Cognition 115 (2010) 197–201

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Cognition

journal homepage: www.elsevier .com/locate /COGNIT

Brief article

Investigating the importance of the least supported phoneme onvisual word naming

Mark Yates *

Dept. of Psychology, University of South Alabama, 307 University Blvd North, LSCB 320, Mobile, AL 36688, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 19 June 2009Revised 23 October 2009Accepted 1 December 2009

Keywords:Visual word recognitionPhonological processingPhonological neighborhood

0010-0277/$ - see front matter � 2009 Elsevier B.Vdoi:10.1016/j.cognition.2009.12.002

* Tel.: +1 251 460 7872.E-mail address: myates@usouthal.edu

The least supported phoneme refers to the phoneme position within a word with which thefewest phonological neighbors overlap. Recently, it has been argued that the number ofneighbors coinciding with the least supported phoneme is a critical determinant of pro-nunciation latencies. The current research tested this claim by comparing naming latenciesto words that differed in terms of the number of neighbors overlapping with their leastsupported phoneme. The results revealed that words where many neighbors overlappedwere named more rapidly than those where few neighbors overlapped. These results areexplained using the dual-route cascaded model of reading aloud.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

The role that phonology plays on visual word process-ing has been an active topic of research. One way that thishas been studied is by assessing the effect of phonologicalneighborhood variables on a variety of word recognitiontasks. Two phonological neighborhood variables that havereceived recent attention are neighborhood density andspread. Density refers to the number of words that differby a phoneme from the target word. Neighborhood spreadrefers to the number of phoneme positions within a wordthat can be changed to form a neighbor. Research hasshown that phonological neighborhood density and spreadfacilitate visual word recognition and reading (Mulatti,Reynolds, & Besner, 2006; Yates, 2005, 2009; Yates, Friend,& Ploetz, 2008a, 2008b).

In relation to the naming task, Yates et al. (2008b) ar-gued that both the density and spread effect can be under-stood in terms of the least supported phoneme (LSP). TheLSP is defined as the phoneme within a word that hasthe fewest number of neighbors overlapping with it. Forexample, the word geese has the following phonological

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neighbors: cease, lease, niece, peace, gas, goose, and guess.The first phoneme, /g/, overlaps with gas, goose, and guess.The second phoneme, /i/, overlaps with cease, lease, niece,and peace. The third phoneme, /s/, overlaps with all sevenneighbors. For the word geese, the LSP is the /g/ phonemeas it overlaps with fewer neighbors than the other twophonemes. Yates et al. showed that after the number ofneighbors overlapping with the LSP was covaried out therewas no longer an effect of phonological spread. Further-more, they showed that previous reports of a phonologicalneighborhood density effect (Mulatti et al., 2006; Yates,2005) are also non significant after the LSP variable iscovaried out.

To explain why the number of neighbors overlappingwith the LSP is important, Yates et al. (2008b) used thedual-route cascaded (DRC) model of reading aloud (Colt-heart, Rastle, Perry, Langdon, & Ziegler, 2001). This modelconsists of two routes by which the pronunciation for aword may be generated. The lexical route bases the pro-nunciation on the phonological code for the word withinthe phonological output lexicon. The grapheme–phonemecorrespondence (GPC) route uses a set of rules to converta word’s graphemes to phonemes. Both of these routespass activation to a shared phoneme system consisting ofa string of phoneme units. The model’s naming latency is

Table 1Means and (standard deviations) for the control variables of the experi-mental stimuli.

Control variables LSP� LSP+

Frequency of occurrenceCELEX 7.43 (6.97) 7.57 (6.12)Kucera and Francis 6.93 (7.42) 8.40 (10.58)Estimated 365.53 (69.65) 369.33 (78.51)

198 M. Yates / Cognition 115 (2010) 197–201

given as the cycle on which a phoneme in each of the pho-neme positions reaches threshold.

