Predicting Noun and Verb Latencies: Influential Variables and Task Effects

Abstract

Listen

Translate article

Predicting Noun and Verb Latencies: Influential Variables and Task Effects Natalie Kacinik (kacinn01@student.ucr.edu) Christine Chiarello (christine.chiarello@ucr.edu) University of California, Riverside Life Sciences Psychology Building Riverside, CA 92521-0426, U.S.A Abstract Natural language comprehension involves processing a multitude of words that vary along many dimensions, some of which may reflect statistical regularities in language. These variables may differ in their relative importance across various types of words and tasks. This study used a multiple regression approach to investigate potentially important predictors of noun-verb processing across naming, grammatical categorization, and sentence completion tasks. Although there were some indications of different predictors for nouns vs verb processing, the strongest predictors of response latencies were primarily determined by the types of processing most important for a given task. One variable of particular interest was the newly created Noun-Verb Distributional Difference (NVDD) metric developed by Chiarello et al. (1999). NVDD values reflect statistical regularities in language regarding the typicality of the contexts in which nouns and verbs tend to occur. The results suggest that although noun-verb typicality, as assessed via the NVDD, is a valid measure of regularities in noun-verb contexts within a linguistic corpus, individuals may not be very sensitive to this dimension in standard psycholinguistic processing tasks. Introduction Single word recognition is a central component of language processing. The typical approach has been to use a naming or lexical decision (LD) task and a factorial design to investigate the processing effect of one or more variables such as familiarity or imageability while holding other potentially confounding variables constant. In addition, most single word recognition research has tended to use words of different parts of speech without considering grammatical class (e.g., nouns vs. verbs), or has focused on concrete, imageable nouns. Natural language comprehension, however, involves processing a multitude of words varying along many dimensions. These dimensions may be relatively more or less important for various word types, and their relative importance is likely to vary across different forms of language processing (e.g., word pronunciation vs grammatical identification vs sentence integration). With a few exceptions (e.g., Balota & Chumbley, 1984; Balota, Cortese, & Pilotti, 1999), there have been few attempts to investigate the relative importance of various orthographic and semantic dimensions for responding to words across tasks using multiple regression procedures. This approach provides the opportunity to study many variables simultaneously, to determine which lexical dimensions account for the greatest amount of variance in reaction time (RT) and accuracy for a particular task, and to assess whether the variance accounted for is unique, or is shared by other variables. Such a regression approach was used in the present study to investigate the relative importance of different lexical dimensions across three language tasks. To our knowledge no prior regression study has examined whether various predictor variables are equally applicable to words of different grammatical class. This is an important issue because neuropsychological research has shown that nouns and verbs appear to be processed differently in the brain (e.g., Daniele et al., 1994; Koenig & Lehmann, 1996; Sereno, 1999). It is unclear whether these differences are due to neurally separate noun and verb processing systems, or whether these processing differences are mainly due to different semantic dimensions that covary with word class. Investigating several potentially relevant dimensions using a regression approach may be informative regarding these processing differences between nouns and verbs. One possible reason word recognition research has generally been limited to concrete, imageable nouns, is the lack of word norming corpora available for other word types. A recent study by Chiarello, Shears, and Lund (1999), however, provides imageability ratings, frequency values from the Usenet text corpus of the Lund and Burgess (1996) Hyperspace Analog to Language (HAL) model, and a new measure of noun-verb distributional typicality (the Noun- Verb Distributional Difference, NVDD, metric), for a set of 1197 words: 555 “pure” nouns, 427 “pure” verbs, and 215 words “balanced” for noun-verb usage, as classified by the Francis and Kucera (FK, 1982) norms. Noun-Verb Distributional Typicality The new measure of noun-verb usage developed by Chiarello et al. (1999) uses context vectors from the Lund and Burgess (1996) HAL model, where words occurring in similar phrasal and sentential contexts are nearby in high dimensional context space. Context distances were computed between each word and each of the 555 “pure” nouns (according to Francis & Kucera, 1982) and averaged to get a mean noun context distance score. Mean verb distance scores were similarly obtained by computing and