Journal of Psycholinguistic Research, Vol. 33, No. 1, January 2004 (© 2004)

Neighborhood Effects on Nonword VisualProcessing in a Language with Shallow Orthography

Lisa S. Arduino1,2,3 and Cristina Burani1

Neighborhood size and neighborhood frequency were orthogonally varied in two experiments onItalian nonwords. In Experiment 1, an inhibitory effect of neighborhood frequency on visual lexicaldecision was found: The presence of one high-frequency neighbor increased response latencies anderror rates to nonwords. By contrast, no effect of neighborhood size and no neighborhood size �neighborhood frequency interaction were found. In Experiment 2, a facilitatory effect of neighborhoodsize on nonword naming was shown: Naming latencies were faster when nonwords had a large neigh-borhood. In the naming experiment, there was no effect of neighbors’ frequency and no neighborhoodsize � neighborhood frequency interaction. An additional role for bigram frequency was foundwhereas syllable frequency did not give any independent contribution. These results further corrobo-rate the view that, in a language with transparent orthography like Italian, despite a substantial con-tribution of sublexical print-to-sound mapping due to the language’s high regularity/consistency,reading aloud of nonlexical material may benefit from the contribution of the lexical component.

KEY WORDS: Bigram frequency; neighborhood frequency; nonword processing; orthographicneighborhood size; reading aloud.

INTRODUCTION

Recent studies have provided evidence for lexically mediated word naming (read-ing aloud) in Italian, a language with regular print-to-sound mapping. For Italian, asis also the case for less transparent languages, the probability of lexical effects onreading aloud has been shown to be affected by factors such as the list compositionand stimulus properties (see, e.g., Arduino, Burani, & Vallar, 2002; Bates, Burani,D’Amico, & Barca, 2001; Burani, Dovetto, Spuntarelli, & Thornton, 1999; Burani& Laudanna, 2003; Burani, Marcolini, & Stella, 2002; Colombo, 1992; Colombo &Tabossi, 1992; Laudanna, Cermele, & Caramazza, 1997; Tabossi & Laghi, 1992).

750090-6905/04/0100-0075/0 © 2004 Plenum Publishing Corporation

1 Institute for Cognitive Sciences and Technologies, ISTC-CNR, Rome, Italy.2 University of Urbino, Italy.3 To whom all correspondence should be addressed: Università degli Studi di Urbino, Istituto di

Psicologia “Luigi Meschieri,” Via Zeppi, 61029 Urbino, Italy. email: lisa.arduino@uniurb.it

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Few studies have investigated, for the Italian language, whether reading aloudof nonwords is affected by the activation of orthographically similar words. This isa critical test for lexical involvement in reading aloud, given that, in a language withshallow orthography, all new words may be in principle correctly read aloud by rule.The studies by Job, Peressotti, and Cusinato (1998) and Peressotti & Job (2002) didshow lexical effects in Italian nonword naming when the list included both wordsand nonwords: In the latter case, the pronunciation of an Italian nonword containinga context-sensitive rule was influenced by the existence of a similar word.

In languages other than Italian, a number of studies have shown that readingperformance for nonwords is affected by neighborhood size, or N-size; that is, thenumber of words that differ by one letter with respect to a target nonword whilepreserving the identity and position of the other letters (Coltheart, Davelaar,Jonasson, & Besner, 1977). An example of an English nonword with many wordneighbors is SHART (its word neighborhood including SMART, START, SHORT,SHARP, and so forth) whereas TOOSH is a nonword with only one neighbor (i.e.,TOOTH) (Rastle, Harrington, & Coltheart, 2002). Both in English and French, non-words with many neighbors were found to be read aloud faster and with greateraccuracy when compared to nonwords with few word neighbors (Laxon,Masterson, Pool, & Keating, 1992; McCann & Besner, 1987; Peereman & Content,1995; Weekes, 1997). However, there is some debate regarding whether neighbor-hood effects on nonword naming necessarily reflect lexical retrieval or may insteadbe due to effects of orthographic structure on the procedures involved in nonlexicalreading. For example, it is possible that the frequency of sublexical units(e.g., graphemes or bigrams) may play a role: Nonwords with many orthographicneighbors might be faster to name because they contain more common spelling-to-sound correspondences (Andrews, 1992, 1997; Peereman & Content, 1995).

The need to disentangle lexical versus sublexical effects on reading aloud iscritical in Italian, a language with transparent orthography. A measure of ortho-graphic transitional probability such as bigram frequency [i.e., the frequency withwhich adjacent pairs of letters (bigrams) occur in printed texts] is in Italian a goodapproximation to print-to-sound frequency. In Italian, individual letters usuallycorrespond to one grapheme, and for the great majority they are regularly andconsistently translated into the same phoneme. Thus, one-letter graphemes aregood print-to-sound mapping units. Analogously, a given bigram is usually trans-lated into the same phonemes, irrespective of word contexts. Cases of two possible

4 These case involve the letters c and g, pronounced as /k/ or /t�/, or as /g/, /d�/ or/�/, respectively,depending on the following letter. The two cases whose correct translation necessitates a three-letter context involve the orthographic sequence of two consonants: sc and gl, translated witheither two phonemes (/sk/ and /gl/, respectively) or with a single phoneme (/�/ and /ʎ/, respect-ively) depending on the following letter. The double letters cc and gg are also translated into twodifferent geminate phonemes on the basis of the following letter.

