├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── R ├── data.R └── gen_sim_lnorm_functions.R ├── README.md ├── data ├── df_F1data.rda ├── df_VMJG18E1.rda ├── df_VMJG18E2.rda ├── df_VMJG18E3.rda ├── df_VMJG18E4.rda ├── df_VMJG18E5.rda ├── df_VMJG18E6.rda ├── df_VMJG18E7.rda ├── df_contrasts1.rda ├── df_contrasts2.rda ├── df_contrasts3.rda ├── df_contrasts4.rda ├── df_contrasts5.rda ├── df_contrasts6.rda ├── df_contrasts7.rda ├── df_dillonE1.rda ├── df_dillonrep.rda ├── df_discreteagrmt.rda ├── df_dutch.rda ├── df_english.rda ├── df_fedorenko06.rda ├── df_gg05e1.rda ├── df_gg05e1_full.rda ├── df_gibsonwu.rda ├── df_gibsonwu2.rda ├── df_hindi.rda ├── df_hindi_full.rda ├── df_levykeller13E1.rda ├── df_levykeller13E2.rda ├── df_persianE1.rda ├── df_polarity.rda ├── df_smithE1.rda ├── df_smithE2.rda ├── df_swets08.rda └── dillonE1ttnested.rda ├── inst └── REFERENCES.bib ├── lingpsych.Rproj └── man ├── df_F1data.Rd ├── df_VMJG18E1.Rd ├── df_VMJG18E2.Rd ├── df_VMJG18E3.Rd ├── df_VMJG18E4.Rd ├── df_VMJG18E5.Rd ├── df_VMJG18E6.Rd ├── df_VMJG18E7.Rd ├── df_contrasts1.Rd ├── df_contrasts2.Rd ├── df_contrasts3.Rd ├── df_contrasts4.Rd ├── df_contrasts5.Rd ├── df_contrasts6.Rd ├── df_contrasts7.Rd ├── df_dillonE1.Rd ├── df_dillonrep.Rd ├── df_discreteagrmt.Rd ├── df_dutch.Rd ├── df_english.Rd ├── df_fedorenko06.Rd ├── df_gg05e1.Rd ├── df_gg05e1_full.Rd ├── df_gibsonwu.Rd ├── df_gibsonwu2.Rd ├── df_hindi.Rd ├── df_hindi_full.Rd ├── df_levykeller13E1.Rd ├── df_levykeller13E2.Rd ├── df_persianE1.Rd ├── df_polarity.Rd ├── df_smithE1.Rd ├── df_smithE2.Rd ├── dillonE1ttnested.Rd ├── gen_fake_lnorm2x2x2.Rd └── gen_sim_lnorm2.Rd /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: lingpsych 2 | Title: Data and Functions used in the Book "Linear Mixed Models in Linguistics and Psychology: A Comprehensive Introduction" 3 | Version: 0.0.0.9000 4 | Authors@R: 5 | c(person(given = "Shravan", 6 | family = "Vasishth", 7 | role = c("aut", "cre"), 8 | email = "vasishth.1@osu.edu", 9 | comment = c(ORCID = "0000-0003-2027-1994")), 10 | person(given = "Daniel", 11 | family = "Schad", 12 | role = c("aut"), 13 | email = "danieljschad@gmail.com", 14 | comment = c(ORCID = "0000-0003-2586-6823")), 15 | person(given = "Audrey", 16 | family = "Bürki-Forschini", 17 | role = c("aut"), 18 | email = "buerki@uni-potsdam.de", 19 | comment = c(ORCID = "0000-0002-1390-2826")), 20 | person(given = "Reinhold", 21 | family = "Kliegl", 22 | role = c("aut"), 23 | email = "reinhold.kliegl@gmail.com", 24 | comment = c(ORCID = "0000-0002-0180-8488"))) 25 | Description: Data and Functions used in the Book "Linear Mixed Models in Linguistics and Psychology: A Comprehensive Introduction". 26 | License: MIT + file LICENSE 27 | Encoding: UTF-8 28 | LazyData: true 29 | Imports: 30 | Rdpack, 31 | MASS 32 | Suggests: 33 | devtools, 34 | usethis, 35 | dplyr, 36 | Rdpack 37 | RdMacros: Rdpack 38 | RoxygenNote: 7.1.1 39 | Depends: 40 | R (>= 3.0) 41 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2021 2 | COPYRIGHT HOLDER: Shravan Vasishth 3 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2021 Shravan Vasishth 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(gen_fake_lnorm2x2x2) 4 | export(gen_sim_lnorm2) 5 | -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | #' Data from an experiment involving an agreement attraction design with Likert ratings and accuracy of response \insertCite{AnnaLphd;textual}{lingpsych} 2 | #' 3 | #' The data set, from experiment 1 in chapter 4 of \insertCite{AnnaLphd;textual}{lingpsych}, contains the responses from 4,296 subjects who participated in an internet-based study that elicited Likert ratings and question-response accuracies for a single sentence they were shown. There were two levels of a factor called attractor (levels: match and mismatch) and two levels of another factor called interference (levels: interference and no interference). The full data set and code, including preprocessing steps, are available from: https://osf.io/eawg5/. 4 | #' 5 | #' @format A data frame with 4,296 rows and 7 variables: 6 | #' \describe{ 7 | #' \item{item}{Item id.} 8 | #' \item{subj}{Subject id.} 9 | #' \item{attractor}{A factor with two levels, match and mismatch.} 10 | #' \item{interference}{A factor with two levels, interference and no interference.} 11 | #' \item{rating}{A Likert scale rating of the acceptability of the sentence.} 12 | #' \item{rating}{A Likert scale rating of the acceptability of the sentence.} 13 | #' \item{accuracy}{Question response accuracy.} 14 | #' \item{Sentence}{The sentence that the subject was shown.} 15 | #' } 16 | #' @references 17 | #' \insertAllCited{} 18 | "df_discreteagrmt" 19 | 20 | #' Data from an experiment with F1 frequency in Hertz of vowels produced by male and females speakers of different languages \insertCite{johnson2011quantitative;textual}{lingpsych} 21 | #' 22 | #' The data set is from \insertCite{johnson2011quantitative;textual}{lingpsych}, contains the F1 frequencies recorded from 19 male subjects and 19 female subjects who produced vowels in their native languages. 23 | #' 24 | #' @format A data frame with 19 rows and 4 variables: 25 | #' \describe{ 26 | #' \item{female}{F1 frequency data in Hertz from females.} 27 | #' \item{male}{F1 frequency data in Hertz from males.} 28 | #' \item{vowel}{The vowel produced.} 29 | #' \item{language}{The language spoken by the speaker.} 30 | #' } 31 | #' @references 32 | #' \insertAllCited{} 33 | "df_F1data" 34 | 35 | #' Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in English. \insertCite{grodner;textual}{lingpsych} 36 | #' 37 | #' The data set is from Experiment 1 in \insertCite{grodner;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in English. There are 42 subjects who read 16 items in a Latin square design. The data are from the relative clause verb, which is the critical region of interest. 38 | #' 39 | #' @format A data frame with 672 rows and 4 variables: 40 | #' \describe{ 41 | #' \item{subject}{The subject id, ranging from 1 to 42.} 42 | #' \item{item}{The item id, ranging from 1 to 16.} 43 | #' \item{condition}{Condition labels; objgap represents object relatives, and subjgap represents subject relatives.} 44 | #' \item{rawRT}{The reading times at the critical region (the relative clause verb) in milliseconds.} 45 | #' } 46 | #' @references 47 | #' \insertAllCited{} 48 | "df_gg05e1" 49 | 50 | #' Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in English. \insertCite{grodner;textual}{lingpsych} 51 | #' 52 | #' The data set is from Experiment 1 in \insertCite{grodner;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in English. There are 42 subjects who read 16 items in a Latin square design. The data are for all the words in the two sentence types. 53 | #' 54 | #' @format A data frame with 10331 rows and 10 variables: 55 | #' \describe{ 56 | #' \item{subject}{The subject id, ranging from 1 to 42.} 57 | #' \item{item}{The item id, ranging from 1 to 16.} 58 | #' \item{condition}{Condition labels; objgap represents object relatives, and subjgap represents subject relatives.} 59 | #' \item{word_position}{The word position in each sentence; in object relatives, the critical region (the relative clause verb) is in position 6, and in subject relatives, the critical region is in position 4.} 60 | #' \item{rawRT}{The reading times at each word, in milliseconds.} 61 | #' \item{qcorrect}{Comprehension question response accuracy, coded as 0 (incorrect) or 1 (correct).} 62 | #' } 63 | #' @references 64 | #' \insertAllCited{} 65 | "df_gg05e1_full" 66 | 67 | #' Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in Chinese. \insertCite{gibsonwu;textual}{lingpsych} 68 | #' 69 | #' The data set is from an experiment reported in \insertCite{gibsonwu;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in Chinese. There are 37 subjects who read 15 items in a Latin square design. The data are from the relative clause head noun, which is the critical region of interest. 70 | #' 71 | #' @format A data frame with 547 rows and 4 variables: 72 | #' \describe{ 73 | #' \item{subj}{The subject id, ranging from 1 to 40.} 74 | #' \item{item}{The item id, ranging from 1 to 16.} 75 | #' \item{type}{Condition labels; obj-ext represents object relatives, and subj-ext represents subject relatives.} 76 | #' \item{rt}{The reading times at the critical region (the relative clause head noun) in milliseconds.} 77 | #' } 78 | #' @references 79 | #' \insertAllCited{} 80 | "df_gibsonwu" 81 | 82 | #' Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in Chinese. \insertCite{VasishthetalPLoSOne2013;textual}{lingpsych} 83 | #' 84 | #' The data set is from a replication attempt of experiment by \insertCite{gibsonwu;textual}{lingpsych}; the replication attempt was reported in \insertCite{VasishthetalPLoSOne2013;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in Chinese. There are 40 subjects who read 15 items in a Latin square design. The data are from the relative clause head noun, which is the critical region of interest. 85 | #' 86 | #' @format A data frame with 595 rows and 6 variables: 87 | #' \describe{ 88 | #' \item{subj}{The subject id, ranging from 1 to 40.} 89 | #' \item{item}{The item id, ranging from 1 to 16.} 90 | #' \item{condition}{Condition labels; obj-ext represents object relatives, and subj-ext represents subject relatives.} 91 | #' \item{pos}{The word position of the critical region in the sentence.} 92 | #' \item{rt}{The reading times at the critical region (the relative clause head noun) in milliseconds.} 93 | #' \item{region}{The label for the critical region (head noun) in the sentence.} 94 | #' } 95 | #' @references 96 | #' \insertAllCited{} 97 | "df_gibsonwu2" 98 | 99 | #' Data from an eyetracking reading experiment that records total reading times in milliseconds at an auxiliary verb in so-called low and high interference conditions. \insertCite{DillonEtAl2013;textual}{lingpsych} 100 | #' 101 | #' The data set is from an eyetracking experiment by \insertCite{DillonEtAl2013;textual}{lingpsych}, and contains reading times (total reading times) from a design that orginally had eight conditions. There are 40 subjects who read 48 items in a Latin square design. The data shown here focus on two conditions from the experiment, high and low interference conditions in a subject-verb number agreement configuration in ungrammatical sentences. 102 | #' 103 | #' @format A data frame with 2,855 rows and 5 variables: 104 | #' \describe{ 105 | #' \item{subj}{The subject id, ranging from 1 to 40.} 106 | #' \item{item}{The item id, ranging from 1 to 48. Each item id is prefixed by dillonE1.} 107 | #' \item{rt}{The total reading times at the critical region (auxiliary verb) in milliseconds.} 108 | #' \item{int}{The two levels of the interference manipulation, low and high.} 109 | #' \item{expt}{The experiment id.} 110 | 111 | #' } 112 | #' @references 113 | #' \insertAllCited{} 114 | "df_dillonE1" 115 | 116 | #' Data from a self-paced reading experiment that records log reading times in log milliseconds of grammatical and ungrammatical double center embeddings in English. \insertCite{VSLK08;textual}{lingpsych} 117 | #' 118 | #' The data set is from a self-paced reading study by \insertCite{VSLK08;textual}{lingpsych}, and contains reading times from a design that had two conditions, grammatical and ungrammatical double center embeddings in English. There are 48 subjects who read 16 items in a Latin square design. The data shown here focus on log reading times at the noun phrase following the final verb in a double center embedding. 119 | #' 120 | #' @format A data frame with 768 rows and 4 variables: 121 | #' \describe{ 122 | #' \item{subject}{The subject id, ranging from 1 to 49.} 123 | #' \item{item}{The item id, ranging from 1 to 16.} 124 | #' \item{condition}{Grammatical conditions are coded as +1, and ungrammatical conditions as -1.} 125 | #' \item{NP1}{The log reading times at the noun phase following the final verb in a double center embedding.} 126 | #' } 127 | #' @references 128 | #' \insertAllCited{} 129 | "df_english" 130 | 131 | #' Data from a self-paced reading experiment that records log reading times in log milliseconds of grammatical and ungrammatical double center embeddings in Dutch. \insertCite{FrankEtAl2015;textual}{lingpsych} 132 | #' 133 | #' The data set is from a self-paced reading study by \insertCite{FrankEtAl2015;textual}{lingpsych}, and contains reading times from a design that had two conditions, grammatical and ungrammatical double center embeddings in Dutch. There are 24 subjects who read 16 items in a Latin square design. The data shown here focus on log reading times at the noun phrase following the final verb in a double center embedding. 134 | #' 135 | #' @format A data frame with 372 rows and 4 variables: 136 | #' \describe{ 137 | #' \item{subject}{The subject id, ranging from 1 to 24. Each subject id is preceded by an s.} 138 | #' \item{item}{The item id, ranging from 1 to 16. Each subject id is preceded by an i.} 139 | #' \item{condition}{Grammatical conditions are coded as +1, and ungrammatical conditions as -1.} 140 | #' \item{NP1}{The log reading times at the noun phase following the final verb in a double center embedding.} 141 | #' } 142 | #' @references 143 | #' \insertAllCited{} 144 | "df_dutch" 145 | 146 | #' Data from the Potsdam-Allahabad Hindi Corpus, an eyetracking reading experiment that records various reading measures for a newspaper corpus in Hindi. \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych} 147 | #' 148 | #' The data set is from an eyetracking study by \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}, and contains various reading measures for a newspaper corpus in Hindi. The data are from a larger data set and contain 10 subjects who read 83 items. The reading measures shown are for each word in each sentence. 149 | #' 150 | #' @format A data frame with 27,066 rows and 33 variables: 151 | #' \describe{ 152 | #' \item{subj}{The subject id, ranging from 1 to 10.} 153 | #' \item{expt}{The experiment id.} 154 | #' \item{item}{The item id, ranging from 1 to 83.} 155 | #' \item{lang}{The language being investigated.} 156 | #' \item{session}{There were two sessions, one preceded by an experiment on Urdu, and one followed by Urdu.} 157 | #' \item{trial}{Trial id.} 158 | #' \item{roi}{Region of interest in the sentence.} 159 | #' \item{FFD}{(First-Fixation Duration) Duration of the first fixation on a position if (and only if) the fixation was progressive. Zero otherwise.} 160 | #' \item{FFP}{(First Fixation Progressive) 0 if material downstream was viewed before the first fixation on this position, 1 otherwise.} 161 | #' \item{SFD}{(Single Fixation Duration) Duration of the fixation on a position if it was the *only* fixation on this region, i.e. if no subsequent fixation on this position followed. Zero if there were several fixations on this region.} 162 | #' \item{FPRT}{(First Pass Reading Time, Gaze Duration) Sum of all first-pass fixation durations on a region before *any* other region is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 163 | #' \item{RBRT}{(Right Bounded Reading Time) Sum of all first-pass fixation durations on a position before another position to the *right* is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 164 | #' \item{TFT}{(Total Fixation Time) Sum of all fixation durations on a region.} 165 | #' \item{RPD}{(Regression Path Duration, Go-Past Duration) Sum of all first-pass fixation durations on a position n and all preceding positions in the time period between the first fixation on n and the first fixation on anything to the right of n.} 166 | #' \item{CRPD}{(Cumulative Regression Path Duration) The CRPD of position n is the total amount of time a participant spent reading the sentence until reaching region n+1. It is the sum of the RPDs of all preceding regions and the RPD of the current region.} 167 | #' \item{RRT}{(Re-reading Time) Sum of all second-pass fixation durations. (RRT = TFT - FPRT)} 168 | #' \item{RRTP}{(ReReading Time Progressive) Sum of all second-pass fixation durations on a position that took place *before* a fixation on a region further to the right. (RRTP = RBRT - FPRT)} 169 | #' \item{RRTR}{(ReReading Time Regressive) Sum of all second-pass fixation durations on a position that occured *after* a fixation on a region further to the right. (RRTR = TFT - RBRT)} 170 | #' \item{RBRC}{(Right-Bounded Regression Count) Number of regressions from this position given *before* any region further to the right has been fixated.} 171 | #' \item{TRC}{(Total Regression Count) Total number of regressions from this position.} 172 | #' \item{LPRT}{(Last-pass reading time).} 173 | #' \item{syll_len}{Syllable length.} 174 | #' \item{word_lex}{The word read.} 175 | #' \item{word_complex}{Word complexity (continuous measure).} 176 | #' \item{word_freq}{Word frequency.} 177 | #' \item{type_freq}{Type frequency.} 178 | #' \item{word_bifreq}{Word bigram frequency.} 179 | #' \item{type_bifreq}{Type bigram frequency.} 180 | #' \item{word_len}{Word length.} 181 | #' \item{PB}{??.} 182 | #' \item{IC}{Integration cost.} 183 | #' \item{SC}{Storage cost.} 184 | #' } 185 | #' @references 186 | #' \insertAllCited{} 187 | "df_hindi" 188 | 189 | #' Data from the Potsdam-Allahabad Hindi Corpus, an eyetracking reading experiment that records various reading measures for a newspaper corpus in Hindi. \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych} 190 | #' 191 | #' The data set is from an eyetracking study by \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}, and contains various reading measures for a newspaper corpus in Hindi. The data are from 30 subjects who read 83 items. The reading measures shown are for each word in each sentence. 