Preverbal syntactic complexity leads to local coherence effects

References

• Altmann, G. T., & Kamide, Y. (1999). Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition, 73(3), 247–264. https://doi.org/10.1016/S0010-0277(99)00059-1
   PubMed Web of Science ®Google Scholar
• Altmann, G. T., & Kamide, Y. (2007). The real-time mediation of visual attention by language and world knowledge: Linking anticipatory (and other) eye movements to linguistic processing. Journal of Memory and Language, 57(4), 502–518. https://doi.org/10.1016/j.jml.2006.12.004
   Web of Science ®Google Scholar
• Anand, P., Chung, S., & Wagers, M. (2020). Widening the net: Challenges for gathering linguistic data in the digital age. Response to NSF SBE.
   Google Scholar
• Apurva, & Husain, S. (2021). Revisiting anti-locality effects: Evidence against prediction-based accounts. Journal of Memory and Language, 121, Article 104280. https://doi.org/10.1016/j.jml.2021.104280
   Google Scholar
• Apurva, & Husain, S. (2022). Effect of noncanonical word order and argument proximity on processing of SOV languages. In P. Chandra (Ed.), Variation in south asian languages: From macro to micro-differences. Springer Nature.
   Google Scholar
• Avetisyan, S., Lago, S., & Vasishth, S. (2020). Does case marking affect agreement attraction in comprehension? Journal of Memory and Language, 112, Article 104087. https://doi.org/10.1016/j.jml.2020.104087
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Bates, D., Machler, 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
   Web of Science ®Google Scholar
• Bhatia, S., & Dillon, B. (2022). Processing agreement in Hindi: When agreement feeds attraction. Journal of Memory and Language, 125, Article 104322. https://doi.org/10.1016/j.jml.2022.104322
   Google Scholar
• Bhatt, R., Narasimhan, B., Palmer, M., Rambow, O., Sharma, D. M., & Xia, F. (2009). A multi-representational and multi-layered treebank for Hindi/Urdu. In Proceedings of the third linguistic annotation workshop (pp. 186–189). Association for Computational Linguistics.
   Google Scholar
• Bicknell, K., & Levy, R. (2009, June). A model of local coherence effects in human sentence processing as consequences of updates from bottom-up prior to posterior beliefs. In Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics (pp. 665–673). Association for Computational Linguistics. https://www.aclweb.org/anthology/N09-1075
   Google Scholar
• Brothers, T., & Kuperberg, G. R. (2021). Word predictability effects are linear, not logarithmic: Implications for probabilistic models of sentence comprehension. Journal of Memory and Language, 116, Article 104174. https://doi.org/10.1016/j.jml.2020.104174
   Google Scholar
• Christiansen, M. H., & Chater, N. (1999). Toward a connectionist model of recursion in human linguistic performance. Cognitive Science, 23(2), 157–205. https://doi.org/10.1207/s15516709cog2302_2.
   Web of Science ®Google Scholar
• Christiansen, M. H., & MacDonald, M. C. (2009). A usage-based approach to recursion in sentence processing. Language Learning, 59(s1), 126–161. https://doi.org/10.1111/lang.2009.59.issue-s1
   Google Scholar
• Christianson, K., Dempsey, J., Tsiola, A., & Goldshtein, M. (2022). What if they're just not that into you (or your experiment)? On motivation and psycholinguistics. In Psychology of learning and motivation-advances in research and theory. Academic Press Inc.
   Google Scholar
• Drummond, A. (2013). Ibex farm. http://spellout.net/ibexfarm
   Google Scholar
• Engelmann, F., & Vasishth, S. (2009). Processing grammatical and ungrammatical center embeddings in English and German: A computational model. In A. Howes, D. Peebles, & R. Cooper (Eds.), Proceedings of 9th international conference on cognitive modeling.
   Google Scholar
• Ferreira, F. (2003). The misinterpretation of noncanonical sentences. Cognitive Psychology, 47(2), 164–203. https://doi.org/10.1016/S0010-0285(03)00005-7
   PubMed Web of Science ®Google Scholar
• Ferreira, F., Bailey, K. G., & Ferraro, V. (2002). Good-enough representations in language comprehension. Current Directions in Psychological Science, 11(1), 11–15. https://doi.org/10.1111/1467-8721.00158
   Web of Science ®Google Scholar
• Ferreira, F., & Patson, N. D. (2007). The ‘good enough’ approach to language comprehension. Language and Linguistics Compass, 1(1–2), 71–83. https://doi.org/10.1111/lnco.2007.1.issue-1-2.
