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Cited by 142Neurobiology of Language and BilingualismSyntax, Semantics, Linguistic VariationReading and Literacy Development

Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study

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Jäger, Lena A., Mertzen, Daniela, Van Dyke, Julie A., Vasishth, Shravan·2020·Journal of Memory and Language
DOI 10.1016/j.jml.2019.104063PMC PMC7583648
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Abstract

Cue-based retrieval theories in sentence processing predict two classes of interference effect: (i) Inhibitory interference is predicted when multiple items match a retrieval cue: cue-overloading leads to an overall slowdown in reading time; and (ii) Facilitatory interference arises when a retrieval target as well as a distractor only partially match the retrieval cues; this partial matching leads to an overall speedup in retrieval time. Inhibitory interference effects are widely observed, but facilitatory interference apparently has an exception: reflexives have been claimed to show no facilitatory interference effects. Because the claim is based on underpowered studies, we conducted a large-sample experiment that investigated both facilitatory and inhibitory interference. In contrast to previous studies, we find facilitatory interference effects in reflexives. We also present a quantitative evaluation of the cue-based retrieval model of Engelmann, Jäger, and Vasishth (2019).

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  • Subject-verb agreement is a crucial part of English grammar and is essential for clear communication.

    “To start with the first fossilized error, subject-verb agreement is a crucial part of English grammar and is essential for clear communication (Jäger et al., 2020).”

    From Overcoming error fossilization in academic writing: strategies for Saudi EFL learners to move beyond the plateau (2024) · cites this paper as Jäger et al., 2020

    The Introduction treats subject–verb agreement as a grammatical dependency and distinguishes grammatical from ungrammatical examples. It does not say that agreement is essential for clear communication; the paper examines sentence-processing mechanisms rather than that broader claim. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 69

