-
Notifications
You must be signed in to change notification settings - Fork 14
/
FreqBook.bib
1087 lines (966 loc) · 44.7 KB
/
FreqBook.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
@book{blitzstein2014introduction,
title={Introduction to probability},
author={Blitzstein, Joseph K and Hwang, Jessica},
year={2014},
publisher={Chapman and Hall/CRC}
}
@article{dillon2012syntactic,
title={Syntactic and semantic predictors of tense in Hindi: An ERP investigation},
author={Dillon, Brian and Nevins, Andrew and Austin, Alison C and Phillips, Colin},
journal={Language and Cognitive Processes},
volume={27},
number={3},
pages={313--344},
year={2012},
publisher={Taylor \& Francis}
}
@article{clark73,
title={The language-as-fixed-effect fallacy: {A} critique of language statistics in psychological research},
author={Clark, Herbert H},
journal={Journal of verbal learning and verbal behavior},
volume={12},
number={4},
pages={335--359},
year={1973},
publisher={Elsevier}
}
@article{JaegerMertzenVanDykeVasishth2019,
Author = {J\"ager, Lena A. and Mertzen, Daniela and Van Dyke, Julie A. and Vasishth, Shravan},
Title = {Interference patterns in subject-verb agreement and reflexives revisited: {A} large-sample study},
Year = {2020},
volume = {111},
journal = {Journal of Memory and Language},
code = {https://osf.io/reavs/},
doi = {https://doi.org/10.1016/j.jml.2019.104063}
}
@book{baguley2012serious,
title={Serious stats: {A} guide to advanced statistics for the behavioral sciences},
author={Baguley, Thomas},
year={2012},
publisher={Macmillan International Higher Education}
}
@book{rosenthal2000contrasts,
title={Contrasts and effect sizes in behavioral research: A correlational approach},
author={Rosenthal, Robert and Rosnow, Ralph L and Rubin, Donald B},
year={2000},
publisher={Cambridge University Press}
}
@book{faraway2016extending,
title={Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models},
author={Faraway, Julian J},
year={2016},
publisher={CRC press}
}
@book{west2006linear,
title={Linear mixed models: a practical guide using statistical software},
author={West, Brady T and Welch, Kathleen B and Galecki, Andrzej T},
year={2015},
edition = {Second edition},
publisher={Chapman and Hall/CRC}
}
@book{WoodGAMMs,
Author = {Simon N. Wood},
Publisher = {CRC Press},
Title = {{Generalized Additive Models: An introduction with R}},
Year = {2006}}
@book{pinheirobates,
Address = {New York},
Author = {Jos{\'e} C. Pinheiro and Douglas M. Bates},
Optannote = {Excellent introduction to nlme package, but it is at a higher level than Raudenbush and Bryk (2002).},
Publisher = {Springer-Verlag},
Title = {Mixed-Effects Models in {S} and {S-PLUS}},
Year = 2000}
@article{henderson1959estimation,
title={The estimation of environmental and genetic trends from records subject to culling},
author={Henderson, Charles R and Kempthorne, Oscar and Searle, Shayle R and Von Krosigk, CM},
journal={Biometrics},
volume={15},
number={2},
pages={192--218},
year={1959},
publisher={JSTOR}
}
@book{venablesripley,
Address = {New York},
Annote = {Terse and tight-lipped. Assumes that you know a lot. It has a brief but excellent section on lme.},
Author = {William N. Venables and Brian D. Ripley},
Publisher = {Springer},
Title = {Modern Applied Statistics with {S-PLUS}},
Year = {2002}}
@unpublished{MertzenEtAl2021,
Author = {Mertzen, Daniela and Paape, Dario and Dillon, Brian W. and Engbert, Ralf and Vasishth, Shravan},
Title = {Syntactic and semantic interference in sentence comprehension: {Support from English and German eye-tracking data}},
Year = {2021},
note = {submitted to Glossa Psycholinguistics},
OPTurl = {}
}
@article{levy2013expectation,
title={Expectation and locality effects in {G}erman verb-final structures},
author={Levy, Roger P and Keller, Frank},
journal={Journal of Memory and Language},
volume={68},
number={2},
pages={199--222},
year={2013},
publisher={Elsevier}
}
@unpublished{LaurinavichyuteVasishth2021,
title={The (ir)reproducibility of published analyses: {A} case study of 57 {JML} articles published between 2019 and 2021},
author={Anna Laurinavichyute and Shravan Vasishth},
year = {2021},
code = {https://osf.io/3bzu8/},
pdf = {https://psyarxiv.com/hf297/},
note = {Submitted to the Journal of Memory and Language}
}
@article{wicherts2011willingness,
title={Willingness to share research data is related to the strength of the evidence and the quality of reporting of statistical results},
author={Wicherts, Jelte M and Bakker, Marjan and Molenaar, Dylan},
journal={PloS ONE},
volume={6},
number={11},
pages={e26828},
year={2011},
publisher={Public Library of Science San Francisco, USA}
}
@article{yarkonigeneralizability,
title={The generalizability crisis},
author={Yarkoni, Tal},
journal={The Behavioral and Brain Sciences},
pages={1--37},
year= {2020}
}
@Article{westfall2017,
AUTHOR = { Westfall, J and Nichols, TE and Yarkoni, Tal},
