Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other...

Short article

Age of acquisition predicts naming and lexical-decisionperformance above and beyond 22 other predictor

variables: An analysis of 2,342 words

Michael J. CorteseUniversity of Nebraska at Omaha, Omaha, NE, USA

Maya M. KhannaCreighton University, Omaha, NE, USA

Age of acquisition (AoA) ratings were obtained and were used in hierarchical regression analyses topredict naming and lexical-decision performance for 2,342 words (from Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004). In the analyses, AoA was included in addition to the set of predictorsused by Balota et al. (2004). AoA significantly predicted latency performance on both tasks above andbeyond the standard predictor set. However, AoA was more strongly related to lexical-decision per-formance than to naming performance. Finally, the previously reported effect of imageability onnaming latencies by Balota et al. was not significant with AoA included as a factor. These resultsare consistent with the idea either that AoA has a semantic/lexical locus or that AoA effectsemerge primarily in situations in which the input–output mapping is arbitrary.

Does the age at which one acquires a word influ-ence the speed to which that word is respondedto in reading aloud and lexical decision?Recently, much attention has been focused onthis and related issues (e.g., Ellis & LambonRalph, 2000; Monaghan & Ellis, 2002; Morrison& Ellis, 1995; Zevin & Seidenberg, 2002, 2004;for reviews see Johnston & Barry, 2006; Juhasz,2006Q1 ). However, the relationship that age ofacquisition (AoA) has with word processing iscomplicated by the fact that performance onword recognition tasks is related to a large andgrowing number of variables, and AoA is

correlated with many of these variables (e.g., fre-quency and imageability). Therefore, it is difficultto assess the effect of AoA while controlling for allother relevant variables.

Part of the reason that researchers have becomeso intrigued with AoA is that its relationship toword recognition performance has importantimplications for theoretical models. For example,Zevin and Seidenberg (2002, 2004) argued thattheir parallel-distributed-processing (PDP)model produces little effect of AoA on namingperformance. Instead, cumulative frequency (i.e.,the summed frequency of occurrences of a word

PQJE231447 TECHSET COMPOSITION LTD, SALISBURY, U.K. 3/22/2007

Correspondence should be addressed to Michael J. Cortese, Department of Psychology, University of Nebraska at Omaha, 6001

Dodge Street, Omaha, NE 68182, USA. E-mail: [email protected]

# 0000 The Experimental Psychology Society 1http://www.psypress.com/qjep DOI:10.1080/17470210701315467

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across development) influences performance of thePDP model. In the model, knowledge is containedin the values of weighted connections that linkunits in the network. The values are adjusted asthe model is exposed to words and their pronun-ciations. While the number of encounters thatthe model has with a word influences performance,the influence of the relative time that a word is firstencountered (i.e., AoA) is a bit more complicated.In a PDP model, the influence of AoA depends onthe nature of the relationship between the inputmode and the output response. Specifically, whenthe link between the input and the output is, ingeneral, unpredictable, AoA has a greater influ-ence than when the link between input andoutput is, in general, more systematic. Forexample, when naming words as quickly and accu-rately as possible, the input is a visual letter string,and the output is a naming response. The relation-ship between letter strings and their pronuncia-tions is fairly predictable in English (Seidenberg& McClelland, 1989) so AoA is expected tohave little or no effect on naming performance.However, Zevin and Seidenberg (2002) foundthat when arbitrary mappings occur betweeninputs and ouputs, AoA will affect performance(also, see Lambon Ralph & Ehsan, 2006). Thatis, Zevin and Seidenberg (2002) obtained AoAeffects in a model when there was little transpar-ency between orthography and phonology (also,see Ellis & Lambon Ralph, 2000) but they didnot obtain AoA effects when the relationshipbetween orthography and phonology was moretransparent. This characteristic is importantbecause all of their simulations were meant toreflect reading aloud. In English, orthographic-to-phonological relationships are fairly systematic,but orthographic-to-semantic relationships andphonological-to-semantic relationships are fairlyarbitrary. Thus, according to a PDP perspective,the computation of a semantic code from ortho-graphy and/or phonology may be expected toproduce an effect of AoA, but the computationof phonology from orthography is not expectedto produce an effect of AoA. Interestingly,because semantic factors relate more strongly tolexical-decision performance than to naming

performance (Balota, Cortese, Sergent-Marshall,Spieler, & Yap, 2004), one might predict AoAto influence lexical-decision performance morethan naming.