  1. Anderson JR, Bothell D, Byrne MD, Douglass S, Lebiere C, & Qin Y (2004). An integrated theory of the mind. Psychological Review, 111 (4), 1036– 60. 15482072
  2. Barr DJ, Levy R, Scheepers C, & Tily HJ (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68 (3), 255– 278. PMC3881361 24403724
  3. Bates D, Kliegl R, Vasishth S, & Baayen H (2015). Parsimonious mixed models. arXiv:1506.04967. · arXiv
  4. Boston MF, Hale JT, Vasishth S, & Kliegl R (2011). Parallel processing and sentence comprehension difficulty. Language and Cognitive Processes, 26 (3), 301– 349.
  5. Brasoveanu A, & Dotlačil J (2019). Formal linguistics and cognitive architecture. New York: Springer-Verlag.
  6. Cohen J (1988). Statistical power analysis for the behavioral sciences (2 ed.). Hillsdale, NJ: Lawrence Erlbaum.
  7. Cunnings I, & Sturt P (2014). Coargumenthood and the processing of reflexives. Journal of Memory and Language, 75, 117– 139.
  8. Cunnings I, & Sturt P (2018). Retrieval interference and sentence interpretation. Journal of Memory and Language, 102, 16– 27.
  9. De Groot A (1956/2014). The meaning of “significance” for different types of research [translated and annotated by Eric-Jan Wagenmakers, Denny Borsboom, Josine Verhagen, Rogier Kievit, Mar1 Bakker, Angelique Cramer, Dora Matzke, Don Mellenbergh, and Han LJ van der Maas]. Acta Psychologica, 148, 188– 194. 24589374
  10. Dillon B (2011). Structured access in sentence comprehension. PhD thesis, University of Maryland, College Park, MD.
  11. Dillon B, Mishler A, Sloggett S, & Phillips C (2013). Contrasting intrusion profiles for agreement and anaphora: Experimental and modeling evidence. Journal of Memory and Language, 69, 85– 103.
  12. Dotlačil J (2018). Building an ACT-R reader for eye-tracking corpus data. Topics in Cognitive Science, 10 (1), 144– 160. 29251440
  13. Engelmann F (2016). Toward an integrated model of sentence processing in reading. Doctoral thesis, Universität Potsdam, Germany.
  14. Engelmann F, Jäger LA, & Vasishth S (2019). The effect of prominence and cue association in retrieval processes: A computational account. Cognitive Science. (Accepted pending minor edits) 31858626
  15. Engelmann F, Vasishth S, Engbert R, & Kliegl R (2013). A framework for modeling the interaction of syntactic processing and eye movement control. Topics in Cognitive Science, 5 (3), 452– 474. 23681560
  16. Freedman LS, Lowe D, & Macaskill P (1984). Stopping rules for clinical trials incorporating clinical opinion. Biometrics, 40 (3), 575– 586. 6518241
  17. Gelman A, & Carlin J (2014). Beyond power calculations assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science, 9 (6), 641– 651. 26186114
  18. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, & Rubin DB (2014). Bayesian data analysis (3rd ed.). Boca Raton, FL: Chapman and Hall/CRC Press.
  19. Glaser YG, Martin RC, Van Dyke JA, Hamilton AC, & Tan Y (2013). Neural basis of semantic and syntactic interference in sentence comprehension. Brain and Language, 126 (3), 314– 326. 23933471 PMC3816580
  20. Gronau QF, Sarafoglou A, Matzke D, Ly A, Boehm U, Marsman M, (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80– 97. 29200501 PMC5699790
  21. Hobbs BP, & Carlin BP (2008). Practical Bayesian design and analysis for drug and device clinical trials. Journal of Biopharmaceutical Statistics, 18 (1), 54– 80. 18161542
  22. Hoekstra R, Morey RD, Rouder JN, & Wagenmakers E-J (2014). Robust misinterpretation of confidence intervals. Psychonomic Bulletin & Review, 21 (5), 1157– 1164. 24420726
  23. Jäger LA, Engelmann F, & Vasishth S (2015). Retrieval interference in reflexive processing: Experimental evidence from Mandarin, and computational modeling. Frontiers in Psychology, 6 (617). PMC4444751 26074829
  24. Jäger LA, Engelmann F, & Vasishth S (2017). Similarity-based interference in sentence comprehension: Literature review and Bayesian meta-analysis. Journal of Memory and Language, 94, 316– 339.
  25. Jeffreys H (1939/1998). The theory of probability. Oxford University Press (Original work published >1939)
  26. Kangasrääsiö A, Jokinen JP, Oulasvirta A, Howes A, & Kaski S (2019). Parameter inference for computational cognitive models with Approximate Bayesian Computation. Cognitive Science, 43 (6), e12738. 31204797 PMC6593436
  27. Kruschke J (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2 ed.). Amsterdam, The Netherlands: Academic Press.
  28. Kush D (2013). Respecting relations: Memory access and antecedent retrieval in incremental sentence processing. Phd thesis, University of Maryland, College Park, MD.
  29. Kush D, & Phillips C (2014). Local anaphor licensing in an SOV language: Implications for retrieval strategies. Frontiers in Psychology, 5 (1252). PMC4220743 25414680
  30. Lago S, Shalom DE, Sigman M, Lau EF, & Phillips C (2015). Agreement attraction in Spanish comprehension. Journal of Memory and Language, 82, 133– 149.
  31. Lee MD, & Wagenmakers E-J (2013). Bayesian cognitive modeling: A practical course. Cambridge, UK: Cambridge University Press.
  32. Lewandowski D, Kurowicka D, & Joe H (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100 (9), 1989– 2001.
  33. Lewis RL, & Vasishth S (2005). An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science, 29 (3), 375– 419. 21702779
  34. Lewis RL, Vasishth S, & Van Dyke J (2006). Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences, 10 (10), 447– 454. 16949330 PMC2239011
  35. Logačev P, & Vasishth S (2016). A multiple-channel model of task-dependent ambiguity resolution in sentence comprehension. Cognitive Science, 40 (2), 266– 298. 25823920
  36. Martin AE, & McElree B (2009). Memory operations that support language comprehension: Evidence from verb-phrase ellipsis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35 (5), 1231– 1239. PMC2849635 19686017
  37. Mätzig P, Vasishth S, Engelmann F, Caplan D, & Burchert F (2018). A computational investigation of sources of variability in sentence comprehension difficulty in aphasia. Topics in Cognitive Science, 10 (1), 161– 174. 29356427
  38. McElreath R (2016). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton, FL: Chapman and Hall/CRC Press.
  39. McElree B (2000). Sentence comprehension is mediated by content-addressable memory structures. Journal of Psycholinguistic Research, 29 (2), 111– 123. 10709178