TITLE = {Fixing the stimulus-as-fixed-effect fallacy in task fMRI},
JOURNAL = {Wellcome Open Research},
VOLUME = {1},
YEAR = {2017},
NUMBER = {23},
DOI = {10.12688/wellcomeopenres.10298.2}
}
@article{fedorenko2006nature,
title={The nature of working memory capacity in sentence comprehension: {E}vidence against domain-specific working memory resources},
author={Fedorenko, Evelina and Gibson, Edward and Rohde, Douglas},
journal={Journal of memory and language},
volume={54},
number={4},
pages={541--553},
year={2006},
publisher={Elsevier}
}
@article{SafaviEtAlFrontiers2016,
Author = {Molood Sadat Safavi and Samar Husain and Shravan Vasishth},
journal = {Frontiers in Psychology},
Title = {Dependency resolution difficulty increases with distance in {P}ersian separable complex predicates: Implications for expectation and memory-based accounts},
OPTnote = {Special Issue on Encoding and Navigating Linguistic Representations in Memory},
pdf = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00403/full},
code={https://github.com/vasishth/SafaviEtAl2016},
year = {2016},
volume = {7},
issue = {403},
OPTdoi = {10.3389/fpsyg.2016.00403},
abstract = {Delaying the appearance of a verb in a noun-verb dependency tends to increase processing difficulty at the verb; one explanation for this locality effect is decay and/or interference of the noun in working memory. Surprisal, an expectation-based account, predicts that delaying the appearance of a verb either renders it no more predictable or more predictable, leading respectively to a prediction of no effect of distance or a facilitation. Recently, Husain et al (2014) suggested that when the exact identity of the upcoming verb is predictable (strong predictability), increasing argument-verb distance leads to facilitation effects, which is consistent with surprisal; but when the exact identity of the upcoming verb is not predictable (weak predictability), locality effects are seen. We investigated Husain et al.'s proposal using Persian complex predicates (CPs), which consist of a non-verbal element---a noun in the current study---and a verb. In CPs, once the noun has been read, the exact identity of the verb is highly predictable (strong predictability); this was confirmed using a sentence completion study. In two self-paced reading (SPR) and two eye-tracking (ET) experiments, we delayed the appearance of the verb by interposing a relative clause (Expt. 1 and 3) or a long PP (Expt. 2 and 4).
We also included a simple Noun-Verb predicate configuration with the same distance manipulation; here, the exact identity of the verb was not predictable (weak predictability). Thus, the design crossed Predictability Strength and Distance. We found that, consistent with surprisal, the verb in the strong predictability conditions was read faster than in the weak predictability conditions. Furthermore, greater verb-argument distance led to slower reading times; strong predictability did not neutralize or attenuate the locality effects. As regards the effect of distance on dependency resolution difficulty, these four experiments present evidence in favor of working memory accounts of argument-verb dependency resolution, and against the surprisal-based expectation account of Levy (2008). However, another expectation-based measure, entropy, which was computed using the offline sentence completion data, predicts reading times in Experiment 1. We suggest that forgetting due to memory overload leads to greater entropy at the verb.}
}
@article{VBLD07,
Author = {Shravan Vasishth and Sven Bruessow and Richard L. Lewis and Heiner Drenhaus},
Date-Modified = {2009-08-21 11:25:25 +0200},
Journal = {Cognitive Science},
Number = {4},
Title = {Processing Polarity: {H}ow the ungrammatical intrudes on the grammatical},
abstract = {A central question in online human sentence comprehension is: how are linguistic
relations established between different parts of a sentence? Previous work has shown that
this dependency resolution process can be computationally expensive, but the underlying
reasons for this are still unclear. We argue that dependency resolution is mediated by
cue-based retrieval, constrained by independently motivated working memory principles
defined in a cognitive architecture (ACT-R). To demonstrate this, we investigate an
unusual instance of dependency resolution, the processing of negative and positive
polarity items, and confirm a surprising prediction of the cue-based retrieval model:
partial cue-matches, which constitute a kind of similarity-based interference, can give
rise to the intrusion of ungrammatical retrieval candidates, leading to both processing
slow-downs and even errors of judgment that take the form of illusions of grammaticality
in patently ungrammatical structures. A notable achievement is that good quantitative