There are several hypotheses about how AoAinfluences semantic processing. One interestinghypothesis is that earlier acquired words providea structure in semantic memory upon which laterwords build, thus forming a semantic locus. Thesupporters of this semantic-locus hypothesis(e.g., Brysbaert, Van Wijnendaele, & DeDeyne,2000 Q2; Van Loon-Vervoon, 1989) propose thatlater concepts are learned by accessing and relatingto the meaning of already-acquired words. Therepresentations of the earlier acquired words maybe accessed more often due to this characteristic,thus yielding faster response latencies andimproved accuracy rates. Recently, Steyvers andTenenbaum (2005) proposed a slightly differenthypothesis in which AoA is characterized interms of a developing semantic network.According to this view, semantic nodes that areestablished earlier tend to develop more semanticconnections than do later acquired nodes. Therelatively large number of connections for early-acquired nodes will lead to more frequent acti-vations in semantic tasks than occur for lateracquired/less connected nodes. The more frequentsemantic activations of early-acquired conceptswill result in faster response latencies in semantictasks. Thus, we might expect effects of AoA tobe larger in lexical-decision performance than inreading aloud.

It is important to note that there is no consen-sus on how to conceptualize AoA. For example, incontrast to previous ideas about AoA, Zevin andSeidenberg (2002, 2004) consider AoA to be anoutcome variable (i.e., one that is the result ofother factors rather than a predictor of perform-ance on word recognition tasks). They argue thatAoA predicts naming and lexical-decisionperformance because the age at which a word isacquired is affected by many factors that arerelated to performance. To avoid the confluenceof factors associated with AoA, Zevin andSeidenberg (2004) concentrate on frequencytrajectory. Frequency trajectory reflects the

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CORTESE AND KHANNA

distribution of encounters that one has with wordsover time. Some words (e.g., potty) occur relativelyfrequently during early childhood but less soduring adulthood. Other words (e.g., fax) occurfrequently during adulthood but less so duringchildhood. It follows that one would acquiremore rapidly a word that occurs more frequentlyduring childhood than one that occurs less fre-quently. Therefore, one can consider AoA anoutcome variable that is predicted by frequencytrajectory. Indeed, Zevin and Seidenberg (2004)have found that the two variables are correlated.Furthermore, in a reading-aloud task (a relativelysemantic-insensitive task), they found little evi-dence that frequency trajectory affects naminglatencies. In contrast, they found that cumulativefrequency (i.e., the summed frequency of a wordacross development) had a marked effect on per-formance (i.e., reduced naming latencies). Forexample, based on the word frequency norms ofZeno and colleagues (Zeno, Ivens, Millard, &Duvvuri, 1995), down has a relatively high cumu-lative frequency value because it is encounteredfrequently throughout one’s lifetime. In contrast,dent has a relatively low cumulative frequencyvalue because it is not frequently encounteredthroughout the course of a lifetime. It is importantto note that in a study conducted in French,Bonin, Barry, Meot, and Chalard (2004) foundan effect of AoA over and above word frequency,but they found no effect of frequency trajectory.

Our view is that if one conceptualizes AoA asan outcome measure (as did Zevin &Seidenberg, 2004), then the specific variablesthat contribute to AoA (e.g., imageability, fre-quency) can be identified and evaluated separatelyfrom AoA. This is precisely what Zevin andSeidenberg (2004) attempted to do with frequencytrajectory. However, if the effect of AoA on per-formance goes above and beyond the contributedeffects from these variables, then AoA is aunique factor influencing performance and is notsimply an outcome variable. Furthermore, onecould use the same logic as Zevin and

Seidenberg to conceptualize word frequency asan outcome variable. That is, there will also befactors that conspire to determine the frequencywith which a word is used in a language.According to this logic, we might also treat wordfrequency as an outcome variable. The obviousquestion then becomes: Why should it be thatAoA is considered to be more of an outcome vari-able than is word frequency or any other variable?1

