Hostname: page-component-848d4c4894-r5zm4 Total loading time: 0 Render date: 2024-07-01T02:38:17.124Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating the Uniform Information Density hypothesis with complex nominal compounds

Published online by Cambridge University Press:  12 April 2024

John C. B. Gamboa Open the ORCID record for John C. B. Gamboa [Opens in a new window] ,
Leigh B. Fernandez Open the ORCID record for Leigh B. Fernandez [Opens in a new window]  and
Shanley E. M. Allen Open the ORCID record for Shanley E. M. Allen [Opens in a new window]
John C. B. Gamboa*
Affiliation:
University of Kaiserslautern-Landau, Kaiserslautern, Germany
Leigh B. Fernandez
Affiliation:
University of Kaiserslautern-Landau, Kaiserslautern, Germany
Shanley E. M. Allen
Affiliation:
University of Kaiserslautern-Landau, Kaiserslautern, Germany
*
Corresponding author: John C. B. Gamboa; Email: gamboa@rptu.de
Get access Rights & Permissions [Opens in a new window]

Abstract

The Uniform Information Density (UID) hypothesis proposes that speakers communicate by transmitting information close to a constant rate. When choosing between two syntactic variants, it claims that speakers prefer the variant distributing information most evenly, avoiding signal peaks and troughs. If speakers prefer transmitting information uniformly, then comprehenders should also prefer a uniform signal, experiencing difficulty whenever confronted with informational peaks. However, the literature investigating this hypothesis has focused mostly on production, with only a few studies considering comprehension. In this study, we investigate comprehension in two eye-tracking experiments. Participants read sentences of two different lengths, reflecting different degrees of density, containing either a dense structure (a nominal compound, NC) or a structure that spreads the information through more words (a noun followed by a prepositional phrase, PP). Favoring the UID hypothesis, participants gazed longer at text segments following the critical structure when it was an NC than when it was a PP. They also regressed more in sentences containing longer structures. However, the pattern of results was not as clear as expected, potentially reflecting participants’ experience with the denser structure or task differences between production and comprehension. These aspects should be taken into account in future research investigating the UID hypothesis for comprehension.

