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Predicting Noun and Verb Latencies: Influential Variables and Task Effects
Abstract
Natural language comprehension involves processing a multitude of words that vary along many dimensions, some of which may reflect statistical regularities in language.
... The 142 trials were preceded by 8 practice trials comprised of 4 nouns and 4 verbs that were not in the stimulus set. As in Kacinik and Chiarello (2002), participants were instructed that nouns were words naming a quality, person, place or thing, and that verbs were words that refer to an action or the occurrence of an event, and examples were given of each. ...
... Both nouns and verbs had length as a significant predictor. Neither nouns nor verbs demonstrated an effect of phonological typicality, though we replicated the effect of noun-verb distributional typicality for nouns found by Kacinik and Chiarello (2002). Nouns that occurred in more typical contexts were responded to more quickly. ...
... However, there was a marginally significant effect of typicality for verbs, which was in the same direction – meaning that verbs which occurred in more typical noun contexts were responded to more quickly. This is in contrast to the finding by Kacinik and Chiarello (2002), who found that verbs which were more distributionally typical of the verb class were responded to more quickly. Kacinik and Chiarello (2002) used a larger set of words (152 nouns and 137 verbs) and so the additional power of their analyses may have revealed effects that were not significant in our data. ...
Information about the syntactic category of a word can be derived from a number of complementary sources. We focus here on phonological and distributional cues for distinguishing nouns and verbs that have been proposed as useful for language acquisition. In this paper we assessed the extent to which this information affects lexical processing in adults. We hypothesised that the phonological or distributional typicality of a word with respect to its syntactic class would influence lexical access – words that were more typical of their class would be accessed more quickly. We tested this in three tasks: naming, lexical decision, and a noun/verb decision task. Words that were phonologically typical of their syntactic category were responded to more quickly in lexical decision and naming tasks. Distributional typicality related only to the noun/verb decision task.
... Experiment 1 – Speeded Grammatical Classification The speeded grammatical classification task has been used in several previous studies (e.g., Arciuli & Cupples, 2003; Davis & Kelly, 1997; Kacinik & Chiarello, 2002; Sereno & Jongman, 1990 ...
We investigated probabilistic cues to grammatical category (noun vs. verb) in English orthography. These cues are located in both the beginnings and endings of words—as identified in our large-scale corpus analysis. Experiment 1 tested participants' sensitivity to beginning and ending cues while making speeded grammatical classifications. Experiment 2 tested sensitivity to these cues during lexical decisions. For both tasks, words with consistent ending cues (with respect to grammatical category) were processed more quickly and with lower error rates than words with inconsistent ending cues. However, for beginnings, consistent cues resulted in lower errors but no differences in response times. The data reported here point to the multifaceted nature of grammatical category representation and indicate that probabilistic orthographic cues relating to grammatical category have a clear influence on lexical processing particularly when these cues are located at the end of the word.
A procedure that processes a corpus of text and produces numeric vectors containing information about its meanings for each
word is presented. This procedure is applied to a large corpus of natural language text taken from Usenet, and the resulting
vectors are examined to determine what information is contained within them. These vectors provide the coordinates in a high-dimensional
space in which word relationships can be analyzed. Analyses of both vector similarity and multidimensional scaling demonstrate
that there is significant semantic information carried in the vectors. A comparison of vector similarity with human reaction
times in a single-word priming experiment is presented. These vectors provide the basis for a representational model of semantic
memory, hyperspace analogue to language (HAL).
Numerous word recognition studies conducted over the past 2 decades are examined. These studies manipulated lexical familiarity by presenting words of high versus low printed frequency and most reported an interaction between printed frequency and one of several second variables, namely, orthographic regularity, semantic concreteness, or polysemy. However, the direction of these interactions was inconsistent from study to study. Six new experiments clarify these discordant results. The first two demonstrate that words of the same low printed frequency are not always equally familiar to subjects. Instead, subjects' ratings of "experimental familiarity" suggest that many of the low-printed-frequency words used in prior studies varied along this dimension. Four lexical decision experiments reexamine the prior findings by orthogonally manipulating lexical familiarity, as assessed by experiential familiarity ratings, with bigram frequency, semantic concreteness, and number of meanings. The results suggest that of these variables, only experiential familiarity reliably affects word recognition latencies. This in turn suggests that previous inconsistent findings are due to confounding experiential familiarity with a second variable.
Early noncomputational models of word recognition have typically attempted to account for effects of categorical factors such as word frequency (high vs low) and spelling-to-sound regularity (regular vs irregular) More recent computational models that adhere to general connectionist principles hold the promise of being sensitive to underlying item differences that are only approximated by these categorical factors In contrast to earlier models, these connectionist models provide predictions of performance for individual items In the present study, we used the item-level estimates from two connectionist models (Plaut, McClelland, Seidenberg, & Patterson, 1996, Seidenberg & McClelland, 1989) to predict naming latencies on the individual items on which the models were trained The results indicate that the models capture, at best, slightly more variance than simple log frequency and substantially less than the combined predictive power of log frequency, neighborhood density, and orthographic length. The discussion focuses on the importance of examining the item-level performance of word-naming models and possible approaches that may improve the models' sensitivity to such item differences
To what extent is linguistic structure learnable from statistical information in the input? This research investigated the role played by statistical learning in the acquisition of rudimentary phrase structure. One type of statistical cue which might assist in the discovery of hierarchical phrase structure given serially presented input is the dependencies, or predictive relationships, present between form classes within phrases. In order to determine whether learners can use this statistical information, adult and child participants were exposed to an artificial language which contained predictive dependencies as a cue to phrase structure. The results suggest that humans possess statistical learning mechanisms which may assist in the acquisition of this abstract component of natural language.