192 | #' 193 | #' @format A data frame with 79,943 rows and 33 variables: 194 | #' \describe{ 195 | #' \item{subj}{The subject id, ranging from 1 to 10.} 196 | #' \item{expt}{The experiment id.} 197 | #' \item{item}{The item id, ranging from 1 to 83.} 198 | #' \item{lang}{The language being investigated.} 199 | #' \item{session}{There were two sessions, one preceded by an experiment on Urdu, and one followed by Urdu.} 200 | #' \item{trial}{Trial id.} 201 | #' \item{roi}{Region of interest in the sentence.} 202 | #' \item{FFD}{(First-Fixation Duration) Duration of the first fixation on a position if (and only if) the fixation was progressive. Zero otherwise.} 203 | #' \item{FFP}{(First Fixation Progressive) 0 if material downstream was viewed before the first fixation on this position, 1 otherwise.} 204 | #' \item{SFD}{(Single Fixation Duration) Duration of the fixation on a position if it was the *only* fixation on this region, i.e. if no subsequent fixation on this position followed. Zero if there were several fixations on this region.} 205 | #' \item{FPRT}{(First Pass Reading Time, Gaze Duration) Sum of all first-pass fixation durations on a region before *any* other region is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 206 | #' \item{RBRT}{(Right Bounded Reading Time) Sum of all first-pass fixation durations on a position before another position to the *right* is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 207 | #' \item{TFT}{(Total Fixation Time) Sum of all fixation durations on a region.} 208 | #' \item{RPD}{(Regression Path Duration, Go-Past Duration) Sum of all first-pass fixation durations on a position n and all preceding positions in the time period between the first fixation on n and the first fixation on anything to the right of n.} 209 | #' \item{CRPD}{(Cumulative Regression Path Duration) The CRPD of position n is the total amount of time a participant spent reading the sentence until reaching region n+1. It is the sum of the RPDs of all preceding regions and the RPD of the current region.} 210 | #' \item{RRT}{(Re-reading Time) Sum of all second-pass fixation durations. (RRT = TFT - FPRT)} 211 | #' \item{RRTP}{(ReReading Time Progressive) Sum of all second-pass fixation durations on a position that took place *before* a fixation on a region further to the right. (RRTP = RBRT - FPRT)} 212 | #' \item{RRTR}{(ReReading Time Regressive) Sum of all second-pass fixation durations on a position that occured *after* a fixation on a region further to the right. (RRTR = TFT - RBRT)} 213 | #' \item{RBRC}{(Right-Bounded Regression Count) Number of regressions from this position given *before* any region further to the right has been fixated.} 214 | #' \item{TRC}{(Total Regression Count) Total number of regressions from this position.} 215 | #' \item{LPRT}{(Last-pass reading time).} 216 | #' \item{syll_len}{Syllable length.} 217 | #' \item{word_lex}{The word read.} 218 | #' \item{word_complex}{Word complexity (continuous measure).} 219 | #' \item{word_freq}{Word frequency.} 220 | #' \item{type_freq}{Type frequency.} 221 | #' \item{word_bifreq}{Word bigram frequency.} 222 | #' \item{type_bifreq}{Type bigram frequency.} 223 | #' \item{word_len}{Word length.} 224 | #' \item{PB}{??.} 225 | #' \item{IC}{Integration cost.} 226 | #' \item{SC}{Storage cost.} 227 | #' } 228 | #' @references 229 | #' \insertAllCited{} 230 | "df_hindi_full" 231 | 232 | #' Model fit for an eyetracking reading experiment that records total reading times in milliseconds for a 2x2x2 design. \insertCite{DillonEtAl2013;textual}{lingpsych} 233 | #' 234 | #' The data set is a model fit for an English eyetracking reading study by \insertCite{DillonEtAl2013;textual}{lingpsych}. There are 40 subjects who read 48 items in a Latin square design. The data shown here focus on total reading times at the critical region. 235 | #' 236 | #' @format A brms model fit 237 | #' \describe{ 238 | #' } 239 | #' @references 240 | #' \insertAllCited{} 241 | "dillonE1ttnested" 242 | 243 | #' Reading times at the critical regions of the self-paced reading experiment 1 conducted by \insertCite{SafaviEtAlFrontiers2016;textual}{lingpsych}. 244 | #' 245 | #' Reading times at the critical region of sentences of Experiment 1 of \insertCite{SafaviEtAlFrontiers2016;textual}{lingpsych}. 246 | #' 247 | #' 248 | #' The experiment examined sentences with four conditions: particle-verb constructions were created with either 249 | #' short or long distance between the particle and verb, and with the particle being either predictable or unpredictable. 250 | #' 251 | #' All the data are available from https://github.com/vasishth/SafaviEtAl2016. 252 | #' 253 | #' @format A data frame with 1,512 rows and 5 variables: 254 | #' \describe{ 255 | #' \item{subj}{Subject id, ranging from 4 to 45.} 256 | #' \item{item}{Item id, ranging from 1 to 36.} 257 | #' \item{rt}{Reading times in milliseconds} 258 | #' \item{distance}{The factor distance, with levels short and long.} 259 | #' \item{predability}{The factor predictability, with levels predictable and unpredictable.} 260 | #' } 261 | #' 262 | #' @references 263 | #' \insertAllCited{} 264 | "df_persianE1" 265 | 266 | #' Reading times at the critical regions of an eyetracking experiment conducted by \insertCite{VBLD07;textual}{lingpsych}. 267 | #' 268 | #' Reading times at the critical region of sentences of the experiment reported in \insertCite{VBLD07;textual}{lingpsych}. 269 | #' 270 | #' 271 | #' The experiment examined sentences with six conditions: three conditions with negative polarity items (one grammatical, two ungrammatical), 272 | #' and three with positive polarity items (two grammatical, one ungrammatical). 273 | #' 274 | #' The data and associated code are available from https://github.com/vasishth/ProcessingPolarity. 275 | #' 276 | #' @format A data frame with 8,823 rows and 5 variables: 277 | #' \describe{ 278 | #' \item{subject}{Subject id, ranging from 1 to 48.} 279 | #' \item{item}{Item id, ranging from 1 to 36.} 280 | #' \item{condition}{The conditions labels, ranging from a to f.} 281 | #' \item{times}{The different dependent measures from the eyetracking study.} 282 | #' \item{value}{The dependent measure (milliseconds).} 283 | #' } 284 | #' 285 | #' @references 286 | #' \insertAllCited{} 287 | "df_polarity" 288 | 289 | #' Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{smith2021encoding;textual}{lingpsych}. 290 | #' 291 | #' Reading times at the critical region of sentences of experiment 1 reported in \insertCite{smith2021encoding;textual}{lingpsych}. 292 | #' 293 | #' 294 | #' The experiment examined sentences with four conditions (a 2x2 design). 295 | #' One factor, labeled SemFactor, is semantic similarity of a noun 2 with the subject of the sentence (noun 1); the levels are labeled SemSim (semantically similar) and SemDissim 296 | #' (semantically dissimilar). The other factor, labeled N2Factor, marks whether the 297 | #' second noun phrase was singular (N2sg) or plural (N2pl) marked. 298 | #' 299 | #' The data and associated code are available from https://osf.io/hjrkn/. 300 | #' 301 | #' @format A data frame with 3,441 rows and 5 variables: 302 | #' \describe{ 303 | #' \item{Participant}{Subject id.} 304 | #' \item{StimSet}{Item id.} 305 | #' \item{RT}{Reading times in milliseconds at the critical region.} 306 | #' \item{N2Factor}{The two levels of a factor representing the number marking on the second noun.} 307 | #' \item{SemFactor}{The two levels of a factor representing the number marking on the second noun.} 308 | #' } 309 | #' 310 | #' @references 311 | #' \insertAllCited{} 312 | "df_smithE1" 313 | 314 | #' Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{smith2021encoding;textual}{lingpsych}. 315 | #' 316 | #' Reading times at the critical region of sentences of experiment 2 reported in \insertCite{smith2021encoding;textual}{lingpsych}. 317 | #' 318 | #' 319 | #' The experiment examined sentences with eight conditions (a 2x2x2 design). 320 | #' One factor, labeled SemFactor, is semantic similarity of a noun 2 with the subject of the sentence (noun 1); the levels are labeled SemSim (semantically similar) and SemDissim 321 | #' (semantically dissimilar). The second factor, labeled N2Factor, marks whether the 322 | #' second noun phrase was singular (N2sg) or plural (N2pl) marked. The third factor 323 | #' is VerbFactor, which marks whether the verb is singular (Vsg) or plural (Vpl) marked. 324 | #' 325 | #' The data and associated code are available from https://osf.io/hjrkn/. 326 | #' 327 | #' @format A data frame with 3,459 rows and 6 variables: 328 | #' \describe{ 329 | #' \item{Participant}{Subject id.} 330 | #' \item{StimSet}{Item id.} 331 | #' \item{RT}{Reading times in milliseconds at the critical region.} 332 | #' \item{N2Factor}{The two levels of a factor representing the number marking on the second noun.} 333 | #' \item{SemFactor}{The two levels of a factor representing the number marking on the second noun.} 334 | #' \item{VerbFactor}{The two levels of a factor representing the number marking on the verb.} 335 | #' } 336 | #' 337 | #' @references 338 | #' \insertAllCited{} 339 | "df_smithE2" 340 | 341 | #' Reading times at the critical region () of a self-paced reading experiment conducted by \insertCite{fedorenko06;textual}{lingpsych}. 342 | #' 343 | #' Reading times at the critical region of sentences of experiment 2 reported in \insertCite{fedorenko06;textual}{lingpsych}. 344 | #' 345 | #' 346 | #' The experiment examined sentences with eight conditions (a 2x2x2 design). 347 | #' This self-paced experiment examined sentences with eight conditions (a 2x2x2 design). 348 | #' One factor is relative clause type (object or subject relative); 349 | #' another factor is memory load (three or one items held in memory); 350 | #' the third factor is nountype, which represents items held in memory that 351 | #' are either similar or dissimilar to the nouns in the sentences. 352 | #' 353 | #'#' 354 | #' @format A data frame with 1,440 rows and 7 variables: 355 | #' \describe{ 356 | #' \item{subj}{Subject id.} 357 | #' \item{item}{Item id.} 358 | #' \item{RT}{Reading times in milliseconds at the critical region.} 359 | #' \item{rctype}{The two levels of a factor representing the relative clause type.} 360 | #' \item{nountype}{The two levels of a factor representing the noun type (name or occupation).} 361 | #' \item{load}{The two levels of a factor representing memory load (three words or one word).} 362 | #' \item{region}{The critical region in the sentence.} 363 | #' } 364 | #' 365 | #' @references 366 | #' \insertAllCited{} 367 | "df_fedorenko06" 368 | 369 | 370 | #' Total reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{levy2013expectation}{lingpsych}. 371 | #' 372 | #' Total reading times at the critical region of sentences of experiment 1 reported in \insertCite{levy2013expectation;textual}{lingpsych}. 373 | #' 374 | #' The experiment examined sentences with four conditions (a 2x2 design). 375 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 376 | #' One factor is whether a dative phrase is in the main or subordinate clause; 377 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 378 | #' 379 | #' The conditions (simplified) are: 380 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 381 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 382 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 383 | #' d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 384 | #' den Fussball versteckt... 385 | #' 386 | #' @format A data frame with 672 rows and 5 variables: 387 | #' \describe{ 388 | #' \item{subj}{Subject id.} 389 | #' \item{item}{Item id.} 390 | #' \item{dat}{The two levels of a factor representing whether 391 | #' a dative phrase is in the main or subordinate clause.} 392 | #' \item{adj}{The two levels of a factor representing whether an adjust is in the main or subordinate clause.} 393 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 394 | #' } 395 | #' 396 | #' @references 397 | #' \insertAllCited{} 398 | "df_levykeller13E1" 399 | 400 | #' Total reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{levy2013expectation}{lingpsych}. 401 | #' 402 | #' Total reading times at the critical region of sentences of experiment 2 reported in \insertCite{levy2013expectation;textual}{lingpsych}. 403 | #' 404 | #' The experiment examined sentences with four conditions (a 2x2 design). 405 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 406 | #' One factor is whether a dative phrase is in the main or subordinate clause; 407 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 408 | #' 409 | #' The conditions (simplified) are: 410 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 411 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 412 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 413 | #' d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 414 | #' den Fussball versteckt... 415 | #' 416 | #' @format A data frame with 672 rows and 5 variables: 417 | #' \describe{ 418 | #' \item{subj}{Subject id.} 419 | #' \item{item}{Item id.} 420 | #' \item{dat}{The two levels of a factor representing whether 421 | #' a dative phrase is in the main or subordinate clause.} 422 | #' \item{adj}{The two levels of a factor representing whether an adjust is in the main or subordinate clause.} 423 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 424 | #' } 425 | #' 426 | #' @references 427 | #' \insertAllCited{} 428 | "df_levykeller13E2" 429 | 430 | #' Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 431 | #' 432 | #' Reading times at the critical region of sentences of experiment 1 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 433 | #' 434 | #' The experiment examined sentences with four conditions (a 2x2 design). 435 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 436 | #' One factor is whether a dative phrase is in the main or subordinate clause; 437 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 438 | #' 439 | #' The conditions (simplified) are: 440 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 441 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 442 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 443 | #' d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 444 | #' den Fussball versteckt... 445 | #' 446 | #' @format A data frame with 672 rows and 4 variables: 447 | #' \describe{ 448 | #' \item{subj}{Subject id.} 449 | #' \item{item}{Item id.} 450 | #' \item{cond}{The four condition levels.} 451 | #' \item{rt}{Reading times in milliseconds at the critical region.} 452 | #' } 453 | #' 454 | #' @references 455 | #' \insertAllCited{} 456 | "df_VMJG18E1" 457 | 458 | #' Reading times at the critical region (verb) of aeyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 459 | #' 460 | #' Reading times at the critical region of sentences of experiment 2 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 461 | #' 462 | #' The experiment examined sentences with four conditions (a 2x2 design). 463 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 464 | #' One factor is whether a dative phrase is in the main or subordinate clause; 465 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 466 | #' 467 | #' The conditions (simplified) are: 468 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 469 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 470 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 471 | #' d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 472 | #' den Fussball versteckt... 473 | #' 474 | #' @format A data frame with 670 rows and 4 variables: 475 | #' \describe{ 476 | #' \item{subj}{Subject id.} 477 | #' \item{item}{Item id.} 478 | #' \item{condition}{The four condition levels.} 479 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 480 | #' } 481 | #' 482 | #' @references 483 | #' \insertAllCited{} 484 | "df_VMJG18E2" 485 | 486 | #' Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 487 | #' 488 | #' Reading times at the critical region of sentences of experiment 3 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 489 | #' 490 | #' The experiment examined sentences with four conditions (a 2x2 design). 491 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 492 | #' One factor is whether a dative phrase is in the main or subordinate clause; 493 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 494 | #' 495 | #' The conditions (simplified) are: 496 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat der Mitschuler, der den Fussball versteckt hat, ... 497 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat der Mitschuler, der [ADJ zur Ahndung] den Fussball versteckt hat, ... 498 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 499 | #' d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 500 | #' den Fussball versteckt hat, ... 501 | #' 502 | #' @format A data frame with 672 rows and 4 variables: 503 | #' \describe{ 504 | #' \item{subj}{Subject id.} 505 | #' \item{item}{Item id.} 506 | #' \item{cond}{The four condition levels.} 507 | #' \item{rt}{Reading times in milliseconds at the critical region.} 508 | #' } 509 | #' 510 | #' @references 511 | #' \insertAllCited{} 512 | "df_VMJG18E3" 513 | 514 | #' Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 515 | #' 516 | #' Reading times at the critical region of sentences of experiment 4 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 517 | #' 518 | #' The experiment examined sentences with four conditions (a 2x2 design). 