In the present study, we chose to examine therelationship that AoA, objective frequency, subjec-tive frequency, and imageability have with namingand lexical-decision performance. Rather thanattempting to cross these variables in a factorialdesign, we employed multiple-regression tech-niques to analyse a large number of items. First,we collected AoA estimates for 3,000 monosylla-bic words. Then, we accessed the Balota et al.(2004) data set of monosyllabic words foryounger adults naming and lexical-decisionlatencies and accuracy rates. We conducted step-wise hierarchical regression analyses on this dataset to examine the independent influence ofAoA, the frequency measures, imageability, anda host of standard predictor variables (e.g.,length, spelling–sound consistency, etc.). It isimportant to note that in their multiple regressionanalyses, one reason that Balota et al. did not assessAoA was because AoA norms for their corpus ofmonosyllabic words were not available at thetime. There are now 2,342 words for which therewere values for both the predictor variablesemployed by Balota et al. and AoA. We aremaking the AoA ratings available for the 3,000words, but the analyses reported in this paperwill be on the 2,342 words common to ourratings and to the Balota et al. (2004) data set.Balota et al. also noted that another reason forexcluding AoA from their analyses involved thecontroversy about whether AoA predicts perform-ance beyond that of cumulative frequency (e.g.,Zevin & Balota, 2002 Q3). Here, we do not assumethat cumulative frequency has priority over AoA.Furthermore, our view is that, regardless of one’s

1 We thank Andy Ellis for pointing out this line of reasoning.

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ANALYSIS OF 2,342 WORDS

particular theoretical position, the analyses con-ducted here will be of considerable interest toresearchers involved in word processing.

We should also note that we decided to includeAoA in the third step of the analysis along withimageability. There were two reasons for doingso. First, there has been considerable debate inthe literature regarding whether effects of image-ability are instead attributable to AoA (cf. Ellis& Monaghan, 2002; Monaghan & Ellis, 2002;Strain, Patterson, & Seidenberg, 2002). Second,proponents of the semantic-locus hypothesis ofAoA (e.g., Brysbaert, Lange, & VanWijnendaele, 2000; Van Loon-Vervoon, 1989)posit that later concepts are learned by accessingand relating to the meaning of already-acquiredwords. Therefore, it seems beneficial to enterthese two variables together and after the otherphonological onset (e.g., voicing, stop, etc.) andlexical predictors (e.g., frequency, orthographicneighborhood size, etc.) have been assessed.

In support of our view that AoA is a semanticvariable, we predict that AoA will affect lexical-decision performance more than naming. Asnoted above, PDP models can produce AoAeffects when the mapping between input andoutput is arbitrary (such as is the case in ortho-graphic-to-semantic mappings). Furthermore,the lexical-decision task places an emphasis onmeaning and factors related to meaning (cf.,Chumbley & Balota, 1984; Balota et al., 2004).In English, the mapping between orthographyand semantics and also between phonology andsemantics is fairly arbitrary. In addition, thesemantic-locus hypothesis (e.g., Brysbaert et al.,2000) as well as the developing network hypoth-esis (Steyvers & Tenenbaum, 2005Q4 ) posit thatAoA has a semantic basis and so should berelated more strongly to lexical-decisionperformance.

Regarding naming performance, the predic-tions are less clear. Assuming that AoA has asemantic basis, then one might expect either asmall effect of AoA on naming performance orno effect at all. Also, it is important to note thatimageability and AoA are correlated (r ¼ .49 inour 3,000-word corpus), and the effect of

imageability on naming latencies in the Balotaet al. 2004, study was small but significant. It ispossible that including AoA as a factor in theanalysis may reduce the variance accounted forby imageability. We note that as we were involvedin the collection of both the imageability and AoAnorms, the procedures for collecting both sets ofnorms were very similar. Specifically, the samewords were rated in each norm set, and theywere rated by the same number of participants.

NORMS FOR AGE OF ACQUISITION

Method

ParticipantsParticipants were 32 College of Charleston under-graduates who participated for course credit.