   Google Scholar
• Fischler, I. S., & Bloom, P. A. (1979). Automatic and attentional processes in the effects of sentence contexts on word recognition. Journal of Verbal Learning and Verbal Behavior, 18(1), 1–20. https://doi.org/10.1016/S0022-5371(79)90534-6
   Google Scholar
• Frank, S. L., Trompenaars, T., & Vasishth, S. (2016). Cross-linguistic differences in processing double-embedded relative clauses: Working-memory constraints or language statistics? Cognitive Science, 40(3), 554–578. https://doi.org/10.1111/cogs.2016.40.issue-3
   PubMed Web of Science ®Google Scholar
• Frazier, L. (1985). Syntactic complexity. In L. K. D. Dowty, & A. Zwicky (Eds.), Natural language parsing (pp. 129–189). Cambridge University Press.
   Google Scholar
• Futrell, R., Gibson, E., & Levy, R. (2020). Lossy-context surprisal: An information-theoretic model of memory effects in sentence processing. Cognitive Science, 44(3). https://doi.org/10.1111/cogs.12814.
   Google Scholar
• Gibson, E. (1998). Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1), 1–76. https://doi.org/10.1016/S0010-0277(98)00034-1
   PubMed Web of Science ®Google Scholar
• Gibson, E. (2006). The interaction of top-down and bottom-up statistics in the resolution of syntactic category ambiguity. Journal of Memory and Language, 54(3), 363–388. https://doi.org/10.1016/j.jml.2005.12.005
   Web of Science ®Google Scholar
• Gibson, E., Piantadosi, S. T., Brink, K., Bergen, L., Lim, E., & Saxe, R. (2013). A noisy-channel account of crosslinguistic word-order variation. Psychological Science, 24(7), 1079–1088. https://doi.org/10.1177/0956797612463705
   PubMed Web of Science ®Google Scholar
• Gibson, E., & Thomas, J. (1999). Memory limitations and structural forgetting: The perception of complex ungrammatical sentences as grammatical. Language and Cognitive Processes, 14(3), 225–248. https://doi.org/10.1080/016909699386293
   Web of Science ®Google Scholar
• Grodner, D., & Gibson, E. (2005). Consequences of the serial nature of linguistic input for sentenial complexity. Cognitive Science, 29(2), 261–290. https://doi.org/10.1207/s15516709cog0000_7
   PubMed Web of Science ®Google 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). Association for Computational Linguistics.
   Google Scholar
• Halekoh, U., & Højsgaard, S. (2014). A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models – the R package pbkrtest. Journal of Statistical Software, 59(9), 1–32. https://doi.org/10.18637/jss.v059.i09
   PubMed Web of Science ®Google Scholar
• Häussler, J., & Bader, M. (2015). An interference account of the missing-VP effect. Frontiers in Psychology, 6, 766. https://doi.org/10.3389/fpsyg.2015.00766
   PubMed Web of Science ®Google Scholar
• Hsiao, Y., & MacDonald, M. C. (2013). Experience and generalization in a connectionist model of Mandarin Chinese relative clause processing. Frontiers in Psychology, 4, 767. https://doi.org/10.3389/fpsyg.2013.00767
   PubMed Web of Science ®Google Scholar
• Huettig, F. (2015). Four central questions about prediction in language processing. Brain Research, 1626, 118–135. https://doi.org/10.1016/j.brainres.2015.02.014
   PubMed Web of Science ®Google Scholar
• Husain, S., Vasishth, S., & Srinivasan, N. (2014). Strong expectations cancel locality effects: Evidence from Hindi. PloS One, 9(7), Article e100986. https://doi.org/10.1371/journal.pone.0100986
   Google Scholar
• Jäger, L., Chen, Z., Li, Q., Lin, C. J. C., & Vasishth, S. (2015). The subject-relative advantage in Chinese: Evidence for expectation-based processing. Journal of Memory and Language, 79–80, 97–120. https://doi.org/10.1016/j.jml.2014.10.005
   Web of Science ®Google Scholar
• Just, M. A., & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychological Review, 99(1), 122–149. https://doi.org/10.1037/0033-295X.99.1.122
   PubMed Web of Science ®Google Scholar
• Just, M. A., Carpenter, P. A., & Woolley, J. D. (1982). Paradigms and processes in reading comprehension. Journal of Experimental Psychology: General, 111(2), 228. https://doi.org/10.1037/0096-3445.111.2.228
   PubMed Web of Science ®Google Scholar
• Kachru, Y. (2006). Hindi. John Benjamins Publishing Company.