519 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 520 | #' One factor is whether a dative phrase is in the main or subordinate clause; 521 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 522 | #' 523 | #' The conditions (simplified) are: 524 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat der Mitschuler, der den Fussball versteckt hat, ... 525 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat der Mitschuler, der [ADJ zur Ahndung] den Fussball versteckt hat, ... 526 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 527 | #' d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 528 | #' den Fussball versteckt hat, ... 529 | #' 530 | #' @format A data frame with 655 rows and 4 variables: 531 | #' \describe{ 532 | #' \item{subj}{Subject id.} 533 | #' \item{item}{Item id.} 534 | #' \item{condition}{The four condition levels.} 535 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 536 | #' } 537 | #' 538 | #' @references 539 | #' \insertAllCited{} 540 | "df_VMJG18E4" 541 | 542 | #' Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 543 | #' 544 | #' Reading times at the critical region of sentences of experiment 5 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 545 | #' 546 | #' The experiment examined sentences with four conditions (a 2x2 design). 547 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 548 | #' One factor is whether a dative phrase is in the main or subordinate clause; 549 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 550 | #' 551 | #' The conditions (simplified) are: 552 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 553 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 554 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 555 | #' d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 556 | #' den Fussball versteckt hat, ... 557 | #' 558 | #' @format A data frame with 672 rows and 4 variables: 559 | #' \describe{ 560 | #' \item{subj}{Subject id.} 561 | #' \item{item}{Item id.} 562 | #' \item{cond}{The four condition levels.} 563 | #' \item{word}{The word read in the critical region.} 564 | #' \item{rt}{Reading times in milliseconds at the critical region.} 565 | #' } 566 | #' 567 | #' @references 568 | #' \insertAllCited{} 569 | "df_VMJG18E5" 570 | 571 | #' Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 572 | #' 573 | #' Reading times at the critical region of sentences of experiment 6 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 574 | #' 575 | #' The experiment examined sentences with four conditions (a 2x2 design). 576 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 577 | #' One factor is whether a dative phrase is in the main or subordinate clause; 578 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 579 | #' 580 | #' The conditions (simplified) are: 581 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 582 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 583 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 584 | #' d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 585 | #' den Fussball versteckt hat, ... 586 | #' 587 | #' @format A data frame with 670 rows and 4 variables: 588 | #' \describe{ 589 | #' \item{subj}{Subject id.} 590 | #' \item{item}{Item id.} 591 | #' \item{condition}{The four condition levels.} 592 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 593 | #' } 594 | #' 595 | #' @references 596 | #' \insertAllCited{} 597 | "df_VMJG18E6" 598 | 599 | #' Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}. 600 | #' 601 | #' Reading times at the critical region of sentences of experiment 7 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 602 | #' 603 | #' The experiment examined sentences with four conditions (a 2x2 design). 604 | #' This eyetracking experiment examined sentences with four conditions (a 2x2 design). 605 | #' One factor is whether a dative phrase is in the main or subordinate clause; 606 | #' another factor is whether an adjunct phrase is in the main or subordinate clause. 607 | #' 608 | #' The conditions (simplified) are: 609 | #' a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 610 | #' b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 611 | #' c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 612 | #' d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 613 | #' den Fussball versteckt hat, ... 614 | #' 615 | #' @format A data frame with 2,384 rows and 4 variables: 616 | #' \describe{ 617 | #' \item{subj}{Subject id.} 618 | #' \item{item}{Item id.} 619 | #' \item{condition}{The four condition levels.} 620 | #' \item{TFT}{Total reading times in milliseconds at the critical region.} 621 | #' } 622 | #' 623 | #' @references 624 | #' \insertAllCited{} 625 | "df_VMJG18E7" 626 | 627 | #' Reading times at the critical regions of an eyetracking experiment conducted by \insertCite{JaegerMertzenVanDykeVasishth2019;textual}{lingpsych}. 628 | #' 629 | #' Reading times at the critical region of sentences of the experiment reported in \insertCite{JaegerMertzenVanDykeVasishth2019;textual}{lingpsych}. 630 | #' 631 | #' 632 | #' The experiment, which is a replication attempt of Dillon et al. 2013, examined sentences with eight conditions. 633 | #' 634 | #' The data and associated code are available from https://osf.io/reavs/. 635 | #' 636 | #' @format A data frame with 8,628 rows and 8 variables: 637 | #' \describe{ 638 | #' \item{subj}{Subject id.} 639 | #' \item{item}{Item id.} 640 | #' \item{cond}{The conditions labels, ranging from a to h.} 641 | #' \item{acc}{Question-response accuracy.} 642 | #' \item{roi}{The region of interest.} 643 | #' \item{FPRT}{First-pass reading time.} 644 | #' \item{TFT}{Total fixation time.} 645 | #' \item{FPR}{First-pass regression.} 646 | #' } 647 | #' 648 | #' @references 649 | #' \insertAllCited{} 650 | "df_dillonrep" 651 | 652 | #' Data from a self-paced reading experiment that records reading times in milliseconds at the post-critical region. \insertCite{swets2008underspecification;textual}{bcogsci} 653 | #' 654 | #' The data set is from a self-paced reading experiment by \insertCite{swets2008underspecification;textual}{bcogsci}, and contains reading times from a 3x3 design. 655 | #' 656 | #' @format A data frame with 5,184 rows and 6 variables: 657 | #' \describe{ 658 | #' \item{subj}{The subject id.} 659 | #' \item{item}{The item id.} 660 | #' \item{resp.RT}{Response times to questions.} 661 | #' \item{qtype}{The three levels of the between-subjects factor, question type.} 662 | #' \item{attachment}{The three levels of the within-subjects factor, attachment type.} 663 | #' \item{RT}{Reading times at the post-critical region.} 664 | #' } 665 | #' @references 666 | #' \insertAllCited{} 667 | "df_swets08" 668 | 669 | 670 | 671 | #' Simulated data with response times as a function of a between-subject 2-level factor F. 672 | #' 673 | #' The condition means are exactly 0.8 and 0.4 seconds. 674 | #' 675 | #' The data were simulated using the following R-code: 676 | #' 677 | #' set.seed(123) 678 | #' 679 | #' d <- mvrnorm(n=5, mu=c(0.8, 0.4), Sigma=diag(2)*0.2^2, empirical=TRUE) 680 | #' 681 | #' df_contrasts1 <- d %>% as.data.frame() %>% gather(key="F", value="DV") %>% 682 | #' mutate(id=1:nrow(.), F=factor(F)) 683 | #' 684 | #' levels(df_contrasts1$F) <- c("F1", "F2") 685 | #' 686 | #' @format A data frame with 10 rows and 3 variables: 687 | #' \describe{ 688 | #' \item{F}{Between subject factor with factor levels F1 and F2} 689 | #' \item{DV}{Dependent variable, response time in seconds} 690 | #' \item{id}{Subject index} 691 | #' } 692 | #' 693 | #' @references 694 | #' \insertAllCited{} 695 | "df_contrasts1" 696 | 697 | 698 | #' Simulated data with response times from a lexical decision task as a function of a 3-level between-subject factor "word class". 699 | #' 700 | #' The simulated data contains data from word classes "nouns", "verbs", and "adjectives". Word class is manipulated between subjects here (usually it is a within-subject manipulation). 701 | #' The condition means are exactly 500, 450, and 400 milliseconds for nouns, verbs, and adjectives words. 702 | #' 703 | #' The data were simulated using the following R-code: 704 | #' 705 | #' set.seed(123) 706 | #' 707 | #' d2 <- mvrnorm(n=4, mu=c(500, 450, 400), 708 | #' Sigma=diag(3)*20^2, empirical=TRUE) 709 | #' 710 | #' df_contrasts2 <- d2 %>% as.data.frame() %>% 711 | #' gather(key="F", value="DV") %>% 712 | #' mutate(id=1:nrow(.), F=factor(F)) 713 | #' 714 | #' levels(df_contrasts2$F) <- c("nouns", "verbs", "adjectives") 715 | #' 716 | #' df_contrasts2$DV <- round(df_contrasts2$DV) 717 | #' 718 | #' @format A data frame with 12 rows and 3 variables: 719 | #' \describe{ 720 | #' \item{F}{Between subject factor word class with factor levels "nouns", "verbs", and "adjectives"} 721 | #' \item{DV}{Dependent variable, response time in milliseconds} 722 | #' \item{id}{Subject index} 723 | #' } 724 | #' 725 | #' @references 726 | #' \insertAllCited{} 727 | "df_contrasts2" 728 | 729 | 730 | #' Simulated data with response times as a function of a 4-level between-subject factor. 731 | #' 732 | #' The condition means are exactly 10, 20, 10, and 40. 733 | #' It could be assumed that the four factor levels `F1` to `F4` reflect levels of word frequency, including the levels `low`, `medium-low`, `medium-high`, and `high` frequency words, and that the dependent variable (DV) reflects some response time. 734 | #' 735 | #' The data were simulated using the following R-code: 736 | #' 737 | #' set.seed(123) 738 | #' 739 | #' d3 <- mvrnorm(n=5, mu=c(10, 20, 10, 40), Sigma=diag(4)*10^2, empirical=TRUE) 740 | #' 741 | #' df_contrasts3 <- d3 %>% as.data.frame() %>% gather(key="F", value="DV") %>% 742 | #' mutate(id=1:nrow(.), F=factor(F)) 743 | #' 744 | #' levels(df_contrasts3$F) <- c("F1", "F2", "F3", "F4") 745 | #' 746 | #' @format A data frame with 12 rows and 3 variables: 747 | #' \describe{ 748 | #' \item{F}{Between subject factor with factor four levels (F1, F2, F3, F4)} 749 | #' \item{DV}{Dependent variable} 750 | #' \item{id}{Subject index} 751 | #' } 752 | #' 753 | #' @references 754 | #' \insertAllCited{} 755 | "df_contrasts3" 756 | 757 | 758 | #' Simulated data with response times from a 2 x 2 between subject design. 759 | #' 760 | #' The condition means are exactly 10, 20, 10, and 40; means and standard deviations are exactly the same as in `df_contrasts3`. 761 | #' 762 | #' The data were simulated using the following R-code: 763 | #' 764 | #' set.seed(123) 765 | #' 766 | #' d4 <- mvrnorm(n=5, mu=c(10, 20, 10, 40), Sigma=diag(4)*10^2, empirical=TRUE) 767 | #' 768 | #' df_contrasts4 <- d4 %>% as.data.frame() %>% gather(key="A", value="DV") %>% 769 | #' mutate(id=1:nrow(.), B=factor(A), A=factor(A)) 770 | #' 771 | #' levels(df_contrasts4$A) <- c("A1","A1","A2","A2") 772 | #' 773 | #' levels(df_contrasts4$B) <- c("B1","B2","B1","B2") 774 | #' 775 | #' @format A data frame with 12 rows and 3 variables: 776 | #' \describe{ 777 | #' \item{A}{Between subject factor with two levels (A1, A2)} 778 | #' \item{B}{Between subject factor with two levels (B1, B2)} 779 | #' \item{DV}{Dependent variable} 780 | #' \item{id}{Subject index} 781 | #' } 782 | #' 783 | #' @references 784 | #' \insertAllCited{} 785 | "df_contrasts4" 786 | 787 | 788 | #' Simulated data with response times with a 2-level between subject factor and IQ as covariate: no interaction. 789 | #' 790 | #' In the data, the influence of IQ on response time is exactly the same in both conditions F1 and F2. However, the mean response time and mean IQ differs between conditions F1 and F2. 791 | #' 792 | #' The data were simulated using the following R-code: 793 | #' 794 | #' set.seed(123) 795 | #' 796 | #' N <- 30 797 | #' 798 | #' mu1 <- c(225, 85) 799 | #' 800 | #' mu2 <- c(200,115) 801 | #' 802 | #' sd1 <- 40 803 | #' 804 | #' sd2 <- 15 805 | #' 806 | #' r1 <- r2 <- -0.4 807 | #' 808 | #' sigma1 <- matrix(c(sd1^2,r1*sd1*sd2,r1*sd1*sd2,sd2^2),nrow=2) 809 | #' 810 | #' sigma2 <- matrix(c(sd1^2,r2*sd1*sd2,r2*sd1*sd2,sd2^2),nrow=2) 811 | #' 812 | #' d5a <- mvrnorm(n=N, mu=mu1, Sigma=sigma1, empirical=TRUE) 813 | #' 814 | #' d5b <- mvrnorm(n=N, mu=mu2, Sigma=sigma2, empirical=TRUE) 815 | #' 816 | #' df_contrasts5 <- rbind(data.frame(F="F1", d5a), data.frame(F="F2", d5b)) 817 | #' 818 | #' names(df_contrasts5)[2:3] <- c("RT","IQ") 819 | #' 820 | #' df_contrasts5$id <- 1:nrow(df_contrasts5) 821 | #' 822 | #' df_contrasts5$F <- factor(df_contrasts5$F) 823 | #' 824 | #' @format A data frame with 60 rows and 4 variables: 825 | #' \describe{ 826 | #' \item{F}{Between subject factor with factor two levels (F1, F2)} 827 | #' \item{RT}{Dependent variable response time (RT)} 828 | #' \item{IQ}{Between-subject covariate (IQ)} 829 | #' \item{id}{Subject index} 830 | #' } 831 | #' 832 | #' @references 833 | #' \insertAllCited{} 834 | "df_contrasts5" 835 | 836 | 837 | #' Simulated data with response times with a 2-level between subject factor and IQ as covariate: interaction present. 838 | #' 839 | #' In the data, the influence of IQ on response time differs between conditions F1 and F2. 840 | #' 841 | #' The data were simulated using the following R-code: 842 | #' 843 | #' set.seed(123) 844 | #' 845 | #' N <- 30 846 | #' 847 | #' mu1 <- c(200,100) 848 | #' 849 | #' mu2 <- c(220,100) 850 | #' 851 | #' sd1 <- 40 852 | #' 853 | #' sd2 <- 15 854 | #' 855 | #' r1 <- 0.0 856 | #' 857 | #' r2 <- -0.6 858 | #' 859 | #' sigma1 <- matrix(c(sd1^2,r1*sd1*sd2,r1*sd1*sd2,sd2^2),nrow=2) 860 | #' 861 | #' sigma2 <- matrix(c(sd1^2,r2*sd1*sd2,r2*sd1*sd2,sd2^2),nrow=2) 862 | #' 863 | #' d6a <- mvrnorm(n=N, mu=mu1, Sigma=sigma1, empirical=TRUE) 864 | #' 865 | #' d6b <- mvrnorm(n=N, mu=mu2, Sigma=sigma2, empirical=TRUE) 866 | #' 867 | #' df_contrasts6 <- rbind(data.frame(F="F1", d6a), data.frame(F="F2", d6b)) 868 | #' 869 | #' names(df_contrasts6)[2:3] <- c("RT","IQ") 870 | #' 871 | #' df_contrasts6$id <- 1:nrow(df_contrasts6) 872 | #' 873 | #' df_contrasts6$F <- factor(df_contrasts6$F) 874 | #' 875 | #' @format A data frame with 60 rows and 4 variables: 876 | #' \describe{ 877 | #' \item{F}{Between subject factor with factor two levels (F1, F2)} 878 | #' \item{RT}{Dependent variable response time (RT)} 879 | #' \item{IQ}{Between-subject covariate (IQ)} 880 | #' \item{id}{Subject index} 881 | #' } 882 | #' 883 | #' @references 884 | #' \insertAllCited{} 885 | "df_contrasts6" 886 | 887 | 888 | #' Simulated data with a binomial response from a 2 x 2 between subject design. 889 | #' 890 | #' The dependent variable could be successful task performance. The condition means are exactly 0.2, 0.5, 0.2, and 0.8. 891 | #' 892 | #' The data were simulated using the following R-code: 893 | #' 894 | #' set.seed(123) 895 | #' 896 | #' N <- 50 897 | #' 898 | #' d7 <- data.frame(A =factor(rep(c("A1","A1","A2","A2"), each=N)), 899 | #' 900 | #' B =factor(rep(c("B1","B2","B1","B2"), each=N)), 901 | #' 902 | #' pDV= rep(c( 0.2, 0.5, 0.2, 0.8), each=N)) 903 | #' 904 | #' tmp <- c(seq(0,1,length=N)[sample(N)], seq(0,1,length=N)[sample(N)], 905 | #' 906 | #' seq(0,1,length=N)[sample(N)], seq(0,1,length=N)[sample(N)]) 907 | #' 908 | #' d7$pDV <- ifelse(d7$pDV > tmp, 1, 0) 909 | #' 910 | #' d7$id <- 1:nrow(d7) 911 | #' 912 | #' df_contrasts7 <- tibble(d7) 913 | #' 914 | #' @format A data frame with 12 rows and 3 variables: 915 | #' \describe{ 916 | #' \item{A}{Between subject factor with two levels (A1, A2)} 917 | #' \item{B}{Between subject factor with two levels (B1, B2)} 918 | #' \item{pDV}{Dependent variable: successful task performance (0 = no success versus 1 = success)} 919 | #' \item{id}{Subject index} 920 | #' } 921 | #' 922 | #' @references 923 | #' \insertAllCited{} 924 | "df_contrasts7" 925 | -------------------------------------------------------------------------------- /R/gen_sim_lnorm_functions.R: -------------------------------------------------------------------------------- 1 | #' Generate lognormal data from a two-condition repeated measures design (Latin square) 2 | #' 3 | #' This function takes parameters from a previously fitted model 4 | #' and returns simulated data. 5 | #' 6 | #' @param nitem Number of items, must be divisible by two. 7 | #' @param nsubj Number of subjects, must be divisible by two. 8 | #' @param beta The intercept and slope of the fixed effects. 9 | #' @param Sigma_u The variance-covariance matrix for subject random effects. 10 | #' @param Sigma_w The variance-covariance matrix for item random effects. 11 | #' @param sigma_e The standard deviation of the residuals. 12 | #' @return A data frame containing simulated data. 13 | #' @export 14 | gen_sim_lnorm2<-function(nitem=16, 15 | nsubj=42, 16 | beta=NULL, 17 | Sigma_u=NULL, # subject vcov matrix 18 | Sigma_w=NULL, # item vcov matrix 19 | sigma_e=NULL){ 20 | 21 | ## prepare data frame for a two-condition latin square: 22 | g1<-data.frame(item=1:nitem, 23 | cond=rep(c("a","b"),nitem/2)) 24 | g2<-data.frame(item=1:nitem, 25 | cond=rep(c("b","a"),nitem/2)) 26 | 27 | 28 | ## assemble data frame: 29 | gp1<-g1[rep(seq_len(nrow(g1)), 30 | nsubj/2),] 31 | gp2<-g2[rep(seq_len(nrow(g2)), 32 | nsubj/2),] 33 | 34 | simdat<-rbind(gp1,gp2) 35 | 36 | ## add subject column: 37 | simdat$subj<-rep(1:nsubj,each=nitem) 38 | 39 | ## add contrast coding: 40 | simdat$so<-ifelse(simdat$cond=="a",-1/2,1/2) 41 | 42 | ## subject random effects: 43 | u<-MASS::mvrnorm(n=length(unique(simdat$subj)), 44 | mu=c(0,0),Sigma=Sigma_u) 45 | 46 | ## item random effects 47 | w<-MASS::mvrnorm(n=length(unique(simdat$item)), 48 | mu=c(0,0),Sigma=Sigma_w) 49 | 50 | ## generate data row by row: 51 | N<-dim(simdat)[1] 52 | rt<-rep(NA,N) 53 | for(i in 1:N){ 54 | rt[i] <- rlnorm(1,beta[1] + 55 | u[simdat[i,]$subj,1] + 56 | w[simdat[i,]$item,1] + 57 | (beta[2]+u[simdat[i,]$subj,2]+ 58 | w[simdat[i,]$item,2])*simdat$so[i], 59 | sigma_e) 60 | } 61 | simdat$rt<-rt 62 | simdat$subj<-factor(simdat$subj) 63 | simdat$item<-factor(simdat$item) 64 | simdat} 65 | 66 | #' Generate lognormal data from an eight-condition repeated measures design; the function contains contrast coding for a particular experiment design by Dillon et al. 2013. 