fits are achieved without adjusting the key model parameters.},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/Vasishth-Bruessow-Lewis-Drenhaus-CogSci2008.pdf},
Volume = {32},
issue = {4},
Year = {2008},
pages = {685--712},
code = {https://github.com/vasishth/ProcessingPolarity}
}
@article{jager2020interference,
title={Interference patterns in subject-verb agreement and reflexives revisited: {A} large-sample study},
author={J{\"a}ger, Lena A and Mertzen, Daniela and Van Dyke, Julie A and Vasishth, Shravan},
journal={Journal of Memory and Language},
volume={111},
pages={104063},
year={2020},
publisher={Elsevier}
}
@article{smith2021encoding,
title={Encoding interference effects support self-organized sentence processing},
author={Smith, Garrett and Franck, Julie and Tabor, Whitney},
journal={Cognitive Psychology},
volume={124},
pages={101356},
year={2021},
publisher={Elsevier}
}
@article{hammerly2019grammaticality,
title={The grammaticality asymmetry in agreement attraction reflects response bias: {E}xperimental and modeling evidence},
author={Hammerly, Christopher and Staub, Adrian and Dillon, Brian W.},
journal={Cognitive psychology},
volume={110},
pages={70--104},
year={2019},
publisher={Elsevier}
}
@article{NicenboimEtAlCogSci2018,
year = {2018},
author = {Bruno Nicenboim and Shravan Vasishth and Felix Engelmann and Katja Suckow},
journal = {Cognitive Science},
volume = {42},
issue = {S4},
page = {1075-1100},
title = {Exploratory and confirmatory analyses in sentence processing: {A case study of number interference in German}},
doi = {10.1111/cogs.12589}
}
@article{HusainVasishthNarayanan2015,
title={Integration and prediction difficulty in {H}indi sentence comprehension: {E}vidence from an eye-tracking corpus},
author={Samar Husain and Shravan Vasishth and Narayanan Srinivasan},
volume = {8(2)},
issue = {3},
pages = {1--12},
journal = {Journal of Eye Movement Research},
year={2015},
abstract = {This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension diffi- culty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty.},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/HusainEtAlETHindiJEMR2015.pdf},
code={https://github.com/vasishth/HusainEtAlJEMR2015}
}
@article{nieuwenhuis2011erroneous,
title={Erroneous analyses of interactions in neuroscience: {A} problem of significance},
author={Nieuwenhuis, Sander and Forstmann, Birte U and Wagenmakers, Eric-Jan},
journal={Nature Neuroscience},
volume={14},
number={9},
pages={1105--1107},
year={2011},
publisher={Nature Research}
}
@Article{influenceme,
title = {influence.ME: Tools for Detecting Influential Data in
Mixed Effects Models},
author = {Rense Nieuwenhuis and Manfred {Te Grotenhuis} and Ben
Pelzer},
year = {2012},
journal = {R Journal},
volume = {4},
number = {2},
pages = {38-47},
}
@Misc{Bolker2018,
Author = {Ben Bolker},
Title = {https://github.com/bbolker/mixedmodels-misc/blob/master/notes/contrasts.rmd},
Urldate = {June 10, 2018},
Year = {2018}
}
@book{fox1997applied,
title={Applied regression analysis, linear models, and related methods.},
author={Fox, John},
year={1997},
publisher={Sage Publications, Inc}
}
@Manual{friendly_matlib,
title = {matlib: Matrix Functions for Teaching and Learning Linear Algebra and
Multivariate Statistics},
author = {Michael Friendly and John Fox and Phil Chalmers},
year = {2020},
note = {R package version 0.9.3},
url = {https://CRAN.R-project.org/package=matlib},
}
@book{DraperSmith,
Address = {New York},
Author = {Norman R. Draper and Harry Smith},
Publisher = {Wiley},
Title = {Applied Regression Analysis},
Year = {1998}}
@article{rabe2020hypr,
title={hypr: An R package for hypothesis-driven contrast coding},
author={Rabe, Maximilian M and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold and Schad, Daniel J.},
journal={Journal of Open Source Software},
volume={5},
number={48},
pages={2134},
year={2020}
}
@article{heister2012analysing,
Author = {Heister, Julian and W{\"u}rzner, Kay-Michael and Kliegl, Reinhold},
Date-Added = {2019-07-17 15:29:04 +0200},
Date-Modified = {2019-07-17 15:30:03 +0200},
Journal = {Visual word recognition},
Pages = {102--130},
Title = {Analysing large datasets of eye movements during reading},
Volume = {2},
Year = {2012}
}
@book{snedecor1967statistical,
Address = {Ames, Iowa},
Author = {Snedecor, George W and Cochran, William G},
Date-Added = {2019-02-18 15:54:37 +0100},
Date-Modified = {2019-02-18 15:55:52 +0100},
Publisher = {Iowa State University Press},
Title = {Statistical Methods},
Year = {1967}
}
@book{dobson2011introduction,
Author = {Dobson, Annette J and Barnett, Adrian},
Publisher = {CRC press},
Title = {An introduction to generalized linear models},
Year = {2011}
}
@article{schad2020capitalize,
title={How to capitalize on a priori contrasts in linear (mixed) models: A tutorial},
author={Schad, Daniel J. and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold},
