Abstract
ABSTRACTSixteen years ago, Sereno and Rayner (2003. Measuring word recognition in reading: eye movements and event-related potentials. Trends in Cognitive Sciences, 7(11), 489–493) illustrated how “by means of review and comparison” eye movement (EM) and event-related potential (ERP) studies may advance our understanding of visual word recognition. Attempts to simultaneously record EMs and ERPs soon followed. Recently, this co-registration approach has also been transferred to fMRI and oscillatory EEG. With experimental settings close to natural reading, co-registration enables us to directly integrate insights from EM and neuroimaging studies. This should extend current experimental paradigms by moving the field towards studying sentence-level processing including effects of context and parafoveal preview. This article will introduce the basic principles and applications of co-registration and selectively review how this approach may shed light on one of the most controversially discussed issues in reading research, contextual predictions in online language processing.
Highlights
Sixteen years ago, Sereno and Rayner (2003) pointed out how “by means of review and comparison” eye movements (EMs) and event-related potentials (ERPs) may advance our understanding of the “what”, “when” and “how” of visual word recognition in natural reading
Trends in Cognitive Sciences, 7(11), 489–493) illustrated how “by means of review and comparison” eye movement (EM) and event-related potential (ERP) studies may advance our understanding of visual word recognition
The time-course of visual word recognition reported in traditional ERP studies exceeds normal fixation durations during natural reading, impeding its integration into the processing timeline as derived from EM studies
Summary
Sereno and Rayner (2003) pointed out how “by means of review and comparison” eye movements (EMs) and event-related potentials (ERPs) may advance our understanding of the “what”, “when” and “how” of visual word recognition in natural reading. We focus on contextual predictions in sentence comprehension and objectives to determine the time-course of top-down and bottom-up mechanisms: (i) disentangling contextbased integration from pre-activation and (ii) investigating additive and interactive effects of supposedly bottom-up and top-down determinants of visual word recognition.
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