   Google Scholar
• Kaiser, E. (2014). Experimental paradigms in psycholinguistics. In R. J. Podesva, & D. Sharma (Eds.), Research methods in linguistics (pp. 135–168). Cambridge University Press.
   Google Scholar
• Kamide, Y., & Kukona, A. (2018). The influence of globally ungrammatical local syntactic constraints on real-time sentence comprehension: Evidence from the visual world paradigm and reading. Cognitive Science, 42(8), 2976–2998. https://doi.org/10.1111/cogs.2018.42.issue-8
   PubMed Web of Science ®Google Scholar
• Karimi, H., & Ferreira, F. (2016). Good-enough linguistic representations and online cognitive equilibrium in language processing. Quarterly Journal of Experimental Psychology, 69(5), 1013–1040. https://doi.org/10.1080/17470218.2015.1053951
   PubMed Web of Science ®Google Scholar
• Keshtiari, N., & Vasishth, S. (2012). Reactivation of antecedents by overt vs. null pronouns: Evidence from Persian. Journal of Language Modelling, 1(2), 243–266. https://doi.org/10.15398/jlm.v1i2.54.
   Google Scholar
• Kliegl, R., Grabner, E., Rolfs, M., & Engbert, R. (2004). Length, frequency, and predictability effects of words on eye movements in reading. European Journal of Cognitive Psychology, 16(1-2), 262–284. https://doi.org/10.1080/09541440340000213
   Web of Science ®Google Scholar
• Konieczny, L. (2000). Locality and parsing complexity. Journal of Psycholinguistic Research, 29(6), 627–645. https://doi.org/10.1023/A:1026528912821
   PubMed Web of Science ®Google Scholar
• Konieczny, L., Müller-Feldmeth, D., Hachmann, W., Schwarzkopf, S., & Wolfer, S. (2009, January). Local syntactic coherence interpretation. Evidence from a visual world study. In Proceedings of the 31th annual conference of the cognitive science society (pp. 1133–1138). Cognitive Science Society.
   Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Kurumada, C., & Jaeger, T. F. (2015). Communicative efficiency in language production: Optional case-marking in Japanese. Journal of Memory and Language, 83, 152–178. https://doi.org/10.1016/j.jml.2015.03.003
   Web of Science ®Google Scholar
• Kutas, M., DeLong, K. A., & Smith, N. J. (2011). A look around at what lies ahead: Prediction and predictability in language processing. In M. Bar (Ed.), Predictions in the brain: Using our past to generate a future (pp. 190–207). Oxford University Press.
   Google Scholar
• Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203–205. https://doi.org/10.1126/science.7350657.
   PubMed Web of Science ®Google Scholar
• Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161–163. https://doi.org/10.1038/307161a0.
   PubMed Web of Science ®Google Scholar
• Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177. https://doi.org/10.1016/j.cognition.2007.05.006
   PubMed Web of Science ®Google Scholar
• Levy, R. (2013). Memory and surprisal in human sentence comprehension. In R. P. G. van Gompel (Ed.), Sentence processing. Psychology Press.
   Google Scholar
• Levy, R., & Keller, F. (2013). Expectation and locality effects in German verb-final structures. Journal of Memory and Language, 68(2), 199–222. https://doi.org/10.1016/j.jml.2012.02.005
   PubMed Web of Science ®Google Scholar
• Lewis, R. L., & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science, 29(3), 375–419. https://doi.org/10.1207/s15516709cog0000_25
   PubMed Web of Science ®Google Scholar
• Lewis, S., & Phillips, C. (2015). Aligning grammatical theories and language processing models. Journal of Psycholinguistic Research, 44(1), 27–46. https://doi.org/10.1007/s10936-014-9329-z
   PubMed Web of Science ®Google Scholar
• Logačev, P., & Vasishth, S. (2016). Understanding underspecification: A comparison of two computational implementations. Quarterly Journal of Experimental Psychology, 69(5), 996–1012. https://doi.org/10.1080/17470218.2015.1134602
   PubMed Web of Science ®Google Scholar
• MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109(1), 35–54. https://doi.org/10.1037/0033-295X.109.1.35
   PubMed Web of Science ®Google Scholar
• Marslen-Wilson, W. (1973). Linguistic structure and speech shadowing at very short latencies. Nature, 244(5417), 522–523. https://doi.org/10.1038/244522a0.
