PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Finite-state transducers in language and speech processing
Computational Linguistics
Entropy rate constancy in text
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A probabilistic earley parser as a psycholinguistic model
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Variation of entropy and parse trees of sentences as a function of the sentence number
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A noisy-channel model of rational human sentence comprehension under uncertain input
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
OpenFst: a general and efficient weighted finite-state transducer library
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
E-Z Reader: A cognitive-control, serial-attention model of eye-movement behavior during reading
Cognitive Systems Research
Why long words take longer to read: the role of uncertainty about word length
CMCL '12 Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics
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A number of results in the study of realtime sentence comprehension have been explained by computational models as resulting from the rational use of probabilistic linguistic information. Many times, these hypotheses have been tested in reading by linking predictions about relative word difficulty to word-aggregated eye tracking measures such as go-past time. In this paper, we extend these results by asking to what extent reading is well-modeled as rational behavior at a finer level of analysis, predicting not aggregate measures, but the duration and location of each fixation. We present a new rational model of eye movement control in reading, the central assumption of which is that eye movement decisions are made to obtain noisy visual information as the reader performs Bayesian inference on the identities of the words in the sentence. As a case study, we present two simulations demonstrating that the model gives a rational explanation for between-word regressions.