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Topics in semantic representation
 Psychological Review
, 2007
"... Accounts of language processing have suggested that it requires retrieving concepts from memory in response to an ongoing stream of information. This can be facilitated by inferring the gist of a sentence, conversation, or document, and using that computational problem underlying the extraction and ..."
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Cited by 183 (15 self)
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Accounts of language processing have suggested that it requires retrieving concepts from memory in response to an ongoing stream of information. This can be facilitated by inferring the gist of a sentence, conversation, or document, and using that computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads us to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure. Many aspects of perception and cognition can be understood by considering the computational problem that is addressed by a particular human capacity (Andersion, 1990; Marr, 1982). Perceptual capacities such as identifying shape from shading (Freeman, 1994), motion perception
Word Learning as Bayesian Inference
 In Proceedings of the 22nd Annual Conference of the Cognitive Science Society
, 2000
"... The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word’s referents, by making rational inductive inferences that integrate pr ..."
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Cited by 175 (33 self)
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The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word’s referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account’s predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.
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 151 (26 self)
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Theorybased Bayesian models of inductive reasoning 2 Theorybased Bayesian models of inductive reasoning
Learning overhypotheses with hierarchical Bayesian models
"... Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models th ..."
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Cited by 116 (38 self)
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Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses — overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.
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 65 (11 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.
Bayesian models of cognition
"... For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational a ..."
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Cited by 54 (2 self)
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For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty
Sensitivity to Sampling in Bayesian Word Learning
"... thank members of the UBC Baby Cognition Lab for their help with data collection, and Paul Bloom, Geoff Hall, and Terry Regier for helpful discussion. We owe a particular debt to Liz Bonawitz, for discussions and pilot work on an earlier version of this work. ..."
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Cited by 40 (8 self)
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thank members of the UBC Baby Cognition Lab for their help with data collection, and Paul Bloom, Geoff Hall, and Terry Regier for helpful discussion. We owe a particular debt to Liz Bonawitz, for discussions and pilot work on an earlier version of this work.
A hierarchical Bayesian model of human decisionmaking on an optimal stopping problem
 Cognitive Science
, 2006
"... Wiener diffusion accounts of human decisionmaking are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain MonteCarlo methods for posterior sampling. By analyzing ..."
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Cited by 36 (6 self)
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Wiener diffusion accounts of human decisionmaking are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain MonteCarlo methods for posterior sampling. By analyzing seminal data from a brightness discrimination task, we show how the Bayesian approach offers several avenues for extending and improving diffusion models. These possibilities include the hierarchical modeling of stimulus properties, and modeling the role of contaminant processes in generating experimental data. We also argue that the Bayesian approach challenges some basic assumptions of previous diffusion models, involving how variability in decisionmaking should be interpreted. We conclude that adopting a Bayesian approach to relating diffusion models and human decisionmaking data will sharpen the theoretical and empirical questions, and improve our understanding of a basic human cognitive ability. BAYESIAN DIFFUSION DECISIONMAKING 2
Bayesian approaches to associative learning: From passive to active learning
 Learning & Behavior
, 2008
"... Traditional associationist models represent an organism’s knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses. Many traditional associationist mode ..."
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Cited by 29 (7 self)
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Traditional associationist models represent an organism’s knowledge state by a single strength of association on each associative link. Bayesian models instead represent knowledge by a distribution of graded degrees of belief over a range of candidate hypotheses. Many traditional associationist models assume that the learner is passive, adjusting strengths of association only in reaction to stimuli delivered by the environment. Bayesian models, on the other hand, can describe how the learner should actively probe the environment to learn optimally. The first part of this article reviews two Bayesian accounts of backward blocking, a phenomenon that is challenging for many traditional theories. The broad Bayesian framework, in which these models reside, is also selectively reviewed. The second part focuses on two formalizations of optimal active learning: maximizing either the expected information gain or the probability gain. New analyses of optimal active learning by a Kalman filter and by a noisylogic gate show that these two Bayesian models make different predictions for some environments. The Kalman filter predictions are disconfirmed in at least one case. Bayesian formalizations of learning are a revolutionary advance over traditional approaches. Bayesian models assume that the learner maintains multiple candidate hypotheses with differing degrees of belief, unlike traditional
The rat as particle filter
"... The core tenet of Bayesian modeling is that subjects represent beliefs as distributions over possible hypotheses. Such models have fruitfully been applied to the study of learning in the context of animal conditioning experiments (and analogously designed human learning tasks), where they explain ph ..."
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Cited by 23 (2 self)
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The core tenet of Bayesian modeling is that subjects represent beliefs as distributions over possible hypotheses. Such models have fruitfully been applied to the study of learning in the context of animal conditioning experiments (and analogously designed human learning tasks), where they explain phenomena such as retrospective revaluation that seem to demonstrate that subjects entertain multiple hypotheses simultaneously. However, a recent quantitative analysis of individual subject records by Gallistel and colleagues cast doubt on a very broad family of conditioning models by showing that all of the key features the models capture about even simple learning curves are artifacts of averaging over subjects. Rather than smooth learning curves (which Bayesian models interpret as revealing the gradual tradeoff from prior to posterior as data accumulate), subjects acquire suddenly, and their predictions continue to fluctuate abruptly. These data demand revisiting the model of the individual versus the ensemble, and also raise the worry that more sophisticated behaviors thought to support Bayesian models might also emerge artifactually from averaging over the simpler behavior of individuals. We suggest that the suddenness of changes in subjects ’ beliefs (as expressed in conditioned behavior) can be modeled by assuming they are conducting inference using sequential Monte Carlo sampling with a small number of samples — one, in our simulations. Ensemble behavior resembles exact Bayesian models since, as in particle filters, it averages over many samples. Further, the model is capable of exhibiting sophisticated behaviors like retrospective revaluation at the ensemble level, even given minimally sophisticated individuals that do not track uncertainty from trial to trial. These results point to the need for more sophisticated experimental analysis to test Bayesian models, and refocus theorizing on the individual, while at the same time clarifying why the ensemble may be of interest. 1