   PubMed Web of Science ®Google Scholar
• Miller, G. A., & Chomsky, N. (1963). Finitary models of language users. In R. Bush, R. D. Luce, & E. Galanter (Eds.), Handbook of mathematical psychology (Vol. 2, pp. 419–492). Wiley.
   Google Scholar
• Nakatani, K., & Gibson, E. (2010). An on-line study of japanese nesting complexity. Cognitive Science, 34(1), 94–112. https://doi.org/10.1111/j.1551-6709.2009.01067.x
   PubMed Web of Science ®Google Scholar
• Norcliffe, E., Harris, A. C., & Jaeger, T. F. (2015). Cross-linguistic psycholinguistics and its critical role in theory development: Early beginnings and recent advances. Language, Cognition and Neuroscience, 30(9), 1009–1032. https://doi.org/10.1080/23273798.2015.1080373
   Web of Science ®Google Scholar
• Paape, D., & Vasishth, S. (2016). Local coherence and preemptive digging-in effects in German. Language and Speech, 59(3), 387–403. https://doi.org/10.1177/0023830915608410.
   PubMed Web of Science ®Google Scholar
• Pandharipande, R., & Kachru, Y. (1977). Relational grammar, ergativity, and Hindi-Urdu. Lingua, 41(3-4), 217–238. https://doi.org/10.1016/0024-3841(77)90080-8
   Web of Science ®Google Scholar
• Parker, D. (2019). Two minds are not always better than one: Modeling evidence for a single sentence analyzer. Glossa a Journal of General Linguistics, 4(1), 64. https://doi.org/10.5334/gjgl.766
   Web of Science ®Google Scholar
• Piantadosi, S. T., Tily, H., & Gibson, E. (2011). Word lengths are optimized for efficient communication. Proceedings of the National Academy of Sciences, 108(9), 3526–3529. https://doi.org/10.1073/pnas.1012551108
   PubMed Web of Science ®Google 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
   PubMed Web of Science ®Google Scholar
• Pickering, M. J., & Gambi, C. (2018). Predicting while comprehending language: A theory and review. Psychological Bulletin, 144(10), 1002–1044. https://doi.org/10.1037/bul0000158
   PubMed Web of Science ®Google Scholar
• Rayner, K., Slattery, T. J., Drieghe, D., & Liversedge, S. P. (2011). Eye movements and word skipping during reading: effects of word length and predictability. Journal of Experimental Psychology: Human Perception and Performance, 37(2), 514–528. https://doi.org/10.1037/a0020990
   PubMed Web of Science ®Google Scholar
• R Core Team (2013). R: A language and environment for statistical computing [Computer software manual]. http://www.R-project.org/.
   Google Scholar
• Ros, I., Santesteban, M., Fukumura, K., & Laka, I. (2015). Aiming at shorter dependencies: The role of agreement morphology. Language, Cognition and Neuroscience, 30(9), 1156–1174. https://doi.org/10.1080/23273798.2014.994009
   Web of Science ®Google Scholar
• Schütze, C. T., & Sprouse, J. (2014). Judgment data. In R. J. Podesva, & D. Sharma (Eds.), Research methods in linguistics (pp. 27–50). Cambridge University Press.
   Google Scholar
• Schwanenflugel, P. J., & Lacount, K. L. (1988). Semantic relatedness and the scope of facilitation for upcoming words in sentences. Journal of Experimental Psychology: Learning Memory and Cognition, 14(2), 344–354. https://doi.org/10.1037/0278-7393.14.2.344
   Web of Science ®Google Scholar
• Schwanenflugel, P. J., & Shoben, E. J. (1985). The influence of sentence constraint on the scope of facilitation for upcoming words. Journal of Memory and Language, 24(2), 232–252. https://doi.org/10.1016/0749-596X(85)90026-9
   Web of Science ®Google Scholar
• Sharma, K., Futrell, R., & Husain, S. (2020). What determines the order of verbal dependents in Hindi? Effects of efficiency in comprehension and production. In Proceedings of cognitive modeling and computational linguistics (CMCL). Association of Computational Linguistics.