Keywords

Sentence processingNominal compoundsEye tracking
Type
Original Article
Information
Applied Psycholinguistics , First View , pp. 1 - 46
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allan, D. (2006). Oxford placement test 1: Audio CD. Oxford, UK: Oxford University Press.Google Scholar
Andrews, S., Veldre, A., & Clarke, I. E. (2020). Measuring lexical quality: The role of spelling ability. Behavior Research Methods, 52(6), 2257–2282. https://doi.org/10.3758/s13428-020-01387-3 CrossRefGoogle ScholarPubMed
Baddeley, A. (2011). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29. https://doi.org/10.1146/annurev-psych-120710-100422 CrossRefGoogle ScholarPubMed
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 CrossRefGoogle ScholarPubMed
Bartolic, L. (1978). Nominal compounds in technical English. In Trimble, L., Trimble, M., & Drobnic, K. (Eds.), English for specific purposes: Science and technology (pp. 257–277). Corvallis, OR: Oregon State University.Google Scholar
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. (R package version 1.1-18-1). https://doi.org/10.18637/jss.v067.i01 CrossRefGoogle Scholar
Bauer, L., & Tarasova, E. (2013). The meaning link in nominal compounds. SKASE Journal of Theoretical Linguistics, 10(3), 2–18. Retrieved from http://www.skase.sk/Volumes/JTL24/pdf_doc/01.pdf Google Scholar
Berg, T. (2016). The semantic structure of English and German compounds: Same or different? Studia Neophilologica, 88(2), 148–164. https://doi.org/10.1080/00393274.2015.1135758 CrossRefGoogle Scholar
Bhatia, V. K. (1992). Pragmatics of the use of nominals in academic and professional genres. In Bouton, L. F. & Kachru, Y. (Eds.), Pragmatics and language learning: Monograph series (Vol. 3, pp. 217–230). Urbana, IL, USA: University of Illinois. Retrieved from https://eric.ed.gov/?id=ED395531 Google Scholar
Biber, D., & Gray, B. (2010). Challenging stereotypes about academic writing: Complexity, elaboration, explicitness. Journal of English for Academic Purposes, 9(1), 2–20. https://doi.org/10.1016/j.jeap.2010.01.001 CrossRefGoogle Scholar
Biber, D., & Gray, B. (2011). Grammatical change in the noun phrase: The influence of written language use. English Language & Linguistics, 15(2), 223–250. https://doi.org/10.1017/S1360674311000025 CrossRefGoogle Scholar
Blignaut, P., Holmqvist, K., Nyström, M., & Dewhurst, R. (2014). Improving the accuracy of video-based eye tracking in real time through post-calibration regression. In Horsley, M., Eliot, M., Knight, B. A., & Reilly, R. (Eds.), Current trends in eye tracking research (pp. 77–100). Cham: Springer. https://doi.org/10.1007/978-3-319-02868-2_5 CrossRefGoogle Scholar
Brekle, H. E. (1976). Generative Satzsemantik im System der englischen Nominalkomposition [Generative sentential semantics in the English nominal system]. Munich: Fink.Google Scholar
Carrió Pastor, M. L. (2008). English complex noun phrase interpretation by Spanish learners. Revista Española de Lingüística Aplicada, 21, 27–44. Retrieved from https://dialnet.unirioja.es/servlet/articulo?codigo=2925910 Google Scholar
Carrió Pastor, M. L., & Candel Mora, M. Á. (2013). Variation in the translation patterns of English complex noun phrases into Spanish in a specific domain. Languages in Contrast, 13(1), 28–45. https://doi.org/10.1075/lic.13.1.02car CrossRefGoogle Scholar
Christianson, K., Luke, S. G., Hussey, E. K., & Wochna, K. L. (2017). Why reread? Evidence from garden-path and local coherence structures. The Quarterly Journal of Experimental Psychology, 70(7), 1380–1405. https://doi.org/10.1080/17470218.2016.1186200 CrossRefGoogle ScholarPubMed
Collins, M. X. (2014). Information density and dependency length as complementary cognitive models. Journal of Psycholinguistic Research, 43, 651–681. https://doi.org/10.1007/s10936-013-9273-3 CrossRefGoogle ScholarPubMed
Cook, A. E., & Wei, W. (2019). What can eye movements tell us about higher level comprehension? Vision, 3(3), 45.CrossRefGoogle ScholarPubMed
Craney, T. A., & Surles, J. G. (2002). Model-dependent variance inflation factor cutoff values. Quality Engineering, 14(3), 391–403. https://doi.org/10.1081/QEN-120001878 CrossRefGoogle Scholar
Dan. (2020). Get Reading Measures [Online forum post]. Retrieved 21 June 2021 from https://www.sr-research.com/support/showthread.php?tid=26 Google Scholar
de Almeida, R. G., & Libben, G. (2005). Changing morphological structures: The effect of sentence context on the interpretation of structurally ambiguous English trimorphemic words. Language and Cognitive Processes, 20, 373–394. https://doi.org/10.1080/01690960444000232 CrossRefGoogle Scholar