67 | #' 68 | #' This function takes parameters from a previously fitted model 69 | #' and returns simulated data. 70 | #' 71 | #' @param ncond Number of conditions (eight). 72 | #' @param nitem Number of items, must be divisible by eight. 73 | #' @param nsubj Number of subjects, must be divisible by eight. 74 | #' @param beta The intercept and slopes of the fixed effects. 75 | #' @param Sigma_u The variance-covariance matrix for subject random effects. 76 | #' @param Sigma_w The variance-covariance matrix for item random effects. 77 | #' @param sigma_e The standard deviation of the residuals. 78 | #' @return A data frame containing simulated data. 79 | #' @export 80 | ## assumes that no. of subjects and no. of items is divisible by 8. 81 | gen_fake_lnorm2x2x2<-function(ncond=8, 82 | nitem=NULL, 83 | nsubj=NULL, 84 | beta=NULL, 85 | Sigma_u=NULL, # subject vcov matrix 86 | Sigma_w=NULL, # item vcov matrix 87 | sigma_e=NULL){ 88 | grouping<-matrix(rep(NA,ncond*ncond),ncol=ncond) 89 | grouping[1,]<-1:8 90 | grouping[2,]<-c(2:8,1) 91 | grouping[3,]<-c(3:8,1:2) 92 | grouping[4,]<-c(4:8,1:3) 93 | grouping[5,]<-c(5:8,1:4) 94 | grouping[6,]<-c(6:8,1:5) 95 | grouping[7,]<-c(7:8,1:6) 96 | grouping[8,]<-c(8,1:7) 97 | 98 | ## prepare data frame for 8 condition latin square: 99 | g1<-data.frame(item=1:nitem, 100 | cond=rep(grouping[1,],nitem/ncond)) 101 | g2<-data.frame(item=1:nitem, 102 | cond=rep(grouping[2,],nitem/ncond)) 103 | g3<-data.frame(item=1:nitem, 104 | cond=rep(grouping[3,],nitem/ncond)) 105 | g4<-data.frame(item=1:nitem, 106 | cond=rep(grouping[4,],nitem/ncond)) 107 | g5<-data.frame(item=1:nitem, 108 | cond=rep(grouping[5,],nitem/ncond)) 109 | g6<-data.frame(item=1:nitem, 110 | cond=rep(grouping[6,],nitem/ncond)) 111 | g7<-data.frame(item=1:nitem, 112 | cond=rep(grouping[7,],nitem/ncond)) 113 | g8<-data.frame(item=1:nitem, 114 | cond=rep(grouping[8,],nitem/ncond)) 115 | 116 | 117 | ## assemble data frame: 118 | gp1<-g1[rep(seq_len(nrow(g1)), 119 | nsubj/ncond),] 120 | gp2<-g2[rep(seq_len(nrow(g2)), 121 | nsubj/ncond),] 122 | gp3<-g3[rep(seq_len(nrow(g3)), 123 | nsubj/ncond),] 124 | gp4<-g4[rep(seq_len(nrow(g4)), 125 | nsubj/ncond),] 126 | gp5<-g5[rep(seq_len(nrow(g5)), 127 | nsubj/ncond),] 128 | gp6<-g6[rep(seq_len(nrow(g6)), 129 | nsubj/ncond),] 130 | gp7<-g7[rep(seq_len(nrow(g7)), 131 | nsubj/ncond),] 132 | gp8<-g8[rep(seq_len(nrow(g8)), 133 | nsubj/ncond),] 134 | 135 | simdat<-rbind(gp1,gp2,gp3,gp4,gp5,gp6,gp7,gp8) 136 | 137 | ## add subjects: 138 | simdat$subj<-rep(1:nsubj,each=nitem) 139 | 140 | ## add contrast coding: 141 | simdat$Dep <- ifelse(simdat$cond %in% c(1:4), 142 | 1, -1) # main effect of dependency type: agr=1, refl=-1 143 | simdat$Gram <- ifelse(simdat$cond %in% c(1,2,5,6), 144 | -1, 1) # main effect of grammaticality: gram=-1, ungram=1 145 | simdat$DepxGram <- ifelse(simdat$cond %in% c(3:6), 1, -1) 146 | simdat$Int_gram_refl <- ifelse(simdat$cond %in% c(5), 1, 147 | ifelse (simdat$cond %in% c(6), -1, 0)) 148 | simdat$Int_gram_agr <- ifelse(simdat$cond %in% c(1), 1, 149 | ifelse(simdat$cond %in% c(2), -1, 0)) 150 | simdat$Int_ungram_refl <- ifelse(simdat$cond %in% c(8), 1, 151 | ifelse(simdat$cond %in% c(7), -1, 0)) 152 | simdat$Int_ungram_agr <- ifelse(simdat$cond %in% c(4), 1, 153 | ifelse(simdat$cond %in% c(3), -1, 0)) 154 | ## subject random effects: 155 | u<-MASS::mvrnorm(n=length(unique(simdat$subj)), 156 | mu=c(rep(0,8)),Sigma=Sigma_u) 157 | 158 | ## item random effects 159 | w<-MASS::mvrnorm(n=length(unique(simdat$item)), 160 | mu=c(rep(0,8)),Sigma=Sigma_w) 161 | 162 | ## generate data row by row: 163 | N<-dim(simdat)[1] 164 | rt<-rep(NA,N) 165 | for(i in 1:N){ 166 | rt[i] <- rlnorm(1,beta[1] + 167 | u[simdat[i,]$subj,1] + 168 | w[simdat[i,]$item,1] + 169 | (beta[2]+u[simdat[i,]$subj,2]+ 170 | w[simdat[i,]$item,2])*simdat$Dep[i]+ 171 | (beta[3]+u[simdat[i,]$subj,3]+ 172 | w[simdat[i,]$item,3])*simdat$Gram[i]+ 173 | (beta[4]+u[simdat[i,]$subj,4]+ 174 | w[simdat[i,]$item,4])*simdat$DepxGram[i]+ 175 | (beta[5]+u[simdat[i,]$subj,5]+ 176 | w[simdat[i,]$item,5])*simdat$Int_gram_refl[i]+ 177 | (beta[6]+u[simdat[i,]$subj,6]+ 178 | w[simdat[i,]$item,6])*simdat$Int_gram_agr[i]+ 179 | (beta[7]+u[simdat[i,]$subj,7]+ 180 | w[simdat[i,]$item,7])*simdat$Int_ungram_refl[i]+ 181 | (beta[8]+u[simdat[i,]$subj,8]+ 182 | w[simdat[i,]$item,8])*simdat$Int_ungram_agr[i], 183 | 184 | sigma_e) 185 | } 186 | simdat$rt<-rt 187 | simdat$subj<-factor(simdat$subj) 188 | simdat$item<-factor(simdat$item) 189 | simdat 190 | } 191 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # lingpsych 2 | Datasets and models used in the book [Linear Mixed Models for Linguistics and Psychology: A Comprehensive Introduction](https://vasishth.github.io/Freq_CogSci/) 3 | 4 | ## Installation 5 | 6 | lingpsych has not yet been released in CRAN. Install the 7 | development version from [GitHub](https://github.com/) with: 8 | 9 | ``` r 10 | # install.packages("devtools") 11 | devtools::install_github("vasishth/lingpsych") 12 | ``` 13 | 14 | Alternatively, download the zip archive, unzip it, and then in the R console run (the files has to be in the current working directory that R is in): 15 | 16 | devtools::install("lingpsych-main") -------------------------------------------------------------------------------- /data/df_F1data.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vasishth/lingpsych/92060c6a6abe4dfe4f13a8b7ad8ea23c2ec17743/data/df_F1data.rda -------------------------------------------------------------------------------- /data/df_VMJG18E1.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vasishth/lingpsych/92060c6a6abe4dfe4f13a8b7ad8ea23c2ec17743/data/df_VMJG18E1.rda 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author={Blitzstein, Joseph K and Hwang, Jessica}, 4 | year={2014}, 5 | publisher={Chapman and Hall/CRC} 6 | } 7 | 8 | 9 | @article{fedorenko06, 10 | title={The nature of working memory capacity in sentence comprehension: {E}vidence against domain-specific working memory resources}, 11 | author={Fedorenko, Evelina and Gibson, Edward and Rohde, Douglas}, 12 | journal={Journal of Memory and Language}, 13 | volume={54}, 14 | number={4}, 15 | pages={541--553}, 16 | year={2006}, 17 | publisher={Elsevier} 18 | } 19 | 20 | @article{swets2008underspecification, 21 | title={Underspecification of syntactic ambiguities: {E}vidence from self-paced reading}, 22 | author={Swets, Benjamin and Desmet, Timothy and Clifton, Charles and Ferreira, Fernanda}, 23 | journal={Memory and Cognition}, 24 | volume={36}, 25 | number={1}, 26 | pages={201--216}, 27 | year={2008}, 28 | publisher={Springer} 29 | } 30 | 31 | 32 | @article{SafaviEtAlFrontiers2016, 33 | Author = {Molood Sadat Safavi and Samar Husain and Shravan Vasishth}, 34 | journal = {Frontiers in Psychology}, 35 | Title = {Dependency resolution difficulty increases with distance in {P}ersian separable complex predicates: Implications for expectation and memory-based accounts}, 36 | OPTnote = {Special Issue on Encoding and Navigating Linguistic Representations in Memory}, 37 | pdf = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00403/full}, 38 | code={https://github.com/vasishth/SafaviEtAl2016}, 39 | year = {2016}, 40 | volume = {7}, 41 | issue = {403}, 42 | OPTdoi = {10.3389/fpsyg.2016.00403}, 43 | 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). 44 | 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.} 45 | } 46 | 47 | @article{levy2013expectation, 48 | title={Expectation and locality effects in {G}erman verb-final structures}, 49 | author={Levy, Roger P and Keller, Frank}, 50 | journal={Journal of Memory and Language}, 51 | volume={68}, 52 | number={2}, 53 | pages={199--222}, 54 | year={2013}, 55 | publisher={Elsevier} 56 | } 57 | 58 | 59 | @article{smith2021encoding, 60 | title={Encoding interference effects support self-organized sentence processing}, 61 | author={Smith, Garrett and Franck, Julie and Tabor, Whitney}, 62 | journal={Cognitive Psychology}, 63 | volume={124}, 64 | pages={101356}, 65 | year={2021}, 66 | publisher={Elsevier} 67 | } 68 | 69 | 70 | @article{VBLD07, 71 | Author = {Shravan Vasishth and Sven Bruessow and Richard L. Lewis and Heiner Drenhaus}, 72 | Date-Modified = {2009-08-21 11:25:25 +0200}, 73 | Journal = {Cognitive Science}, 74 | Number = {4}, 75 | Title = {Processing Polarity: {H}ow the ungrammatical intrudes on the grammatical}, 76 | abstract = {A central question in online human sentence comprehension is: how are linguistic 77 | relations established between different parts of a sentence? Previous work has shown that 78 | this dependency resolution process can be computationally expensive, but the underlying 79 | reasons for this are still unclear. We argue that dependency resolution is mediated by 80 | cue-based retrieval, constrained by independently motivated working memory principles 81 | defined in a cognitive architecture (ACT-R). To demonstrate this, we investigate an 82 | unusual instance of dependency resolution, the processing of negative and positive 83 | polarity items, and confirm a surprising prediction of the cue-based retrieval model: 84 | partial cue-matches, which constitute a kind of similarity-based interference, can give 85 | rise to the intrusion of ungrammatical retrieval candidates, leading to both processing 86 | slow-downs and even errors of judgment that take the form of illusions of grammaticality 87 | in patently ungrammatical structures. A notable achievement is that good quantitative 88 | fits are achieved without adjusting the key model parameters.}, 89 | pdf = {https://onlinelibrary.wiley.com/doi/full/10.1080/03640210802066865}, 90 | Volume = {32}, 91 | issue = {4}, 92 | Year = {2008}, 93 | pages = {685--712}, 94 | code = {https://github.com/vasishth/ProcessingPolarity} 95 | } 96 | 97 | @article{JaegerMertzenVanDykeVasishth2019, 98 | Author = {J\"ager, Lena A. and Mertzen, Daniela and Van Dyke, Julie A. and Vasishth, Shravan}, 99 | Title = {Interference patterns in subject-verb agreement and reflexives revisited: {A} large-sample study}, 100 | Year = {2020}, 101 | volume = {111}, 102 | journal = {Journal of Memory and Language}, 103 | code = {https://osf.io/reavs/}, 104 | doi = {https://doi.org/10.1016/j.jml.2019.104063} 105 | } 106 | 107 | 108 | @article{HusainVasishthNarayanan2015, 109 | title={Integration and prediction difficulty in {H}indi sentence comprehension: {E}vidence from an eye-tracking corpus}, 110 | author={Samar Husain and Shravan Vasishth and Narayanan Srinivasan}, 111 | volume = {8(2)}, 112 | issue = {3}, 113 | pages = {1--12}, 114 | journal = {Journal of Eye Movement Research}, 115 | year={2015}, 116 | 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.}, 117 | pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/HusainEtAlETHindiJEMR2015.pdf}, 118 | code={https://github.com/vasishth/HusainEtAlJEMR2015} 119 | } 120 | 121 | 122 | @Article{influenceme, 123 | title = {influence.ME: Tools for Detecting Influential Data in 124 | Mixed Effects Models}, 125 | author = {Rense Nieuwenhuis and Manfred {Te Grotenhuis} and Ben 126 | Pelzer}, 127 | year = {2012}, 128 | journal = {R Journal}, 129 | volume = {4}, 130 | number = {2}, 131 | pages = {38-47}, 132 | } 133 | 134 | @Misc{Bolker2018, 135 | Author = {Ben Bolker}, 136 | Title = {https://github.com/bbolker/mixedmodels-misc/blob/master/notes/contrasts.rmd}, 137 | Urldate = {June 10, 2018}, 138 | Year = {2018} 139 | } 140 | 141 | @Manual{friendly_matlib, 142 | title = {matlib: Matrix Functions for Teaching and Learning Linear Algebra and 143 | Multivariate Statistics}, 144 | author = {Michael Friendly and John Fox and Phil Chalmers}, 145 | year = {2020}, 146 | note = {R package version 0.9.3}, 147 | url = {https://CRAN.R-project.org/package=matlib}, 148 | } 149 | 150 | @article{rabe2020hypr, 151 | title={hypr: An R package for hypothesis-driven contrast coding}, 152 | author={Rabe, Maximilian M and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold and Schad, Daniel J.}, 153 | journal={Journal of Open Source Software}, 154 | volume={5}, 155 | number={48}, 156 | pages={2134}, 157 | year={2020} 158 | } 159 | 160 | 161 | @article{heister2012analysing, 162 | Author = {Heister, Julian and W{\"u}rzner, Kay-Michael and Kliegl, Reinhold}, 163 | Date-Added = {2019-07-17 15:29:04 +0200}, 164 | Date-Modified = {2019-07-17 15:30:03 +0200}, 165 | Journal = {Visual word recognition}, 166 | Pages = {102--130}, 167 | Title = {Analysing large datasets of eye movements during reading}, 168 | Volume = {2}, 169 | Year = {2012} 170 | } 171 | 172 | @book{snedecor1967statistical, 173 | Address = {Ames, Iowa}, 174 | Author = {Snedecor, George W and Cochran, William G}, 175 | Date-Added = {2019-02-18 15:54:37 +0100}, 176 | Date-Modified = {2019-02-18 15:55:52 +0100}, 177 | Publisher = {Iowa State University Press}, 178 | Title = {Statistical Methods}, 179 | Year = {1967} 180 | } 181 | 182 | 183 | @book{dobson2011introduction, 184 | Author = {Dobson, Annette J and Barnett, Adrian}, 185 | Publisher = {CRC press}, 186 | Title = {An introduction to generalized linear models}, 187 | Year = {2011} 188 | } 189 | 190 | @article{schad2020capitalize, 191 | title={How to capitalize on a priori contrasts in linear (mixed) models: A tutorial}, 192 | author={Schad, Daniel J. and Vasishth, Shravan and Hohenstein, Sven and Kliegl, Reinhold}, 193 | journal={Journal of Memory and Language}, 194 | volume={110}, 195 | pages={104038}, 196 | year={2020}, 197 | publisher={Elsevier} 198 | } 199 | 200 | 201 | @phdthesis{AnnaLphd, 202 | Type = {dissertation}, 203 | Title = {Similarity-based interference and faulty encoding accounts of sentence processing}, 204 | Author = {Anna Laurinavichyute}, 205 | School = {University of Potsdam}, 206 | Year = {2020}, 207 | } 208 | 209 | @book{powerbookcohen, 210 | address = {Hillsdale, NJ}, 211 | author = {Jacob Cohen}, 212 | edition = 2, 213 | publisher = {Lawrence Erlbaum}, 214 | title = {{Statistical power analysis for the behavioral sciences}}, 215 | year = 1988 216 | } 217 | 218 | 219 | 220 | @book{rice1995mathematical, 221 | title={{Mathematical statistics and data analysis}}, 222 | author={Rice, John A.}, 223 | year={1995}, 224 | publisher={Duxbury press Belmont, CA} 225 | } 226 | 227 | @article{barr2013, 228 | title={Random effects structure for confirmatory hypothesis testing: {K}eep it maximal}, 229 | author={Barr, Dale J and Levy, Roger and Scheepers, Christoph and Tily, Harry J}, 230 | journal={Journal of Memory and Language}, 231 | volume={68}, 232 | number={3}, 233 | pages={255--278}, 234 | year={2013}, 235 | publisher={Elsevier} 236 | } 237 | 238 | @article{hsiao03, 239 | Author = {Fanny Pai-Fang Hsiao and Edward Gibson}, 240 | Journal = {Cognition}, 241 | Pages = {3--27}, 242 | Title = {Processing relative clauses in {C}hinese}, 243 | Volume = {90}, 244 | Year = {2003}} 245 | 246 | @article{gibsonthomas99, 247 | Author = {Edward Gibson and James Thomas}, 248 | Journal = {Language and Cognitive Processes}, 249 | Pages = {225--248}, 250 | Title = {Memory Limitations and Structural Forgetting: The Perception of Complex Ungrammatical Sentences as Grammatical}, 251 | Volume = {14(3)}, 252 | Year = {1999}} 253 | 254 | @Article{VSLK08, 255 | author = {Shravan Vasishth and Katja Suckow and Richard L. Lewis and Sabine Kern}, 256 | title = {Short-term forgetting in sentence comprehension: {C}rosslinguistic evidence from head-final structures}, 257 | journal = {Language and Cognitive Processes}, 258 | year = {2011}, 259 | OPTkey = {}, 260 | volume = {25}, 261 | OPTnumber = {4}, 262 | pages = {533-567}, 263 | OPTmonth = {}, 264 | OPTannote = {}, 265 | pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/Vasishth-Suckow-Lewis-Kern-LCP2010.pdf}, 266 | 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‚Äîbased 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.}, 267 | code = {https://osf.io/r3cg9/} 268 | } 269 | 270 | @article{FrankEtAl2015, 271 | author = {Stefan L. Frank and Thijs Trompenaars and Shravan Vasishth}, 272 | title = {Cross-linguistic differences in processing double-embedded relative clauses: {W}orking-memory constraints or language statistics?}, 273 | year = {2015}, 274 | pages = {554-578}, 275 | volume = {40}, 276 | doi = {10.1111/cogs.12247}, 277 | 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.