Word Length and Lexical Competition: Longer is the Same as Shorter

Vitevitch, Michael S.; Stamer, Melissa K.; Sereno, Joan A.

doi:10.1177/0023830908099070

Cited by 22 publications

(30 citation statements)

References 51 publications

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“…We further grant that much has been learned by studying how the variables that contribute to those relationships influence processing. Consider all of the work investigating the influence of word frequency on various language and memory processes (dating back at least to Lester, 1922), studies of word-length on processing (e.g., Vitevitch, Stamer, & Sereno, 2008), all of the work on orthographic and phonological neighborhood density on processing (e.g., Laxon, Coltheart, & Keating, 1988; Pisoni, Nusbaum, Luce, & Slowiaczek, 1985), and the work on neighborhood frequency on processing (e.g., Grainger, O’Regan, Jacobs, & Segui, 1989). What appears to be lacking are studies that demonstrate that relationships between variables—such as the observation that high frequency words tend to have many phonological neighbors (e.g., Frauenfelder et al, 1993; Landauer & Streeter, 1973)—directly influence processing.…”

Section: Introductionmentioning

confidence: 99%

Insights into failed lexical retrieval from network science

Vitevitch

Chan

Goldstein

2014

Cognitive Psychology

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Previous network analyses of the phonological lexicon (Vitevitch, 2008) observed a web-like structure that exhibited assortative mixing by degree: words with dense phonological neighborhoods tend to have as neighbors words that also have dense phonological neighborhoods, and words with sparse phonological neighborhoods tend to have as neighbors words that also have sparse phonological neighborhoods. Given the role that assortative mixing by degree plays in network resilience, we examined instances of real and simulated lexical retrieval failures in computer simulations, analysis of a slips-of-the-ear corpus, and three psycholinguistic experiments for evidence of this network characteristic in human behavior. The results of the various analyses support the hypothesis that the structure of words in the mental lexicon influences lexical processing. The implications of network science for current models of spoken word recognition, language processing, and cognitive psychology more generally are discussed.

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Section: Introductionmentioning

confidence: 99%

Insights into failed lexical retrieval from network science

Vitevitch

Chan

Goldstein

2014

Cognitive Psychology

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“…However, addressing this issue in the typical way, by using small sets of tightly controlled stimuli (e.g. Garlock, Walley, & Metsala, 2001; Mirman, Kittredge, & Dell, 2010; Vitevitch, 2002a, 2003; Vitevitch et al, 2004; Vitevitch & Rodriguez, 2005; Vitevitch & Stamer, 2006; Vitevitch, Stamer, & Sereno, 2008) may carry its own problems, as this accentuates the impact of item-specific stimulus characteristics, and may reduce the replicability of the findings (cf. Baus et al, 2008; Spieler & Balota, 2000; Vitevitch & Stamer, 2006).…”

Section: Introductionmentioning

confidence: 99%

The ageing neighbourhood: phonological density in naming

Gordon

Kurczek

2013

Language, Cognition and Neuroscience

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Aging affects the ability to retrieve words for production, despite maintainence of lexical knowledge. In this study, we investigate the influence of lexical variables on picture naming accuracy and latency in adults ranging in age from 22 to 86 years. In particular, we explored the influence of phonological neighborhood density, which has been shown to exert competitive effects on word recognition, but to facilitate word production, a finding with implications for models of the lexicon. Naming responses were slower and less accurate for older participants, as expected. Target frequency also played a strong role, with facilitative frequency effects becoming stronger with age. Neighborhood density interacted with age, such that naming was slower for high-density than low-density items, but only for older subjects. Explaining this finding within an interactive activation model suggests that, as we age, the ability of activated neighbors to facilitate target production diminishes, while their activation puts them in competition with the target.

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“…, each word has additional neighbors, but only a few were listed for illustrative purposes). Much psycholinguistic research has demonstrated the influence of neighborhood density on spoken word recognition (e.g., Cluff & Luce, 1990; Goldinger, Luce, & Pisoni, 1989; Luce & Pisoni, 1998; Vitevitch, 2002a, 2003; Vitevitch & Luce, 1999; Vitevitch & Rodríguez, 2005; Vitevitch, Stamer & Sereno, in press) and other language processes (e.g., Storkel, Armbruster & Hogan, 2006; Vitevitch, 1997, 2002b; Vitevitch & Stamer, 2006). …”

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The influence of the phonological neighborhood clustering coefficient on spoken word recognition.

Chan¹,

Vitevitch²

2009

Journal of Experimental Psychology: Human Perception and Perfor

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Clustering coefficient-a measure derived from the new science of networks-refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (i.e., few neighbors are neighbors of each other) were more accurately identified than words with a high clustering coefficient (i.e., many neighbors are neighbors of each other). In a lexical decision task, words with a low clustering coefficient were responded to more quickly than words with a high clustering coefficient. These findings suggest that the structure of the lexicon, that is the similarity relationships among neighbors of the target word measured by clustering coefficient, influences lexical access in spoken word recognition. Simulations of the TRACE and Shortlist models of spoken word recognition failed to account for the present findings. A framework for a new model of spoken word recognition is proposed.Several models of spoken word recognition view the mental lexicon as a collection of arbitrarily ordered phonological representations, and the process of lexical retrieval as a special instance of pattern matching (e.g., TRACE: McClelland & Elman, 1986; Shortlist: Norris, 1994). In these accounts, acoustic-phonetic input activates several phonological word-forms "…which are roughly consistent with the bottom-up input" (Norris, 1994; pg. 201). The candidate words then compete among each other (in some cases via an inhibitory mechanism among activated word-forms) until the activation level of one candidate exceeds that of the other candidates, indicating that a representation that best (though not necessarily correctly) matches the input has been found.Although this perspective of the mental lexicon has advanced our understanding of spoken word recognition and other related processes, it is not the only way to view the mental lexicon. Indeed, an early model of word recognition proposed by Forster (1978; page 3) suggested that: "[a] structured information-retrieval system permits speakers to recognize words in their language effortlessly and easily." The idea that the lexicon may not be a collection of arbitrarily ordered phonological representations, but may instead be structured in such a way to influence the process of spoken word recognition can also be found in a more recent model of spoken word recognition-the neighborhood activation model (Luce & Pisoni, 1998; page 1)-which assumes "…that similarity relations among the sound patterns of spoken words represent one Correspondence should be addressed to: Michael S. Vitevitch, Ph.D., Spoken Language Laboratory, Department of Psychology, 1415 Jayhawk Blvd., University of Kansas, Lawrence, KS 66045, mvitevit@ku.edu, ph: 785-864-9312. Publisher's Disclaimer: The following manuscript is the final accepted manuscript. It has not been subjected to the fin...

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Word Length and Lexical Competition: Longer is the Same as Shorter

Cited by 22 publications

References 51 publications

Insights into failed lexical retrieval from network science

Insights into failed lexical retrieval from network science

The ageing neighbourhood: phonological density in naming

The influence of the phonological neighborhood clustering coefficient on spoken word recognition.

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