journal={Journal of Memory and Language},
volume={110},
pages={104038},
year={2020},
publisher={Elsevier}
}
@phdthesis{AnnaLphd,
Type = {dissertation},
Title = {Similarity-based interference and faulty encoding accounts of sentence processing},
Author = {Anna Laurinavichyute},
School = {University of Potsdam},
Year = {2020},
}
@book{powerbookcohen,
address = {Hillsdale, NJ},
author = {Jacob Cohen},
edition = 2,
publisher = {Lawrence Erlbaum},
title = {{Statistical power analysis for the behavioral sciences}},
year = 1988
}
@book{monty,
title={An introduction to linear regression analysis},
author={D. C. Montgomery and E. A. Peck and G. G. Vining},
edition = {5th},
year={2012},
publisher={Wiley},
address = {Hoboken, NJ}
}
@article{mccullagh2019generalized,
title={Generalized linear models},
author={McCullagh, Peter and Nelder, J.A.},
year={2019},
publisher={Routledge},
address = {Boca Raton, Florida}
}
@book{seber2012linear,
title={Linear regression analysis},
author={Seber, George AF and Lee, Alan J},
volume={329},
year={2012},
publisher={John Wiley \& Sons}
}
@book{rice1995mathematical,
title={{Mathematical statistics and data analysis}},
author={Rice, John A.},
year={1995},
publisher={Duxbury press Belmont, CA}
}
@article{barr2013,
title={Random effects structure for confirmatory hypothesis testing: {K}eep it maximal},
author={Barr, Dale J and Levy, Roger and Scheepers, Christoph and Tily, Harry J},
journal={Journal of Memory and Language},
volume={68},
number={3},
pages={255--278},
year={2013},
publisher={Elsevier}
}
@article{hsiao03,
Author = {Fanny Pai-Fang Hsiao and Edward Gibson},
Journal = {Cognition},
Pages = {3--27},
Title = {Processing relative clauses in {C}hinese},
Volume = {90},
Year = {2003}}
@article{gibsonthomas99,
Author = {Edward Gibson and James Thomas},
Journal = {Language and Cognitive Processes},
Pages = {225--248},
Title = {Memory Limitations and Structural Forgetting: The Perception of Complex Ungrammatical Sentences as Grammatical},
Volume = {14(3)},
Year = {1999}}
@Article{VSLK08,
author = {Shravan Vasishth and Katja Suckow and Richard L. Lewis and Sabine Kern},
title = {Short-term forgetting in sentence comprehension: {C}rosslinguistic evidence from head-final structures},
journal = {Language and Cognitive Processes},
year = {2011},
OPTkey = {},
volume = {25},
OPTnumber = {4},
pages = {533-567},
OPTmonth = {},
OPTannote = {},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/Vasishth-Suckow-Lewis-Kern-LCP2010.pdf},
abstract = {Seven experiments using self-paced reading and eyetracking suggest that omitting the middle verb in a double centre embedding leads to easier processing in English but leads to greater difficulty in German. One commonly accepted explanation for the English pattern‚ based on data from offline acceptability ratings and due to Gibson and Thomas (1999)‚Äîis that working-memory overload leads the comprehender to forget the prediction of the upcoming verb phrase (VP), which reduces working-memory load. We show that this VP-forgetting hypothesis does an excellent job of explaining the English data, but cannot account for the German results. We argue that the English and German results can be explained by the parser's adaptation to the grammatical properties of the languages; in contrast to English, German subordinate clauses always have the verb in clause-final position, and this property of German may lead the German parser to maintain predictions of upcoming VPs more robustly compared to English. The evidence thus argues against language-independent forgetting effects in online sentence processing; working-memory constraints can be conditioned by countervailing influences deriving from grammatical properties of the language under study.},
code = {https://osf.io/r3cg9/}
}
@article{FrankEtAl2015,
author = {Stefan L. Frank and Thijs Trompenaars and Shravan Vasishth},
title = {Cross-linguistic differences in processing double-embedded relative clauses: {W}orking-memory constraints or language statistics?},
year = {2015},
pages = {554-578},
volume = {40},
doi = {10.1111/cogs.12247},
abstract = {An English double-embedded relative clause from which the middle verb is omitted can often be processed more easily than its grammatical counterpart, a phenomenon known as the grammaticality illusion. This effect has been found to be reversed in German, suggesting that the illusion is language specific rather than a consequence of universal working memory constraints. We present results from three self-paced reading experiments which show that Dutch native speakers also do not show the grammaticality illusion in Dutch, whereas both German and Dutch native speakers do show the illusion when reading English sentences. These findings provide evidence against working memory constraints as an explanation for the observed effect in English. We propose an alternative account based on the statistical patterns of the languages involved. In support of this alternative, a single recurrent neural network model that is trained on both Dutch and English sentences indeed predicts the cross-linguistic difference in grammaticality effect.},