Dijkgraaf, A., Hartsuiker, R. J., & Duyck, W. (2017). Predicting upcoming information in native-language and non-native-language auditory word recognition. Bilingualism: Language and Cognition, 20(5), 917–930. https://doi.org/10.1017/S1366728916000547 CrossRefGoogle Scholar
Downing, P. (1977). On the creation and use of English compound nouns. Language, 53(4), 810–842. https://doi.org/10.2307/412913 CrossRefGoogle Scholar
Estes, Z., & Jones, L. L. (2006). Priming via relational similarity: A copper horse is faster when seen through a glass eye. Journal of Memory and Language, 55(1), 89–101. https://doi.org/10.1016/j.jml.2006.01.004 CrossRefGoogle Scholar
Ferreira, V. S., & Dell, G. S. (2000). Effect of ambiguity and lexical availability on syntactic and lexical production. Cognitive Psychology, 40(4), 296–340. https://doi.org/10.1006/cogp.1999.0730 CrossRefGoogle ScholarPubMed
Fox, J., & Weisberg, S. (2019). An R companion to applied regression (3rd ed.). Thousand Oaks, CA: Sage.Google Scholar
Frank, A. F., & Jaeger, T. F. (2008). Speaking rationally: Uniform information density as an optimal strategy for language production. In Proceedings of the annual meeting of the Cognitive Science Society (Vol. 30). Retrieved from https://escholarship.org/uc/item/7d08h6j4 Google Scholar
Frank, S. L., Monaghan, P., & Tsoukala, C. (2019). Neural network models of language acquisition and processing. In Hagoort, P. (Ed.), Human language: From genes and brain to behavior (pp. 277–293). Cambridge, MA: MIT Press. Retrieved from https://www.mpi.nl/publications/item_3347596 CrossRefGoogle Scholar
Gagné, C. L., & Shoben, E. J. (1997). Influence of thematic relations on the comprehension of modifier–noun combinations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(1), 71–87. https://doi.org/10.1037/0278-7393.23.1.71 Google Scholar
Gagné, C. L., & Spalding, T. L. (2009). Constituent integration during the processing of compound words: Does it involve the use of relational structures? Journal of Memory and Language, 60(1), 20–35. https://doi.org/10.1016/j.jml.2008.07.003 CrossRefGoogle Scholar
Geer, S. E., Gleitman, H., & Gleitman, L. (1972). Paraphrasing and remembering compound words. Journal of Verbal Learning and Verbal Behavior, 11(3), 348–355. https://doi.org/10.1016/S0022-5371(72)80097-5 CrossRefGoogle Scholar
Gibson, E. (2001). The dependency locality theory: A distance-based theory of linguistic complexity. In Marantz, A. P., Miyashita, Y., & O’Neil, W. (Eds.), Image, language, brain: Papers from the first mind articulation project symposium (pp. 95–126). Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/3654.003.0008 CrossRefGoogle Scholar
Gibson, E., Futrell, R., Piandadosi, S. T., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019). How efficiency shapes human language. Trends in Cognitive Sciences, 23(5), 389–407. https://doi.org/10.1016/j.tics.2019.02.003 CrossRefGoogle ScholarPubMed
Gledhill, C. J. (2000). Collocations in science writing. Tübingen: Gunter Narr Verlag.Google Scholar
Gleitman, L. R., & Gleitman, H. (1970). Phrase and paraphrase: Some innovative uses of language. New York: Norton.Google Scholar
Gordon, P. C., Hendrick, R., Johnson, M., & Lee, Y. (2006). Similarity-based interference during language comprehension: Evidence from eye tracking during reading. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(6), 1304.Google ScholarPubMed
Granville Hatcher, A. (1960). An introduction to the analysis of English noun compounds. Word, 16(3), 356–373. https://doi.org/10.1080/00437956.1960.11659738 CrossRefGoogle Scholar
Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the second meeting of the North American chapter of the Association for Computational Linguistics on Language Technologies (pp. 1–8). Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.3115/1073336.1073357 Google Scholar
Hornof, A. J., & Halverson, T. (2002). Cleaning up systematic error in eye-tracking data by using required fixation locations. Behavior Research Methods, Instruments, & Computers, 34(4), 592–604. https://doi.org/10.3758/BF03195487 CrossRefGoogle ScholarPubMed
Horsella, M., & Pérez, F. (1991). Nominal compounds in chemical English literature: Toward an approach to text typology. English for Specific Purposes, 10(2), 125–138. https://doi.org/10.1016/0889-4906(91)90005-H CrossRefGoogle Scholar