}, 278 | journal = {Cognitive Science}, 279 | code = {https://github.com/vasishth/StanJAGSexamples/tree/master/FrankEtAlCogSci2015}, 280 | pdf = {http://stefanfrank.info/pubs/GrammaticalityIllusion.pdf} 281 | } 282 | 283 | @article{VasishthetalPLoSOne2013, 284 | author = {Vasishth, Shravan AND Chen, Zhong AND Li, Qiang AND Guo, Gueilan}, 285 | journal = {PLoS ONE}, 286 | publisher = {Public Library of Science}, 287 | title = {Processing {C}hinese Relative Clauses: {E}vidence for the Subject-Relative Advantage}, 288 | year = {2013}, 289 | month = {10}, 290 | volume = {8}, 291 | pdf = {http://dx.doi.org/10.1371%2Fjournal.pone.0077006}, 292 | pages = {1--14}, 293 | number = {10}, 294 | code = {http://www.ling.uni-potsdam.de/~vasishth/code/PLoSOneVasishthetaldata.zip} 295 | } 296 | 297 | @article{SchadEtAlcontrasts, 298 | Author = {Daniel J. Schad and Shravan Vasishth and Sven Hohenstein and Reinhold Kliegl}, 299 | journal = {Journal of Memory and Language}, 300 | Title = {How to capitalize on a priori contrasts in linear (mixed) models: {A} tutorial}, 301 | Year = {2020}, 302 | volume = {110}, 303 | code = {https://osf.io/7ukf6/}, 304 | pdf = {https://arxiv.org/abs/1807.10451} 305 | } 306 | 307 | @article{DillonEtAl2013, 308 | title={Contrasting intrusion profiles for agreement and anaphora: {E}xperimental and modeling evidence}, 309 | author={Dillon, Brian and Mishler, Alan and Sloggett, Shayne and Phillips, Colin}, 310 | journal={Journal of Memory and Language}, 311 | volume={69}, 312 | number={2}, 313 | pages={85--103}, 314 | year={2013}, 315 | publisher={Elsevier} 316 | } 317 | 318 | 319 | @article{hannesBEAP, 320 | title={{Balancing Type I Error and Power in Linear Mixed Models}}, 321 | author={Hannes Matuschek and Reinhold Kliegl and Shravan Vasishth and R. Harald Baayen and Douglas M. Bates}, 322 | doi = {10.1016/j.jml.2017.01.001}, 323 | pdf = {http://www.sciencedirect.com/science/article/pii/S0749596X17300013}, 324 | 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.}, 325 | year={2017}, 326 | volume={94}, 327 | pages={305--315}, 328 | journal ={Journal of Memory and Language} 329 | } 330 | 331 | 332 | @unpublished{BatesEtAlParsimonious, 333 | Author = {Bates, Douglas M. and Kliegl, Reinhold and Vasishth, Shravan and Baayen, Harald}, 334 | Note = {Unpublished manuscript}, 335 | Title = {Parsimonious mixed models}, 336 | Year = {2015}, 337 | pdf = {http://arxiv.org/abs/1506.04967}, 338 | abstract = {The analysis of experimental data with mixed-effects models requires 339 | decisions about the specification of the appropriate random-effects structure. 340 | Recently, Barr, et al 2013, recommended fitting `maximal' 341 | models with all possible random effect components included. Estimation of 342 | maximal models, however, may not converge. We show that failure to converge 343 | typically is not due to a suboptimal estimation algorithm, but is 344 | a consequence of attempting to fit a model that is too complex to be properly 345 | supported by the data, irrespective of whether estimation is based on maximum 346 | likelihood or on Bayesian hierarchical modeling with uninformative or weakly 347 | informative priors. Importantly, even under convergence, overparameterization 348 | may lead to uninterpretable models. We provide diagnostic tools for detecting 349 | overparameterization and guiding model simplification. Finally, we clarify 350 | that the simulations on which Barr et al. base their recommendations are 351 | atypical for real data. A detailed example is provided of how subject-related 352 | attentional fluctuation across trials may further qualify 353 | statistical inferences about fixed effects, and of how such nonlinear effects 354 | can be accommodated within the mixed-effects modeling framework.} 355 | } 356 | 357 | 358 | @article{cumming2014new, 359 | title={The new statistics: Why and how}, 360 | author={Cumming, Geoff}, 361 | journal={Psychological science}, 362 | volume={25}, 363 | number={1}, 364 | pages={7--29}, 365 | year={2014}, 366 | publisher={Sage Publications Sage CA: Los Angeles, CA} 367 | } 368 | 369 | @article{cumming2009confidence, 370 | title={Confidence intervals: Better answers to better questions}, 371 | author={Cumming, Geoff and Fidler, Fiona}, 372 | journal={Zeitschrift f{\"u}r Psychologie/Journal of Psychology}, 373 | volume={217}, 374 | number={1}, 375 | pages={15--26}, 376 | year={2009}, 377 | publisher={Hogrefe \& Huber Publishers} 378 | } 379 | 380 | 381 | @article{hoenigheisey, 382 | Author = {John M. Hoenig and Dennis M. Heisey}, 383 | Journal = {The American Statistician}, 384 | Pages = {19--24}, 385 | Title = {The Abuse of Power: {T}he pervasive fallacy of power calculations for data analysis}, 386 | Volume = {55}, 387 | issue = {1}, 388 | Year = 2001} 389 | 390 | 391 | @book{morin2016probability, 392 | title={Probability: For the Enthusiastic Beginner}, 393 | author={Morin, David J}, 394 | year={2016}, 395 | publisher={Createspace Independent Publishing Platform} 396 | } 397 | @book{millermiller, 398 | title={John E. Freund's Mathematical Statistics with Applications}, 399 | author={Miller, I. and Miller, M.}, 400 | year={2004}, 401 | publisher={Prentice Hall} 402 | } 403 | @book{fox2009mathematical, 404 | title={A mathematical primer for social statistics}, 405 | author={Fox, John}, 406 | number={159}, 407 | year={2009}, 408 | publisher={Sage} 409 | } 410 | 411 | @book{kolmogorov2018foundations, 412 | title={Foundations of the Theory of Probability: Second English Edition}, 413 | author={Kolmogorov, Andre{\u\i} Nikolaevich}, 414 | year={2018}, 415 | publisher={Courier Dover Publications} 416 | } 417 | 418 | @Manual{designr, 419 | title = {designr: {B}alanced Factorial Designs}, 420 | author = {Maximilian M. Rabe and Reinhold Kliegl and Schad Daniel}, 421 | year = {2021}, 422 | note = {R package version 0.1.11}, 423 | url = {https://maxrabe.com/designr}, 424 | } 425 | 426 | @article{JaegerEngelmannVasishth2017, 427 | Author = {J{\"a}ger, Lena A. and Engelmann, Felix and Vasishth, Shravan}, 428 | Title = {Similarity-based interference in sentence comprehension: {Literature review and Bayesian meta-analysis}}, 429 | pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/JaegerEngelmannVasishthJML2017.pdf}, 430 | 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.}, 431 | Year = {2017}, 432 | volume = {94}, 433 | pages = {316-339}, 434 | journal={Journal of Memory and Language}, 435 | code = {https://github.com/vasishth/MetaAnalysisJaegerEngelmannVasishth2017}, 436 | doi = {https://doi.org/10.1016/j.jml.2017.01.004} 437 | } 438 | 439 | @article{benjamin2018redefine, 440 | title={Redefine statistical significance}, 441 | 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}, 442 | journal={Nature Human Behaviour}, 443 | volume={2}, 444 | number={1}, 445 | pages={6}, 446 | year={2018}, 447 | publisher={Nature Publishing Group} 448 | } 449 | @article{NicenboimRoettgeretal, 450 | Author = {Bruno Nicenboim and Timo B. Roettger and Shravan Vasishth}, 451 | Title = {Using meta-analysis for evidence synthesis: {The case of incomplete neutralization in German}}, 452 | Year = {2018}, 453 | journal = {Journal of Phonetics}, 454 | doi = {https://doi.org/10.1016/j.wocn.2018.06.001}, 455 | url = {https://osf.io/g5ndw/}, 456 | pdf = {https://mfr.osf.io/render?url=https://osf.io/4k25w/?action=download%26mode=render}, 457 | volume = {70}, 458 | pages = {39-55} 459 | } 460 | 461 | @unpublished{VasishthEngelmann2020, 462 | title={Sentence comprehension as a cognitive process: {A} computational approach}, 463 | author={Shravan Vasishth and Felix Engelmann}, 464 | year={2020}, 465 | note={Under contract with Cambridge University Press}, 466 | url = {https://vasishth.github.io/sccp/} 467 | } 468 | 469 | @book{fieller, 470 | Address = {Boca Raton, FL}, 471 | Author = {Nick Fieller}, 472 | Publisher = {CRC Press}, 473 | Title = {Basics of matrix algebra for statistics with {R}}, 474 | Year = {2016}} 475 | 476 | @book{moore2013mathematics, 477 | title={A mathematics course for political and social research}, 478 | author={Moore, Will H and Siegel, David A}, 479 | year={2013}, 480 | publisher={Princeton University Press} 481 | } 482 | 483 | @book{gill2006essential, 484 | title={Essential mathematics for political and social research}, 485 | author={Gill, Jeff}, 486 | year={2006}, 487 | publisher={Cambridge University Press Cambridge} 488 | } 489 | 490 | @article{hedges1984estimation, 491 | title = {Estimation of effect size under nonrandom sampling: The effects of censoring studies yielding statistically insignificant mean differences}, 492 | author = {Hedges, Larry V}, 493 | journal = {Journal of Educational Statistics}, 494 | volume = {9}, 495 | number = {1}, 496 | pages = {61--85}, 497 | year = {1984}, 498 | publisher = {Sage Publications Sage CA: Thousand Oaks, CA} 499 | } 500 | 501 | @article{lane1978estimating, 502 | title = {Estimating effect size: Bias resulting from the significance criterion in editorial decisions}, 503 | author = {Lane, David M and Dunlap, William P}, 504 | journal = {British Journal of Mathematical and Statistical Psychology}, 505 | volume = {31}, 506 | number = {2}, 507 | pages = {107--112}, 508 | year = {1978}, 509 | publisher = {Wiley Online Library} 510 | } 511 | 512 | @article{powerfailure, 513 | title = {Power failure: why small sample size undermines the reliability of neuroscience}, 514 | 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}, 515 | journal = {Nature Reviews Neuroscience}, 516 | volume = {14}, 517 | number = {5}, 518 | pages = {365--376}, 519 | year = {2013}, 520 | publisher = {Nature Publishing Group} 521 | } 522 | 523 | @article{ioannidis2008most, 524 | title = {Why most discovered true associations are inflated}, 525 | author = {Ioannidis, John PA}, 526 | journal = {Epidemiology}, 527 | volume = {19}, 528 | number = {5}, 529 | pages = {640--648}, 530 | year = {2008}, 531 | publisher = {LWW} 532 | } 533 | 534 | @article{VasishthMertzenJaegerGelman2018, 535 | Author = {Vasishth, Shravan and Mertzen, Daniela and J\"ager, Lena A. and Gelman, Andrew}, 536 | journal = {Journal of Memory and Language}, 537 | url = {https://osf.io/eyphj/}, 538 | doi = {https://doi.org/10.1016/j.jml.2018.07.004}, 539 | Title = {The statistical significance filter leads to overoptimistic expectations of replicability}, 540 | Year = {2018}, 541 | volume = {103}, 542 | pages = {151-175} 543 | } 544 | 545 | @article{gibsonwu, 546 | title={Processing {C}hinese relative clauses in context}, 547 | author={Gibson, Edward and Wu, H-H Iris}, 548 | journal={Language and Cognitive Processes}, 549 | volume={28}, 550 | number={1-2}, 551 | pages={125--155}, 552 | year={2013}, 553 | publisher={Taylor \& Francis} 554 | } 555 | 556 | 557 | 558 | @book{Royall, 559 | Author = {Richard Royall}, 560 | Publisher = {Chapman and Hall, CRC 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publisher={John Wiley \& Sons} 593 | } 594 | 595 | 596 | @book{Gelman14, 597 | Author = {Andrew Gelman and John B. Carlin and Hal S. Stern and David B. Dunson and Aki Vehtari and Donald B. Rubin}, 598 | Edition = {Third}, 599 | Publisher = {Chapman and Hall/CRC}, 600 | address = {Boca Raton, FL}, 601 | Title = {Bayesian Data Analysis}, 602 | Year = {2014}} 603 | 604 | @book{kruschke2014doing, 605 | title={Doing {B}ayesian data analysis: {A tutorial with R, JAGS, and Stan}}, 606 | author={Kruschke, John}, 607 | year={2014}, 608 | publisher={Academic Press} 609 | } 610 | 611 | 612 | @Manual{R-base, 613 | title = {R: A Language and Environment for Statistical Computing}, 614 | author = {{R Core Team}}, 615 | organization = {R Foundation for Statistical Computing}, 616 | address = {Vienna, Austria}, 617 | year = {2019}, 618 | url = {https://www.R-project.org/}, 619 | } 620 | @Manual{R-bayesplot, 621 | title = {bayesplot: {Plotting for Bayesian Models}}, 622 | author = {Jonah Gabry and Tristan Mahr}, 623 | year = {2018}, 624 | note = {R package version 1.6.0}, 625 | url = {https://CRAN.R-project.org/package=bayesplot}, 626 | } 627 | @Manual{R-bookdown, 628 | title = {bookdown: Authoring Books and Technical Documents with R Markdown}, 629 | author = {Yihui Xie}, 630 | year = {2019}, 631 | note = {R package version 0.12}, 632 | url = {https://CRAN.R-project.org/package=bookdown}, 633 | } 634 | @Manual{R-brms, 635 | title = {brms: Bayesian Regression Models using 'Stan'}, 636 | author = {Paul-Christian B{\"u}rkner}, 637 | year = {2019}, 638 | note = {R package version 2.8.0}, 639 | url = {https://CRAN.R-project.org/package=brms}, 640 | } 641 | @Manual{R-citr, 642 | title = {citr: RStudio Add-in to Insert Markdown Citations}, 643 | author = {Frederik Aust}, 644 | year = {2019}, 645 | note = {R package version 0.3.2}, 646 | url = {https://CRAN.R-project.org/package=citr}, 647 | } 648 | @Manual{R-dplyr, 649 | title = {dplyr: A Grammar of Data Manipulation}, 650 | author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller}, 651 | year = {2019}, 652 | note = {R package version 0.8.3}, 653 | url = {https://CRAN.R-project.org/package=dplyr}, 654 | } 655 | @Manual{R-DT, 656 | title = {DT: A Wrapper of the JavaScript Library 'DataTables'}, 657 | author = {Yihui Xie and Joe Cheng and Xianying Tan}, 658 | year = {2018}, 659 | note = {R package version 0.5}, 660 | url = {https://CRAN.R-project.org/package=DT}, 661 | } 662 | @Manual{R-extraDistr, 663 | title = {extraDistr: Additional Univariate and Multivariate Distributions}, 664 | author = {Tymoteusz Wolodzko}, 665 | year = {2019}, 666 | note = {R package version 1.8.11}, 667 | url = {https://CRAN.R-project.org/package=extraDistr}, 668 | } 669 | @Manual{R-forcats, 670 | title = {forcats: Tools for Working with Categorical Variables (Factors)}, 671 | author = {Hadley Wickham}, 672 | year = {2019}, 673 | note = {R package version 0.4.0}, 674 | url = {https://CRAN.R-project.org/package=forcats}, 675 | } 676 | @Manual{R-gdtools, 677 | title = {gdtools: Utilities for Graphical Rendering}, 678 | author = {David Gohel and Hadley Wickham and Lionel Henry and Jeroen Ooms}, 679 | year = {2019}, 680 | note = {R package version 0.2.0}, 681 | url = {https://CRAN.R-project.org/package=gdtools}, 682 | } 683 | @Manual{R-ggplot2, 684 | title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics}, 685 | 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}, 686 | year = {2019}, 687 | note = {R package version 3.2.0}, 688 | url = {https://CRAN.R-project.org/package=ggplot2}, 689 | } 690 | @Manual{R-htmlwidgets, 691 | title = {htmlwidgets: HTML Widgets for R}, 692 | author = {Ramnath Vaidyanathan and Yihui Xie and JJ Allaire and Joe Cheng and Kenton Russell}, 693 | year = {2018}, 694 | note = {R package version 1.3}, 695 | url = {https://CRAN.R-project.org/package=htmlwidgets}, 696 | } 697 | @Manual{R-knitr, 698 | title = {knitr: A General-Purpose Package for Dynamic Report Generation in R}, 699 | author = {Yihui Xie}, 700 | year = {2019}, 701 | note = {R package version 1.24}, 702 | url = {https://CRAN.R-project.org/package=knitr}, 703 | } 704 | @Manual{R-MASS, 705 | title = {MASS: Support Functions and Datasets for Venables and Ripley's MASS}, 706 | author = {Brian Ripley}, 707 | year = {2019}, 708 | note = {R package version 7.3-51.4}, 709 | url = {https://CRAN.R-project.org/package=MASS}, 710 | } 711 | @Manual{R-miniUI, 712 | title = {miniUI: Shiny UI Widgets for Small Screens}, 713 | author = {Joe Cheng}, 714 | year = {2018}, 715 | note = {R package version 0.1.1.1}, 716 | url = {https://CRAN.R-project.org/package=miniUI}, 717 | } 718 | @Manual{R-purrr, 719 | title = {purrr: Functional Programming Tools}, 720 | author = {Lionel Henry and Hadley Wickham}, 721 | year = {2019}, 722 | note = {R package version 0.3.2}, 723 | url = {https://CRAN.R-project.org/package=purrr}, 724 | } 725 | @Manual{R-Rcpp, 726 | title = {Rcpp: Seamless R and C++ Integration}, 727 | author = {Dirk Eddelbuettel and Romain Francois and JJ Allaire and Kevin Ushey and Qiang Kou and Nathan Russell and Douglas Bates and John Chambers}, 728 | year = {2019}, 729 | note = {R package version 1.0.2}, 730 | url = {https://CRAN.R-project.org/package=Rcpp}, 731 | } 732 | @Manual{R-readr, 733 | title = {readr: Read Rectangular Text Data}, 734 | author = {Hadley Wickham and Jim Hester and Romain Francois}, 735 | year = {2018}, 736 | note = {R package version 1.3.1}, 737 | url = {https://CRAN.R-project.org/package=readr}, 738 | } 739 | @Manual{R-rmarkdown, 740 | title = {rmarkdown: Dynamic Documents for R}, 741 | author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, 742 | year = {2019}, 743 | note = {R package version 1.14}, 744 | url = {https://CRAN.R-project.org/package=rmarkdown}, 745 | } 746 | @Manual{R-rstan, 747 | title = {rstan: R Interface to Stan}, 748 | author = {Jiqiang Guo and Jonah Gabry and Ben Goodrich}, 749 | year = {2019}, 750 | note = {R package version 2.19.2}, 751 | url = {https://CRAN.R-project.org/package=rstan}, 752 | } 753 | @Manual{R-servr, 754 | title = {servr: A Simple HTTP Server to Serve Static Files or Dynamic Documents}, 755 | author = {Yihui Xie}, 756 | year = {2019}, 757 | note = {R package version 0.15}, 758 | url = {https://CRAN.R-project.org/package=servr}, 759 | } 760 | 761 | @article{carpenter2017stan, 762 | title={Stan: {A} probabilistic programming language}, 763 | author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen}, 764 | journal={Journal of Statistical Software}, 765 | volume={76}, 766 | number={1}, 767 | year={2017}, 768 | publisher={Columbia Univ., New York, NY (United States); Harvard Univ., Cambridge, MA (United States)} 769 | } 770 | 771 | 772 | 773 | @Manual{R-StanHeaders, 774 | title = {StanHeaders: C++ Header Files for Stan}, 775 | author = {Ben Goodrich and Andrew Gelman and Bob Carpenter and Matt Hoffman and Daniel Lee and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell and Marco Inacio and Mitzi Morris and Jeffrey Arnold and Rob Goedman and Brian Lau and Rob Trangucci and Jonah Gabry and Alp Kucukelbir and Robert Grant and Dustin Tran and Michael Malecki and Yuanjun Gao}, 776 | year = {2019}, 777 | note = {R package version 2.18.1-10}, 778 | url = {https://CRAN.R-project.org/package=StanHeaders}, 779 | } 780 | @Manual{R-stringr, 781 | title = {stringr: Simple, Consistent Wrappers for Common String Operations}, 782 | author = {Hadley Wickham}, 783 | year = {2019}, 784 | note = {R package version 1.4.0}, 785 | url = {https://CRAN.R-project.org/package=stringr}, 786 | } 787 | @Manual{R-tibble, 788 | title = {tibble: Simple Data Frames}, 789 | author = {Kirill Müller and Hadley Wickham}, 790 | year = {2019}, 791 | note = {R package version 2.1.3}, 792 | url = {https://CRAN.R-project.org/package=tibble}, 793 | } 794 | @Manual{R-tidyr, 795 | title = {tidyr: Easily Tidy Data with 'spread()' and 'gather()' Functions}, 796 | author = {Hadley Wickham and Lionel Henry}, 797 | year = {2019}, 798 | note = {R package version 0.8.3}, 799 | url = {https://CRAN.R-project.org/package=tidyr}, 800 | } 801 | @Manual{R-tidyverse, 802 | title = {tidyverse: Easily Install and Load the 'Tidyverse'}, 803 | author = {Hadley Wickham}, 804 | year = {2017}, 805 | note = {R package version 1.2.1}, 806 | url = {https://CRAN.R-project.org/package=tidyverse}, 807 | } 808 | @Manual{R-webshot, 809 | title = {webshot: Take Screenshots of Web Pages}, 810 | author = {Winston Chang}, 811 | year = {2018}, 812 | note = {R package version 0.5.1}, 813 | url = {https://CRAN.R-project.org/package=webshot}, 814 | } 815 | -------------------------------------------------------------------------------- /lingpsych.