Neighborhood Effects on Nonword Visual Processing 77

translations are few, and no more than a two- or three-letter context is needed forthe correct phonological translation to be assigned.4

This structure of the spelling-to-sound Italian system has two main implica-tions. First, the great majority of Italian orthographic neighbors share both theorthography and the pronunciation of the common segment. Hence, the neigh-borhood of a given Italian stimulus, differently from English and French, willrarely include neighbors with inconsistent pronunciations (see Job et al., 1998).Second, for a given target stimulus, be it a word or a nonword, the pronunciationresulting from lexically based reading (i.e., from lexical activation of similar wordunits) usually converges in a consistent way with the pronunciation resulting fromnonlexical reading. With the exception of words including the rare bigrams thatare correctly pronounced on a trigram basis (see footnote 4), the pronunciation ofa given bigram on the basis of neighborhood will usually not conflict in Italianwith inconsistent pronunciations resulting from translating the same bigram asembedded in other word contexts. Thus, reading aloud of Italian stimuli will usu-ally involve convergence rather than competition between pronunciations pro-vided by lexical and nonlexical sources, respectively.

Given these premises, the experimental predictions for the Italian language dif-fer somewhat from those for less transparent languages. In contrast with the absenceof bigram frequency effects in reading aloud English nonwords (Andrews, 1992;Weekes, 1997), some role for bigram frequency could be expected in reading Italiannonwords aloud. The frequency of bigrams is expected to exert some role in Italianbecause of constituting an index of frequency of print-to-sound correspondence,analogously to average grapheme frequency, which is an estimate of the frequencyof nonlexical correspondences for English (Weekes, 1997). However, an effect ofthe sublexical measure of bigram frequency on nonword naming would not neces-sarily exclude a concurrent role for lexically activated units like word neighbors. Itis thus possible that lexical neighborhood and sublexical frequency measures bothcontribute to reading efficiency (see also Weekes, 1997), with a relatively moresubstantial nonlexical contribution in highly consistent languages.

The hypothesis of the current study was that, in a language with transparentorthography such as Italian, nonword naming benefits from lexical activation ofword neighborhood, with an additional contribution of the sublexical measure ofbigram frequency, and with the two measures not reducible to each other. In orderto demonstrate this hypothesis, two experiments on nonwords were carried out,employing two different tasks; that is, naming (Experiment 2) and visual lexicaldecision (Experiment 1).

The adoption of the lexical decision task may appear surprising. Although thefocus of our investigation is on the process of reading aloud in a shallow ortho-graphy, the lexical decision task was included in order to qualify the lexical effectsthat were expected in the naming task. Results at lexical decision were expectednot to differ from results found in other languages. Hence, bigrams were predicted

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not to play a significant role as functional units of analysis in visual recognition ofnonwords, when lexical factors such as neighborhood are accounted for (see,e.g., Andrews, 1992; Gernsbacher, 1984; Rice & Robinson, 1975; see also, forno effects of bigram frequency on lexical decision to Italian low-frequencywords, Burani & Cafiero, 1991, Experiment 2). By contrast, and differently fromEnglish (Andrews, 1992), the frequency of bigrams, as possible units of nonlexi-cal orthography-to-phonology conversion, was expected to contribute to readingnonwords aloud, with high-frequency bigrams speeding up reading.

Two different measures of word neighborhood (i.e., neighborhood size andneighborhood frequency) were investigated. Both neighborhood size and neigh-borhood frequency have been found to affect lexical processing, with contrastingresults across different studies and different tasks (see Andrews, 1997, for a reviewlimited to studies of real words). While neighborhood size has frequently beenstudied since the seminal work of Coltheart et al. (1977), the investigation ofneighborhood frequency is more recent. In several studies, Grainger and co-workers(Grainger, 1990, 1992; Grainger, O’Regan, Jacobs, & Segui, 1989; Grainger &Segui, 1990) found that neighborhood frequency—namely, the frequency of neigh-bors relative to the target word frequency—rather than neighborhood size was thecritical factor in determining word recognition latencies.

While many studies have involved word stimuli, few studies have investigatednonwords. Those effects of neighborhood on lexical decision to nonwords thathave been observed appear to be inconsistent with each other. On the one hand,Coltheart et al. (1977) found that lexical decision times were longer for nonwordsthat had many neighbors than for nonwords with few neighbors. This pattern ofresults has frequently been replicated since (Andrews, 1989; Carreiras, Perea, &Grainger, 1997; Forster & Shen, 1996; Sears, Hino, & Lupker, 1995), although theinhibitory effect of a dense neighborhood may disappear when nonwords differingin neighborhood size are matched for bigram frequency (Andrews, 1992). On theother hand, the effect of the printed frequency of neighbors on both reaction timesand accuracy to nonwords was shown by Grainger & Jacobs (1996). The authorsfound a neighborhood frequency effect, either facilitatory or inhibitory, dependingon the number of high-frequency word neighbors, on lexical decision to nonwords.More agreement is found among the studies that have assessed the effects of neigh-borhood on nonword naming. All the studies (Laxon et al., 1992; McCann &Besner, 1987; Peereman & Content, 1995; Weekes, 1997) have found facilitatoryeffects of neighborhood size on naming latencies and accuracy.

In the current study, opposite results were expected for the two tasks.Similar to what has been found for other languages when both neighborhoodsize and neighbors’ frequency were simultaneously varied, an effect of neigh-borhood frequency with no effect of bigram frequency was expected for lexicaldecision on nonwords. By contrast, independent facilitatory effects of bothneighborhood size and bigram frequency were expected for nonword naming.

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EXPERIMENT 1 (LEXICAL DECISION)

If lexical decision is based on information stored in the orthographic inputcomponent, with no involvement of print-to-sound conversion, differences inthe writing systems should not lead to different patterns of results acrossdifferent languages. By simultaneously varying neighborhood size and neigh-borhood frequency within the same experimental set, we expected, in line withGrainger & Jacobs’s (1996) findings, that the printed frequency of word neigh-bors would affect lexical decision to nonword stimuli, with the direction of theneighborhood frequency effect, either facilitatory or inhibitory, being influencedby the number of high-frequency neighbors.