Within the DRC model, Yates et al. (2008b) argued thatas a word is activated in the phonological lexicon it willactivate its constituent phonemes within the phonemesystem. As these phoneme units pass activation back tothe phonological lexicon, words sharing these phonemes(i.e., the phonological neighbors) will become activated.These neighbors will in turn pass activation back to thephoneme system increasing the activation for the targetword’s phonemes. Thus, the more neighbor overlap thereis for a given phoneme unit the more activation it will re-ceive from the neighbors, and the faster it will reach thenaming threshold. In the case of the LSP, it has the fewestneighbors overlapping with it, and therefore, will receiveless activation from the phonological neighbors than theother phonemes do. Furthermore, because the LSP’s activa-tion level rises slowly, it will provide less activation for theneighbors that do overlap with it. Additionally, theseneighbors will receive inhibition from the phonemes thatare competing with the LSP in the phoneme system. Thismeans the neighbors overlapping with the LSP will be lessactivated than other neighbors, further decreasing the acti-vation passed from the phonological lexicon to the LSP.Consequently, the LSP will reach the naming thresholdmore slowly than the other phonemes. As a pronunciationcannot be given until all phonemes have reached thresh-old, the amount of time it takes the LSP to reach thresholdis a direct determinant of the naming latency.

Yates et al. (2008b) showed that a modified version ofthe DRC model could indeed produce the effect of both pho-nological neighborhood spread and density. In the modifiedversion, the excitatory activation from the phoneme systemto the phonological output lexicon was increased. Addition-ally, the activation from the GPC route had to be eliminatedor greatly attenuated. More importantly, they showed thatafter the LSP variable was covaried out, the model no longerproduced a density or spread effect.

Although the LSP variable looks to be a promising mea-sure of phonological processing, as of yet there has been noexperiment designed to explicitly test the effect of this var-iable. Instead, evidence that the LSP variable affects nam-ing has been limited to the strategy discussed abovewhere it has been used as a covariate. Thus, what is clearlyneeded is a direct test of whether the number of neighborsoverlapping with the LSP affects naming latencies, and ifso, can the DRC model simulate the effect. The currentstudy was designed to answer these questions.

Familiarity 6.53 (0.85) 6.68 (0.45)Age of acquisition 4.17 (0.92) 4.41 (1.02)Imageability 4.63 (1.40) 4.28 (1.50)Feedforward rime consistency 0.85 (0.27) 0.88 (0.17)Feedback rime consistency 0.75 (0.29) 0.71 (0.26)Number of phonemes 3.33 (0.72) 3.20 (0.41)Number of letters 4.47 (0.64) 4.33 (0.49)OLD20 1.60 (0.22) 1.55 (0.20)Neighborhood density

Phonological 12.53 (5.72) 14.80 (3.53)Orthographic 5.27 (4.43) 6.53 (2.92)Phonographic 3.27 (3.67) 4.20 (2.96)

SpreadPhonological 2.47 (0.52) 2.67 (0.49)Orthographic 2.33 (1.40) 2.47 (1.19)

2. Experiment

2.1. Method

Participants: The participants were 20 undergraduatesat the University of South Alabama who earned coursecredit for their participation. All participants were nativespeakers of English and reported having normal or cor-rected to normal vision.

Materials: The stimuli consisted of 30 words. Half of thewords had few phonological neighbors overlapping with

their LSP (hereafter referred to as LSP�, M = 1.8,SD = 0.68) and the other half had many (hereafter referredto as LSP+, M = 6.5, SD = 1.4). This difference was significantF(1, 28) = 146.42, p < 0.001. The words were controlled onCELEX frequency (Baayen, Piepenbrock, & Gulikers, 1995),Kucera and Francis frequency (Kucera & Francis, 1967),estimated frequency (Balota, Pilotti, & Cortese, 2001),familiarity (Nusbaum, Pisoni, & Davis, 1984), age of acqui-sition (Cortese & Khanna, 2008), imageability (Cortese &Fugett, 2004), feedforward rime consistency, feedbackrime consistency, number of phonemes, number of letters,OLD20 orthographic similarity (Yarkoni, Balota, & Yap,2008), neighborhood density for phonological, ortho-graphic, and phonographic neighborhood, and both phono-logical and orthographic neighborhood spread. Theconsistency measures were obtained from Kessler, Trei-man, and Mullennix (2008). All neighborhood measureswere obtained from the N-Watch program (Davis, 2005)with the exception of OLD20 and phonographic neighbor-hood density which were obtained from the English Lexi-con Project database (Balota et al., 2007). Finally, the twoword sets were matched closely on spelling to sound reg-ularity with six of the LSP� words being irregular and fourof the LSP+ being irregular. There were no significant dif-ferences between the groups of words on any of the controlvariables, all p > 0.20. Finally, it is important to note thatthe words were matched on initial phoneme. See Table 1for a list of control variables and the Appendix A for stimuliand reaction times.