  40. McElree B (2006). Accessing recent events In Ross BH (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 46, pp. 155– 200). San Diego, CA: Elsevier.
  41. Nicenboim B, Logačev P, Gattei C, & Vasishth S (2016). When high-capacity readers slow down and low-capacity readers speed up: Working memory differences in unbounded dependencies. Frontiers in Psychology, 7 (280). PMC4782223 27014113
  42. Nicenboim B, & Vasishth S (2016). Statistical methods for linguistic research: Foundational ideas - Part II. Language and Linguistics Compass, 10 (11), 591– 613.
  43. Nicenboim B, Vasishth S, Engelmann F, & Suckow K (2018). Exploratory and confirmatory analyses in sentence processing: A case study of number interference in German. Cognitive Science, 42, 1075– 1100. 29411408
  44. Nicenboim B, Vasishth S, & Rosler F (2019). Are words pre-activated probabilistically during sentence comprehension? Evidence from new data and a Bayesian random-effects meta-analysis using publicly available data. https://psyarxiv.com/2atrh/. 32251629
  45. Nieuwland MS, Politzer-Ahles S, Heyselaar E, Segaert K, Darley E, Kazanina N, (2018). Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468. 29631695 PMC5896878
  46. Palestro JJ, Sederberg PB, Osth AF, Van Zandt T, & Turner BM (2018). Likelihood-free methods for cognitive science. Cham, Switzerland: Springer.
  47. Parker D (2018). A memory-based explanation of antecedent-ellipsis mismatches: New insights from computational modeling. Glossa: A Journal of General Linguistics, 3 (1), 129.
  48. Parker D, & Phillips C (2016). Negative polarity illusions and the format of hierarchical encodings in memory. Cognition, 157, 321– 339. 27721173
  49. Parker D, & Phillips C (2017). Reflexive attraction in comprehension is selective. Journal of Memory and Language, 94, 272– 290.
  50. Patil U, Hanne S, Burchert F, Bleser RD, & Vasishth S (2016). A computational evaluation of sentence comprehension deficits in aphasia. Cognitive Science, 40, 5– 50. 26016698
  51. Patil U, Vasishth S, & Lewis RL (2016). Retrieval interference in syntactic processing: The case of reflexive binding in English. Frontiers in Psychology, 7. PMC4881398 27303315
  52. Roberts S, & Pashler H (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107 (2), 358– 367. 10789200
  53. Schad DJ, Betancourt M, & Vasishth S (2019). Towards a principled Bayesian workflow: A tutorial for Cognitive Science. arXiv:1904.12765. 32551748 · arXiv
  54. Schad DJ, Hohenstein S, Vasishth S, & Kliegl R (2019). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language. (In Press)
  55. Spiegelhalter DJ, Freedman LS, & Parmar MK (1994). Bayesian approaches to randomized trials. Journal of the Royal Statistical Society. Series A (Statistics in Society), 157 (3), 357– 416.
  56. Sturt P (2003). The time-course of the application of binding constraints in reference resolution. Journal of Memory and Language, 48, 542– 562.
  57. Tan Y, Martin RC, & Van Dyke JA (2017). Semantic and syntactic interference in sentence comprehension: A comparison of working memory models. Frontiers in Psychology, 8, 198. 28261133 PMC5309252
  58. Turner R, Spiegelhalter D, Smith G, & Thompson S (2008). Bias modelling in evidence synthesis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 172 (1), 21– 47. PMC2667303 19381328
  59. Van Dyke JA (2007). Interference effects from grammatically unavailable constituents during sentence processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33 (2), 407– 430. PMC2077343 17352621
  60. Van Dyke JA, & Lewis RL (2003). Distinguishing effects of structure and decay on attachment and repair: A cue-based parsing account of recovery from misanalyzed ambiguities. Journal of Memory and Language, 49, 285– 316.
  61. Van Dyke JA, & McElree B (2011). Cue-dependent interference in comprehension. Journal of Memory and Language, 65 (3), 247– 263. 21927535 PMC3171743
  62. Vasishth S (2019). Using Approximate Bayesian Computation for estimating parameters in the cue-based retrieval model of sentence processing. MethodsX. PMC7152701 32300544
  63. Vasishth S, Bruessow S, Lewis RL, & Drenhaus H (2008). Processing polarity: How the ungrammatical intrudes on the grammatical. Cognitive Science, 32 (4), 685– 712. 21635350
  64. Vasishth S, & Lewis RL (2006). Argument-head distance and processing complexity: Explaining both locality and antilocality effects. Language, 82 (4), 767– 794.
  65. Vasishth S, Mertzen D, Jäger LA, & Gelman A (2018). The statistical significance filter leads to overoptimistic expectations of replicability. Journal of Memory and Language, 103, 151– 175.
  66. Vasishth S, Nicenboim B, Beckman ME, Li F, & Kong EJ (2018). Bayesian data analysis in the phonetic sciences: A tutorial introduction. Journal of Phonetics, 71, 141– 161. PMC6124675 30197458
  67. 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. 28603341 PMC5461930
  68. von der Malsburg T, & Vasishth S (2013). Scanpaths reveal syntactic underspecification and reanalysis strategies. Language and Cognitive Processes, 28 (10), 1545– 1578.
  69. Wagers MW, Lau EF, & Phillips C (2009). Agreement attraction in comprehension: Representations and processes. Journal of Memory and Language, 61, 206– 237.

Cited by

Sorted by citations

Authors
Jäger, L. A., Mertzen, D., Van Dyke, J. A., & Vasishth, S.
Affiliations
University of Potsdam (DE)
Corresponding author
Vasishth, Shravan
ORCID
Venue
Journal of Memory and Language
Publisher
Elsevier BV
Published
2020-04
Volume
111
Pages
104063
ISSN
0749-596X (ISSN-L)
DOI
10.1016/j.jml.2019.104063
PMC
PMC7583648
OpenAlex
W2996001645
Cited by
142
References
5
Citations by year
2026: 12 · 2025: 17 · 2024: 19 · 2023: 18 · 2022: 27 · 2021: 37 · 2020: 9 · 2019: 3
FWCI
11.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Memory and Language (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • publish.UP (University of Potsdam) (submittedVersion, cc-by-nc-nd)
Funders
  • Deutsche Forschungsgemeinschaft
  • Volkswagen Foundation
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
  • Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
  • Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
Keywords
subject-verb agreement, reflexive pronouns, cue-based retrieval, sentence processing, reading time

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