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18
A Probabilistic Model of Syntactic and Semantic Acquisition from ChildDirected Utterances and their Meanings
"... This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of childdirected utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they d ..."
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Cited by 12 (1 self)
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This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of childdirected utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model. We use the CCG grammatical framework and train a nonparametric Bayesian model of parse structure with online variational Bayesian expectation maximization. When tested on utterances from the CHILDES corpus, our learner outperforms a stateoftheart semantic parser. In addition, it models such aspects of child acquisition as “fast mapping,” while also countering previous criticisms of statistical syntactic learners. 1
Bayes and blickets: Effects of knowledge on causal induction in children and adults
"... People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal acc ..."
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Cited by 2 (1 self)
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People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4yearolds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults ’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.
Cognitive control over learning: Creating, clustering and generalizing taskset structure
 Psychological Review
, 2013
"... Learning and executive functions such as taskswitching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as ..."
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Cited by 2 (1 self)
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Learning and executive functions such as taskswitching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or tasksets, needed for cognitive control. We investigate this question from 3 complementary angles. First, we develop a new contexttaskset (CTS) model, inspired by nonparametric Bayesian methods, specifying how the learner might infer hidden structure (hierarchical rules) and decide to reuse or create new structure in novel situations. Second, we develop a neurobiologically explicit network model to assess mechanisms of such structured learning in hierarchical frontal cortex and basal ganglia circuits. We systematically explore the link between these modeling levels across task demands. We find that the network provides an approximate implementation of highlevel CTS computations, with specific neural mechanisms modulating distinct CTS parameters. Third, this synergism yields predictions about the nature of human optimal and suboptimal choices and response times during learning and taskswitching. In particular, the models suggest that participants spontaneously build taskset structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide experimental evidence for these predictions and show that CTS provides a good quantitative fit to human sequences of choices. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities and, thus, potentially longterm rather than shortterm optimality.
Learning the context of a category
"... This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans ..."
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This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans learn context specific conceptual representations. 1
A tutorial introduction to Bayesian models of cognitive development
"... We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, an ..."
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We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.
Perception, Action and Utility: The Tangled Skein
, 2011
"... Normative theories of learning and decisionmaking are motivated by a computationallevel analysis of the task facing an animal: what should the animal do to maximize future reward? However, much of the recent excitement in this field originates in how the animal arrives at its decisions and reward ..."
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Normative theories of learning and decisionmaking are motivated by a computationallevel analysis of the task facing an animal: what should the animal do to maximize future reward? However, much of the recent excitement in this field originates in how the animal arrives at its decisions and reward predictions—algorithmic questions about which the computationallevel analysis is silent.
ARTICLE Communicated by Nando de Freitas Multistability and Perceptual Inference
"... Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. Ho ..."
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Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many realworld situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability—stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic samplegenerating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena—gammalike stochastic switching, patchy percepts, fusion, and traveling waves—can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing samplebased posterior approximations in the brain. 1
Reviewed by:
, 2012
"... doi: 10.3389/fncom.2012.00024 Selectionist and evolutionary approaches to brain function: a critical appraisal ..."
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doi: 10.3389/fncom.2012.00024 Selectionist and evolutionary approaches to brain function: a critical appraisal
Opinion Article
, 2011
"... One of the most influential research programs in psychology is that of Tversky and Kahneman’s (1973, 1983) on heuristics and biases in decisionmaking. Two characteristics of this program are, first, compelling empirical demonstrations that in some decisionmaking situations naïve observers violate ..."
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One of the most influential research programs in psychology is that of Tversky and Kahneman’s (1973, 1983) on heuristics and biases in decisionmaking. Two characteristics of this program are, first, compelling empirical demonstrations that in some decisionmaking situations naïve observers violate the rules of classic probability (CP) theory and, second, that corresponding behavior can be explained with simple heuristics. Tversky and Kahneman’s work has led to a vast literature on what is the basis for psychological process in decisionmaking. Note that their work, however impactful, has not settled the debate of whether CP