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: No 4 | SaveWorkspace: No 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | LineEndingConversion: Posix 18 | 19 | BuildType: Package 20 | PackageUseDevtools: Yes 21 | PackageInstallArgs: --no-multiarch --with-keep.source 22 | PackageRoxygenize: rd,collate,namespace 23 | -------------------------------------------------------------------------------- /man/df_F1data.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_F1data} 5 | \alias{df_F1data} 6 | \title{Data from an experiment with F1 frequency in Hertz of vowels produced by male and females speakers of different languages \insertCite{johnson2011quantitative;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 19 rows and 4 variables: 9 | \describe{ 10 | \item{female}{F1 frequency data in Hertz from females.} 11 | \item{male}{F1 frequency data in Hertz from males.} 12 | \item{vowel}{The vowel produced.} 13 | \item{language}{The language spoken by the speaker.} 14 | } 15 | } 16 | \usage{ 17 | df_F1data 18 | } 19 | \description{ 20 | The dataset is from \insertCite{johnson2011quantitative;textual}{lingpsych}, contains the F1 frequencies recorded from 19 male subjects and 19 female subjects who produced vowels in their native languages. 21 | } 22 | \references{ 23 | \insertAllCited{} 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/df_VMJG18E1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E1} 5 | \alias{df_VMJG18E1} 6 | \title{Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 672 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{cond}{The four condition levels.} 13 | \item{rt}{Reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E1 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 1 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_VMJG18E2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E2} 5 | \alias{df_VMJG18E2} 6 | \title{Reading times at the critical region (verb) of aeyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 670 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{condition}{The four condition levels.} 13 | \item{TFT}{Total reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E2 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 2 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_VMJG18E3.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E3} 5 | \alias{df_VMJG18E3} 6 | \title{Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 672 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{cond}{The four condition levels.} 13 | \item{rt}{Reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E3 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 3 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat der Mitschuler, der den Fussball versteckt hat, ... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat der Mitschuler, der [ADJ zur Ahndung] den Fussball versteckt hat, ... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt hat, ... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_VMJG18E4.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E4} 5 | \alias{df_VMJG18E4} 6 | \title{Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 655 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{condition}{The four condition levels.} 13 | \item{TFT}{Total reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E4 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 4 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat der Mitschuler, der den Fussball versteckt hat, ... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat der Mitschuler, der [ADJ zur Ahndung] den Fussball versteckt hat, ... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt hat, ... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_VMJG18E5.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E5} 5 | \alias{df_VMJG18E5} 6 | \title{Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 672 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{cond}{The four condition levels.} 13 | \item{word}{The word read in the critical region.} 14 | \item{rt}{Reading times in milliseconds at the critical region.} 15 | } 16 | } 17 | \usage{ 18 | df_VMJG18E5 19 | } 20 | \description{ 21 | Reading times at the critical region of sentences of experiment 5 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 22 | } 23 | \details{ 24 | The experiment examined sentences with four conditions (a 2x2 design). 25 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 26 | One factor is whether a dative phrase is in the main or subordinate clause; 27 | another factor is whether an adjunct phrase is in the main or subordinate clause. 28 | 29 | The conditions (simplified) are: 30 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 31 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 32 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 33 | d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 34 | den Fussball versteckt hat, ... 35 | } 36 | \references{ 37 | \insertAllCited{} 38 | } 39 | \keyword{datasets} 40 | -------------------------------------------------------------------------------- /man/df_VMJG18E6.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E6} 5 | \alias{df_VMJG18E6} 6 | \title{Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 670 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{condition}{The four condition levels.} 13 | \item{TFT}{Total reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E6 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 6 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt hat, ... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_VMJG18E7.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_VMJG18E7} 5 | \alias{df_VMJG18E7} 6 | \title{Reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{VasishthMertzenJaegerGelman2018}{lingpsych}.} 7 | \format{ 8 | A data frame with 2,384 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{condition}{The four condition levels.} 13 | \item{TFT}{Total reading times in milliseconds at the critical region.} 14 | } 15 | } 16 | \usage{ 17 | df_VMJG18E7 18 | } 19 | \description{ 20 | Reading times at the critical region of sentences of experiment 7 reported in \insertCite{VasishthMertzenJaegerGelman2018;textual}{lingpsych}. 21 | } 22 | \details{ 23 | The experiment examined sentences with four conditions (a 2x2 design). 24 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 25 | One factor is whether a dative phrase is in the main or subordinate clause; 26 | another factor is whether an adjunct phrase is in the main or subordinate clause. 27 | 28 | The conditions (simplified) are: 29 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 30 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 31 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat der Mitschuler, der [DAT dem Sohn] den Fussball versteckt hat, ... 32 | d. Nachdem der Lehrer [ ] [ ] ... hat der Mitschuler, der [ADJ zur Ahndung] [DAT dem Sohn] 33 | den Fussball versteckt hat, ... 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_contrasts1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts1} 5 | \alias{df_contrasts1} 6 | \title{Simulated data with response times as a function of a between-subject 2-level factor F.} 7 | \format{ 8 | A data frame with 10 rows and 3 variables: 9 | \describe{ 10 | \item{F}{Between subject factor with factor levels F1 and F2} 11 | \item{DV}{Dependent variable, response time in seconds} 12 | \item{id}{Subject index} 13 | } 14 | } 15 | \usage{ 16 | df_contrasts1 17 | } 18 | \description{ 19 | The condition means are exactly 0.8 and 0.4 seconds. 20 | } 21 | \details{ 22 | The data were simulated using the following R-code: 23 | 24 | set.seed(123) 25 | 26 | d <- mvrnorm(n=5, mu=c(0.8, 0.4), Sigma=diag(2)*0.2^2, empirical=TRUE) 27 | 28 | df_contrasts1 <- d %>% as.data.frame() %>% gather(key="F", value="DV") %>% 29 | mutate(id=1:nrow(.), F=factor(F)) 30 | 31 | levels(df_contrasts1$F) <- c("F1", "F2") 32 | } 33 | \references{ 34 | \insertAllCited{} 35 | } 36 | \keyword{datasets} 37 | -------------------------------------------------------------------------------- /man/df_contrasts2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts2} 5 | \alias{df_contrasts2} 6 | \title{Simulated data with response times from a lexical decision task as a function of a 3-level between-subject factor "word class".} 7 | \format{ 8 | A data frame with 12 rows and 3 variables: 9 | \describe{ 10 | \item{F}{Between subject factor word class with factor levels "nouns", "verbs", and "adjectives"} 11 | \item{DV}{Dependent variable, response time in milliseconds} 12 | \item{id}{Subject index} 13 | } 14 | } 15 | \usage{ 16 | df_contrasts2 17 | } 18 | \description{ 19 | The simulated data contains data from word classes "nouns", "verbs", and "adjectives". Word class is manipulated between subjects here (usually it is a within-subject manipulation). 20 | The condition means are exactly 500, 450, and 400 milliseconds for nouns, verbs, and adjectives words. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | d2 <- mvrnorm(n=4, mu=c(500, 450, 400), 28 | Sigma=diag(3)*20^2, empirical=TRUE) 29 | 30 | df_contrasts2 <- d2 %>% as.data.frame() %>% 31 | gather(key="F", value="DV") %>% 32 | mutate(id=1:nrow(.), F=factor(F)) 33 | 34 | levels(df_contrasts2$F) <- c("nouns", "verbs", "adjectives") 35 | 36 | df_contrasts2$DV <- round(df_contrasts2$DV) 37 | } 38 | \references{ 39 | \insertAllCited{} 40 | } 41 | \keyword{datasets} 42 | -------------------------------------------------------------------------------- /man/df_contrasts3.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts3} 5 | \alias{df_contrasts3} 6 | \title{Simulated data with response times as a function of a 4-level between-subject factor.} 7 | \format{ 8 | A data frame with 12 rows and 3 variables: 9 | \describe{ 10 | \item{F}{Between subject factor with factor four levels (F1, F2, F3, F4)} 11 | \item{DV}{Dependent variable} 12 | \item{id}{Subject index} 13 | } 14 | } 15 | \usage{ 16 | df_contrasts3 17 | } 18 | \description{ 19 | The condition means are exactly 10, 20, 10, and 40. 20 | It could be assumed that the four factor levels `F1` to `F4` reflect levels of word frequency, including the levels `low`, `medium-low`, `medium-high`, and `high` frequency words, and that the dependent variable (DV) reflects some response time. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | d3 <- mvrnorm(n=5, mu=c(10, 20, 10, 40), Sigma=diag(4)*10^2, empirical=TRUE) 28 | 29 | df_contrasts3 <- d3 %>% as.data.frame() %>% gather(key="F", value="DV") %>% 30 | mutate(id=1:nrow(.), F=factor(F)) 31 | 32 | levels(df_contrasts3$F) <- c("F1", "F2", "F3", "F4") 33 | } 34 | \references{ 35 | \insertAllCited{} 36 | } 37 | \keyword{datasets} 38 | -------------------------------------------------------------------------------- /man/df_contrasts4.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts4} 5 | \alias{df_contrasts4} 6 | \title{Simulated data with response times from a 2 x 2 between subject design.} 7 | \format{ 8 | A data frame with 12 rows and 3 variables: 9 | \describe{ 10 | \item{A}{Between subject factor with two levels (A1, A2)} 11 | \item{B}{Between subject factor with two levels (B1, B2)} 12 | \item{DV}{Dependent variable} 13 | \item{id}{Subject index} 14 | } 15 | } 16 | \usage{ 17 | df_contrasts4 18 | } 19 | \description{ 20 | The condition means are exactly 10, 20, 10, and 40; means and standard deviations are exactly the same as in `df_contrasts3`. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | d4 <- mvrnorm(n=5, mu=c(10, 20, 10, 40), Sigma=diag(4)*10^2, empirical=TRUE) 28 | 29 | df_contrasts4 <- d4 %>% as.data.frame() %>% gather(key="A", value="DV") %>% 30 | mutate(id=1:nrow(.), B=factor(A), A=factor(A)) 31 | 32 | levels(df_contrasts4$A) <- c("A1","A1","A2","A2") 33 | 34 | levels(df_contrasts4$B) <- c("B1","B2","B1","B2") 35 | } 36 | \references{ 37 | \insertAllCited{} 38 | } 39 | \keyword{datasets} 40 | -------------------------------------------------------------------------------- /man/df_contrasts5.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts5} 5 | \alias{df_contrasts5} 6 | \title{Simulated data with response times with a 2-level between subject factor and IQ as covariate: no interaction.} 7 | \format{ 8 | A data frame with 60 rows and 4 variables: 9 | \describe{ 10 | \item{F}{Between subject factor with factor two levels (F1, F2)} 11 | \item{RT}{Dependent variable response time (RT)} 12 | \item{IQ}{Between-subject covariate (IQ)} 13 | \item{id}{Subject index} 14 | } 15 | } 16 | \usage{ 17 | df_contrasts5 18 | } 19 | \description{ 20 | In the data, the influence of IQ on response time is exactly the same in both conditions F1 and F2. However, the mean response time and mean IQ differs between conditions F1 and F2. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | N <- 30 28 | 29 | mu1 <- c(225, 85) 30 | 31 | mu2 <- c(200,115) 32 | 33 | sd1 <- 40 34 | 35 | sd2 <- 15 36 | 37 | r1 <- r2 <- -0.4 38 | 39 | sigma1 <- matrix(c(sd1^2,r1*sd1*sd2,r1*sd1*sd2,sd2^2),nrow=2) 40 | 41 | sigma2 <- matrix(c(sd1^2,r2*sd1*sd2,r2*sd1*sd2,sd2^2),nrow=2) 42 | 43 | d5a <- mvrnorm(n=N, mu=mu1, Sigma=sigma1, empirical=TRUE) 44 | 45 | d5b <- mvrnorm(n=N, mu=mu2, Sigma=sigma2, empirical=TRUE) 46 | 47 | df_contrasts5 <- rbind(data.frame(F="F1", d5a), data.frame(F="F2", d5b)) 48 | 49 | names(df_contrasts5)[2:3] <- c("RT","IQ") 50 | 51 | df_contrasts5$id <- 1:nrow(df_contrasts5) 52 | 53 | df_contrasts5$F <- factor(df_contrasts5$F) 54 | } 55 | \references{ 56 | \insertAllCited{} 57 | } 58 | \keyword{datasets} 59 | -------------------------------------------------------------------------------- /man/df_contrasts6.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts6} 5 | \alias{df_contrasts6} 6 | \title{Simulated data with response times with a 2-level between subject factor and IQ as covariate: interaction present.} 7 | \format{ 8 | A data frame with 60 rows and 4 variables: 9 | \describe{ 10 | \item{F}{Between subject factor with factor two levels (F1, F2)} 11 | \item{RT}{Dependent variable response time (RT)} 12 | \item{IQ}{Between-subject covariate (IQ)} 13 | \item{id}{Subject index} 14 | } 15 | } 16 | \usage{ 17 | df_contrasts6 18 | } 19 | \description{ 20 | In the data, the influence of IQ on response time differs between conditions F1 and F2. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | N <- 30 28 | 29 | mu1 <- c(200,100) 30 | 31 | mu2 <- c(220,100) 32 | 33 | sd1 <- 40 34 | 35 | sd2 <- 15 36 | 37 | r1 <- 0.0 38 | 39 | r2 <- -0.6 40 | 41 | sigma1 <- matrix(c(sd1^2,r1*sd1*sd2,r1*sd1*sd2,sd2^2),nrow=2) 42 | 43 | sigma2 <- matrix(c(sd1^2,r2*sd1*sd2,r2*sd1*sd2,sd2^2),nrow=2) 44 | 45 | d6a <- mvrnorm(n=N, mu=mu1, Sigma=sigma1, empirical=TRUE) 46 | 47 | d6b <- mvrnorm(n=N, mu=mu2, Sigma=sigma2, empirical=TRUE) 48 | 49 | df_contrasts6 <- rbind(data.frame(F="F1", d6a), data.frame(F="F2", d6b)) 50 | 51 | names(df_contrasts6)[2:3] <- c("RT","IQ") 52 | 53 | df_contrasts6$id <- 1:nrow(df_contrasts6) 54 | 55 | df_contrasts6$F <- factor(df_contrasts6$F) 56 | } 57 | \references{ 58 | \insertAllCited{} 59 | } 60 | \keyword{datasets} 61 | -------------------------------------------------------------------------------- /man/df_contrasts7.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_contrasts7} 5 | \alias{df_contrasts7} 6 | \title{Simulated data with a binomial response from a 2 x 2 between subject design.} 7 | \format{ 8 | A data frame with 12 rows and 3 variables: 9 | \describe{ 10 | \item{A}{Between subject factor with two levels (A1, A2)} 11 | \item{B}{Between subject factor with two levels (B1, B2)} 12 | \item{pDV}{Dependent variable: successful task performance (0 = no success versus 1 = success)} 13 | \item{id}{Subject index} 14 | } 15 | } 16 | \usage{ 17 | df_contrasts7 18 | } 19 | \description{ 20 | The dependent variable could be successful task performance. The condition means are exactly 0.2, 0.5, 0.2, and 0.8. 