Semantic networks produced from human data have statistical properties that cannot be easily captured by spatial representations. We explore a probabilistic approach to semantic representation that explicitly models the probability with which words occur in different contexts, and hence captures the probabilistic relationships between words. We show that this representation has statistical properties consistent with the large-scale structure of semantic networks constructed by humans, and trace the origins of these properties.

Abstract not found

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

We explore the consequences of viewing semantic association as the result of attempting to predict the concepts likely to arise in a particular context. We argue that the success of existing accounts of semantic representation comes as a result of indirectly addressing this problem, and show that a closer correspondence to human data can be obtained by taking a probabilistic approach that explicitly models the generative structure of language.

People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference. 1

How do children learn how to play hide and seek? At age 3-4, children do not typically have perspective taking ability, so their hiding ability should be extremely limited. We show through a case study that a 3 1/2 year old child can, in fact, play a credible game of hide and seek, even though she does not seem to have perspective taking ability. We propose that children are able to learn how to play hide and seek by learning the features and relations of objects (e.g., containment, under) and use that information to play a credible game of hide and seek. We model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. We also take the “hiding ” model and use it as the basis for a “seeking ” model. We suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.

We propose a causal Bayesian model of false belief reasoning in children. This model realizes theory of mind as the rational use of intuitive theories and supports causal prediction, explanation, and theory revision. The model undergoes an experience-driven false belief transition. We investigate the relationship between prediction, explanation, and surprise; this is used to interpret an empirical study of children’s explanations in an extension of the false belief task. Our study includes the standard outcome, surprising to younger children, and a novel “Psychic Sally ” condition that challenges older children with an unexpected outcome. In everyday life, humans constantly attribute unobservable mental states to one another, and use them to

Representativeness is a central explanatory construct in cognitive science but suffers from the lack of a principled theoretical account. Here we present a formal definition of one sense of representativeness -- what it means to be a good example of a process or category in the context of Bayesian inference. This analysis clarifies the relation between representativeness as an intuitive statistical heuristic and normative principles of inductive inference. It also leads to strong quantitative predictions about people 's judgments, which compare favorably to alternative accounts based on likelihood or similarity when evaluated on data from two experiments. Why do people think that Linda, the politically active, single, outspoken, and very bright 31-year-old, is more likely to be a feminist bankteller than to be a bankteller, even though this is logically impossible? Why do we think that the sequence HHTHT is more likely than the sequence HHHHH to be produced by flipping a fa...

This thesis addresses the cognitive basis of syntactic adaptation, which biases speakers to repeat their own syntactic constructions and those of their conversational partners. I address two types of syntactic adaptation: short-term priming and longterm adaptation. I develop two metrics for syntactic adaptation within a speaker and between speakers in dialogue: one for short-term priming effects that decay quickly, and one for long-term adaptation over the course of a dialogue. Both methods estimate adaptation in large datasets consisting of transcribed human-human dialogue annotated with syntactic information. Two such corpora in English are used: Switchboard, a collection of spontaneous phone conversation, and HCRC Map Task, a set of task-oriented dialogues in which participants describe routes on a map to one another. I find both priming and long-term adaptation in both corpora, confirming well-known experimental results (e.g., Bock, 1986b). I extend prior work by showing that syntactic priming effects not only apply to selected syntactic constructions that are alternative realizations of the same semantics, but still hold when a broad

Different kinds of knowledge are relevant in different inductive contexts. Previous models of category-based induction have focused on judgments about taxonomic properties, but other kinds of models are needed for other kinds of properties. We present a new model of reasoning about causally transmitted properties. Our first experiment shows that the model predicts judgments about a disease-related property when only causal information is available. Our second experiment uses a disease-related property and a genetic property in a setting where both causal and taxonomic information are available. Our new model accounts only for judgments about the disease property, and a taxonomic model accounts only for judgments about the genetic property. This double dissociation suggests that qualitatively different models are needed to account for property induction. Any familiar thing can be thought about in a multitude of ways. A cat is a creature that climbs trees, eats