Project description
In cognitive psychology, the eyes have been said to provide a window to the mind. Eye fixations, assessed with eye-tracking technology, is used to investigate reading times (RTs) in natural reading. In particular, eye-tracking allows researchers to probe the dependence of RTs for a given target word as a function of its context. Given that RTs are used as a proxy for the cognitive demands of reading – words that are easy to understand are read more quickly than those that are harder to process – these studies shed light on how different contexts impact on the understanding of a text. For example, how do people read and understand articles on the BBC, legal documents and letters from the NHS?
Traditionally, researchers have only focussed on RTs for particular target words that have been manipulated in a study, ignoring the RTs of the remaining words. This leaves an extremely rich dataset of RTs completely untapped. In this project, we will process the RTs from a large number of eye-tracking experiments collected at the University of Nottingham. Crucially, the project will deliver a principled data-cleaning algorithm. This is particularly important since different labs pre-process their data differently, which poses a substantial challenge to reproduce their results and to compare results from different labs.
Once EMNED (English Mathematics Nottingham Eye-tracking Database) is created, the data will be analysed with several reading models including the EZ reader to gain insight into reading in a natural context. The project will be undertaken in close collaboration with Prof Kathy Conklin in the School of English.