UC San Diego

Moving one's eyes while reading is one of the most complex everyday tasks humans face. To perform efficiently, readers must make decisions about when and where to move their eyes every 200-300ms. Over the past decades, it has been demonstrated that these fine-grained decisions are influenced by a range of linguistic properties of the text, and measuring eye movements during reading has become one of the primary methods of studying online sentence comprehension. However, it is still largely unclear why linguistic variables affect the eye movement record in the ways they do. The present work begins to answer this question by presenting a rational framework for understanding eye movement control in reading, in which probabilistic language knowledge plays a crucial role. Specifically, the task of reading is taken to be one of sentence identification: readers move their eyes to efficiently obtain visual input, which they combine with probabilistic language knowledge through Bayesian inference to yield posterior beliefs about sentence form and structure. Simulations with implemented models within this framework demonstrate that it can provide a principled account of many aspects of reading behavior, including the influence of a number of linguistic variables. In addition, the framework suggests a novel explanation for one of the least understood aspects of eye movements in reading - regressive eye movements - and we present evidence from an eye tracking corpus to support this proposal