Method

Materials

Fifty-six Italian monomorphemic five-letter words were selected from acorpus of Italian written language of 1.5 million tokens (Istituto di LinguisticaComputazionale, CNR, 1989). Half of the words were of high frequencywhereas the other half had a low frequency. By changing the first letter in eachword we obtained 56 orthographically legal pronounceable nonwords that variedalong two dimensions: 1. neighborhood size, or N-size (i.e., the number ofItalian words that share all except one letter). Half of the nonwords had an aver-age N value of 1.8 (low neighborhood size; range, 1–3; e.g., the nonwordFRETE has only the following word neighbor: PRETE) whereas the remaininghalf had an average N value of 6.3 (high neighborhood size; range, 4–11; e.g.,the nonword BENTO has the following word neighbors: VENTO, MENTO,SENTO, TENTO, CENTO, PENTO, LENTO). 2. Neighborhood frequency, orN-frequency (i.e., the presence of one neighbor that had a printed frequencygreater than 100 occurrences per 1.5 million). The nonword stimuli had eitherone word neighbor with a printed frequency greater than 100 (high-frequencyneighbor) or no such word neighbor (low-frequency neighbor). The mean fre-quency of the higher frequency neighbor in the two groups (high- and low-frequency neighbor) were 364 (range, 100–946) and 28.8 (range, 5–60) per1.5 million, respectively.

The two experimental variables were crossed in a 2 (neighborhood size) � 2(neighborhood frequency) factorial design with 14 stimuli in each of the four con-ditions. While the great majority of word neighbors in each group differed from thecorresponding nonword for the letter in first position, a small proportion of wordneighbors (about 20% on average in each group, evenly distributed across items)differed with respect to the nonword for one letter in a word position other than thefirst. The four categories of stimuli were matched for orthographic/phonological

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structure.5 All stimuli were bisyllabic, with no ambiguity in the placement of stress.In Italian bisyllables, stress is always placed on the first syllable. (The few cases inwhich stress is on the last syllable are explicitly marked by a sign on the last vowelof the second syllable).

The nonword mean bigram frequencies, calculated on word tokens in anyword position and transformed on the basis of the natural logarithm, were extractedfrom a corpus of Italian written language (Istituto di Linguistica Computazionale,CNR, 1989; see also Barca, Burani, & Arduino, 2002). While three groups wereeasily matched for mean bigram frequency, the fourth group, namely the set thatincluded nonwords with both a high-frequency neighbor and high N-size, could notbe matched because these nonwords have a significantly higher mean bigram fre-quency relative to the other three sets. When the values of all the items were con-sidered, a significant correlation was found between mean (log) bigram frequencyand (log) N-size (r � 0.44, t(54) � 3.57, p � 0.005), and a marginally significantcorrelation between mean (log) bigram frequency and (log) N-frequency (r � 0.21,t(54) � 1.58, p � 0.07) (see also Frauenfelder, Baayen, Hellwig, & Schreuder,1993, for a correlation of bigram frequency with N-size, stronger than the correla-tion with word frequency). The stimuli, with values for the relevant variables andindividual reaction times are reported in Appendix A.

The 56 experimental nonwords were inserted in an experimental list thatalso included 16 nonword fillers five- or six-letters long and 72 filler words ofmedium-low frequency matched for length and orthographic/phonologicalstructure with the nonwords. The list was presented to participants through dif-ferent blocks. Nonword stimuli belonging to the four experimental sets wereequally distributed across blocks.

Participants and Procedure

Forty-nine students from Rome universities took part in the experimentand were paid for their collaboration. They were native speakers of Italian withnormal or corrected vision. The standard lexical decision procedure was used,with participants instructed to respond as rapidly and as accurately as possiblewhether a string of letters formed an Italian word or not. The presentation ofstimuli and response recording were controlled by the M.E.L. program(version 2.0) run on an IBM PC. Each trial began with a central fixation pointthat remained on the screen for 400 ms, followed 600 ms later by the targetstring in upper case letters, which remained on the screen for at least 1 s. If theparticipant did not give any answer in that time, the feedback “Fuori tempo”

5 The four experimental sets were also matched for the following conditions: 1. the difference interms of visual and phonological features between the letters we changed and the letter that origi-nated the highest frequency neighbor of such nonword; 2. the number of neighbors whose fre-quency was equal to zero; 3. the grammatical categories of the neighbors (nouns, adjectives, verbs).

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(“Out of time”) appeared on the screen. Furthermore, when the participantmade an error, the word “Errore” (“Error”) appeared on the computer screen.There were two training blocks composed of 12 stimuli each (six words andsix nonwords).

Results

Mean reaction times and percentage of errors are shown in Table I. A two-way analysis of variance (ANOVA), by participants (F1) and by items (F2),showed a significant inhibitory effect of neighborhood frequency: F1(1,48) �22.42, MSE � 303.4, p � 0.001; F2(1,52) � 4.52, MSE � 445.4, p � 0.04.Participants were slower in rejecting nonwords with one high-frequency neighbor,independent of the numerosity of neighbors. Neither neighborhood size nor theinteraction between neighborhood size and neighborhood frequency reached sig-nificance (both F1 and F2 �1).

The analyses performed on error rates showed the same pattern of results.The ANOVAs by participants (F1) and by items (F2) showed the same inhibitoryeffect of neighborhood frequency as on reaction times: F1(1,48) � 5.52,MSE � 0.54, p � 0.02; F2(1,52) � 6.07, MSE � 1.62, p � 0.02. Participantsmade more errors in rejecting a nonword that had a high-frequency neighbor,regardless of whether the neighborhood was large or small. Neither neighborhoodsize (p � 0.10 both by participants and by items) nor the interaction neighbor-hood size � neighborhood frequency (p � 0.10) were significant.