Inhoff, A. W., Radach, R., & Heller, D. (2000). Complex compounds in German: Interword spaces facilitate segmentation but hinder assignment of meaning. Journal of Memory and Language, 42(1), 23–50. https://doi.org/10.1006/jmla.1999.2666 CrossRefGoogle Scholar
Iraola Azpiroz, M., Allen, S. E. M., Katsika, K., & Fernandez, L. B. (2019a). Psycholinguistic approaches to production and comprehension in bilingual adults and children. Linguistic Approaches to Bilingualism, 9(4/5), 505–513. https://doi.org/10.1075/bct.117.01azp CrossRefGoogle Scholar
Iraola Azpiroz, M., Allen, S. E. M., Katsika, K., & Fernandez, L. B. (Eds.). (2019b). Special issue of Linguistic Approaches to Bilingualism: Psycholinguistic approaches to production and comprehension in bilingual adults and children. Linguistic Approaches to Bilingualism, 9(4/5). https://doi.org/10.1075/bct.117 Google Scholar
Ito, A., Corley, M., & Pickering, M. J. (2018). A cognitive load delays predictive eye movements similarly during L1 and L2 comprehension. Bilingualism: Language and Cognition, 21(2), 251–264. https://doi.org/10.1017/S1366728917000050 CrossRefGoogle Scholar
Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology, 61(1), 23–62. https://doi.org/10.1016/j.cogpsych.2010.02.002 CrossRefGoogle ScholarPubMed
Jared, D., & O’Donnell, K. (2017). Skilled adult readers activate the meanings of high-frequency words using phonology: Evidence from eye tracking. Memory & Cognition, 45, 334–346. https://doi.org/10.3758/s13421-016-0661-4 CrossRefGoogle ScholarPubMed
Jones, D., Farrand, P., Stuart, G., & Morris, N. (1995). Functional equivalence of verbal and spatial information in serial short-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(4), 1008–1018. https://psycnet.apa.org/doi/10.1037/0278-7393.21.4.1008 Google ScholarPubMed
Kaan, E., & Grüter, T. (2021). Prediction in second language processing and learning: Advances and directions. In Kaan, E. & Grüter, T. (Eds.), Prediction in second language processing and learning (pp. 1–24). Amsterdam: John Benjamins. https://doi.org/10.1075/bpa.12.01kaa CrossRefGoogle Scholar
Kim, H., & Grüter, T. (2021). Predictive processing of implicit causality in a second language: A visual-world eye-tracking study. Studies in Second Language Acquisition, 43(1), 133–154. https://doi.org/10.1017/S0272263120000443 CrossRefGoogle Scholar
Krott, A., Libben, G., Jarema, G., Dressler, W., Schreuder, R., & Baayen, H. (2004). Probability in the grammar of German and Dutch: Interfixation in triconstituent compounds. Language and Speech, 47(1), 83–106. https://doi.org/10.1177/00238309040470010401 CrossRefGoogle ScholarPubMed
Kuperberg, G. R., & Jaeger, T. F. (2016). What do we mean by prediction in language comprehension? Language, Cognition and Neuroscience, 31(1), 32–59. https://doi.org/10.1080/23273798.2015.1102299 CrossRefGoogle ScholarPubMed
Kuperman, V., Bertram, R., & Baayen, R. H. (2008). Morphological dynamics in compound processing. Language and Cognitive Processes, 23(7), 1089–1132. https://doi.org/10.1080/01690960802193688 CrossRefGoogle Scholar
Kuznetsova, A., Brockhoff, P. B., Christensen, R. H., et al. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. (R package version 3.0.1) https://doi.org/10.18637/jss.v082.i13 CrossRefGoogle Scholar
Kvam, A. M. (1990). Three-part noun combinations in English, composition – meaning – stress. English Studies: A Journal of English Language and Literature, 71(2), 152–161. https://doi.org/10.1080/00138389008598684 CrossRefGoogle Scholar
Lees, R. B. (1960). The grammar of English nominalizations. Bloomington: Indiana University Press.Google Scholar
Lees, R. B. (1970). Problems in the grammatical analysis of English nominal compounds. In Bierwisch, M. & Heidolph, K. E. (Eds.), Progress in linguistics (pp. 174–186). The Hague: Mouton. https://doi.org/10.1515/9783111350219.174 CrossRefGoogle Scholar
Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: A quick and valid lexical test for advanced learners of English. Behavior Research Methods, 44(2), 325–343. https://doi.org/10.3758/s13428-011-0146-0 CrossRefGoogle ScholarPubMed
Levi, J. N. (1973). Where do all those other adjectives come from? In Corum, C., Smith-Stark, T. C., & Weiser, A. (Eds.), Papers from the 9th regional meeting of the Chicago Linguistic Society (pp. 332–345). Retrieved from https://www.ingentaconnect.com/contentone/cls/pcls/1973/00000009/00000001/art00030 Google Scholar
Levi, J. N. (1978). The syntax and semantics of complex nominals. New York: Academic Press.Google Scholar
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006 CrossRefGoogle ScholarPubMed