21 | } 22 | \details{ 23 | The data were simulated using the following R-code: 24 | 25 | set.seed(123) 26 | 27 | N <- 50 28 | 29 | d7 <- data.frame(A =factor(rep(c("A1","A1","A2","A2"), each=N)), 30 | 31 | B =factor(rep(c("B1","B2","B1","B2"), each=N)), 32 | 33 | pDV= rep(c( 0.2, 0.5, 0.2, 0.8), each=N)) 34 | 35 | tmp <- c(seq(0,1,length=N)[sample(N)], seq(0,1,length=N)[sample(N)], 36 | 37 | seq(0,1,length=N)[sample(N)], seq(0,1,length=N)[sample(N)]) 38 | 39 | d7$pDV <- ifelse(d7$pDV > tmp, 1, 0) 40 | 41 | d7$id <- 1:nrow(d7) 42 | 43 | df_contrasts7 <- tibble(d7) 44 | } 45 | \references{ 46 | \insertAllCited{} 47 | } 48 | \keyword{datasets} 49 | -------------------------------------------------------------------------------- /man/df_dillonE1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_dillonE1} 5 | \alias{df_dillonE1} 6 | \title{Data from an eyetracking reading experiment that records total reading times in milliseconds at an auxiliary verb in so-called low and high interference conditions. \insertCite{DillonEtAl2013;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 2,855 rows and 5 variables: 9 | \describe{ 10 | \item{subj}{The subject id, ranging from 1 to 40.} 11 | \item{item}{The item id, ranging from 1 to 48. Each item id is prefixed by dillonE1.} 12 | \item{rt}{The total reading times at the critical region (auxiliary verb) in milliseconds.} 13 | \item{int}{The two levels of the interference manipulation, low and high.} 14 | \item{expt}{The experiment id.} 15 | } 16 | } 17 | \usage{ 18 | df_dillonE1 19 | } 20 | \description{ 21 | The dataset is from an eyetracking experiment by \insertCite{DillonEtAl2013;textual}{lingpsych}, and contains reading times (total reading times) from a design that orginally had eight conditions. There are 40 subjects who read 48 items in a Latin square design. The data shown here focus on two conditions from the experiment, high and low interference conditions in a subject-verb number agreement configuration in ungrammatical sentences. 22 | } 23 | \references{ 24 | \insertAllCited{} 25 | } 26 | \keyword{datasets} 27 | -------------------------------------------------------------------------------- /man/df_dillonrep.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_dillonrep} 5 | \alias{df_dillonrep} 6 | \title{Reading times at the critical regions of an eyetracking experiment conducted by \insertCite{JaegerMertzenVanDykeVasishth2019;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 8,628 rows and 8 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{cond}{The conditions labels, ranging from a to h.} 13 | \item{acc}{Question-response accuracy.} 14 | \item{roi}{The region of interest.} 15 | \item{FPRT}{First-pass reading time.} 16 | \item{TFT}{Total fixation time.} 17 | \item{FPR}{First-pass regression.} 18 | } 19 | } 20 | \usage{ 21 | df_dillonrep 22 | } 23 | \description{ 24 | Reading times at the critical region of sentences of the experiment reported in \insertCite{JaegerMertzenVanDykeVasishth2019;textual}{lingpsych}. 25 | } 26 | \details{ 27 | The experiment, which is a replication attempt of Dillon et al. 2013, examined sentences with eight conditions. 28 | 29 | The data and associated code are available from https://osf.io/reavs/. 30 | } 31 | \references{ 32 | \insertAllCited{} 33 | } 34 | \keyword{datasets} 35 | -------------------------------------------------------------------------------- /man/df_discreteagrmt.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_discreteagrmt} 5 | \alias{df_discreteagrmt} 6 | \title{Data from an experiment involving an agreement attraction design with Likert ratings and accuracy of response \insertCite{AnnaLphd;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 4,296 rows and 7 variables: 9 | \describe{ 10 | \item{item}{Item id.} 11 | \item{subj}{Subject id.} 12 | \item{attractor}{A factor with two levels, match and mismatch.} 13 | \item{interference}{A factor with two levels, interference and no interference.} 14 | \item{rating}{A Likert scale rating of the acceptability of the sentence.} 15 | \item{rating}{A Likert scale rating of the acceptability of the sentence.} 16 | \item{accuracy}{Question response accuracy.} 17 | \item{Sentence}{The sentence that the subject was shown.} 18 | } 19 | } 20 | \usage{ 21 | df_discreteagrmt 22 | } 23 | \description{ 24 | The dataset, from experiment 1 in chapter 4 of \insertCite{AnnaLphd;textual}{lingpsych}, contains the responses from 4,296 subjects who participated in an internet-based study that elicited Likert ratings and question-response accuracies for a single sentence they were shown. There were two levels of a factor called attractor (levels: match and mismatch) and two levels of another factor called interference (levels: interference and no interference). The full data-set and code, including preprocessing steps, are available from: https://osf.io/eawg5/. 25 | } 26 | \references{ 27 | \insertAllCited{} 28 | } 29 | \keyword{datasets} 30 | -------------------------------------------------------------------------------- /man/df_dutch.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_dutch} 5 | \alias{df_dutch} 6 | \title{Data from a self-paced reading experiment that records log reading times in log milliseconds of grammatical and ungrammatical double center embeddings in Dutch. \insertCite{FrankEtAl2015;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 372 rows and 4 variables: 9 | \describe{ 10 | \item{subject}{The subject id, ranging from 1 to 24. Each subject id is preceded by an s.} 11 | \item{item}{The item id, ranging from 1 to 16. Each subject id is preceded by an i.} 12 | \item{condition}{Grammatical conditions are coded as +1, and ungrammatical conditions as -1.} 13 | \item{NP1}{The log reading times at the noun phase following the final verb in a double center embedding.} 14 | } 15 | } 16 | \usage{ 17 | df_dutch 18 | } 19 | \description{ 20 | The dataset is from a self-paced reading study by \insertCite{FrankEtAl2015;textual}{lingpsych}, and contains reading times from a design that had two conditions, grammatical and ungrammatical double center embeddings in Dutch. There are 24 subjects who read 16 items in a Latin square design. The data shown here focus on log reading times at the noun phrase following the final verb in a double center embedding. 21 | } 22 | \references{ 23 | \insertAllCited{} 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/df_english.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_english} 5 | \alias{df_english} 6 | \title{Data from a self-paced reading experiment that records log reading times in log milliseconds of grammatical and ungrammatical double center embeddings in English. \insertCite{VSLK08;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 768 rows and 4 variables: 9 | \describe{ 10 | \item{subject}{The subject id, ranging from 1 to 49.} 11 | \item{item}{The item id, ranging from 1 to 16.} 12 | \item{condition}{Grammatical conditions are coded as +1, and ungrammatical conditions as -1.} 13 | \item{NP1}{The log reading times at the noun phase following the final verb in a double center embedding.} 14 | } 15 | } 16 | \usage{ 17 | df_english 18 | } 19 | \description{ 20 | The dataset is from a self-paced reading study by \insertCite{VSLK08;textual}{lingpsych}, and contains reading times from a design that had two conditions, grammatical and ungrammatical double center embeddings in English. There are 48 subjects who read 16 items in a Latin square design. The data shown here focus on log reading times at the noun phrase following the final verb in a double center embedding. 21 | } 22 | \references{ 23 | \insertAllCited{} 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/df_fedorenko06.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_fedorenko06} 5 | \alias{df_fedorenko06} 6 | \title{Reading times at the critical region () of a self-paced reading experiment conducted by \insertCite{fedorenko06;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 1,440 rows and 7 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{RT}{Reading times in milliseconds at the critical region.} 13 | \item{rctype}{The two levels of a factor representing the relative clause type.} 14 | \item{nountype}{The two levels of a factor representing the noun type (name or occupation).} 15 | \item{load}{The two levels of a factor representing memory load (three words or one word).} 16 | \item{region}{The critical region in the sentence.} 17 | } 18 | } 19 | \usage{ 20 | df_fedorenko06 21 | } 22 | \description{ 23 | Reading times at the critical region of sentences of experiment 2 reported in \insertCite{fedorenko06;textual}{lingpsych}. 24 | } 25 | \details{ 26 | The experiment examined sentences with eight conditions (a 2x2x2 design). 27 | This self-paced experiment examined sentences with eight conditions (a 2x2x2 design). 28 | One factor is relative clause type (object or subject relative); 29 | another factor is memory load (three or one items held in memory); 30 | the third factor is nountype, which represents items held in memory that 31 | are either similar or dissimilar to the nouns in the sentences. 32 | 33 | #' 34 | } 35 | \references{ 36 | \insertAllCited{} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/df_gg05e1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_gg05e1} 5 | \alias{df_gg05e1} 6 | \title{Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in English. \insertCite{grodner;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 672 rows and 4 variables: 9 | \describe{ 10 | \item{subject}{The subject id, ranging from 1 to 42.} 11 | \item{item}{The item id, ranging from 1 to 16.} 12 | \item{condition}{Condition labels; objgap represents object relatives, and subjgap represents subject relatives.} 13 | \item{rawRT}{The reading times at the critical region (the relative clause verb) in milliseconds.} 14 | } 15 | } 16 | \usage{ 17 | df_gg05e1 18 | } 19 | \description{ 20 | The dataset is from Experiment 1 in \insertCite{grodner;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in English. There are 42 subjects who read 16 items in a Latin square design. The data are from the relative clause verb, which is the critical region of interest. 21 | } 22 | \references{ 23 | \insertAllCited{} 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/df_gg05e1_full.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_gg05e1_full} 5 | \alias{df_gg05e1_full} 6 | \title{Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in English. \insertCite{grodner;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 10331 rows and 10 variables: 9 | \describe{ 10 | \item{subject}{The subject id, ranging from 1 to 42.} 11 | \item{item}{The item id, ranging from 1 to 16.} 12 | \item{condition}{Condition labels; objgap represents object relatives, and subjgap represents subject relatives.} 13 | \item{word_position}{The word position in each sentence; in object relatives, the critical region (the relative clause verb) is in position 6, and in subject relatives, the critical region is in position 4.} 14 | \item{rawRT}{The reading times at each word, in milliseconds.} 15 | \item{qcorrect}{Comprehension question response accuracy, coded as 0 (incorrect) or 1 (correct).} 16 | } 17 | } 18 | \usage{ 19 | df_gg05e1_full 20 | } 21 | \description{ 22 | The dataset is from Experiment 1 in \insertCite{grodner;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in English. There are 42 subjects who read 16 items in a Latin square design. The data are for all the words in the two sentence types. 23 | } 24 | \references{ 25 | \insertAllCited{} 26 | } 27 | \keyword{datasets} 28 | -------------------------------------------------------------------------------- /man/df_gibsonwu.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_gibsonwu} 5 | \alias{df_gibsonwu} 6 | \title{Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in Chinese. \insertCite{gibsonwu;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 547 rows and 4 variables: 9 | \describe{ 10 | \item{subj}{The subject id, ranging from 1 to 40.} 11 | \item{item}{The item id, ranging from 1 to 16.} 12 | \item{type}{Condition labels; obj-ext represents object relatives, and subj-ext represents subject relatives.} 13 | \item{rt}{The reading times at the critical region (the relative clause head noun) in milliseconds.} 14 | } 15 | } 16 | \usage{ 17 | df_gibsonwu 18 | } 19 | \description{ 20 | The dataset is from an experiment reported in \insertCite{gibsonwu;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in Chinese. There are 37 subjects who read 15 items in a Latin square design. The data are from the relative clause head noun, which is the critical region of interest. 21 | } 22 | \references{ 23 | \insertAllCited{} 24 | } 25 | \keyword{datasets} 26 | -------------------------------------------------------------------------------- /man/df_gibsonwu2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_gibsonwu2} 5 | \alias{df_gibsonwu2} 6 | \title{Data from a self-paced reading experiment that records reading times in milliseconds of subject and object relative clauses in Chinese. \insertCite{VasishthetalPLoSOne2013;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 595 rows and 6 variables: 9 | \describe{ 10 | \item{subj}{The subject id, ranging from 1 to 40.} 11 | \item{item}{The item id, ranging from 1 to 16.} 12 | \item{condition}{Condition labels; obj-ext represents object relatives, and subj-ext represents subject relatives.} 13 | \item{pos}{The word position of the critical region in the sentence.} 14 | \item{rt}{The reading times at the critical region (the relative clause head noun) in milliseconds.} 15 | \item{region}{The label for the critical region (head noun) in the sentence.} 16 | } 17 | } 18 | \usage{ 19 | df_gibsonwu2 20 | } 21 | \description{ 22 | The dataset is from a replication attempt of experiment by \insertCite{gibsonwu;textual}{lingpsych}; the replication attempt was reported in \insertCite{VasishthetalPLoSOne2013;textual}{lingpsych}, and contains reading times from a self-paced reading experiment on subject and object relative clauses in Chinese. There are 40 subjects who read 15 items in a Latin square design. The data are from the relative clause head noun, which is the critical region of interest. 23 | } 24 | \references{ 25 | \insertAllCited{} 26 | } 27 | \keyword{datasets} 28 | -------------------------------------------------------------------------------- /man/df_hindi.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_hindi} 5 | \alias{df_hindi} 6 | \title{Data from the Potsdam-Allahabad Hindi Corpus, an eyetracking reading experiment that records various reading measures for a newspaper corpus in Hindi. \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 27,066 rows and 33 variables: 9 | \describe{ 10 | \item{subj}{The subject id, ranging from 1 to 10.} 11 | \item{expt}{The experiment id.} 12 | \item{item}{The item id, ranging from 1 to 83.} 13 | \item{lang}{The language being investigated.} 14 | \item{session}{There were two sessions, one preceded by an experiment on Urdu, and one followed by Urdu.} 15 | \item{trial}{Trial id.} 16 | \item{roi}{Region of interest in the sentence.} 17 | \item{FFD}{(First-Fixation Duration) Duration of the first fixation on a position if (and only if) the fixation was progressive. Zero otherwise.} 18 | \item{FFP}{(First Fixation Progressive) 0 if material downstream was viewed before the first fixation on this position, 1 otherwise.} 19 | \item{SFD}{(Single Fixation Duration) Duration of the fixation on a position if it was the *only* fixation on this region, i.e. if no subsequent fixation on this position followed. Zero if there were several fixations on this region.} 20 | \item{FPRT}{(First Pass Reading Time, Gaze Duration) Sum of all first-pass fixation durations on a region before *any* other region is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 21 | \item{RBRT}{(Right Bounded Reading Time) Sum of all first-pass fixation durations on a position before another position to the *right* is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 22 | \item{TFT}{(Total Fixation Time) Sum of all fixation durations on a region.} 23 | \item{RPD}{(Regression Path Duration, Go-Past Duration) Sum of all first-pass fixation durations on a position n and all preceding positions in the time period between the first fixation on n and the first fixation on anything to the right of n.} 24 | \item{CRPD}{(Cumulative Regression Path Duration) The CRPD of position n is the total amount of time a participant spent reading the sentence until reaching region n+1. It is the sum of the RPDs of all preceding regions and the RPD of the current region.} 25 | \item{RRT}{(Re-reading Time) Sum of all second-pass fixation durations. (RRT = TFT - FPRT)} 26 | \item{RRTP}{(ReReading Time Progressive) Sum of all second-pass fixation durations on a position that took place *before* a fixation on a region further to the right. (RRTP = RBRT - FPRT)} 27 | \item{RRTR}{(ReReading Time Regressive) Sum of all second-pass fixation durations on a position that occured *after* a fixation on a region further to the right. (RRTR = TFT - RBRT)} 28 | \item{RBRC}{(Right-Bounded Regression Count) Number of regressions from this position given *before* any region further to the right has been fixated.} 29 | \item{TRC}{(Total Regression Count) Total number of regressions from this position.} 30 | \item{LPRT}{(Last-pass reading time).} 31 | \item{syll_len}{Syllable length.} 32 | \item{word_lex}{The word read.} 33 | \item{word_complex}{Word complexity (continuous measure).} 34 | \item{word_freq}{Word frequency.} 35 | \item{type_freq}{Type frequency.} 36 | \item{word_bifreq}{Word bigram frequency.} 37 | \item{type_bifreq}{Type bigram frequency.} 38 | \item{word_len}{Word length.} 39 | \item{PB}{??.