In order to assess whether bigram frequency contributed to the above effect,an analysis of covariance (ANCOVA) was performed with the mean RTsobtained for each experimental item as the dependent variable. Neighborhoodfrequency (High or Low) and neighborhood size (High or Low) were entered asclassification variables, and mean bigram frequency for each nonword wasentered as a covariate. Although slightly reduced by entering bigram frequencyas covariate, the main effect of neighborhood frequency still turned out to bemarginally significant: F(1,51) � 3.71, MSE � 620.4, p � 0.06. Neighborhoodsize and the interaction between neighborhood frequency and neighborhood size

Table I. Mean Lexical-Decision Times (in ms) and Percentage of Errors(in Parentheseis) for Nonword Stimuli in Experiment 1

HN LN

HF 628 (4.4) 629 (2.6)LF 619 (1.7) 614 (1.7)

HN, high neighborhood size; LN, low neighborhood size; HF, high-frequency neighbor, LF,low-frequency neighbor.

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were not significant (F � 1 in both cases). Bigram frequency made no signifi-cant contribution to the explained variance (F � 1).

A second ANCOVA performed with Error rates for each experimental itemas the dependent variable, neighborhood frequency and neighborhood size asclassification variables, and mean bigram frequency as a covariate resulted in a sig-nificant effect of neighborhood frequency: F(1,51) � 5.03, MSE � 1.32, p � 0.03.Neighborhood size was not significant (F � 1). The interaction of neighborhoodfrequency by neighborhood size was also not significant (p � 0.10). Mean non-word bigram frequency did not account for a significant portion of the explainedvariance (p � 0.10).

Discussion

The lexical decision experiment on Italian nonwords showed a significantinhibitory effect of neighborhood frequency on both reaction times and accuracyand no effect of either neighborhood size or bigram frequency.

The absence of a role for bigram frequency in lexical decision is consistentwith results from other languages (Andrews, 1992; Gernsbacher, 1984; Rice &Robinson, 1975). In comparing the results for neighborhood frequency and neigh-borhood size with other data in the literature, some considerations should be made.A first set of results (Andrews, 1989; Coltheart et al., 1977; Forster & Sheri, 1996;Sears et al., 1995) showed an inhibitory effect of neighborhood size in lexical deci-sion to nonwords. However, these studies only varied neighborhood size, withoutconsidering neighborhood frequency. Our results showing a role for neighborhoodfrequency with no role for neighborhood size and no N-size by N-frequency inter-action indicate that, when both neighborhood frequency and bigram frequency areequated (like in our LF/HN vs. LF/LN sets, see Table I), the effect of N-size onlexical decision performance may disappear [t(1,26) � 0.55, p � 0.1, one-tailed,for the difference in RTs; Error rates are identical in the two sets].

The neighborhood frequency effect on lexical decision to Italian nonwordsseems consistent with the dual-route cascaded (DRC) model, although the effectsof neighborhood frequency on nonword decision have not been taken into accountin this framework (Coltheart, Curtis, Atkins, & Haller, 1993; Coltheart, Rastle,Perry, Langdon, & Ziegler, 2001; Coltheart & Rastle, 1994; Rastle & Coltheart,1999). In the DRC model, the activation of a nonword’s neighbor causes somelengthening of the variable deadline for response. The deadline is expressed as thenumber of processing cycles necessary for the system in order to produce an out-put (“Yes” or “No”) and can be lengthened or shortened as a function of how rap-idly the total activation of the visual word recognition system is rising. In decidingon a nonword with many neighbors, the overall activation in the lexicon rises rap-idly and reaches a value that makes the deadline for “NO” response significantlylonger relative to when the nonword has very few or no neighbors at all. Thus, a

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lengthening of the deadline is predicted in response to a nonword with a highneighborhood size. In this framework, the presence of a high-frequency neighborwould further contribute to a fast rise of lexical activation, thus contributing tolengthening the deadline. Whether a large neighborhood or the presence of a high-frequency member in the neighborhood make differential contributions to the ris-ing of activation and to the consequent extension of the deadline is an empiricalissue. However, it is conceivable that the effect of neighborhood size on deadlinelengthening is modulated or attenuated by the presence of one high-frequencyneighbor that strongly contributes to the rapid rise of activation. The effect ofneighborhood size might be overcome by the fact that one high-frequency neigh-bor has a stronger effect on lengthening the variable deadline assumed by themodel (Coltheart et al., 1993, 2001).

Our results are also compatible with the computational model proposedby Grainger & Jacobs (1996), the only one that predicts and simulates lexicaldecision to nonwords when both neighborhood size and neighborhood fre-quency are taken into account. Within the multiple read-out model (MROM)reaction times to both words and nonwords are determined by the total activa-tion of the lexicon depending on three criterial variables. Because the totalactivation of the orthographic input lexicon is correlated with the number ofneighbors, the model predicts an inhibitory effect of neighborhood size thatwould be in turn influenced by the total number of high-frequency neighborsrelative to the total number of neighbors for each nonword. By virtue of theintralevel inhibitory connections, nonwords with many high-frequency neigh-bors will activate these lexical entries, which will in turn inhibit each other.This mechanism of lateral inhibition is responsible for a decrease of activationin the lexicon. Consequently, when more than one high-frequency neighbor isactivated, a facilitatory effect of neighborhood frequency is expected (seeGrainger & Jacobs, 1996, Experiment 2A). By contrast, when only one high-frequency neighbor is activated, the mechanism of mutual inhibition betweenword units, predicted by the MROM model, would not operate, and the singlestrongly activated lexical entry would increase the overall activation in the lex-icon, with consequent lengthening of the deadline. Hence, the inhibitory effectof the high-frequency neighbor on nonword decision that was found in ourstudy is expected (see Grainger & Jacob’s Experiment 2B, in which nonwordswith one high-frequency neighbor, in line with our results, showed a tendencytoward inhibition).

