Purpose The purpose of this study was to differentiate effects of phonotactic probability, the likelihood of occurrence of a sound sequence, and neighborhood density, the number of words that sound similar to a given word, on adult word learning. A second purpose was to determine what aspect of word learning (viz., triggering learning, formation of an initial representation, or integration with existing representations) was influenced by each variable. Method Thirty-two adults were exposed to 16 nonwords paired with novel objects in a story context. The nonwords orthogonally varied in phonotactic probability and neighborhood density. Learning was measured following 1, 4, and 7 exposures in a picture-naming task. Partially correct (i.e., 2 of 3 phonemes correct) and completely correct responses (i.e., 3 of 3 phonemes correct) were analyzed together and independently to examine emerging and partial representations of new words versus complete and accurate representations of new words. Results Analysis of partially correct and completely correct responses combined showed that adults learned a lower proportion of high-probability nonwords than low-probability nonwords (i.e., high-probability disadvantage) and learned a higher proportion of high-density nonwords than low-density nonwords (i.e., high-density advantage). Separate analysis of partially correct responses yielded an effect of phonotactic probability only, whereas analysis of completely correct responses yielded an effect of neighborhood density only. Conclusions These findings suggest that phonological and lexical processing influence different aspects of word learning. In particular, phonotactic probability may aid in triggering new learning, whereas neighborhood density may influence the integration of new lexical representations with existing representations.

This study tests the claim that children acquire collections of phonologically similar word forms, namely, dense neighborhoods. Age of acquisition (AoA) norms were obtained from two databases: parent report of infant and toddler production and adult self-ratings of AoA. Neighborhood density, word frequency, word length, Density × Frequency and Density × Length were analyzed as potential predictors of AoA using linear regression. Early acquired words were higher in density, higher in word frequency, and shorter in length than late acquired words. Significant interactions provided evidence that the lexical factors predicting AoA varied, depending on the type of word being learned. The implication of these findings for lexical acquisition and language learning are discussed.

The influence of phonological (i.e., individual sounds), lexical (i.e., whole-word forms), and semantic (i.e., meaning) characteristics on the words known by infants age 1;4 to 2;6 was examined, using an existing database (Dale & Fenson, 1996). For each noun, word frequency, two phonological (i.e., positional segment average, biphone average), two lexical (i.e., neighborhood density, word length), and four semantic variables (i.e., semantic set size, connectivity, probability resonance, resonance strength) were computed. Regression analyses showed that more infants knew (1) words composed of low probability sounds and sound pairs, (2) shorter words with high neighborhood density, (3) words that were semantically related to other words, both in terms of the number and strength of semantic connections. Moreover, the effect of phonological variables was constant across age; whereas the effect of lexical and semantic variables changed across age.Keywords word learning; vocabulary; phonotactic probability; neighborhood density; semantics Three types of representations appear to play a role in word learning: phonological, lexical, and semantic (e.g., Gupta & MacWhinney, 1997). Phonological representations refer to individual sounds (e.g., /k/, /ae/, /t/). Lexical representations refer to whole-word forms (e.g., / kaet/). Semantic representations refer to the meaning or referent of a word (e.g., 'small furry four-legged pet'). The simplest example of word learning is one in which a novel object labeled by a correctly perceived and articulated novel sound sequence is learned. In this case, it is assumed that when the novel word is encountered it activates existing phonological representations. These, in turn, activate lexical representations; however, a novel word will not exactly match an existing lexical representation. Likewise, a novel word will not exactly match an existing semantic representation. Thus, the formation of new lexical and semantic representations presumably is initiated. Word learning consists of creating new lexical and semantic representations, linking these new representations to one another, and integrating these new representations with existing phonological, lexical, and semantic representations.A limitation of current models of word learning is that many aspects of word learning are not fully specified, particularly as related to the phonological, lexical, and semantic characteristics of the ambient language. That is, many models do not specify whether the influence of ambient characteristics on the formation of new representations is similar across phonological, lexical, and semantic representations, and whether this influence might change over developmental time as more words are acquired. In part, these aspects of word learning are underspecified because the necessary data are limited. The goal of this paper is to provide preliminary evidence to address these three issues to promote elaboration of existing models as well as future research. Phonological, Lexical, and Semantic Charac...

The goal of this research was to disentangle effects of phonotactic probability, the likelihood of occurrence of a sound sequence, and neighborhood density, the number of phonologically similar words, in lexical acquisition. Two word learning experiments were conducted with 4-year-old children. Experiment 1 manipulated phonotactic probability while holding neighborhood density and referent characteristics constant. Experiment 2 manipulated neighborhood density while holding phonotactic probability and referent characteristics constant. Learning was tested at two time points (immediate vs. retention) in both a naming and referent identification task, although only data from the referent identification task were analyzed due to poor performance in the naming task. Results showed that children were more accurate learning rare sound sequences than common sound sequences and this was consistent across time points. In contrast, the effect of neighborhood density varied by time. Children were more accurate learning sparse sound sequences than dense sound sequences at the immediate test point but accuracy for dense sound sequences significantly improved by the retention test without further training. It was hypothesized that phonotactic probability and neighborhood density influenced different cognitive processes that underlie lexical acquisition. Keywords phonotactic probability; neighborhood density; lexical acquisition; vocabulary Models of spoken word recognition and production typically incorporate three types of representations: phonological, lexical, and semantic (e.g., Dell, 1988;Gupta & MacWhinney, 1997;Levelt, 1989;Luce, Goldinger, Auer, & Vitevitch, 2000;Magnuson, Tanenhaus, Aslin, & Dahan, 2003;McClelland & Elman, 1986;Norris, 1994). The phonological representation corresponds to information about individual sounds, with models varying in the specific sound unit chosen (e.g., phonetic features, context specific allophones, phonemes). Assuming a phoneme unit of representation, the phonological representation of a word such as "seal" would consist of three separate sound units, namely / s/, /i/, and /l/. The lexical representation corresponds to the whole-word form as a single unit. Thus, the lexical representation of "seal" would be a single lexical unit, specifically / sil/. Finally, the semantic representation corresponds to the meaning of the word, which for "seal" would include information such as "ocean mammal with webbed flippers."Correspondence should be addressed to: Holly Storkel, Ph.D., Associate Professor, Department of Speech-Language-Hearing: Sciences and Disorders, University of Kansas, 3001 Dole Human Development Center, 1000 Sunnyside Avenue, Lawrence,. 785-864-3974 (fax). hstorkel@ku.edu. NIH Public Access Author ManuscriptLang Cogn Process. Author manuscript; available in PMC 2012 January 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptRecent studies of adult and child spoken word recognition and production suggest that formrelated variables influence ret...