Levy, R., & Jaeger, T. F. (2006). Speakers optimize information density through syntactic reduction. In Schölkopf, B., Platt, J. C., & Hoffman, T. (Eds.), Proceedings of the 19th International Conference on Neural Information Processing Systems (pp. 849–856). Cambridge, MA: MIT Press. Retrieved from https://proceedings.neurips.cc/paper/2006/hash/c6a01432c8138d46ba39957a8250e027-Abstract.html Google Scholar
Limaye, M., & Pompian, R. (1991). Brevity versus clarity: The comprehensibility of nominal compounds in business and technical prose. The Journal of Business Communication, 28(1), 7–21. https://doi.org/10.1177/002194369102800102 CrossRefGoogle Scholar
Lo, S., & Andrews, S. (2015). To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology, 6, 1171. https://doi.org/10.3389/fpsyg.2015.01171 CrossRefGoogle ScholarPubMed
Lopopolo, A., Frank, S. L., van den Bosch, A., & Willems, R. M. (2017). Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain. PLOS One, 12(5), e0177794. https://doi.org/10.1371/journal.pone.0177794 CrossRefGoogle ScholarPubMed
Meister, C., Pimentel, T., Haller, P., Jäger, L., Cotterell, R., & Levy, R. (2021). Revisiting the Uniform Information Density hypothesis. In Moens, M.-F., Huang, X., Specia, L., & Tau Yih, S. W. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 963–980). Stroudsburg, PA: Association for Computational Linguistics. http://doi.org/10.18653/v1/2021.emnlp-main.74 CrossRefGoogle Scholar
Merkx, D., & Frank, S. L. (2021). Human sentence processing: Recurrence or attention? In Chersoni, E., Hollenstein, N., Jacobs, C., Oseki, Y., Prévot, L., & Santus, E. (Eds.), Proceedings of the workshop on cognitive modeling and computational linguistics (pp. 12–22). Online. https://doi.org/10.18653/v1/2021.cmcl-1.2 CrossRefGoogle Scholar
Mitsugi, S. (2020). Generating predictions based on semantic categories in a second language: A case of numeral classifiers in Japanese. International Review of Applied Linguistics in Language Teaching, 58(3), 323–349. https://doi.org/10.1515/iral-2017-0118 CrossRefGoogle Scholar
Montero, B. (1996). Technical communication: Complex nominals used to express new concepts in scientific English-causes and ambiguity in meaning. The ESPecialist, 17(1), 57–72. Retrieved from https://revistas.pucsp.br/index.php/esp/article/view/9476/7042 Google Scholar
Olshtain, E. (1981). English nominal compounds and the ESL/EFL reader. In Hines, M. & Rutherford, W. (Eds.), On TESOL ’81: Selected papers from the fifteenth Annual Conference of Teachers of English to Speakers of other Languages (pp. 153–168). Washington, DC: TESOL. Retrieved from https://eric.ed.gov/?id=ED223079 Google Scholar
Paape, D., & Vasishth, S. (2022). Does conscious rereading lead to targeted regressions in garden-path sentences? Data from a novel stop-and-reread paradigm. PsyArXiv preprint. https://doi.org/10.31234/osf.io/vjyfz Google Scholar
Pérez Ruiz, L. (2006). Unravelling noun strings: Toward an approach to the description of complex noun phrases in technical writing. ES: Revista de Filología Inglesa, 27(1), 163–174. Retrieved from https://dialnet.unirioja.es/servlet/articulo?codigo=2210385 Google Scholar
Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. In Verhoeven, L., Elbro, C., & Reitsma, P. (Eds.), Precursors of functional literacy (pp. 189–213). Amsterdam: Benjamins. https://doi.org/10.1075/swll.11.14per CrossRefGoogle Scholar
Piantadosi, S. T., Tily, H., & Gibson, E. (2012). The communicative function of ambiguity in language. Cognition, 122(3), 280–291. https://doi.org/10.1016/j.cognition.2011.10.004 CrossRefGoogle ScholarPubMed
Pickering, M. J., & Traxler, M. J. (1998). Plausibility and recovery from garden paths: An eye-tracking study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(4), 940. https://doi.org/10.1037/0278-7393.24.4.940 Google Scholar
R Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria. Retrieved from https://www.r-project.org/ (Version 3.4.0)Google Scholar
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog. Retrieved from https://github.com/openai/gpt-2 Google Scholar
Salager, F. (1984). Compound nominal phrases in scientific-technical literature: Proportion and rationale. In Pugh, A. K. & Ulijn, J. M. (Eds.), Reading for professional purposes: Studies in native and foreign languages (pp. 136–145). London: Heinemann.Google Scholar
Schaeffer, M., Nitzke, J., Tardel, A., Oster, K., Gutermuth, S., & Hansen-Schirra, S. (2019). Eye-tracking revision processes of translation students and professional translators. Perspectives, 27(4), 589–603. https://doi.org/10.1080/0907676X.2019.1597138 CrossRefGoogle Scholar