EXPERIMENT 2 (NAMING)

The naming task is sensitive to the differences among writing systems. In amodel in which both the lexical and the nonlexical procedures feed the same

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component for phonemic output—the Phoneme system, in Coltheart et al.’s (1993,2001) DRC model—both lexical and sublexical factors contribute to reading aloud.However, the relative contribution of lexical and nonlexical processes may be dif-ferent according to the extent of orthographic transparency/regularity of a given lan-guage. If the activation of both reading pathways is assumed for all writing systems,regardless of orthographic transparency, a facilitatory effect of the number of lexi-cal candidates sharing orthographical characteristics (i.e., an effect of neighborhoodsize) is expected to be found for Italian as well as for other languages. However, theproperties of the transparent Italian writing system could result in some more sub-stantial contribution of the nonlexical procedure to the generation of a pronunciation.Because no overall mismatch is expected in the outputs of the two reading routines,an additive role for the frequency of print-to-sound mapping units, like bigrams,may be predicted. Thus, a contribution of high-frequency bigrams to fast readingaloud would be evident in pooling the phonological output that derives from thesublexical mapping, with phonological information coming from lexical activation.

A role for neighborhood frequency is not predicted by Coltheart et al.’s(1993, 2001) DRC model of naming. One-route parallel distributed processingmodels of reading aloud would predict lower error scores and faster processingfor low-frequency words with higher-frequency neighbors (Sears et al., 1999).However, predictions have not been made for nonword stimuli. Empirical dataare also rare. Frederiksen & Kroll (1976) studied the reading of nonwordsderived from English words by randomly changing one vowel; more frequentlythe first. The nonwords varied for having a neighbor (the word from which theywere derived) that could belong to one of four different frequency classes. Noeffect of the neighbor’s frequency on nonword naming times was found.

In the absence of both theoretical predictions and empirical data, it couldbe assumed that high-frequency whole-word units, instead of contributing to theactivation of similar units, would rather play the role, in the output lexicon, ofphonological competitors of the target nonword. Hence, either no role or aninhibitory role for neighborhood frequency is expected in naming (see alsoPeressotti & Job, 1997).

Method

Materials

The experimental design was the same as in Experiment 1, with four sets ofnonwords arranged in a 2 (neighborhood size) � 2 (neighborhood frequency) fac-torial design. Most stimuli were the same used in Experiment 1. Only a few weredifferent in order to match the experimental groups for initial phonemes. Therewere 15 nonwords in each experimental set. The majority of word neighbors ineach group differed from the corresponding nonword for the letter in first position.

Neighborhood Effects on Nonword Visual Processing 85

However, a small proportion of word neighbors (about 20% on average in eachgroup, evenly distributed across items) differed with respect to the nonword forone letter in word positions other than the first.

All the nonwords were five letters long, except one per experimental condi-tion that was six letters long. The different categories of stimuli were matched fororthographic/phonological structure and for initial phonemes. When possible, ini-tial phonemes in the four sets were matched one-to-one. When not possible, thesame proportion of voiced versus unvoiced initial phonemes was included in eachset. Nonwords in the four sets were also matched for the additional factors illus-trated in footnote 5. All stimuli were bisyllabic and stressed on the first syllable.

Stimuli could not be matched across groups for mean bigram frequency.Three groups were matched while the fourth group, namely the set that includednonwords with both one high-frequency neighbor and high N-size, had a signifi-cantly higher bigram frequency. A significant correlation was found between mean(log) bigram frequency and (log) N-size (r � 0.40, t(56) � 3.29, p � 0.005), anda marginally significant correlation between mean (log) bigram frequency and(log) N-frequency (r � 0.18, t(56) � 1.4, p � 0.09) (see Frauenfelder et al., 1993).The stimuli, along with values for the relevant variables and individual reactiontimes, are reported in Appendix B.

The experimental nonwords were presented within a larger list, whichshould allow lexical effects, if any, to show up (Job et al., 1998; Tabossi &Laghi, 1992): 50% of the list consisted in real words and about 15% of thewords had a stress pattern not predictable by rule, but only obtainable throughlexical look-up. Words and nonwords were matched for length and syllabicstructure. The final list, composed of 240 stimuli (120 words and 120 non-words), was presented to participants through different blocks.

Participants and Procedure

Twenty-three students from Rome universities took part in the experimentand were paid for their collaboration. No one had participated in the previousexperiment. Each participant was instructed to read aloud each letter string.Stimuli presentation and responses recording were controlled by the M.E.L. pro-gram (version 2.0) run on an IBM PC with a voice-key connected to the key-board to record naming times. A microphone was attached to the voice key andto a taperecorder for recording the session. The stimuli incorrectly pronouncedwere eliminated from subsequent analyses. The stimuli appeared in capital let-ters in the center of the computer screen, preceded by a fixation point (400 ms).They stayed on the computer screen for a maximum of 1 s. If the participant didnot produce any answer within that time, the feedback “Fuori tempo” (“Out oftime”) appeared on the screen. The experiment was introduced by two trainingsessions, each including 12 stimuli (six words and six nonwords).

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Results

Mean RTs (after discarding responses under 200 ms) for the different cate-gories of correctly named nonwords are reported in Table II. A two-way ANOVAwas performed, by participants (F1) and by items (F2). The ANOVAs showed aneffect of neighborhood size. Nonwords with many neighbors were read aloudfaster than nonwords with few neighbors: F1(1,22) � 16.7, MSE � 632.5,p � 0.001; F2(1,56) � 8.6, MSE � 719.6, p � 0.01. Neither neighborhood fre-quency (p � 0.10 by participants; F2 � 1 by items) nor the interaction betweenneighborhood size and neighborhood frequency (p � 0.10 by participants; F2 � 1by items) were statistically reliable. Errors were too few to be analyzed.

