tailieunhanh - Báo cáo khoa học: "A Rational Model of Eye Movement Control in Reading"

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. . | A Rational Model of Eye Movement Control in Reading Klinton Bicknell and Roger Levy Department of Linguistics University of California San Diego 9500 Gilman Dr La Jolla CA 92093-0108 kbicknell rlevy @ Abstract 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. 1 Introduction The language processing tasks of reading listening and even speaking are remarkably difficult. Good performance at each one requires integrating a range of types of probabilistic information and making incremental predictions on the basis of noisy incomplete input. Despite these requirements empirical work has shown that humans perform very well . Tanenhaus Spivey-Knowlton Eberhard Sedivy 1995 . Sophisticated models have been developed that explain many of these effects using the tools of computational linguistics and large-scale corpora to make normative predictions for optimal performance in these tasks Genzel Charniak 2002 2003 Keller 2004 Levy Jaeger 2007 Jaeger 2010 . To the extent that the behavior of these models looks like human behavior it suggests that humans are

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