Procedure: The stimuli were displayed on a computermonitor using an IBM compatible computer running E-Prime experimental software (Schneider, Eschman, & Zuc-colotto, 2002). The stimuli were displayed in Courier New18 point font. Participants gave their responses using amicrophone that was connected to a response box. Re-sponse times were measured from stimulus onset untilthe onset of acoustic energy.

A fixation point (plus sign) was shown for 1000 ms tobegin each trial. Immediately after the fixation point, the

M. Yates / Cognition 115 (2010) 197–201 199

word was displayed in lowercase. Once the word was pro-nounced, it was removed from the screen. The experi-menter then coded the response as correct, incorrect, ormicrophone error. Following this, the next trial began. Be-fore viewing the experimental items, participants namedten practice stimuli. None of the practice stimuli was usedin the experimental session.

2.2. Results and discussion

Outliers were defined as responses that were less than250 ms or greater than 1000 ms and represented 3.0% ofthe data. These values were not used in any analyses. Forthe participants analyses, number of neighbors overlap-ping with the LSP was treated as a within-participants fac-tor, but it was treated as a between-items factor forpurposes of the items analyses. Additionally, the controlvariables from Table 1 and regularity were all included ascovariates when conducting items analyses. Equipment er-rors (e.g., the microphone did not register the response ofthe participant) occurred on 3.7% of the trials and werenot included in the data analyses. Finally, true errors(e.g., real pronunciation errors) occurred on only 3.5% ofthe trials.

For the latency analysis, only correct responses wereused. The results show that LSP+ words were named morerapidly (M = 531, SD = 93) than were LSP� words (M = 552,SD = 98). This effect was significant for both the partici-pants analysis F(1, 19) = 24.37, p < 0.001, MSE = 180.18and items analysis F(1, 11) = 5.71, p = 0.036, MSE = 607.62.Inspection of the pronunciation errors showed that partic-ipants made 4.3% errors to LSP+ and 2.7% errors to LSP�words. This difference was not significant for participantsF(1, 19) = 2.02, p = 0.171, MSE = 0.31 or items F(1, 11) =1.55, p = 0.240, MSE = 2.56.

The results of the naming experiment clearly show thatnaming latencies are faster for words with many neighborsoverlapping with their LSP. It is important to note thatthese words do not differ on overall phonological neigh-borhood density. It is believed that this LSP effect occursbecause as the number of neighbors coinciding with theLSP increases the time it takes the phoneme to reachthreshold decreases.

3. DRC simulations

The words used in the experiment were presented toDRC 1.2, which is the most recent version of the model(Mousikou, Coltheart, & Saunders, in press). The word tirewas not included because it is not in the model’s lexicon.The results revealed that LSP� words were named in77.3 cycles, and LSP+ words were named in 75.5 cycles.This effect was not significant, F < 1. It appears that theDRC 1.2 model with its standard parameter set is not ableto simulate the LSP effect. This is not surprising as Yateset al. (2008b) were also unable to simulate the effects ofphonological neighborhood spread or density using thestandard parameter set in DRC 1.0. They argued that in or-der for the model to simulate phonological neighborhoodeffects the excitatory activation from the phoneme system