In order to assess whether bigram frequency contributed to naming laten-cies, an ANCOVA was performed with the mean RTs obtained for each experi-mental item as the dependent variable. Neighborhood frequency (High or Low)and neighborhood size (High or Low) were entered as classification variables,and mean bigram frequency for each nonword was entered as a covariate. Incontrast with results found for lexical decision, the results for naming showedthat mean bigram frequency accounted for a significant portion of the varianceexplained: F(1,55) � 5.39, p � 0.02. However, the main effect of neighborhoodsize was still significant: F(1,55) � 4.11, MSE � 668.3, p � 0.047. Neitherneighborhood frequency nor the interaction between neighborhood size andneighborhood frequency were significant (both F � 1).

Additional Analyses

Recently, the role of the syllable as a unit in visual word recognition, atleast in languages with regular grapheme-to-phoneme correspondences and well-defined syllable boundaries has been described. Experimental results on nonwordsare scarce. Carreiras, Alvarez, and De Vega (1993), by orthogonally varying thefrequency of between-syllables bigrams and the frequency of syllables (whileholding constant the frequency of bigrams within syllables), showed that naminglatencies were faster for nonwords with high-frequency syllables than for non-words with low-frequency syllables (Experiments 4 and 5). They also showed thatthe frequency of (between-syllables) bigrams could not account for this effect.

Table II. Mean Naming Times (in ms) for Nonword Stimuli in Experiment 2

HN LN

HF 565 589LF 575 591

HN, high neighborhood size; LN, low neighborhood size; HF, high-frequency neighbor; LF,low-frequency neighbor.

Neighborhood Effects on Nonword Visual Processing 87

Thus Carreiras et al. (1993) claimed that the syllable is an important sublexicalaccess unit for Spanish readers and perhaps also for readers of other shalloworthographies.

In order to assess whether syllable frequency could be responsible for (at leastpart of) the effects we found, a series of additional analyses were conducted,assessing whether the role of bigram frequency could be independent of that ofsyllable frequency or whether one would be reduced to the other. For each non-word, the mean frequency of the two syllables was calculated. The frequency ofeach syllable, calculated on word tokens in the appropriate word position (eitherfirst or second) in the corpus of Italian written language (Istituto di LinguisticaComputazionale, CNR, 1989), was derived from Stella & Job (2000). Mean sylla-ble frequency was not matched across groups [mean (log) syllable frequency: 9.39for HF/HN, 8.23 for HF/LN, 7.45 for LF/HN, and 7.85 for LF/LN]. A significantlyhigher mean syllable frequency for nonwords in the high neighbor frequency con-ditions [F(1,56) � 6.58, MSE � 3.07, p � 0.05] and a marginally significant inter-action between neighborhood frequency and neighborhood size [F(1.56) � 2.96,MSE � 3.07, p � 0.09] were found, indicating that nonwords with both a high-frequency neighbor and a high N-size also had a higher mean syllable frequency.

In order to assess whether mean syllable frequency was correlated with thetwo independent variables (i.e., neighborhood size and neighborhood frequency),further correlation analyses were performed. A significant correlation betweenmean (log) syllable frequency and (log) N-frequency (r � 0.26, t(58) � 2.06,p � 0.05, one-tailed), and a marginally significant correlation between mean (log)syllable frequency and (log) N-size (r � 0.24, t(58) � 1.91, p � 0.06, one-tailed)were found. A strong correlation between mean (log) syllable frequency and mean(log) bigram frequency (r � 0.55, t(58) � 5.05, p � 0.0001) was also found.

The two measures of bigram and syllable frequency were found to be highlycorrelated. Hence, in order to assess whether syllable frequency contributed tonaming latencies, an additional separate ANCOVA was performed with the meanRTs for each experimental item as the dependent variable, neighborhood fre-quency (High or Low) and neighborhood size (High or Low) as classificationvariables, and mean syllable frequency for each nonword as a covariate. In con-trast with results of the ANCOVA in which mean bigram frequency was enteredas the covariate, the results of this second ANCOVA showed that mean syllablefrequency did not account for a significant portion of the variance explained[F(1,55) � 1.97, p � 0.1], leaving the main effect of neighborhood size stable.

Discussion

Two main results deserve discussion. First, an effect of neighborhood size onnonword naming was found, with no effect of neighborhood frequency and nointeraction between the two variables. Second, and differently from both our results

88 Arduino and Burani

at lexical decision and from data on nonword naming in English (Andrews, 1992),a contribution of bigram frequency to the speed of Italian nonword naming wasfound. The analysis of covariance, while pointing out a strong effect of bigram fre-quency, also showed that bigram frequency was not fully responsible for the effectof neighborhood size. This was also evident in the difference in naming latencies[t(1.28) � �1.78, p � 0.04, one-tailed] between nonwords (LF-HN vs. LF-LN, seeTable II) that were perfectly matched for bigram frequency but differed for N-size.

As shown by a second analysis of covariance, syllable frequency did notgive any additional independent contribution to nonword naming. This indicatesthat, when other measures such as neighborhood frequency and size are taken intoaccount, no more space is left for a role of syllable frequency, a measure corre-lated to the latter two that were not accounted for by Carreiras et al. (1993).

