joshua abbott  

Computational Cognitive Science Lab
Department of Psychology
5429 Tolman Hall
University of California, Berkeley

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Bayesian Generalization and Word Learning
The Bayesian generalization framework has been successful in explaining how people generalize a property from a few observed stimuli to novel stimuli, across several different domains. To create a Bayesian generalization model, modelers typically specify a hypothesis space and prior probability distribution for each specific domain. However, this raises two problems: the models do not scale beyond the (typically small-scale) domain that they were designed for, and the explanatory power of the models is reduced by their reliance on a hand-coded hypothesis space and prior. To solve these two problems, we propose a method for deriving hypothesis spaces and priors from large online databases. We evaluate our method by constructing a hypothesis space and prior for a Bayesian word learning model from WordNet, a large online database that encodes the semantic relationships between words as a network. After validating our approach by replicating a previous word learning study, we apply the same model to a new experiment featuring three additional taxonomic domains (clothing, containers, and seats). In both experiments, we found that the same automatically constructed hypothesis space explains the complex pattern of generalization behavior, producing accurate predictions across a total of six different domains.

We also present a system for learning nouns directly from images, using probabilistic predictions generated by visual classifiers as the input to Bayesian word learning, and compare this system to human performance in an automated, large-scale experiment. The system captures a significant proportion of the variance in human responses. Combining the uncertain outputs of the visual classifiers with the ability to identify an appropriate level of abstraction that comes from Bayesian word learning allows the system to outperform alternatives that either cannot deal with visual stimuli or use a more conventional computer vision approach.

J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012.
[abstract] [paper] [slides]

Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. Visually-Grounded Bayesian Word Learning. First International Workshop on Large Scale Visual Recognition and Retrieval. 26th Annual Conference on Neural Information Processing Systems. Lake Tahoe, Nevada. December, 2012.
[paper] [poster]

Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. Visual concept learning: combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems 26, 2013.
[abstract] [paper] [supplementary materials]