1,721,509 research outputs found
Learning and using relational theories
Get PDFKemp, Charles; Goodman, Noah D.; Tenenbaum, Joshua B.Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and how they support inductive inferences about unobserved relations. We describe two experiments that test our approach, and show that it provides a better account of human learning and reasoning than an approach developed by Goodman [1]
What are you trying to tell me? A Bayesian model of how toddlers can simultaneously infer property extension and sampling processes
Get PDFURL to paper on conference site.Young human learners possess a remarkable ability to make inductive inferences from sparse data. Recent research suggests that children’s generalizations are sensitive to the process by which data are generated (i.e., teacher-driven vs. learner-driven sampling; Xu & Tenenbaum, 2007). In general, sampling process and properties of objects are tightly coupled; knowing how the data were sampled can inform your inference about property extensions, and vice versa. In real-world situations, however, both the extension of novel properties and the sampling process may be ambiguous. These situations commonly arise when children are learning socially from adults. How do children confront the challenge of simultaneously inferring both the property extension and the sampling process from a small amount of data? Here we present a Bayesian model showing how this joint inference problem can be solved. Consistent with the predictions of the model, two behavioral experiments suggest that toddlers (mean: 16 months) can use the relationship between a sample and a population to infer both the sampling process and the extent to which a non-obvious object property should be generalized.National Science Foundation (U.S.) (Faculty Early Career Development Award)Templeton Foundation (Award)James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning
Discovering Structure by Learning Sparse Graphs
Get PDFSystems of concepts such as colors, animals, cities, and artifacts are richly structured, and people discover the structure of these domains throughout a lifetime of experience. Discovering structure can be formalized as probabilistic inference about the organization of entities, and previous work has operationalized learning as selection amongst specific candidate
hypotheses such as rings, trees, chains, grids, etc. defined by graph grammars (Kemp & Tenenbaum, 2008). While this model makes discrete choices from a limited set, humans appear to entertain an unlimited range of hypotheses, many without an obvious grammatical description. In this paper, we approach structure discovery as optimization in a continuous space of all possible structures, while encouraging structures to be sparsely connected. When reasoning about animals and cities, the sparse model achieves performance equivalent to more structured approaches. We also explore a large domain of 1000 concepts with broad semantic coverage and no simple structure
Encoding higher-order structure in visual working memory: A probabilistic model
Get PDFWhen encoding a scene into memory, people store both theoverall gist of the scene and detailed information about a few specific objects. Moreover, they use the gist to guide their choice of which specific objects to remember. However, formal models of change detection, like those used to estimate visual working memory capacity, generally assume people represent no higher-order structure about the display and choose which items to encode at random. We present a probabilistic model of change detection that attempts to bridge this gap by formalizing the encoding of both specific items and higher-order information about simple working memory displays. We show that this model successfully predicts change detection performance for individual displays of patterned dots. More generally, we show that it is necessary for the model to encode higher-order structure in order to accurately predict human performance in the change detection task. This work thus confirms and formalizes the role of higher-order structure in visual working memory
Theory Acquisition as Stochastic Search
Get PDFWe present an algorithmic model for the development of children’s
intuitive theories within a hierarchical Bayesian framework,
where theories are described as sets of logical laws
generated by a probabilistic context-free grammar. Our algorithm
performs stochastic search at two levels of abstraction
– an outer loop in the space of theories, and an inner loop in
the space of explanations or models generated by each theory
given a particular dataset – in order to discover the theory
that best explains the observed data. We show that this model
is capable of learning correct theories in several everyday domains,
and discuss the dynamics of learning in the context of
children’s cognitive development.United States. Air Force Office of Scientific Research (AFOSR (FA9550-07-1-0075)United States. Office of Naval Research (ONR (N00014-09-0124)James S. McDonnell Foundation (Causal Learning Collaborative Initiative
Dynamic infinite relational model for time-varying relational data analysis
Get PDFWe propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks. Our proposed model abstracts observed timevarying object-object relationships into relationships between object clusters. We extend the infinite Hidden Markov model to follow dynamic and time-sensitive changes in the structure of the relational data and to estimate a number of clusters simultaneously. We show the usefulness of the model through experiments with synthetic and real-world data sets
Social and Discourse Contributions to the Determination of Reference in Cross-Situational Word Learning
Get PDFHow do children infer the meanings of their first words? Even in infant-directed speech, object nouns are often used in complex contexts with many possible referents and in sentences with many other words. Previous work has argued that children can learn word meanings via cross-situational observation of correlations between words and their referents. While cross-situational associations can sometimes be informative, social cues to what a speaker is talking about can provide a powerful shortcut to word meaning. The current study takes steps toward quantifying the informativeness of cues that signal speakers' chosen referent, including their eye-gaze, the position of their hands, and the referents of their previous utterances. We present results based on a hand-annotated corpus of 24 videos of child-caregiver play sessions with children from 6 to 18 months old, which we make available to researchers interested in similar issues. Our analyses suggest that although they can be more useful than cross-situational information in some contexts, social and discourse information must also be combined probabilistically to be effective in determining reference.National Science Foundation (U.S.) (NSF #DDRIG #0746251)United States. Department of Education (Jacob K. Javits Graduate Fellowship
Surprise! Infants consider possible bases of generalization for a single input example
Get PDFInfants have been shown to generalize from a small number of input examples. However, existing studies allow two possible means of generalization. One is via a process of noting similarities shared by several examples. Alternatively, generalization may reflect an implicit desire to explain the input. The latter view suggests that generalization might occur when even a single input example is surprising, given the learner's current model of the domain. To test the possibility that infants are able to generalize based on a single example, we familiarized 9-month-olds with a single three-syllable input example that contained either one surprising feature (syllable repetition, Experiment 1) or two features (repetition and a rare syllable, Experiment 2). In both experiments, infants generalized only to new strings that maintained all of the surprising features from familiarization. This research suggests that surprise can promote very rapid generalization. Infants have been shown to generalize from a small number of input examples. However, existing studies allow two possible means of generalization
Bootstrapping in a language of thought: A formal model of numerical concept learning
No full textIn acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful representational system. We provide an implemented model that is powerful enough to learn number word meanings and other related conceptual systems from naturalistic data. The model shows that bootstrapping can be made computationally and philosophically well-founded as a theory of number learning. Our approach demonstrates how learners may combine core cognitive operations to build sophisticated representations during the course of development, and how this process explains observed developmental patterns in number word learning.United States. Air Force Office of Scientific Research (Grant FA9550-07-1-0075
Theory learning as stochastic search in the language of thought
Get PDFWe present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While their subsymbolic representations provide a smooth error surface that supports efficient gradient-based learning, our symbolic representations are better suited to capturing children's intuitive theories but give rise to a harder learning problem, which can only be solved by exploratory search. Our algorithm attempts to discover the theory that best explains a set of observed data by performing stochastic search at two levels of abstraction: an outer loop in the space of theories and an inner loop in the space of explanations or models generated by each theory given a particular dataset. We show that this stochastic search is capable of learning appropriate theories in several everyday domains and discuss its dynamics in the context of empirical studies of children's learning.James S. McDonnell Foundation. Causal Learning CollaborativeUnited States. Office of Naval Research (N00014-09-0124)United States. Army Research Office (W911NF-08-1-0242)National Science Foundation (U.S.). Graduate Research Fellowshi
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