Schmidtke, D., Kuperman, V., Gagné, C. L., & Spalding, T. L. (2016). Competition between conceptual relations affects compound recognition: The role of entropy. Psychonomic Bulletin & Review, 23(2), 556–570. https://doi.org/10.3758/s13423-015-0926-0 CrossRefGoogle ScholarPubMed
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x CrossRefGoogle Scholar
Sikos, L., Greenberg, C., Drenhaus, H., & Crocker, M. W. (2017). Information density of encodings: The role of syntactic variation in comprehension. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. J. (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 3168–3173). Austin, TX: Cognitive Science Society.Google Scholar
Swales, J. (1974). Writing scientific English. London: Thomas Nelson and Sons.Google Scholar
Tobin, M. J. (2002). Compliance (COMmunicate PLease wIth less abbreviations, noun clusters, and exclusiveness). American Journal of Respiratory and Critical Care Medicine, 166(12), 1534–1536. https://doi.org/10.1164/rccm.2211001 CrossRefGoogle ScholarPubMed
Traxler, M. J., Morris, R. K., & Seely, R. E. (2002). Processing subject and object relative clauses: Evidence from eye movements. Journal of Memory and Language, 47(1), 69–90. https://doi.org/10.1006/jmla.2001.2836 CrossRefGoogle Scholar
Trimble, L. (1985). English for science and technology: A discourse approach. Cambridge: Cambridge University Press.Google Scholar
Varantola, K. (1984). On noun phrase structures in engineering English. Turku: Turun Yliopisto.Google Scholar
Vasishth, S. (2010). Integration and prediction in head-final structures. In Yamashita, H., Hirose, Y., & Packard, J. (Eds.), Processing and producing head-final structures (pp. 349–367). Dordrecht: Springer. https://doi.org/10.1007/978-90-481-9213-7_16 CrossRefGoogle Scholar
von der Malsburg, T., & Angele, B. (2017). False positives and other statistical errors in standard analyses of eye movements in reading. Journal of Memory and Language, 94, 119–133. https://doi.org/10.1016/j.jml.2016.10.003 CrossRefGoogle ScholarPubMed
Wambach, D., Lamar, M., Swenson, R., Penney, D. L., Kaplan, E., & Libon, D. J. (2011). Digit Span. In Kreutzer, J. S., DeLuca, J., & Caplan, B. (Eds.), Encyclopedia of clinical neuropsychology (pp. 844–849). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79948-3_1288 CrossRefGoogle Scholar
Warren, B. (1978). Semantic patterns of noun-noun compounds. Gothenburg: Acta Universitatis Gothoburgensis.Google Scholar
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & van den Bosch, A. (2016). Prediction during natural language comprehension. Cerebral Cortex, 26(6), 2506–2516. https://doi.org/10.1093/cercor/bhv075 CrossRefGoogle ScholarPubMed
Williams, R. (1984). A cognitive approach to English nominal compounds. In Pugh, A. K. & Ulijn, J. M. (Eds.), Reading for professional purposes: Studies in native and foreign languages (pp. 136–145). London: Heinemann.Google Scholar
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., … Rush, A. M. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 38–45). Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-demos.6 CrossRefGoogle Scholar
Zhang, Y., & Hornof, A. J. (2011). Mode-of-disparities error correction of eye-tracking data. Behavior Research Methods, 43(3), 834–842. https://doi.org/10.3758/s13428-011-0073-0 CrossRefGoogle ScholarPubMed
Zhang, Y., & Hornof, A. J. (2014). Easy post-hoc spatial recalibration of eye tracking data. In Proceedings of the symposium on eye tracking research and applications (pp. 95–98). New York: Association for Computing Machinery. https://doi.org/10.1145/2578153.2578166 CrossRefGoogle Scholar
Supplementary material: File

Gamboa et al. supplementary material 1

Gamboa et al. supplementary material
Download Gamboa et al. supplementary material 1(File)
File 1.5 MB
Supplementary material: File

Gamboa et al. supplementary material 2

Gamboa et al. supplementary material
Download Gamboa et al. supplementary material 2(File)
File 82.6 KB
Supplementary material: File

Gamboa et al. supplementary material 3

Gamboa et al. supplementary material
Download Gamboa et al. supplementary material 3(File)
File 363.7 KB
Supplementary material: File

Gamboa et al. supplementary material 4

Gamboa et al. supplementary material
Download Gamboa et al. supplementary material 4(File)
File 189.3 KB
Supplementary material: File

Gamboa et al. supplementary material 5

Gamboa et al. supplementary material
Download Gamboa et al. supplementary material 5(File)
File 1.1 MB