Studies of Psychology and Behavior ›› 2011, Vol. 9 ›› Issue (1): 53-64.

• Orginal Article • Previous Articles     Next Articles

Probabilistic Linguistic Expectations, Uncertain Input, and Implications for Eye Movements in Reading

Roger Levy   

  1. University of California, San Diego
  • Received:2010-11-27 Online:2011-03-20 Published:2011-03-20
  • Contact: Roger Levy, Department of Linguistics, University of California at San Diego, 9500 Gilman Drive #0108, La Jolla, CA92093-0108, USA, rlevy@ucsd.edu
  • Supported by:
    This article is based on a talk given May 25, 2010, at the fourth China International Conference on Eye Movements at Tianjin Normal University, and I am grateful to feedback from the audience at that talk; All mistakes are my own; The writing of this article was supported by NSF CAREER grant 0953870 to Roger Levy

Probabilistic Linguistic Expectations, Uncertain Input, and Implications for Eye Movements in Reading

Roger Levy   

  1. University of California, San Diego

Abstract: One nearly ubiquitous assumption in models of linguistic comprehension and of eye movement control in reading alike is of partial modularization between word-level and sentence-level processing: that the outcome of word recognition, and thus the input to sentence-level comprehension, is a categorial representation. Yet such a partial modularization throws away residual uncertainty regarding word identity that might potentially be of value to the comprehender further downstream in the sentence. Here I describe a line of research combining computational modeling with experimental eye-tracking work to explore the consequences of removing this partial modularity assumption.

摘要: One nearly ubiquitous assumption in models of linguistic comprehension and of eye movement control in reading alike is of partial modularization between word-level and sentence-level processing: that the outcome of word recognition, and thus the input to sentence-level comprehension, is a categorial representation. Yet such a partial modularization throws away residual uncertainty regarding word identity that might potentially be of value to the comprehender further downstream in the sentence. Here I describe a line of research combining computational modeling with experimental eye-tracking work to explore the consequences of removing this partial modularity assumption.