to the phonological output lexicon needed to be increased.To this end, they increased this parameter from 0.04 to0.15. Additionally, they turned off the GPC route becauseit works in a serial fashion, which leads to greater activa-tion for the phonemes at the beginning of the word. Thesetwo changes were made to the DRC 1.2 model, and thewords from the current experiment were presented tothe model again. Using this modified parameter set, theLSP� words were named in 82.1 cycles, and the LSP+words were named in 82.0 cycles. This difference was notsignificant F < 1. It seems that DRC 1.2 is not able to simu-late the LSP effect using the same parameter changes thatwere made to DRC 1.0 to allow it to simulate phonologicalneighborhood effects. The question is whether DRC 1.2 cansimulate the LSP effect with a different value for theparameter controlling the excitation from the phonemesystem to the phonological lexicon. The answer is yes. Inthe DRC 1.2 model when this parameter was increased to0.4 and the GPC route was turned off, LSP� words werenamed in 70.5 cycles, and LSP + words were named in68.3 cycles, F(1, 27) = 4.51, p = 0.043, MSE = 8.02. It isworth noting that increasing this parameter to greater val-ues than 0.4 increased the size of the effect. To make surethat turning off the GPC was necessary an additional sim-ulation was performed in which the parameter controllingthe excitation between the phoneme system and the pho-nological lexicon was increased to 0.4, but the GPC routewas allowed to run. This simulation revealed that LSP�words were named in 64.2 cycles, whereas the LSP+ wordswere named in 62.2 cycles. Although this effect is in theright direction, it is not significant F < 1. Thus, it seems toproduce the effect in the model requires an increase inthe parameter controlling the excitatory activation be-tween the phoneme system and the phonological outputlexicon as well as the activation from the GPC being turnedoff.

4. Discussion

The research reported here was designed to provide adirect test of whether the LSP variable provides a viableway of explaining phonological neighborhood effects. Theresults from both the experiment as well as simulationsusing the DRC model indicate that the number of neigh-bors overlapping with the LSP has a direct effect on naminglatencies. Specifically, the more neighbors there are over-lapping with the LSP the more rapidly the word will benamed. Crucial to explaining this effect is the interplay be-tween the phonological output lexicon and the phonemesystem (for a discussion of how these two systems influ-ence pseudoword naming see Mulatti, Peressotti, & Job,2007). As a target word’s phonological code is activatedfrom the incoming orthographic code, the phonemes thatare congruent with the word will be activated within thephoneme system. As these phonemes are shared with theword’s phonological neighbors, the activation passed fromthe phoneme system to the phonological output lexiconwill activate the word’s phonological neighbors. However,for this to happen, there needs to be sufficient excitatoryactivation from the phoneme level to the phonologicallexicon. In the standard parameter set of the DRC 1.2

200 M. Yates / Cognition 115 (2010) 197–201

model, this is not the case. Instead, there is much moreinhibition than excitation. This is why the model was un-able to simulate the effect with the standard parameterset. However, once the excitatory activation between thephoneme system and the phonological lexicon was in-creased, the model was able to simulate the effect becauseof the increased activation of the phonological neighbors.As the phonological neighbors become active they feedactivation back to the phoneme level increasing the activa-tion for the target word’s phonemes. However, a word can-not be named by the model until a phoneme unit withinevery phoneme position reaches the naming criterion. Thisis where the LSP variable becomes important. It is arguedthat the LSP will be slowest phoneme to reach threshold.Because of this, the time it takes this phoneme to reachthreshold will have a direct impact on the naming latency.Consequently, if a word’s LSP has many phonologicalneighbors, it will receive a boost in activation from theseneighbors and reach the naming criterion more rapidly.This leads to faster naming latencies to LSP+ words thanto LSP� words.

There are a few other points that are worth discussing.First, it seems that in order to understand how phonologi-cal similarity influences word recognition, we need tomove beyond simply looking at neighborhood density ef-fects. In fact, Yates et al. (2008b) argued that phonologicalneighborhood density effects in word naming were reallyattributable to LSP processing. The results of the currentresearch support this claim. Second, although the DRCmodel was able to simulate the LSP effect, it could onlydo so with the GPC route turned off. Without the GPC routethe model is not able to pronounce nonwords or simulateother important effects such as the regularity effect. Thisis a problem the model has had in relation to previous sim-ulations of both orthographic and phonological neighbor-hood effects (Coltheart et al., 2001; Mulatti et al., 2006;Yates et al., 2008b). Although these researchers have re-ported parameter sets that allow the model to simulatevarious neighborhood effects, the adjustments that mustbe made to the model prevent it from simulating other ef-fects. Whether there is a parameter set that will allow themodel to simulate neighborhood effects while continuingto simulate other effects remains to be seen. Finally, itshould be made clear that the LSP effect seems to be lim-ited to the naming task. As discussed in previous research,the effects of both density and spread are significant in thelexical decision task once the LSP variable is accounted for(Yates, 2009; Yates et al., 2008b). This indicates that the ef-fect phonological neighbors have on word recognition mayhave different theoretical explanations based on the task inquestion. The results of the research reported here add tothe argument that the LSP variable seems to be the mech-anism by which phonological neighbors facilitate wordnaming. Future research should seek to more clearly delin-eate how phonological neighbors influence word recogni-tion in other tasks such as lexical decision and semanticcategorization.