} 40 | \item{IC}{Integration cost.} 41 | \item{SC}{Storage cost.} 42 | } 43 | } 44 | \usage{ 45 | df_hindi 46 | } 47 | \description{ 48 | The dataset is from an eyetracking study by \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}, and contains various reading measures for a newspaper corpus in Hindi. The data are from a larger data-set and contain 10 subjects who read 83 items. The reading measures shown are for each word in each sentence. 49 | } 50 | \references{ 51 | \insertAllCited{} 52 | } 53 | \keyword{datasets} 54 | -------------------------------------------------------------------------------- /man/df_hindi_full.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_hindi_full} 5 | \alias{df_hindi_full} 6 | \title{Data from the Potsdam-Allahabad Hindi Corpus, an eyetracking reading experiment that records various reading measures for a newspaper corpus in Hindi. \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}} 7 | \format{ 8 | A data frame with 79,943 rows and 33 variables: 9 | \describe{ 10 | \item{subj}{The subject id, ranging from 1 to 10.} 11 | \item{expt}{The experiment id.} 12 | \item{item}{The item id, ranging from 1 to 83.} 13 | \item{lang}{The language being investigated.} 14 | \item{session}{There were two sessions, one preceded by an experiment on Urdu, and one followed by Urdu.} 15 | \item{trial}{Trial id.} 16 | \item{roi}{Region of interest in the sentence.} 17 | \item{FFD}{(First-Fixation Duration) Duration of the first fixation on a position if (and only if) the fixation was progressive. Zero otherwise.} 18 | \item{FFP}{(First Fixation Progressive) 0 if material downstream was viewed before the first fixation on this position, 1 otherwise.} 19 | \item{SFD}{(Single Fixation Duration) Duration of the fixation on a position if it was the *only* fixation on this region, i.e. if no subsequent fixation on this position followed. Zero if there were several fixations on this region.} 20 | \item{FPRT}{(First Pass Reading Time, Gaze Duration) Sum of all first-pass fixation durations on a region before *any* other region is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 21 | \item{RBRT}{(Right Bounded Reading Time) Sum of all first-pass fixation durations on a position before another position to the *right* is fixated. (What exactly constitutes a first pass is determined by the parameter 'regressiveFirstPass'.)} 22 | \item{TFT}{(Total Fixation Time) Sum of all fixation durations on a region.} 23 | \item{RPD}{(Regression Path Duration, Go-Past Duration) Sum of all first-pass fixation durations on a position n and all preceding positions in the time period between the first fixation on n and the first fixation on anything to the right of n.} 24 | \item{CRPD}{(Cumulative Regression Path Duration) The CRPD of position n is the total amount of time a participant spent reading the sentence until reaching region n+1. It is the sum of the RPDs of all preceding regions and the RPD of the current region.} 25 | \item{RRT}{(Re-reading Time) Sum of all second-pass fixation durations. (RRT = TFT - FPRT)} 26 | \item{RRTP}{(ReReading Time Progressive) Sum of all second-pass fixation durations on a position that took place *before* a fixation on a region further to the right. (RRTP = RBRT - FPRT)} 27 | \item{RRTR}{(ReReading Time Regressive) Sum of all second-pass fixation durations on a position that occured *after* a fixation on a region further to the right. (RRTR = TFT - RBRT)} 28 | \item{RBRC}{(Right-Bounded Regression Count) Number of regressions from this position given *before* any region further to the right has been fixated.} 29 | \item{TRC}{(Total Regression Count) Total number of regressions from this position.} 30 | \item{LPRT}{(Last-pass reading time).} 31 | \item{syll_len}{Syllable length.} 32 | \item{word_lex}{The word read.} 33 | \item{word_complex}{Word complexity (continuous measure).} 34 | \item{word_freq}{Word frequency.} 35 | \item{type_freq}{Type frequency.} 36 | \item{word_bifreq}{Word bigram frequency.} 37 | \item{type_bifreq}{Type bigram frequency.} 38 | \item{word_len}{Word length.} 39 | \item{PB}{??.} 40 | \item{IC}{Integration cost.} 41 | \item{SC}{Storage cost.} 42 | } 43 | } 44 | \usage{ 45 | df_hindi_full 46 | } 47 | \description{ 48 | The dataset is from an eyetracking study by \insertCite{HusainVasishthNarayanan2015;textual}{lingpsych}, and contains various reading measures for a newspaper corpus in Hindi. The data are from 30 subjects who read 83 items. The reading measures shown are for each word in each sentence. 49 | } 50 | \references{ 51 | \insertAllCited{} 52 | } 53 | \keyword{datasets} 54 | -------------------------------------------------------------------------------- /man/df_levykeller13E1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_levykeller13E1} 5 | \alias{df_levykeller13E1} 6 | \title{Total reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{levy2013expectation}{lingpsych}.} 7 | \format{ 8 | A data frame with 672 rows and 5 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{dat}{The two levels of a factor representing whether 13 | a dative phrase is in the main or subordinate clause.} 14 | \item{adj}{The two levels of a factor representing whether an adjust is in the main or subordinate clause.} 15 | \item{TFT}{Total reading times in milliseconds at the critical region.} 16 | } 17 | } 18 | \usage{ 19 | df_levykeller13E1 20 | } 21 | \description{ 22 | Total reading times at the critical region of sentences of experiment 1 reported in \insertCite{levy2013expectation;textual}{lingpsych}. 23 | } 24 | \details{ 25 | The experiment examined sentences with four conditions (a 2x2 design). 26 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 27 | One factor is whether a dative phrase is in the main or subordinate clause; 28 | another factor is whether an adjunct phrase is in the main or subordinate clause. 29 | 30 | The conditions (simplified) are: 31 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 32 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 33 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 34 | d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 35 | den Fussball versteckt... 36 | } 37 | \references{ 38 | \insertAllCited{} 39 | } 40 | \keyword{datasets} 41 | -------------------------------------------------------------------------------- /man/df_levykeller13E2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_levykeller13E2} 5 | \alias{df_levykeller13E2} 6 | \title{Total reading times at the critical region (verb) of an eyetracking experiment conducted by \insertCite{levy2013expectation}{lingpsych}.} 7 | \format{ 8 | A data frame with 672 rows and 5 variables: 9 | \describe{ 10 | \item{subj}{Subject id.} 11 | \item{item}{Item id.} 12 | \item{dat}{The two levels of a factor representing whether 13 | a dative phrase is in the main or subordinate clause.} 14 | \item{adj}{The two levels of a factor representing whether an adjust is in the main or subordinate clause.} 15 | \item{TFT}{Total reading times in milliseconds at the critical region.} 16 | } 17 | } 18 | \usage{ 19 | df_levykeller13E2 20 | } 21 | \description{ 22 | Total reading times at the critical region of sentences of experiment 2 reported in \insertCite{levy2013expectation;textual}{lingpsych}. 23 | } 24 | \details{ 25 | The experiment examined sentences with four conditions (a 2x2 design). 26 | This eyetracking experiment examined sentences with four conditions (a 2x2 design). 27 | One factor is whether a dative phrase is in the main or subordinate clause; 28 | another factor is whether an adjunct phrase is in the main or subordinate clause. 29 | 30 | The conditions (simplified) are: 31 | a. Nachdem der Lehrer [ADJ zur Ahndung] [DAT dem Sohn] ... hat Hans Gerntner ... den Fussball versteckt und somit... 32 | b. Nachdem der Lehrer [ ] [DAT dem Sohn] ... hat Hans Gerntner ... [ADJ zur Ahndung] den Fussball versteckt... 33 | c. Nachdem der Lehrer [ADJ zur Ahndung] [ ] ... hat Hans Gerntner ... [DAT dem Sohn] den Fussball versteckt... 34 | d. Nachdem der Lehrer [ ] [ ] ... hat Hans Gerntner ... [ADJ zur Ahndung] [DAT dem Sohn] 35 | den Fussball versteckt... 36 | } 37 | \references{ 38 | \insertAllCited{} 39 | } 40 | \keyword{datasets} 41 | -------------------------------------------------------------------------------- /man/df_persianE1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_persianE1} 5 | \alias{df_persianE1} 6 | \title{Reading times at the critical regions of the self-paced reading experiment 1 conducted by \insertCite{SafaviEtAlFrontiers2016;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 1,512 rows and 5 variables: 9 | \describe{ 10 | \item{subj}{Subject id, ranging from 4 to 45.} 11 | \item{item}{Item id, ranging from 1 to 36.} 12 | \item{rt}{Reading times in milliseconds} 13 | \item{distance}{The factor distance, with levels short and long.} 14 | \item{predability}{The factor predictability, with levels predictable and unpredictable.} 15 | } 16 | } 17 | \usage{ 18 | df_persianE1 19 | } 20 | \description{ 21 | Reading times at the critical region of sentences of Experiment 1 of \insertCite{SafaviEtAlFrontiers2016;textual}{lingpsych}. 22 | } 23 | \details{ 24 | The experiment examined sentences with four conditions: particle-verb constructions were created with either 25 | short or long distance between the particle and verb, and with the particle being either predictable or unpredictable. 26 | 27 | All the data are available from https://github.com/vasishth/SafaviEtAl2016. 28 | } 29 | \references{ 30 | \insertAllCited{} 31 | } 32 | \keyword{datasets} 33 | -------------------------------------------------------------------------------- /man/df_polarity.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_polarity} 5 | \alias{df_polarity} 6 | \title{Reading times at the critical regions of an eyetracking experiment conducted by \insertCite{VBLD07;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 8,823 rows and 5 variables: 9 | \describe{ 10 | \item{subject}{Subject id, ranging from 1 to 48.} 11 | \item{item}{Item id, ranging from 1 to 36.} 12 | \item{condition}{The conditions labels, ranging from a to f.} 13 | \item{times}{The different dependent measures from the eyetracking study.} 14 | \item{value}{The dependent measure (milliseconds).} 15 | } 16 | } 17 | \usage{ 18 | df_polarity 19 | } 20 | \description{ 21 | Reading times at the critical region of sentences of the experiment reported in \insertCite{VBLD07;textual}{lingpsych}. 22 | } 23 | \details{ 24 | The experiment examined sentences with six conditions: three conditions with negative polarity items (one grammatical, two ungrammatical), 25 | and three with positive polarity items (two grammatical, one ungrammatical). 26 | 27 | The data and associated code are available from https://github.com/vasishth/ProcessingPolarity. 28 | } 29 | \references{ 30 | \insertAllCited{} 31 | } 32 | \keyword{datasets} 33 | -------------------------------------------------------------------------------- /man/df_smithE1.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_smithE1} 5 | \alias{df_smithE1} 6 | \title{Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{smith2021encoding;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 3,441 rows and 5 variables: 9 | \describe{ 10 | \item{Participant}{Subject id.} 11 | \item{StimSet}{Item id.} 12 | \item{RT}{Reading times in milliseconds at the critical region.} 13 | \item{N2Factor}{The two levels of a factor representing the number marking on the second noun.} 14 | \item{SemFactor}{The two levels of a factor representing the number marking on the second noun.} 15 | } 16 | } 17 | \usage{ 18 | df_smithE1 19 | } 20 | \description{ 21 | Reading times at the critical region of sentences of experiment 1 reported in \insertCite{smith2021encoding;textual}{lingpsych}. 22 | } 23 | \details{ 24 | The experiment examined sentences with four conditions (a 2x2 design). 25 | One factor, labeled SemFactor, is semantic similarity of a noun 2 with the subject of the sentence (noun 1); the levels are labeled SemSim (semantically similar) and SemDissim 26 | (semantically dissimilar). The other factor, labeled N2Factor, marks whether the 27 | second noun phrase was singular (N2sg) or plural (N2pl) marked. 28 | 29 | The data and associated code are available from https://osf.io/hjrkn/. 30 | } 31 | \references{ 32 | \insertAllCited{} 33 | } 34 | \keyword{datasets} 35 | -------------------------------------------------------------------------------- /man/df_smithE2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{df_smithE2} 5 | \alias{df_smithE2} 6 | \title{Reading times at the critical region (verb) of a self-paced reading experiment conducted by \insertCite{smith2021encoding;textual}{lingpsych}.} 7 | \format{ 8 | A data frame with 3,459 rows and 6 variables: 9 | \describe{ 10 | \item{Participant}{Subject id.} 11 | \item{StimSet}{Item id.} 12 | \item{RT}{Reading times in milliseconds at the critical region.} 13 | \item{N2Factor}{The two levels of a factor representing the number marking on the second noun.} 14 | \item{SemFactor}{The two levels of a factor representing the number marking on the second noun.} 15 | \item{VerbFactor}{The two levels of a factor representing the number marking on the verb.} 16 | } 17 | } 18 | \usage{ 19 | df_smithE2 20 | } 21 | \description{ 22 | Reading times at the critical region of sentences of experiment 2 reported in \insertCite{smith2021encoding;textual}{lingpsych}. 23 | } 24 | \details{ 25 | The experiment examined sentences with eight conditions (a 2x2x2 design). 26 | One factor, labeled SemFactor, is semantic similarity of a noun 2 with the subject of the sentence (noun 1); the levels are labeled SemSim (semantically similar) and SemDissim 27 | (semantically dissimilar). The second factor, labeled N2Factor, marks whether the 28 | second noun phrase was singular (N2sg) or plural (N2pl) marked. The third factor 29 | is VerbFactor, which marks whether the verb is singular (Vsg) or plural (Vpl) marked. 30 | 31 | The data and associated code are available from https://osf.io/hjrkn/. 32 | } 33 | \references{ 34 | \insertAllCited{} 35 | } 36 | \keyword{datasets} 37 | -------------------------------------------------------------------------------- /man/dillonE1ttnested.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{dillonE1ttnested} 5 | \alias{dillonE1ttnested} 6 | \title{Model fit for an eyetracking reading experiment that records total reading times in milliseconds for a 2x2x2 design. \insertCite{DillonEtAl2013;textual}{lingpsych}} 7 | \format{ 8 | A brms model fit 9 | \describe{ 10 | } 11 | } 12 | \usage{ 13 | dillonE1ttnested 14 | } 15 | \description{ 16 | The dataset is a model fit for an English eyetracking reading study by \insertCite{DillonEtAl2013;textual}{lingpsych}. There are 40 subjects who read 48 items in a Latin square design. The data shown here focus on total reading times at the critical region. 17 | } 18 | \references{ 19 | \insertAllCited{} 20 | } 21 | \keyword{datasets} 22 | -------------------------------------------------------------------------------- /man/gen_fake_lnorm2x2x2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gen_sim_lnorm_functions.R 3 | \name{gen_fake_lnorm2x2x2} 4 | \alias{gen_fake_lnorm2x2x2} 5 | \title{Generate lognormal data from an eight-condition repeated measures design; the function contains contrast coding for a particular experiment design by Dillon et al. 2013.} 6 | \usage{ 7 | gen_fake_lnorm2x2x2( 8 | ncond = 8, 9 | nitem = NULL, 10 | nsubj = NULL, 11 | beta = NULL, 12 | Sigma_u = NULL, 13 | Sigma_w = NULL, 14 | sigma_e = NULL 15 | ) 16 | } 17 | \arguments{ 18 | \item{ncond}{Number of conditions (eight).} 19 | 20 | \item{nitem}{Number of items, must be divisible by eight.} 21 | 22 | \item{nsubj}{Number of subjects, must be divisible by eight.} 23 | 24 | \item{beta}{The intercept and slopes of the fixed effects.} 25 | 26 | \item{Sigma_u}{The variance-covariance matrix for subject random effects.} 27 | 28 | \item{Sigma_w}{The variance-covariance matrix for item random effects.} 29 | 30 | \item{sigma_e}{The standard deviation of the residuals.} 31 | } 32 | \value{ 33 | A data frame containing simulated data. 34 | } 35 | \description{ 36 | This function takes parameters from a previously fitted model 37 | and returns simulated data. 38 | } 39 | -------------------------------------------------------------------------------- /man/gen_sim_lnorm2.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gen_sim_lnorm_functions.R 3 | \name{gen_sim_lnorm2} 4 | \alias{gen_sim_lnorm2} 5 | \title{Generate lognormal data from a two-condition repeated measures design (Latin square)} 6 | \usage{ 7 | gen_sim_lnorm2( 8 | nitem = 16, 9 | nsubj = 42, 10 | beta = NULL, 11 | Sigma_u = NULL, 12 | Sigma_w = NULL, 13 | sigma_e = NULL 14 | ) 15 | } 16 | \arguments{ 17 | \item{nitem}{Number of items, must be divisible by two.} 18 | 19 | \item{nsubj}{Number of subjects, must be divisible by two.} 20 | 21 | \item{beta}{The intercept and slope of the fixed effects.} 22 | 23 | \item{Sigma_u}{The variance-covariance matrix for subject random effects.} 24 | 25 | \item{Sigma_w}{The variance-covariance matrix for item random effects.} 26 | 27 | \item{sigma_e}{The standard deviation of the residuals.} 28 | } 29 | \value{ 30 | A data frame containing simulated data. 31 | } 32 | \description{ 33 | This function takes parameters from a previously fitted model 34 | and returns simulated data. 35 | } 36 | --------------------------------------------------------------------------------