The past tense inflection project (PTIP): speeded past tense inflections, imageability ratings, and past tense consistency measures for 2,200 verbs

Abstract The processes involved in past tense verb generation have been central to models of inflectional morphology. However, the empirical support for such models has often been based on studies of accuracy in past tense verb formation on a relatively small set of items. We present the first largescale study of past tense inflection (the Past Tense Inflection Project, or PTIP) that affords response time, accuracy, and error analyses in the generation of the past tense form from the present tense form for over 2,000 verbs. In addition to standard lexical variables (such as word frequency, length, and orthographic and phonological neighborhood), we have also developed new measures of past tense neighborhood consistency and verb imageability for these stimuli, and via regression analyses we demonstrate the utility of these new measures in predicting past tense verb generation. The PTIP can be used to further evaluate existing models, to provide well controlled stimuli for new studies, and to uncover novel theoretical principles in past tense morphology. Keywords Verb processing . Megastudy . Past tense inflection . Item-level variance . Verb consistency . Verb imageability A long-standing question in language acquisition and inflectional morphology is how individuals produce the past tense form of a verb. Past tense inflection (PTI), like spelling-to-sound conversion in English, is quasiregular, meaning that a set of generally applicable descriptive rules are useful for most verbs (e.g., add -ed to the stem form), but there are also some irregular forms (e.g., dodid) and subregularities (as in the eep-ept past tense family: sleep-slept, weep-wept, keep-kept, etc.). Indeed, past tense inflection has been a central focus of the debate between parallel distributed models Although there has been extensive theoretical work in the area of past tense verb generation, experimental work examining response times (RTs) has been relatively limited. For example, in the stem inflection task, participants are asked to produce the past tense (real or hypothetical) of a target verb or novel nonword (e.g., Only a few previous studies of past tense verb inflection have used RT as a dependent variable: Joanisse and Seidenberg One way to address the discrepancies among previous studies, as well as the limitations associated with factorial designs employing relatively few stimuli, is to sample a much larger set of items from the language. Megastudies include stimuli on the order of thousands, rather than 50 to 100, and allow for the effects of variables to be modeled continuously rather than categorically (see In addition to providing a large database of response latencies and accuracies for past tense verb inflection, we also developed two new measures that are important to consider in past tense inflection, consistency and imageability. Similar to the spelling-to-sound consistency measure that has been well-studied in visual word recognition research The second variable that we measured was imageability. Imageability is a variable that reflects the extent to which 152 Behav Res (2013) 45:151-159 one is able to form a mental image of a word, and indeed many imageability norms are already available (e.g., The present study is based on 89 participants' accuracy and RTs for a past tense inflection task with 2,200 verbs. Each participant produced responses to 888 items. For each verb in the PTIP database, we included measures of RT, accuracy, and regularization errors (e.g., saying GRINDED for GRIND), along with the new imageability and consistency measures described above. The PTIP database is useful in examining the specific effects of predictor variables on RT and accuracy and allows for detailed item-level predictions. It is available as supplementary materials with this article for researchers who plan to examine other theoretical questions about past tense inflection, or are hoping to select well-controlled and well-examined stimuli for new studies. These data will serve as both a reference and an impetus for further research in the domain of past tense inflection. Experiment 1 The first experiment was conducted in order to collect imageability rating norms for the verbs in the PTIP database. Method Participants A group of 218 participants were recruited via Amazon's Mechanical Turk (AMT; see Materials The 2,200 words from the PTIP database (see below), plus another 112 words for use in another study, were divided into eight lists of 289 items each. The eight lists were presented as separate jobs in AMT. Procedure Each participant completed one list of the rating task, which was presented in Adobe Flash and appeared after a consent screen in the AMT job description. The instructions were the same as those used in Results The ratings were aggregated across participants for each item (excluding "do not know" responses), so that one mean imageability value was calculated for each verb. These values were used in Experiment 2 (see below). The mean rating across all verbs was 4.28 (SD 0 0.92), and the mean RT across all verbs was 3,191 ms (SD 0 1,695). The overall split-half reliability was r 0 .80, p < .001. Experiment 2 Method Participants A group of 113 native English-speaking college students from the Washington University subject pool participated in Behav Res (2013) 45:151-159 153 the study. After eliminating extreme outliers (less than 80 % accuracy overall; four participants) or participants whose data were subject to recording error (missing sound files from which to code accuracy-20 participants), 89 participants contributed to the final database

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