Both Seidenberg and McClelland (1989) and Plaut, McClelland, Seidenberg,and Patterson (1996) models would predict a facilitatory neighborhood size effect,at least on low-frequency words (see Sears et al., 1999). Within the dual-route cas-caded model of reading aloud (Coltheart et al., 1993, 2001), the effect of neighbor-hood size on nonword naming could be explained as a consequence of the systemworking in a cascade fashion: When subjects are presented with nonword stimuli,visually similar word units receive some activation, both in the orthographic inputand in the phonological output systems. At the level of the phoneme system, whichis shared by the two pathways, the output derived from the nonlexical procedurewill be improved by the activation due to the lexical system. Information derivedfrom grapheme-to-phoneme conversion will thus be incremented with informationfrom the lexical pathway, where orthographically similar words receive someactivation. The absence of a neighborhood frequency effect on nonword naming isconsistent with the findings by Frederiksen and Kroll (1976), and could beexplained, within the DRC model, by assuming no additional facilitatory contribu-tion of a word unit that, having a high level of activation, would constitute morea competitor than a contributor to nonword pronunciation.

GENERAL DISCUSSION

In the current study, the influence of the lexical pathway on nonword read-ing aloud in Italian was shown. Two experiments on Italian bisyllabic nonwordswere carried out, showing contrasting effects of neighborhood size and neighbor-hood frequency in visual lexical decision and naming. When neighborhood sizeand neighborhood frequency were orthogonally varied, an inhibitory effect ofneighborhood frequency was found in lexical decision to nonwords (Experiment1), with no role of neighborhood size nor interaction between the two variables.In contrast, a facilitatory effect of neighborhood size, with no role of neighbor-hood frequency nor interaction between the two factors, was found in naming the

Neighborhood Effects on Nonword Visual Processing 89

same nonwords (Experiment 2). No role for bigram frequency was found in lex-ical decision. By contrast, the frequency of bigrams, which in Italian correspondto units of print-to-sound conversion, contributed to speed up naming latencieswhile not abolishing the facilitatory effect of a large lexical neighborhood.

In lexical decision, nonwords with a high-frequency neighbor required moretime and elicited more errors in being rejected. This result could be accounted forby models of visual word recognition like the dual-route-cascade model (Coltheartet al., 1993, 2001), and the multiple read-out model (Grainger & Jacobs, 1996).In both models, the activation of one high-frequency word neighbor by a nonwordwould contribute to fast increase of lexical activation in the word recognitionsystem, thus lengthening the deadline or time criterion for nonword decision. Thefrequency of a given word unit, rather than the number of activated word repre-sentations, would be the crucial factor leading to an increase of lexical activation.The presence in the neighborhood of only one high-frequency word would also becrucial for avoiding mutual inhibition between highly activated word units, whichcould lead to reduced lexical activation.

In the naming task, nonwords with many neighbors were read aloud fasterthan nonwords with few neighbors, irrespective of neighborhood frequency. Theresults of the naming task—a facilitatory effect of neighborhood size on nonwordnaming latencies with no role for the neighbor’s frequency—provide evidence forlexical activation in the case of newly encountered—nonlexical—stimuli in alanguage characterized by a high degree of orthography-to-phonology trans-parency. The results thus suggest that, even in Italian, a language in which mostnew letter-strings could easily and efficiently be read through nonlexical print-to-sound conversion, the reading of nonwords can benefit by activation of thelexicon. The additional contribution of the frequency of sublexical units such asbigrams (which in Italian constitute potential units for print-to-sound conversion),with the effect of neighborhood size not reducible to that of bigram frequency, pro-vides evidence for the parallel activation of lexical and nonlexical reading routines.

These findings are difficult to reconcile with the view that an effect of neigh-borhood size is an artifact of the frequency of nonlexical correspondences in thelanguage (see also Peereman & Content, 1995). In contrast, the current results favorthe view that the effects of neighborhood size and those of print-to-sound frequencymeasures (such as Italian bigram frequency or English average grapheme fre-quency) are independent of one another (see Weekes, 1997). In the DRC model(Coltheart et al., 1993, 2001; Coltheart & Rastle, 1994; Rastle & Coltheart, 1999),the relative contribution of nonlexical and lexical information is crucial. In thismodel, the complete independence of lexical and nonlexical procedures is not a nec-essary feature, and effects of the lexicon on nonword naming are assumed.Nonword stimuli are not processed exclusively by the nonlexical route. They activ-ate visually similar words (word neighbors) in the orthographic lexicon and these,in turn, activate the corresponding phonological output word units. In the DRC

90 Arduino and Burani

model, lexical activation converges with activation derived from the nonlexical pro-cedure in a final processing stage, the phoneme system, that is shared by the tworoutes. The amount of lexical activation received by each phoneme unit will dependon the word neighbors of the input string. Hence, naming of nonwords with largeneighborhoods will benefit from more activation due to the lexical system.

In the DRC model, the output from the lexical route is able to “aid” or influ-ence nonword reading, but is not sufficient for nonword reading if without supportby the nonlexical pathway. It is conceivable that the relative contribution or speedof the lexical and the nonlexical routes may differ according to the characteristicsof the language involved (see also Ziegler, Perry, & Coltheart, 2000). In an ortho-graphically transparent language such as Italian, the output of the nonlexical pro-cedure, when pooled with the output of lexical activation in the phoneme system,would be in the large majority of cases phonologically congruent. With very fewexceptions (Job et al., 1998), in most cases there will be summation of the resultsprovided by the two routes instead of competition. Thus, the frequency of sublexi-cal units such as bigrams, which correspond to spelling-to-sound mapping units,could exert a specific role by speeding up the contribution of the nonlexical pro-cedure to the phoneme system.