journal = {Cognitive Science},
code = {https://github.com/vasishth/StanJAGSexamples/tree/master/FrankEtAlCogSci2015},
pdf = {http://stefanfrank.info/pubs/GrammaticalityIllusion.pdf}
}
@article{VasishthetalPLoSOne2013,
author = {Vasishth, Shravan AND Chen, Zhong AND Li, Qiang AND Guo, Gueilan},
journal = {PLoS ONE},
publisher = {Public Library of Science},
title = {Processing {C}hinese Relative Clauses: {E}vidence for the Subject-Relative Advantage},
year = {2013},
month = {10},
volume = {8},
pdf = {http://dx.doi.org/10.1371%2Fjournal.pone.0077006},
pages = {1--14},
number = {10},
code = {http://www.ling.uni-potsdam.de/~vasishth/code/PLoSOneVasishthetaldata.zip}
}
@article{SchadEtAlcontrasts,
Author = {Daniel J. Schad and Shravan Vasishth and Sven Hohenstein and Reinhold Kliegl},
journal = {Journal of Memory and Language},
Title = {How to capitalize on a priori contrasts in linear (mixed) models: {A} tutorial},
Year = {2020},
volume = {110},
code = {https://osf.io/7ukf6/},
pdf = {https://arxiv.org/abs/1807.10451}
}
@article{hannesBEAP,
title={{Balancing Type I Error and Power in Linear Mixed Models}},
author={Hannes Matuschek and Reinhold Kliegl and Shravan Vasishth and R. Harald Baayen and Douglas M. Bates},
doi = {10.1016/j.jml.2017.01.001},
pdf = {http://www.sciencedirect.com/science/article/pii/S0749596X17300013},
abstract = {Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments. The advantages of LMMs over ANOVAs, however, come at a cost: Setting up an LMM is not as straightforward as running an ANOVA. One simple option, when numerically possible, is to fit the full variance-covariance structure of random effects (the maximal model; Barr et al., 2013), presumably to keep Type I error down to the nominal {$\alpha$} in the presence of random effects. Although it is true that fitting a model with only random intercepts may lead to higher Type I error, fitting a maximal model also has a cost: it can lead to a significant loss of power. We demonstrate this with simulations and suggest that for typical psychological and psycholinguistic data, models with a random effect structure that is supported by the data have optimal Type I error and power properties.},
year={2017},
volume={94},
pages={305--315},
journal ={Journal of Memory and Language}
}
@unpublished{BatesEtAlParsimonious,
Author = {Bates, Douglas M. and Kliegl, Reinhold and Vasishth, Shravan and Baayen, Harald},
Note = {Unpublished manuscript},
Title = {Parsimonious mixed models},
Year = {2015},
pdf = {http://arxiv.org/abs/1506.04967},
abstract = {The analysis of experimental data with mixed-effects models requires
decisions about the specification of the appropriate random-effects structure.
Recently, Barr, et al 2013, recommended fitting `maximal'
models with all possible random effect components included. Estimation of
maximal models, however, may not converge. We show that failure to converge
typically is not due to a suboptimal estimation algorithm, but is
a consequence of attempting to fit a model that is too complex to be properly
supported by the data, irrespective of whether estimation is based on maximum
likelihood or on Bayesian hierarchical modeling with uninformative or weakly
informative priors. Importantly, even under convergence, overparameterization
may lead to uninterpretable models. We provide diagnostic tools for detecting
overparameterization and guiding model simplification. Finally, we clarify
that the simulations on which Barr et al. base their recommendations are
atypical for real data. A detailed example is provided of how subject-related
attentional fluctuation across trials may further qualify
statistical inferences about fixed effects, and of how such nonlinear effects
can be accommodated within the mixed-effects modeling framework.}
}
@article{cumming2014new,
title={The new statistics: Why and how},
author={Cumming, Geoff},
journal={Psychological science},
volume={25},
number={1},
pages={7--29},
year={2014},
publisher={Sage Publications Sage CA: Los Angeles, CA}
}
@article{Dillon-EtAl-2013,
title={Contrasting intrusion profiles for agreement and anaphora: {E}xperimental and modeling evidence},
author={Dillon, Brian W. and Mishler, Alan and Sloggett, Shayne and Phillips, Colin},
journal={Journal of Memory and Language},
volume={69},
number={2},
pages={85--103},
year={2013},
publisher={Elsevier}
}
@article{MalsburgAngele2016,
title={False positives and other statistical errors in standard analyses of eye movements in reading},
author={{von~der~Malsburg}, Titus and Angele, Bernhard},
