Neighborhood density effects in spoken word recognition in Spanish

Abstract

The present work examined the relationships among familiarity ratings, frequency of occurrence, neighborhood density, and word length in a corpus of Spanish words. The observed relationships were similar to the relationships found among the same variables in English. An auditory lexical decision task was then performed to examine the influence of word frequency, neighborhood density, and neighborhood frequency on spoken word recognition in Spanish. In contrast to the competitive effect of phonological neighborhoods typically observed in English, a facilitative effect of neighborhood density and neighborhood frequency was found in Spanish. Implications for models of spoken word recognition and language disorders are discussed. Keywords Word recognition; SpanishGiven the various differences that exist across the languages of the world, it is important to determine if the same factors affect processing in the same way in all languages. Computational analyses of lexical databases (e.g., Zipf, 1935;Landauer, & Streeter, 1973;Pisoni, Nusbaum, Luce, & Slowiacek, 1985; see also Bard, & Shillcock, 1993;Cutler, & Carter, 1987) can be used in conjunction with data from behavioral experiments to determine if factors such as familiarity, frequency of occurrence, neighborhood density, 1 and word length influence all languages in the same way, thereby better constraining theories of word recognition. The present analysis examined these lexical characteristics in a small subset of words (n = 1584) selected from a Spanish database that contained over 175 000 word types (Sebastián Gallés, Martí-Antonín, Carreiras-Valiña, & Cuetos Vega, 2000 ).2 The present analysis is similar to analyses that have examined English, Dutch (Frauenfelder, Baayen, Hellwig, & Schreuder, 1993), and Japanese (Yoneyama, & Johnson, 2001). In the present analysis a neighbor was defined as a lexical item that was formed by the substitution of a letter into the target word (cf., Landauer, & Streeter, 1973;Luce, & Pisoni, 1998). Word length was measured by the number of letters and the number of syllables in the word. Summary data for the variables that were examined are provided in Table I. Correspondence: Michael S. Vitevitch, Ph.D., Spoken Language Laboratory, Department of Psychology, 1415 Jayhawk Blvd., University of Kansas, Lawrence, KS 66045, USA. Tel: 1 785 864 9312. E-mail: mvitevit@ku.edu. 1 Neighborhood density refers to the number of words that are similar to a target word (Luce, & Pisoni, 1998). Words with many similar words, or neighbors, are said to have a dense neighborhood, whereas words with few neighbors are said to have a sparse neighborhood. 2 This subset was selected because information on all of the variables of interest could be found for only these items. Familiarity ratings were not provided for all of the words in the database. This greatly constrained our analysis. Correlation coefficients (all n = 1584) and significance values (using a Z-test) for the correlations were calculated for the relationships of interest. All repo...

Reconciling phonological neighborhood effects in speech production through single trial analysis

“…There is a large number of studies concerning form similarity in visual (e.g., Andrews, 1992Andrews, , 1989Carreiras et al, 1997;Forster & Shen, 1996;Grainger, 1990;Grainger et al, 1992Grainger et al, , 1989Grainger & Seguí, 1990;Pollatsek et al, 1999;Sears et al, 1995) and auditory word recognition (e.g., Dufour & Frauenfelder, 2010;Goldinger et al, 1989;Landauer & Streeter, 1973;Luce, 1986;Luce & Pisoni, 1998;Vitevitch & Luce, 1999Vitevitch & Rodríguez, 2005;Ziegler et al, 2003;Ziegler & Muneaux, 2007). In comparison, research on PhND effects in the field of speech production is relatively scarce, and the evidence is quite unclear.…”

Comparative Analysis of Networks of Phonologically Similar Words in English and Spanish

“…(Note that in the psycholinguistic literature degree is referred to as neighborhood density.) Interestingly, degree has different influences in Spanish compared to English [22,23], suggesting that the same network structure might have a different influence in a different language as a function of other "structural" characteristics that are not captured in current network measures (e.g., clustering coefficient, degree distribution, etc.). Recall that the lexical networks of several languages consisted of a small giant component (which we will refer to as the giant component), several smaller components (which we will refer to as islands), and many non-connected vertices (which we will refer to as lexical hermits [19]).…”

“…With such different meanings among neighboring vertices in English, perceptual errors might be more costly in English than in Spanish. Given the differences in the characteristics of the two languages, and the different costs in the two languages associated with a phonological error, different (but equally efficient) processing strategies might be implemented in the two languages (see [22,23]). …”

Graph-based term weighting for information retrieval

“…This relation can be statistical (e.g. simple co-occurrence Ferrer i and Solé 2001, or collocation 2 Bordag et al 2003;Dorogovtsev and Mendes 2001;Ferrer i and Solé 2001), syntactic (i Cancho et al 2007; Ferrer i Cancho et al 2004;Widdows and Dorow 2002), semantic (Kozareva et al 2008;Leicht et al 2006;Motter et al 2011;Sigman and Cecchi 2002;Steyvers and Tenenbaum 2005), phonological (Vitevitch and Rodrguez 2005), orthographic (Choudhury et al 2007), discourse (Somasundaran et al 2009), or cognitive (e.g. free-association relations observed in experiments involving humans Sigman and Cecchi 2002;Steyvers and Tenenbaum 2005).…”