How preview space/time translates into preview cost/benefit for fixation durations during reading

Abstract

Eye-movement control during reading depends on foveal and parafoveal information. If the parafoveal preview of the next word is suppressed, reading is less efficient. A linear mixed model (LMM) reanalysis of McDonald (2006) confirmed his observation that preview benefit may be limited to parafoveal words that have been selected as the saccade target. Going beyond the original analyses, in the same LMM, we examined how the preview effect (i.e., the difference in single-fixation duration, SFD, between random-letter and identical preview) depends on the gaze duration on the pretarget word and on the amplitude of the saccade moving the eye onto the target word. There were two key results: (a) The shorter the saccade amplitude (i.e., the larger preview space), the shorter a subsequent SFD with an identical preview; this association was not observed with a random-letter preview. (b) However, the longer the gaze duration on the pretarget word, the longer the subsequent SFD on the target, with the difference between random-letter string and identical previews increasing with preview time. A third pattern—increasing cost of a random-letter string in the parafovea associated with shorter saccade amplitudes—was observed for target gaze durations. Thus, LMMs revealed that preview effects, which are typically summarized under “preview benefit”, are a complex mixture of preview cost and preview benefit and vary with preview space and preview time. The consequence for reading is that parafoveal preview may not only facilitate, but also interfere with lexical access.

Keywords:

• Eye movements
• Reading
• Preview effects
• Linear mixed model
• Boundary paradigm

Acknowledgments

Data and scripts for the analyses can be retrieved at the Potsdam Mind Research Repository (http://read.psych.uni-potsdam.de/pmr2/). The research was supported by Deutsche Forschungsgemeinschaft Grants KL 955/15 and KL 955/18.

Notes

¹ We also specified a model with varying subject-related effects for preview and boundary effects and parameters for the correlation between these variance components. This model converged but some of the correlations were very close to 1. Thus, we suspect that the sample size is not large enough to support such a complex model. The critical three-factor interaction shown in Figure 3 was still significant in this model.

² We note that obviously switching from rmMRA to LMM is not always a guarantee of significant effects.