   Google Scholar
• Slioussar, N. (2018). Forms and features: The role of syncretism in number agreement attraction. Journal of Memory and Language, 101, 51–63. https://doi.org/10.1016/j.jml.2018.03.006
   Web of Science ®Google Scholar
• Smith, G. (2018). A theory of timing effects in a self-organizing model of sentence processing [Doctoral dissertation, University of Connecticut].
   Google Scholar
• Staub, A. (2015). The effect of lexical predictability on eye movements in reading: Critical review and theoretical interpretation. Language and Linguistics Compass, 9(8), 311–327. https://doi.org/10.1111/lnc3.12151.
   Web of Science ®Google Scholar
• Staub, A., & Clifton, C., Jr. (2006). Syntactic prediction in language comprehension: Evidence from either …or. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(2), 425–436. https://doi.org/10.1037/0278-7393.32.2.425
   PubMed Web of Science ®Google Scholar
• Stowe, L. A., Kaan, E., Sabourin, L., & Taylor, R. C. (2018). The sentence wrap-up dogma. Cognition, 176, 232–247. https://doi.org/10.1016/j.cognition.2018.03.011
   PubMed Web of Science ®Google Scholar
• Swets, B., Desmet, T., Clifton, C., & Ferreira, F. (2008). Underspecification of syntactic ambiguities: Evidence from self-paced reading. Memory & Cognition, 36(1), 201–216. https://doi.org/10.3758/MC.36.1.201
   PubMed Web of Science ®Google Scholar
• Tabor, W., Galantucci, B., & Richardson, D. (2004). Effects of merely local syntactic coherence on sentence processing. Journal of Memory and Language, 50(4), 355–370. https://doi.org/10.1016/j.jml.2004.01.001
   Web of Science ®Google Scholar
• Taylor, W. (1953). ‘Cloze’ procedure: A new tool for measuring readability. Journalism Quarterly, 30(4), 415–433. https://doi.org/10.1177/107769905303000401
   Web of Science ®Google Scholar
• Ueno, M., & Polinsky, M. (2009). Does headedness affect processing? A new look at the VO–OV contrast. Journal of Linguistics, 45(3), 675–710. https://doi.org/10.1017/S0022226709990065
   Web of Science ®Google Scholar
• Van Gompel, R. P., Pickering, M. J., & Traxler, M. J. (2000). Unrestricted race: A new model of syntactic ambiguity resolution. In Reading as a perceptual process (pp. 621–648). Elsevier.
   Google Scholar
• Vasishth, S., & Lewis, R. L. (2006). Argument-head distance and processing complexity: Explaining both locality and antilocality effects. Language, 82(4), 767–794. https://doi.org/10.1353/lan.2006.0236
   Web of Science ®Google Scholar
• Vasishth, S., Suckow, K., Lewis, R. L., & Kern, S. (2010). Short-term forgetting in sentence comprehension: Crosslinguistic evidence from verb-final structures. Language and Cognitive Processes, 25(4), 533–567. https://doi.org/10.1080/01690960903310587
   Web of Science ®Google Scholar
• Villata, S., Tabor, W., & Franck, J. (2018). Encoding and retrieval interference in sentence comprehension: Evidence from agreement. Frontiers in Psychology, 9, 2. https://doi.org/10.3389/fpsyg.2018.00002
   PubMed Web of Science ®Google Scholar
• von der Malsburg, T., & Vasishth, S. (2013). Scanpaths reveal syntactic underspecification and reanalysis strategies. Language and Cognitive Processes, 28(10), 1545–1578. https://doi.org/10.1080/01690965.2012.728232
   Web of Science ®Google Scholar
• Yadav, H., Mittal, S., & Husain, S. (2022). A reappraisal of dependency length minimization as a linguistic universal. Open Mind, 6, 147–168. https://doi.org/10.1162/opmi_a_00060.
   Google Scholar
• Yadav, H., Vaidya, A., Shukla, V., & Husain, S. (2020). Word order typology interacts with linguistic complexity: A cross-linguistic corpus study. Cognitive Science, 44(4), Article e12822. https://doi.org/10.1111/cogs.v44.4
   Google Scholar
• Yngve, V. H. (1960). A model and an hypothesis for language structure. Proceedings of the American Philosophical Society, 104(5), 444–466. https://www.jstor.org/stable/985230
   Google Scholar
• Zola, D. (1984). Redundancy and word perception during reading. Perception & Psychophysics, 36(3), 277–284. https://doi.org/10.3758/BF03206369
   PubMed Web of Science ®Google Scholar