Three experiments investigated the impact of five lexical variables (instance dominance, category dominance, word frequency, word length in letters, and word length in syllables) on performance in three different tasks involving word recognition: category verification, lexical decision, and pronunciation. Although the same set of words was used in each task, the relationship of the lexical variables to reaction time varied significantly with the task within which the words were embedded. In particular, the effect of word frequency was minimal in the category verification task, whereas it was significantly larger in the pronunciation task and significantly larger yet in the lexical decision task. It is argued that decision processes having little to do with lexical access accentuate the word-frequency effect in the lexical decision task and that results from this task have questionable value in testing the assumption that word frequency orders the lexicon, thereby affecting time to access the mental lexicon. A simple two-stage model is outlined to account for the role of word frequency and other variables in lexical decision. The model is applied to the results of the reported experiments and some of the most important findings in other studies of lexical decision and pronunciation.
Neuropsychological studies have revealed that brain-damaged patients may show impairments of specific word categories. This study reports the performance of three patients with impairments of the categories noun and verb. The first and second patients, with left frontal lobe atrophy, were impaired in naming and comprehension of verbs. The third patient, with striking atrophy of the left temporal lobe, was disproportionately impaired in naming and comprehension of nouns. These findings suggest that anatomically distinct neural systems in the temporal and frontal lobes of the dominant hemisphere might play a critical role in lexical processing of nouns and verbs, respectively.
Brain processing of grammatical word class was studied analyzing event-related potential (ERP) brain fields. Normal subjects observed a randomized sequence of single German nouns and verbs on a computer screen, while 20-channel ERP field map series were recorded separately for both word classes. Spatial microstate analysis was applied, based on the observation that series of ERP maps consist of epochs of quasi-stable map landscapes and based on the rationale that different map landscapes must have been generated by different neural generators and thus suggest different brain functions. Space-oriented segmentation of the mean map series identified nine successive, different functional microstates, i.e., steps of brain information processing characterized by quasi-stable map landscapes. In the microstate from 116 to 172 msec, noun-related maps differed significantly from verb-related maps along the left-right axis. The results indicate that different neural populations represent different grammatical word classes in language processing, in agreement with clinical observations. This word class differentiation as revealed by the spatial-temporal organization of neural activity occurred at a time after word input compatible with speed of reading.
Although a number of studies have examined lexical asymmetries in hemispheric processing, few have systematically investigated differences between nouns and verbs. Lateralization effects of grammatical class were examined by presenting nouns and verbs of both high and low frequency to either the right or left visual field. Results from both a noun/verb categorization and a lexical decision task revealed a significant visual field by grammatical class interaction. Further analyses revealed that verbs were processed faster in the left compared to the right hemisphere, while there was no hemispheric advantage for the processing of nouns. The present study provides new evidence for the role of grammatical class in lexical processing.
Dissociations between noun and verb processing are not uncommon after brain injury; yet, precise psycholinguistic comparisons of nouns and verbs are hampered by the underrepresentation of verbs in published semantic word norms and by the absence of contemporary estimates for part-of-speech usage. We report herein imageability ratings and rating response times (RTs) for 1,197 words previously categorized as pure nouns, pure verbs, or words of balanced noun-verb usage on the basis of the Francis and Kucera (1982) norms. Nouns and verbs differed in rated imageability, and there was a stronger correspondence between imageability rating and RT for nouns than for verbs. For all word types, the image-rating-RT function implied that subjects employed an image generation process to assign ratings. We also report a new measure of noun-verb typicality that used the Hyperspace Analog to Language (HAL; Lund & Burgess, 1996) context vectors (derived from a large sample of Usenet text) to compute the mean context distance between each word and all of the pure nouns and pure verbs. For a subset of the items, the resulting HAL noun-verb difference score was compared with part-of-speech usage in a representative sample of the Usenet corpus. It is concluded that this score can be used to estimate the extent to which a given word occurs in typical noun or verb sentence contexts in informal contemporary English discourse. The item statistics given in Appendix B will enable experimenters to select representative examples of nouns and verbs or to compare typical with atypical nouns (or verbs), while holding constant or covarying rated imageability.
One of the first problems confronting infant language learners is word segmentation: discovering the boundaries between words. Prior research suggests that 8-month-old infants can detect the statistical patterns that serve as a cue to word boundaries. However, the representational structure of the output of this learning process is unknown. This research assessed the extent to which statistical learning generates novel word-like units, rather than probabilistically-related strings of sounds. Eight-month-old infants were familiarized with a continuous stream of nonsense words with no acoustic cues to word boundaries. A post-familiarization test compared the infants' responses to words versus part-words (sequences spanning a word boundary) embedded either in simple English contexts familiar to the infants (e.g. "I like my tibudo"), or in matched nonsense frames (e.g. "zy fike ny tibudo"). Listening preferences were affected by the context (English versus nonsense) in which the items from the familiarization phase were embedded during testing. A second experiment confirmed that infants can discriminate the simple English contexts and the matched nonsense frames used in Experiment 1. The third experiment replicated the results of Experiment 1 by contrasting the English test frames with non-linguistic frames generated from tone sequences. The results support the hypothesis that statistical learning mechanisms generate word-like units with some status relative to the native language.
Familiarity for nouns and verbs: Not the same as, and better than, frequency
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