Trends in Cognitive Sciences
Measuring word recognition in reading: eye movements and event-related potentials
Section snippets
Eye movements (EMs)
An important contribution to understanding on-line visual language processing has come from EM research where reading behavior is measured via the position, duration, and sequence of eye fixations in text. The EM technique offers certain advantages over traditional behavioral techniques. EMs are a normal part of reading. First, subjects do not have to make decisions about words they read, or name them aloud – procedures that disrupt the flow of reading when used as a secondary task. Second, the
Event-related potentials (ERPs)
ERPs are stimulus-locked averages of the electroencephalogram (EEG) across many presentations of stimuli. They provide a continuous millisecond-by-millisecond record of electrical changes related to on-going perceptual and cognitive processing and can thus index changes related to word recognition in real time 9, 10. ERPs recorded at the scalp via high-density (e.g. 128-channel) sensor arrays provide a measure of processing with fine-grained (external) spatial as well as temporal resolution [11]
Comparisons of EMs and ERPs
Eye fixation time in reading sets constraints for processing but without sufficient temporal resolution. ERPs can provide greater resolution because they are time-locked on a millisecond-by-millisecond basis to brain events during the presentation of a word. For ERP research to advance knowledge about the timing of visual word recognition, however, it must show sensitivity within earlier components. Early, exogenous ERP components have not readily exhibited processing differences arising from
The way forward
EM research, guided by the temporal contours of the reading task, has introduced innovations that use eye contingent display changes – such as the ‘moving window’ [30], ‘boundary’ [7], ‘fast priming’ [31], and ‘disappearing text’ [32] techniques – to explore the intricacies of reading. In ERP research, the increased use of dense-mapping arrays, the application of statistical techniques such as principal component analysis 20, 24, 33 and independent component analysis [34], corroboration from
Acknowledgements
We thank Patrick O'Donnell, Jeffrey Bowers, and the anonymous reviewers for their helpful comments. Preparation of this article was supported by a British Academy grant 30315 to SCS and grants HD17246 and HD26765 to KR.
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