APPENDIX A: NONWORDS USED IN EXPERIMENT 1 (LEXICAL DECISION)

Nonwords Frq N 1 N Bigr. RT

HF/HNLondo 927 4 6 11.19 646Dorte 544 6 8 11.24 616Tegno 325 5 5 10.88 649Macco 100 4 7 11.04 622Greno 187 4 7 11.09 624Rolle 236 6 7 11.16 638Norsa 196 3 4 11.03 622Sarca 162 4 6 11.06 627Rasca 121 5 6 10.94 629Donte 163 5 9 11.51 611Losto 678 5 8 11.23 641Bento 363 7 8 11.02 624Tollo 201 5 7 11.18 624Darta 177 3 4 11.26 617Mean 313 4.7 6.6 11.13 628

HF/LNTadre 946 2 2 10.77 639Lerra 874 2 2 11.26 675Gaese 629 1 1 10.28 634Biume 308 2 2 10.15 613

Neighborhood Effects on Nonword Visual Processing 91

Buogo 221 1 2 9.87 610Zarba 106 2 2 9.68 661Ruoco 263 2 2 10.41 656Dolpa 199 2 2 10.58 602Derro 160 2 2 11.26 605Mibro 162 2 3 10.47 628Bielo 539 1 1 10.69 621Norza 384 1 2 10.54 601Frete 124 1 2 10.89 667Biore 161 1 1 11.04 594Mean 363 1.6 1.9 10.56 629

LF/HNNorgo 18 4 4 10.59 582Vappa 19 4 4 10.52 603Dampa 9 4 4 10.91 606Cenda 28 6 6 11.20 646Fazza 60 5 5 10.24 621Vucca 17 3 4 10.14 656Galpa 9 3 4 10.22 607Lerme 9 4 6 11.15 610Sazzo 55 5 7 10.31 621Nacca 6 7 9 10.89 641Zango 36 5 5 9.89 623Lasto 23 7 11 11.33 654Malco 13 4 8 10.97 583Tecco 41 4 6 10.95 610Mean 25 4.6 5.9 10.67 619

LF/LNVapra 18 1 1 10.89 611Benio 33 1 2 10.85 636Murva 55 1 2 10.13 629Nerpe 22 1 1 10.75 601Dospo 13 1 2 10.97 608Telva 56 2 2 10.63 597Velma 5 1 1 10.67 593Nissa 19 2 2 10.93 618Garva 10 1 3 10.43 631Zulta 12 1 1 9.57 581Rolfo 43 2 2 10.10 609Nusta 40 2 2 10.70 604Niclo 21 1 1 10.46 632Pomma 51 2 2 10.82 650Mean 28 1.4 1.7 10.56 614

HN, high neighborhood size; LN, low neighborhood size; HF, high-frequency neighbor; LF,low-frequency neighbor; Frq, frequency of the neighbor with the highest frequency; N 1, num-ber of neighbors obtained by changing the letter in 1st position; N, total number of neighborsobtained by considering all letter positions; Bigr, mean bigram frequency (transformed on thebasis of the natural logarithm); RT, reaction times.

92 Arduino and Burani

APPENDIX B: NONWORDS USED IN EXPERIMENT 2 (NAMING)

Nonwords Frq N 1 N Bigr. RT

HF/HNTiglia 264 5 8 10.85 620Lonna 951 4 4 11.08 517Londo 927 4 6 11.19 541Mocca 472 5 9 10.79 529Dorte 544 6 8 11.24 591Tollo 201 5 6 11.18 553Macco 100 4 7 11.04 559Greno 187 4 7 11.09 583Rolle 236 6 7 11.16 574Sarca 162 4 6 11.06 592Rasca 121 5 6 10.94 553Donte 163 5 9 11.51 577Losto 678 5 8 11.23 517Bento 363 7 8 11.02 581Dosca 122 4 4 11.10 586Mean 366 4.9 6.9 11.10 565

HF/LNMiazza 237 1 1 10.52 573Tadre 946 2 2 10.77 596Lerra 874 2 2 11.26 536Gaese 629 1 1 10.28 637Biume 308 2 2 10.15 607Zarba 106 2 2 9.68 601Brisi 272 1 1 10.70 593Ruoco 263 2 2 10.41 578Pilla 187 1 2 11.30 550Dolpa 199 2 2 10.58 549Derro 160 2 2 11.26 618Mibro 162 2 3 10.47 602Bielo 539 1 1 10.69 598Norza 384 1 2 10.54 581Biore 161 1 1 11.04 621Mean 362 1.5 1.7 10.64 589

LF/HNVabbia 79 3 3 10.40 603Norgo 18 4 4 10.59 572Vappa 19 4 4 10.52 557Dampa 9 4 4 10.91 595Vucca 17 3 4 10.14 581Galpa 9 3 4 10.22 598Lerme 9 4 6 11.15 544Sazzo 55 5 7 10.31 599 Nacca 6 7 9 10.89 582Lasto 23 7 11 11.33 533Malco 13 4 8 10.97 531

Neighborhood Effects on Nonword Visual Processing 93

Tecco 41 4 6 10.95 566Togna 5 4 6 10.77 593Vusso 45 3 3 10.29 582Ranna 40 6 7 11.06 587Mean 26 4.3 5.7 10.70 575

LF/LNLebbia 69 1 2 10.52 564Vapra 18 1 1 10.89 597Gurro 11 1 1 10.38 614Benio 33 1 2 10.85 642Pomma 30 2 2 10.82 567Murva 55 1 2 10.13 609Nerpe 22 1 1 10.75 604Dospo 13 1 1 10.97 560Telva 56 2 2 10.63 627Velma 5 1 1 10.67 593Nissa 19 2 2 10.93 571Sonca 40 2 4 11.25 584Rolfo 43 2 3 10.10 588Nusta 40 2 2 10.70 574Niclo 21 1 1 10.46 566Mean 32 1.4 1.8 10.67 591

HN, high neighborhood size; LN, low neighborhood size; HF, high-frequency neighbor; LF,low-frequency neighbor; Frq, frequency of the neighbor with the highest frequency; N 1, num-ber of neighbors obtained by changing the letter in 1st position; N, total number of neighborsobtained by considering all letter positions; Bigr, mean bigram frequency (transformed on thebasis of the natural logarithm); RT, reaction times.

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