journal={Journal of Memory and Language},
volume={94},
pages={119--133},
year={2017},
publisher={Elsevier}
}
@article{cumming2009confidence,
title={Confidence intervals: Better answers to better questions},
author={Cumming, Geoff and Fidler, Fiona},
journal={Zeitschrift f{\"u}r Psychologie/Journal of Psychology},
volume={217},
number={1},
pages={15--26},
year={2009},
publisher={Hogrefe \& Huber Publishers}
}
@article{hoenigheisey,
Author = {John M. Hoenig and Dennis M. Heisey},
Journal = {The American Statistician},
Pages = {19--24},
Title = {The Abuse of Power: {T}he pervasive fallacy of power calculations for data analysis},
Volume = {55},
issue = {1},
Year = 2001}
@book{kerns,
Year = {2010},
Author = {G. Jay Kerns},
Title = {Introduction to Probability and Statistics Using R}}
@book{morin2016probability,
title={Probability: For the Enthusiastic Beginner},
author={Morin, David J},
year={2016},
publisher={Createspace Independent Publishing Platform}
}
@book{millermiller,
title={John E. Freund's Mathematical Statistics with Applications},
author={Miller, I. and Miller, M.},
year={2004},
publisher={Prentice Hall}
}
@book{casellaberger,
title={Statistical inference},
author={Casella, George and Berger, Roger L},
year={2002},
edition = {Second Edition},
publisher={Cengage Learning}
}
@article{vasishthlewisLanguage05,
Author = {Shravan Vasishth and Richard L. Lewis},
Date-Modified = {2009-08-21 11:35:57 +0200},
Journal = {Language},
Number = {4},
Optmonth = {December},
Pages = {767-794},
abstract = {Although proximity between arguments and verbs (locality) is a relatively robust determinant
of sentence-processing difficulty (Hawkins 1998, 2001, Gibson 2000), increasing argument-verb
distance can also facilitate processing (Konieczny 2000). We present two self-paced reading
(SPR) experiments involving Hindi that provide further evidence of antilocality, and a third SPR
experiment which suggests that similarity-based interference can attenuate this distance-based
facilitation. A unified explanation of interference, locality, and antilocality effects is proposed
via an independently motivated theory of activation decay and retrieval interference (Anderson
et al. 2004).},
Title = {Argument-head distance and processing complexity: {E}xplaining both locality and antilocality effects},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/Vasishth-Lewis-Language2006.pdf},
Volume = {82},
Year = {2006},
code = {http://www.ling.uni-potsdam.de/~vasishth/code/VasishthLewis2006.zip}
}
@book{fox2009mathematical,
title={A mathematical primer for social statistics},
author={Fox, John},
number={159},
year={2009},
publisher={Sage}
}
@book{kolmogorov2018foundations,
title={Foundations of the Theory of Probability: Second English Edition},
author={Kolmogorov, Andre{\u\i} Nikolaevich},
year={2018},
publisher={Courier Dover Publications}
}
@Manual{designr,
title = {designr: {B}alanced Factorial Designs},
author = {Maximilian M. Rabe and Reinhold Kliegl and Schad Daniel},
year = {2021},
note = {R package version 0.1.11},
url = {https://maxrabe.com/designr},
}
@article{JaegerEngelmannVasishth2017,
Author = {J{\"a}ger, Lena A. and Engelmann, Felix and Vasishth, Shravan},
Title = {Similarity-based interference in sentence comprehension: {Literature review and Bayesian meta-analysis}},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/JaegerEngelmannVasishthJML2017.pdf},
abstract = {We report a comprehensive review of the published reading studies on retrieval interference in reflexive-/reciprocal-antecedent and subject-verb dependencies. We also provide a quantitative random-effects meta-analysis of self-paced and eyetracking reading studies. We show that the empirical evidence is only partly consistent with cue-based retrieval as implemented in the ACT-R-based model of sentence processing by Lewis \& Vasishth 2005 (LV05) and that there are important differences between the reviewed dependency types. In non-agreement subject-verb dependencies, there is evidence for inhibitory interference in configurations where the correct dependent fully matches the retrieval cues. This is consistent with the LV05 cue-based retrieval account. By contrast, in subject-verb agreement as well as in reflexive-/reciprocal-antecedent dependencies, no evidence for interference is found in configurations with a fully cue-matching subject. In configurations with only a partially cue-matching subject or antecedent, the meta-analysis revealed facilitatory interference in subject-verb agreement and inhibitory interference in reflexives/reciprocals. The former is consistent with the LV05 account, but the latter is not. Moreover, the meta-analysis revealed that (i) interference type (proactive versus retroactive) leads to different effects in the reviewed dependency types; and (ii) the prominence of the distractor has an important impact on the interference effect. In sum, the meta-analysis suggests that the LV05 needs important modifications to account for (i) the unexplained interference patterns and (ii) the differences between the dependency types. More generally, the meta-analysis provides a quantitative empirical basis for comparing the predictions of competing accounts of retrieval processes in sentence comprehension.},
