Hierarchical mixtures of experts and the EM algorithm
Neural Computation
An introduction to variational methods for graphical models
Learning in graphical models
A tutorial on learning with Bayesian networks
Learning in graphical models
Approximate learning of dynamic models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data
Machine Learning - Special issue: Unsupervised learning
Eye Tracking Methodology: Theory and Practice
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Probabilistic Independence Networks for Hidden Markov Probability Models
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Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
A survival analysis of fixation times in reading
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
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Random variables and probabilistic decision making are important elements in most theories of reading eye movements, but they tend to receive little theoretical attention. This paper attempts to address this problem by introducing the Stochastic, Hierarchical Architecture for Reading Eye-movements (SHARE). The SHARE framework formalizes reading eye movements as observable outcomes of a latent stochastic process. By modeling eye movements as time-series random variables, the goal of the model is to uncover statistical regularities in the data, which help to identify conditions and constraints the underlying mechanism must satisfy. In the univariate analysis, it is shown that a 3-component Lognormal mixture model provides a good fit to the marginal distribution function of fixation duration, and a hierarchical model is required for modeling saccade length. As a comprehensive model of reading eye movements, SHARE was implemented as an Input-Output Hidden Markov model. With a few simple hypotheses, SHARE is able to capture reading eye-movement patterns of beginning readers and proficient adults, and to reproduce well-known psycholinguistic effects. The rationale of the model, its relations with other modeling endeavors, and its implications are discussed.