simultaneously (cf. Larrauri & Schmajuk, 2008). Third, “learned ” denotes the idea that the allocation of selective processing is retained for future use. The allocation may be context sensitive, so that attention is allocated differently in different contexts. There are many phenomena in human and animal learning that suggest the involvement of learned selective attention. The first part of this chapter briefly reviews some of those phenomena. The emphasis of the chapter is not the empirical phenomena, however. Instead, the focus is on a collection of models that formally express theories of learned attention. These models will be surveyed subsequently. Phenomena suggestive of selective attention in learning There are many phenomena in human and animal learning that suggest that learning involves allocating attention to informative cues, while ignoring uninformative cues. The following subsections indicate the benefits of selective allocation of attention, and illustrate the benefits with particular findings.
Mixture Models of Categorization
 Journal of Mathematical Psychology
, 2002
"... Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (Ashby & AlfonsoReese, 1995). In this article, a family of semiparametric classifiers is investigated where categories ar ..."
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Cited by 31 (0 self)
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Many currently popular models of categorization are either strictly parametric (e.g., prototype models, decision bound models) or strictly nonparametric (e.g., exemplar models) (Ashby & AlfonsoReese, 1995). In this article, a family of semiparametric classifiers is investigated where categories are represented by a finite mixture distribution. The advantage of these mixture models of categorization is that they contain several parametric models and nonparametric models as a special case. Specifically, it is shown that both decision bound models (Ashby & Maddox, 1992, 1993) and the generalized context model (Nosofsky, 1986) can be interpreted as two extreme cases of a common mixture model. Furthermore, many other (semiparametric) models of categorization can be derived from the same generic mixture framework. In this article, several examples are discussed, and a parameter estimation procedure for fitting these models is outlined. To illustrate the approach, several specific models are fitted to a data set collected by McKinley and Nosofsky (1995). The results suggest that semiparametric models are a promising alternative for future model development. Formal models of categorization are often closely related to statistical methods of probability density estimation (Ashby & AlfonsoReese, 1995). In statistics, a distinction is made between parametric estimators, that make strong assumptions about the distribution of the sample data, and nonparametric estimators that make only weak distributional assumptions. In accord with this distinction, Ashby and AlfonsoReese defined parametric classifiers as those classifiers that make strong assumptions about the functional form of the category distributions, and nonparametric classifiers as classifiers that make almost no assumptions about the category form. Prototype models (Reed, 1972) and decision bound models (Ashby & Maddox, 1992, 1993) are parametric classifiers, because they make strong assumptions about category structure. Decision bound models, for example, assume that the category distributions are multivariate normal (see Ashby, 1992, for a motivation). Despite this strong assumption (and the fact that these models can only predict linear or quadratic decision bounds), Ashby and Maddox (1992, 1993)
Structured statistical models of inductive reasoning
"... Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Baye ..."
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Cited by 29 (4 self)
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Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the four models are defined over different kinds of structures that capture different relationships between the categories in a domain. Our framework therefore shows how statistical inference can operate over structured background knowledge, and we argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.