Year = {2017},
volume = {94},
pages = {316-339},
journal={Journal of Memory and Language},
code = {https://github.com/vasishth/MetaAnalysisJaegerEngelmannVasishth2017},
doi = {https://doi.org/10.1016/j.jml.2017.01.004}
}
@article{benjamin2018redefine,
title={Redefine statistical significance},
author={Benjamin, Daniel J and Berger, James O and Johannesson, Magnus and Nosek, Brian A and Wagenmakers, E-J and Berk, Richard and Bollen, Kenneth A and Brembs, Bj{\"o}rn and Brown, Lawrence and Camerer, Colin and others},
journal={Nature Human Behaviour},
volume={2},
number={1},
pages={6},
year={2018},
publisher={Nature Publishing Group}
}
@article{NicenboimRoettgeretal,
Author = {Bruno Nicenboim and Timo B. Roettger and Shravan Vasishth},
Title = {Using meta-analysis for evidence synthesis: {The case of incomplete neutralization in German}},
Year = {2018},
journal = {Journal of Phonetics},
doi = {https://doi.org/10.1016/j.wocn.2018.06.001},
url = {https://osf.io/g5ndw/},
pdf = {https://mfr.osf.io/render?url=https://osf.io/4k25w/?action=download%26mode=render},
volume = {70},
pages = {39-55}
}
@unpublished{VasishthEngelmann2020,
title={Sentence comprehension as a cognitive process: {A} computational approach},
author={Shravan Vasishth and Felix Engelmann},
year={2020},
note={Under contract with Cambridge University Press},
url = {https://vasishth.github.io/sccp/}
}
@book{fieller,
Address = {Boca Raton, FL},
Author = {Nick Fieller},
Publisher = {CRC Press},
Title = {Basics of matrix algebra for statistics with {R}},
Year = {2016}}
@book{moore2013mathematics,
title={A mathematics course for political and social research},
author={Moore, Will H and Siegel, David A},
year={2013},
publisher={Princeton University Press}
}
@book{gill2006essential,
title={Essential mathematics for political and social research},
author={Gill, Jeff},
year={2006},
publisher={Cambridge University Press Cambridge}
}
@article{hedges1984estimation,
title = {Estimation of effect size under nonrandom sampling: The effects of censoring studies yielding statistically insignificant mean differences},
author = {Hedges, Larry V},
journal = {Journal of Educational Statistics},
volume = {9},
number = {1},
pages = {61--85},
year = {1984},
publisher = {Sage Publications Sage CA: Thousand Oaks, CA}
}
@article{bliss1935calculation,
title={The calculation of the dosage-mortality curve},
author={Bliss, Chester Ittner},
journal={Annals of Applied Biology},
volume={22},
number={1},
pages={134--167},
year={1935},
publisher={Wiley Online Library}
}
@article{lane1978estimating,
title = {Estimating effect size: Bias resulting from the significance criterion in editorial decisions},
author = {Lane, David M and Dunlap, William P},
journal = {British Journal of Mathematical and Statistical Psychology},
volume = {31},
number = {2},
pages = {107--112},
year = {1978},
publisher = {Wiley Online Library}
}
@article{powerfailure,
title = {Power failure: why small sample size undermines the reliability of neuroscience},
author = {Button, Katherine S and Ioannidis, John PA and Mokrysz, Claire and Nosek, Brian A and Flint, Jonathan and Robinson, Emma SJ and Munaf{\`o}, Marcus R},
journal = {Nature Reviews Neuroscience},
volume = {14},
number = {5},
pages = {365--376},
year = {2013},
publisher = {Nature Publishing Group}
}
@article{ioannidis2008most,
title = {Why most discovered true associations are inflated},
author = {Ioannidis, John PA},
journal = {Epidemiology},
volume = {19},
number = {5},
pages = {640--648},
year = {2008},
publisher = {LWW}
}
@article{VasishthMertzenJaegerGelman2018,
Author = {Vasishth, Shravan and Mertzen, Daniela and J\"ager, Lena A. and Gelman, Andrew},
journal = {Journal of Memory and Language},
url = {https://osf.io/eyphj/},
doi = {https://doi.org/10.1016/j.jml.2018.07.004},
Title = {The statistical significance filter leads to overoptimistic expectations of replicability},
Year = {2018},
volume = {103},
pages = {151-175}
}
@article{gibsonwu,
title={Processing {C}hinese relative clauses in context},
author={Gibson, Edward and Wu, H-H Iris},
journal={Language and Cognitive Processes},
volume={28},
number={1-2},
pages={125--155},
year={2013},
publisher={Taylor \& Francis}
}
@book{Royall,
Author = {Richard Royall},
Publisher = {Chapman and Hall, CRC Press},
address = {New York},
Title = {Statistical Evidence: {A} likelihood paradigm},
Year = {1997}}
@article{grodner,
Author = {Daniel Grodner and Edward Gibson},
Journal = {Cognitive Science},
Pages = {261--290},