StimuliThe stimuli included 3,000 monosyllabic words.These words were the same as those rated for ima-geability by Cortese and Fugett (2004). Most ofthese words were also rated for subjective fre-quency (Balota, Pilotti, & Cortese, 2001). Inaddition, Balota and colleagues (Balota et al.,2004) have obtained naming latencies, namingaccuracy, lexical-decision latencies, and lexical-decision accuracy from younger and older adultsfor the majority of these words.

EquipmentStimuli were presented on a 19-inch computermonitor that was controlled via a microcomputer.

ProcedureRatings were collected via a microcomputer in acomputer laboratory in two sessions that wereheld less than one week apart. Each sessionlasted between 1.25 and 1.50 hours. A total of1,500 words were rated during each session.Each session consisted of four blocks of 375words. Opportunities for breaks occurred at theend of each block. A different random orderoccurred for each participant.


CORTESE AND KHANNA

On each trial, a word was presented in thecentre of the screen in lowercase letters, and a1–7 scale was presented at the bottom of thescreen for easy reference. The rating was enteredvia a number keypad on the right side of the key-board. The scale employed here was originallydeveloped by Gilhooly and Logie (1980). Theinstructions employed in the current study were(italics added)Q5 :

We acquire words throughout our lives. Some words are acquired

at a very early age, some are acquired later, and others fall in

between. The purpose of this study is to determine the approxi-

mate age for which 3,000 single syllable words have been

acquired.

On each trial of the study, you will be presented with a word.

Try to estimate the age at which you acquired that word accord-

ing to the scale shown at the bottom of the screen. Note that

words acquired at an early age should be given a low rating,

and words acquired at an older age should be given a higher

rating. For any word that you do not know, you should assign

a value of 7. The scale will appear below each word for easy

reference.

-----------------------------------------------

1 2 3 4 5 6 7

AGE 0–2 2–4 4–6 6–8 8–10 10–12 13þ

When making your ratings, try to be as accurate as possible, but

do not spend too much time on any one word. If you have any ques-

tions, ask the experimenter now. Otherwise, PRESS <ENTER>

TO BEGIN THE STUDY

Analyses of AoA ratingsFollowing Cortese and Fugett (2004), latenciesand ratings were eliminated whenever a ratingwas made in less than 300 ms. Speed was notemphasized in the instructions so no upper limitfor response latencies was set. This screening cri-terion eliminated 0.6% of the data. The averagelatency was 2,119.81 ms (SD ¼ 461.03). Oneconcern might be that the data are noisy due tothe number of words that were rated by each par-ticipant. To address this concern, we compared ourratings to other AoA norms for words that werecommon to both sets. There were 778 suchwords available from Bird, Howard, and Franklin(2001), 592 words from the MRC database(Coltheart, 1981), and 663 words fromStadthagen-Gonzalez and Davis (in press)Q6 .Despite the fact that the other studies were

conducted with non-American (but) English-speaking populations, the correlations were high(Bird et al., r ¼ .827; MRC, r ¼ .869; andStadthagen-Gonzalez & Davis Q7, r ¼ .850). Thecorrelation between our ratings and the objectiveratings reported by Morrison, Chappell, andEllis (1997) Q8were somewhat lower (r ¼ .565),but we note that the two sets only had 128words in common, and the Morrison et al.norms were collected on British English children.Overall, these correlations provide evidence ofcongruent validity. Thus, our large-scale studywith 3,000 words produced AoA ratings similarto smaller studies with fewer trials. This suggeststhat participants’ ratings were not negativelyaffected by the length of the testing sessions.Furthermore, we should also note that a numberof published studies have employed a largenumber of trials (e.g., Balota et al., 2004; Balotaet al., in press Q9; Cortese & Fugett, 2004; Kessler,Treiman, & Mullennix, 2002; Treiman,Mullennix, Bijeljac-Babic, & Richmond-Welty,1995). In fact, Cortese and Fugett (2004) collectedimageability norms under very similar conditions.Establishing that these estimates are reliable andvalid also further establishes that 3,000 trialstudies can produce reliable results. Moreover,we found a high degree of interparticipantreliability. This was established by correlatingeach participant’s ratings with the mean ratingsfrom the other 31 participants across the 3,000words. The mean correlation coefficient from the32 participants was .75 with a standard deviationof .07 (the correlation coefficients ranged fromr ¼ .56 to r ¼ .85.).