Acknowledgement

I thank Hope Brasfield for help with data collection.

Appendix A

Item and mean latencies (in ms) from the namingexperiment.

LSP�

LSP+

blouse

556 bind 580 chew 479 choke 507 chop 504 chum 530 claw 562 dirt 487 dough 573 ditch 525 dump 510 gush 550 garb 545 kite 493 link 560 lull 567 loath 580 lure 553 moist 558 mend 538 nest 563 norm 516 shove 542 shave 497 shred 621 shout 511 stow 548 stark 516 tire 497 tomb 486

References

Baayen, R. H., Piepenbrock, R., & Gulikers, L. (1995). The CELEX LexicalDatabase (CD-ROM). Philadelphia, PA: Linguistic Data Consortium,University of Pennsylvania.

Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequencyestimates for 2938 monosyllabic words. Memory and Cognition, 29,639–647.

Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B.,et al. (2007). The English Lexicon Project. Behavior Research Methods,39, 445–459.

Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: Adual route cascaded model of visual word recognition and readingaloud. Psychological Review, 108, 204–256.

Cortese, M. J., & Fugett, A. (2004). Imageability ratings for 3000monosyllabic words. Behavior Research Methods, Instruments &Computers, 36, 384–387.

Cortese, M. J., & Khanna, M. M. (2008). Age of acquisition ratings for 3000monosyllabic words. Behavior Research Methods, 40, 791–794.

Davis, C. J. (2005). N-Watch: A program for deriving neighborhood sizeand other psycholinguistic statistics. Behavior Research Methods, 37,65–70.

Kessler, B., Treiman, R., & Mullennix, J. (2008). Feedback-consistencyeffects in single-word reading. In E. L. Grigorenko & A. J. Naples (Eds.),Single-word reading: Behavioral and biological perspectives(pp. 159–174). Mahwah, NJ, US: Lawrence Erlbaum AssociatesPublishers.

Kucera, H., & Francis, N. (1967). Computational analysis of present-dayAmerican English. Providence: Brown University Press.

Mousikou, P., Coltheart, M., & Saunders, S. (in press). Computationalmodeling of the masked onset priming effect in reading aloud.European Journal of Cognitive Psychology.

Mulatti, C., Peressotti, F., & Job, R. (2007). Zeading and reazing: Which isfaster? The position of the diverging letter in a pseudoworddetermines reading time. The Quarterly Journal of ExperimentalPsychology, 60, 1005–1014.

Mulatti, C., Reynolds, M. G., & Besner, D. (2006). Neighborhood effects inreading aloud: New findings and new challenges for computationalmodels. Journal of Experimental Psychology: Human Perception andPerformance, 32, 799–810.

Nusbaum, H. C., Pisoni, D. B., & Davis, C. K. (1984). Sizing up the Hoosiermental lexicon: Measuring the familiarity of 20,000 words. Inresearch on speech perception progress report no. 10. SpeechResearch Laboratory, Psychology Department, Indiana University,Bloomington, Indiana.

Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime user’s guide.Pittsburgh: Psychology Software Tools Inc..

M. Yates / Cognition 115 (2010) 197–201 201

Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart’s N: Anew measure of orthographic similarity. Psychonomic Bulletin &Review, 15, 971–979.

Yates, M. (2005). Phonological neighbors speed visual word processing:Evidence from multiple tasks. Journal of Experimental Psychology:Learning, Memory, and Cognition, 31, 1385–1397.

Yates, M. (2009). Phonological neighborhood spread facilitates lexicaldecisions. Quarterly Journal of Experimental Psychology, 62,1304–1314.

Yates, M., Friend, J., & Ploetz, D. M. (2008a). The effect of phonologicalneighborhood density on eye movements during reading. Cognition,107, 685–692.

Yates, M., Friend, J., & Ploetz, D. M. (2008b). Phonological neighborsinfluence word naming through the least supported phoneme. Journalof Experimental Psychology: Human Perception and Performance, 34,1599–1608.