Title = {Consequences of the serial nature of linguistic input},
Volume = {29},
Year = {2005}}
@book{pocock2013clinical,
title={Clinical trials: {A} practical approach},
author={Pocock, Stuart J},
year={2013},
publisher={John Wiley \& Sons}
}
@inproceedings{gryllia2015phrasing,
title={On the phrasing properties of Hindi relative clauses.},
author={Gryllia, Stella and F{\'e}ry, Caroline and K{\"u}gler, Frank and Pandey, Pramod}
}
@book{johnson2011quantitative,
title={Quantitative methods in linguistics},
author={Johnson, Keith},
year={2011},
publisher={John Wiley \& Sons}
}
@book{Gelman14,
Author = {Andrew Gelman and John B. Carlin and Hal S. Stern and David B. Dunson and Aki Vehtari and Donald B. Rubin},
Edition = {Third},
Publisher = {Chapman and Hall/CRC},
address = {Boca Raton, FL},
Title = {Bayesian Data Analysis},
Year = {2014}}
@book{kruschke2014doing,
title={Doing {B}ayesian data analysis: {A tutorial with R, JAGS, and Stan}},
author={Kruschke, John},
year={2014},
publisher={Academic Press}
}
@Manual{R-base,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2019},
url = {https://www.R-project.org/},
}
@Manual{R-bayesplot,
title = {bayesplot: {Plotting for Bayesian Models}},
author = {Jonah Gabry and Tristan Mahr},
year = {2018},
note = {R package version 1.6.0},
url = {https://CRAN.R-project.org/package=bayesplot},
}
@Manual{R-bookdown,
title = {bookdown: Authoring Books and Technical Documents with R Markdown},
author = {Yihui Xie},
year = {2019},
note = {R package version 0.12},
url = {https://CRAN.R-project.org/package=bookdown},
}
@Manual{R-brms,
title = {brms: Bayesian Regression Models using 'Stan'},
author = {Paul-Christian B{\"u}rkner},
year = {2019},
note = {R package version 2.8.0},
url = {https://CRAN.R-project.org/package=brms},
}
@Manual{R-citr,
title = {citr: RStudio Add-in to Insert Markdown Citations},
author = {Frederik Aust},
year = {2019},
note = {R package version 0.3.2},
url = {https://CRAN.R-project.org/package=citr},
}
@Manual{R-dplyr,
title = {dplyr: A Grammar of Data Manipulation},
author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
year = {2019},
note = {R package version 0.8.3},
url = {https://CRAN.R-project.org/package=dplyr},
}
@Manual{R-DT,
title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
author = {Yihui Xie and Joe Cheng and Xianying Tan},
year = {2018},
note = {R package version 0.5},
url = {https://CRAN.R-project.org/package=DT},
}
@Manual{R-extraDistr,
title = {extraDistr: Additional Univariate and Multivariate Distributions},
author = {Tymoteusz Wolodzko},
year = {2019},
note = {R package version 1.8.11},
url = {https://CRAN.R-project.org/package=extraDistr},
}
@Manual{R-forcats,
title = {forcats: Tools for Working with Categorical Variables (Factors)},
author = {Hadley Wickham},
year = {2019},
note = {R package version 0.4.0},
url = {https://CRAN.R-project.org/package=forcats},
}
@Manual{R-gdtools,
title = {gdtools: Utilities for Graphical Rendering},
author = {David Gohel and Hadley Wickham and Lionel Henry and Jeroen Ooms},
year = {2019},
note = {R package version 0.2.0},
url = {https://CRAN.R-project.org/package=gdtools},
}
@Manual{R-ggplot2,
title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani},
year = {2019},
note = {R package version 3.2.0},
url = {https://CRAN.R-project.org/package=ggplot2},
}
@Manual{R-htmlwidgets,
title = {htmlwidgets: HTML Widgets for R},
author = {Ramnath Vaidyanathan and Yihui Xie and JJ Allaire and Joe Cheng and Kenton Russell},
year = {2018},
note = {R package version 1.3},
url = {https://CRAN.R-project.org/package=htmlwidgets},
}
@Manual{R-knitr,
title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
author = {Yihui Xie},
year = {2019},
note = {R package version 1.24},
url = {https://CRAN.R-project.org/package=knitr},
}
@Manual{R-MASS,
title = {MASS: Support Functions and Datasets for Venables and Ripley's MASS},
author = {Brian Ripley},
year = {2019},
note = {R package version 7.3-51.4},
url = {https://CRAN.R-project.org/package=MASS},
}
@Manual{R-miniUI,
title = {miniUI: Shiny UI Widgets for Small Screens},
author = {Joe Cheng},
year = {2018},
note = {R package version 0.1.1.1},
url = {https://CRAN.R-project.org/package=miniUI},
}
@Manual{R-purrr,
title = {purrr: Functional Programming Tools},
author = {Lionel Henry and Hadley Wickham},
year = {2019},
note = {R package version 0.3.2},
url = {https://CRAN.R-project.org/package=purrr},
}
@Manual{R-Rcpp,
title = {Rcpp: Seamless R and C++ Integration},
author = {Dirk Eddelbuettel and Romain Francois and JJ Allaire and Kevin Ushey and Qiang Kou and Nathan Russell and Douglas Bates and John Chambers},
year = {2019},
note = {R package version 1.0.2},
url = {https://CRAN.R-project.org/package=Rcpp},
}
@Manual{R-readr,
title = {readr: Read Rectangular Text Data},
author = {Hadley Wickham and Jim Hester and Romain Francois},