Previous evidence suggests that the structure of similarity neighbourhoods in the developing mental lexicon may differ from that of the fully developed lexicon. The similarity relationships used to organize words into neighbourhoods was investigated in  pre-school children (age  ;  to  ; ) using a two alternative forced-choice classification task. Children classified the similarity of test words relative to a standard word to determine neighbourhood membership. The similarity relationship between the test and standard words varied orthogonally in terms of type of similarity and position of overlap. Standard words were drawn from neighbourhoods differing in density. Results showed that dense neighbourhoods were organized by phoneme similarity in the onsetj nucleus or rhyme positions of overlap. In contrast, sparse neighbourhoods appeared to be organized by phoneme similarity in the onsetj nucleus, but manner similarity in the rhyme. These results are integrated with previous findings from infants and adults to propose a developmental course of change in the mental lexicon.

Many recent studies of spoken language processing by children have considered the role of phonotactic probability-the likelihood of occurrence of a sound (phoneme) sequence-and neighborhood density-the number of phonologically similar words-in word recognition (Garlock, Walley, & Metsala, 2001; MainelaArnold, Evans, & Coady, 2008;Metsala, 1997), word production (Edwards, Beckman, & Munson, 2004;Munson, Swenson, & Manthei, 2005;Newman & German, 2005;Zamuner, Gerken, & Hammond, 2004), memory (Gathercole, Frankish, Pickering, & Peaker, 1999;Thomson, Richardson, & Goswami, 2005), and learning (Alt & Plante, 2006;Storkel, 2001Storkel, , 2003Storkel, , 2004aStorkel, , 2009Storkel, Armbrüster, & Hogan, 2006;Storkel & Maekawa, 2005;Swingley & Aslin, 2007). In a number of these studies, phonotactic probability and neighborhood density were calculated using readily available American English adult corpora and online calculators (Balota et al., 2007;Davis, 2005;Vitevitch & Luce, 2004), because comparable child calculators do not exist. However, the validity of the values generated from adult online calculators for child research warrants investigation. Moreover, an understanding of the relationship between values generated from child sources compared with those from adult sources is critical for developmental research, in which researchers seek to compare phonotactic probability and neighborhood density effects across different ages as the lexicon grows.What evidence is there that child phonotactic probability and neighborhood density may differ from adult phonotactic probability and neighborhood density? To our knowledge, no researchers have investigated how phonotactic probability may change with development. However, numerous researchers have considered how neighborhood density may change from childhood to adulthood as the lexicon grows (Charles-Luce & Luce, 1990Luce, , 1995Coady & Aslin, 2003;Dollaghan, 1994). Thus, we begin by examining what is known about neighborhood density changes and then apply the observed patterns to phonotactic probability. Across studies in which lexical growth was examined, there is clear evidence that the number of neighbors increases from childhood to adulthood, meaning that the child neighborhood density for a given word will tend to be lower than the adult neighborhood density for the same word (Charles-Luce & Luce, 1990Luce, , 1995Coady & Aslin, 2003;Dollaghan, 1994). However, it is unknown whether these density differences are constant or variable across words or neighborhoods.One possibility is that child neighborhood density differs from adult neighborhood density by a relatively constant value across neighborhoods. In this case, the difference in density for stimuli identified as sparse versus dense for one age group (e.g., children) will be approximately the same as that for an older age group (e.g., adults). Consider the following hypothetical example: The word mouth, with only 5 neighbors for children, is selected as a sparse word for children, and the word tooth, with 10 neighbo...

Recent research suggests that phonotactic probability (the likelihood of occurrence of a sound sequence) and neighborhood density (the number of words phonologically similar to a given word) influence spoken language processing and acquisition across the lifespan in both normal and clinical populations. The majority of research in this area has tended to focus on controlled laboratory studies rather than naturalistic data such as spontaneous speech samples or elicited probes. One difficulty in applying current measures of phonotactic probability and neighborhood density to more naturalistic samples is the significant correlation between these variables and word length. This study examines several alternative transformations of phonotactic probability and neighborhood density as a means of reducing or eliminating this correlation with word length. Computational analyses of the words in a large database and reanalysis of archival data supported the use of z scores for the analysis of phonotactic probability as a continuous variable and the use of median transformation scores for the analysis of phonotactic probability as a dichotomous variable. Neighborhood density results were less clear with the conclusion that analysis of neighborhood density as a continuous variable warrants further investigation to differentiate the utility of z scores in comparison to median transformation scores. Furthermore, balanced dichotomous coding of neighborhood density was difficult to achieve, suggesting that analysis of neighborhood density as a dichotomous variable should be approached with caution. Recommendations for future application and analyses are discussed.

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