A Predictive Comparison of the Selective Attention Hypothesis and Contextual Priming Theory in Processing English It-Extraposition: An Eye-Tracking Study
DOI:
https://doi.org/10.65405/hnf66a24Keywords:
it-extraposition, eye-tracking, selective attention, contextual priming, Libyan EFL, L2 prediction, surprisalAbstract
When readers encounter an English it-extraposition structure (e.g., It is obvious that the minister resigned), two fundamentally different processing mechanisms could guide the resolution of the cataphoric dependency. The Selective Attention Hypothesis (SAH) holds that expletive it concentrates attentional resources on the matrix predicate, creating an integration cost that spills over onto the extraposed clause. Contextual Priming Theory (CPT), by contrast, claims that the matrix context pre-activates features of the upcoming clause, so that higher predictability reduces downstream effort. We adjudicated between these predictions in the first eye-tracking study of it-extraposition with advanced Libyan learners of English (N = 44). Participants read extraposed and non-extraposed sentences while their eye movements were recorded. Two linear mixed-effects models were constructed: an SAH model incorporating log frequency of the matrix adjective, and a CPT model incorporating offline cloze probability of the embedded-clause verb. The CPT model provided a significantly better fit than a baseline model (Δ − 2LL = 6.52, p = .011), with each one-unit increase in centred predictability reducing total reading time by 0.35 ms (f² = 0.04). The SAH predictor did not approach significance (Δ − 2LL = 1.23, p = .267). Follow-up analyses of early measures, regressions, and adjective-frequency interactions offered no support for an attentional bottleneck. We discuss the results in light of surprisal theory, Bayesian predictive coding, and constraint-based processing, and consider the possibility that SAH effects if present may require larger samples or different operationalisations to detect. The findings confirm that advanced L2 learners exploit the predictive affordances of the matrix context, and they carry direct implications for teaching grammar and reading in the Libyan EFL classroom.
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References
1. Arai, M., Van Gompel, R. P. G., & Scheepers, C. (2007). Priming ditransitive structures in comprehension. Cognitive Psychology, 54(3), 218–250. https://doi.org/10.1016/j.cogpsych.2006.07.001
2. Ali, R. S. (2025). EFL Pre-Service Teachers’ Attitudes Towards Using AI Applications. Al-Farooq Journal of Sciences, 1(1), 93-109.
3. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001
4. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01
5. Birch, S. L., & Rayner, K. (1997). Linguistic focus affects eye movements during reading. Memory & Cognition, 25(5), 653–660. https://doi.org/10.3758/BF03211306
6. Clahsen, H., & Felser, C. (2006). Grammatical processing in language learners. Applied Psycholinguistics, 27(1), 3–42. https://doi.org/10.1017/S0142716406060024
7. Ellis, N. C. (2002). Frequency effects in language processing: A review with implications for theories of implicit and explicit language acquisition. Studies in Second Language Acquisition, 24(2), 143–188. https://doi.org/10.1017/S0272263102002024
8. Elmahdi, O. E. H. (2016). The impact of task-based learning on EFL Libyan students’ writing performance. International Journal of English Linguistics, 6(4), 113–120. https://doi.org/10.5539/ijel.v6n4p113
9. Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498. https://doi.org/10.1111/2041-210X.12504
10. Grüter, T., & Rohde, H. (2021). L2 prediction is not just in the eye of the beholder. Bilingualism: Language and Cognition, 24(5), 869–872. https://doi.org/10.1017/S1366728921000533
11. Hopp, H. (2022). Prediction and grammatical learning in L2 sentence processing. Annual Review of Linguistics, 8, 77–99. https://doi.org/10.1146/annurev-linguistics-031120-115117
12. Inhoff, A. W., & Rayner, K. (1986). Parafoveal word processing during eye fixations in reading: Effects of word frequency. Perception & Psychophysics, 40(6), 431–439. https://doi.org/10.3758/BF03208205
13. Ito, A., & Corley, M. (2022). Learning to predict: The role of statistical learning in L2 sentence processing. Bilingualism: Language and Cognition, 25(4), 641–655. https://doi.org/10.1017/S1366728921000806
14. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329–354. https://doi.org/10.1037/0033-295X.87.4.329
15. Kaan, E., & Grüter, T. (2021). Prediction in second language processing and learning. Bilingualism: Language and Cognition, 24(5), 843–845. https://doi.org/10.1017/S1366728921000454
16. Kamide, Y., Altmann, G. T. M., & Haywood, S. L. (2003). The time-course of prediction in incremental sentence processing: Evidence from anticipatory eye movements. Journal of Memory and Language, 49(1), 133–156. https://doi.org/10.1016/S0749-596X(03)00023-8
17. Kuperberg, G. R. (2020). Tea with milk? A hierarchical generative framework of sequential event comprehension. Topics in Cognitive Science, 12(1), 255–298. https://doi.org/10.1111/tops.12450
18. Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006
19. Latif, M. B., & Saeid, S. A. M. (2026). The Impact of Teaching English Literature as an Elective Course to Fourth-Year ESL Students in a University Context. Al-Farooq Journal of Sciences, 2(3), 1166-1179.
https://doi.org/10.65405/mfh93672
20. MacDonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). The lexical nature of syntactic ambiguity resolution. Psychological Review, 101(4), 676–703. https://doi.org/10.1037/0033-295X.101.4.676
21. Plonsky, L., & Oswald, F. L. (2014). How big is ‘big’? Interpreting effect sizes in L2 research. Language Learning, 64(4), 878–912. https://doi.org/10.1111/lang.12079
22. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372–422. https://doi.org/10.1037/0033-2909.124.3.372
23. Rayner, K., Schotter, E. R., Masson, M. E. J., Potter, M. C., & Treiman, R. (2016). So much to read, so little time: How do we read, and can speed reading help? Psychological Science in the Public Interest, 17(1), 4–34. https://doi.org/10.1177/1529100615623267
24. Roberts, L., & Siyanova-Chanturia, A. (2013). Using eye-tracking to investigate topics in L2 acquisition and processing. Studies in Second Language Acquisition, 35(2), 213–235. https://doi.org/10.1017/S0272263112000861
25. Sanford, A. J., & Sturt, P. (2002). Depth of processing in language comprehension: Not noticing the evidence. Trends in Cognitive Sciences, 6(9), 382–386. https://doi.org/10.1016/S1364-6613(02)01958-7
26. Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D., & Mermelstein, R. J. (2012). A practical guide to calculating Cohen’s f², a measure of local effect size, from PROC MIXED. Frontiers in Psychology, 3, 111. https://doi.org/10.3389/fpsyg.2012.00111
27. van Heuven, W. J. B., Mandera, P., Keuleers, E., & Brysbaert, M. (2014). SUBTLEX-UK: A new and improved word frequency database for British English. Quarterly Journal of Experimental Psychology, 67(6), 1176–1190. https://doi.org/10.1080/17470218.2013.850521












