tailieunhanh - Báo cáo khoa học: "Lexical surprisal as a general predictor of reading time"

Probabilistic accounts of language processing can be psychologically tested by comparing word-reading times (RT) to the conditional word probabilities estimated by language models. Using surprisal as a linking function, a significant correlation between unlexicalized surprisal and RT has been reported (., Demberg and Keller, 2008), but success using lexicalized models has been limited. In this study, phrase structure grammars and recurrent neural networks estimated both lexicalized and unlexicalized surprisal for words of independent sentences from narrative sources. . | Lexical surprisal as a general predictor of reading time Irene Fernandez Monsalve Stefan L. Frank and Gabriella Vigliocco Division of Psychology and Language Sciences University College London ucjtife @ Abstract Probabilistic accounts of language processing can be psychologically tested by comparing word-reading times RT to the conditional word probabilities estimated by language models. Using surprisal as a linking function a significant correlation between unlexicalized surprisal and RT has been reported . Demberg and Keller 2008 but success using lexicalized models has been limited. In this study phrase structure grammars and recurrent neural networks estimated both lexicalized and unlex-icalized surprisal for words of independent sentences from narrative sources. These same sentences were used as stimuli in a self-paced reading experiment to obtain RTs. The results show that lexicalized sur-prisal according to both models is a significant predictor of RT outperforming its un-lexicalized counterparts. 1 Introduction Context-sensitive prediction-based processing has been proposed as a fundamental mechanism of cognition Bar 2007 Faced with the problem of responding in real-time to complex stimuli the human brain would use basic information from the environment in conjunction with previous experience in order to extract meaning and anticipate the immediate future. Such a cognitive style is a well-established finding in low level sensory processing . Kveraga et al. 2007 but has also been proposed as a relevant mechanism in higher order processes such as language. Indeed there is ample evidence to show that human language comprehension is both incremental and predictive. For example on-line detection of semantic or syntactic anomalies can be observed in the brain s EEG signal Hagoort et al. 2004 and eye gaze is directed in anticipation at depictions of plausible sentence completions Kamide et al. 2003 . Moreover probabilistic .