Naming task and lexical-decision taskThe data analysed in the current study were orig-inally obtained by Balota and colleagues (Balotaet al., 2004; Spieler & Balota, 1997 Q10). Here, webriefly describe the main details of the methodsemployed to collect this data.

StimuliThe words used in the naming task consisted of2,870 single-syllable words that were used to



train the PDP models of Seidenberg andMcClelland (1989) and Plaut, McClelland,Seidenberg, and Patterson (1996). The lexical-decision task involved 2,906 words and an equalnumber of nonwords that were matched inlength (between two and eight letters).Nonwords were constructed by changing one tothree letters of each of the words.

ProcedureEach individual participated in two sessions thatoccurred on separate days that were no longerthan 1 week apart from each other. Eachsession consisted of 5 blocks of trials. In thelexical-decision task, Blocks 1–9 consisted of600 stimuli, and Block 10 consisted of 412stimuli. In the naming task, half as many trialswere included in each block. Breaks occurredafter every 150 trials within a block and alsobetween each block. In both tasks, participantswere instructed to respond as quickly and accu-rately as possible. Each trial began with a fixationmark presented to the centre of a computermonitor for 400 ms. A blank screen followedfor 200 ms. Then a stimulus appeared at thecentre of the monitor until the participantresponded. In the naming task, participants pro-nounced each word as quickly and accurately aspossible. Each response triggered a voice keyinterfaced with the computer, which erased thestimulus from the screen. Then, the participantcoded the accuracy of the response via themouse. Correct responses were indicated bypressing the left button on the mouse; incorrectand noise responses (e.g., some extraneous noisesuch as coughing triggered the voice key) wereindicated by pressing the right mouse button.The coding of the response initiated a 1,200-ms intertrial interval. In the lexical-decisiontask, both words and pronounceable nonwords(e.g., blark) were responded to. Each responseoccurred via the keyboard. A key marked“YES” (the “/” key) was used to indicate aword response, and a key marked “NO” (the“z” key) was used to indicate a nonword response.Feedback was provided for incorrect responses.Specifically, a message stating that the response

was incorrect appeared slightly below the fixationmark for 1,500 ms. Then, the participant pressedthe space bar to initiate a 1,200-ms delay inwhich the fixation mark appeared at the centreof the screen. A total of 20 practice trials pre-ceded the experiment, and 2 practice trialsoccurred at the beginning of each block of trials.

Regression analysesIn these analyses, we chose to follow the procedureof Balota et al. (2004) as closely as possible, withthe exception of adding AoA to the list of predic-tor variables. We did this to make the interpret-ation of AoA as straightforward as possible andto minimize the potential for suppressor variables.In the main analyses of interest here, Balota et al.restricted the items to be analysed to only those2,342 items that had a subjective frequency value(Balota et al., 2001) and for which imageabilityratings (Cortese & Fugett, 2004) were available.These are the words that were analysed in thepresent study. For the 2,342 words analysed herethe mean response latency was 468.5 ms (SD ¼

20.7, range ¼ 153.0) for naming and 616.8 ms(SD ¼ 62.0, range ¼ 353.7) for lexical decision.The proportion correct was .96 (SD ¼ .05, range¼ .52) for naming and .92 (SD¼ .08, range¼ .47)for lexical decision.

Predictor variablesPredictor variables included all of those employedby Balota et al. (2004), entered in the same order asthat in Balota et al., except that AoA also wasincluded as a predictor variable in the third stepalong with imageability. Table 1 presents theintercorrelation matrix with the predictor variablesemployed in Steps 2 and 3 of the regression ana-lyses. Four separate stepwise regression analyseswere conducted on the item means for naminglatencies (1) and accuracy rates (2) as well aslexical-decision latencies (3) and accuracy rates(4). We note that, in general the accuracy ratefor naming responses was high, and so theresults from naming-accuracy rates should beinterpreted with caution. We also conducted ananalysis with AoA as the dependent variable.


CORTESE AND KHANNA

Interestingly, the predictor set of 22 variables (6 ofwhich attained statistical significance) predicted73% of the variance in AoA ratings (see Table 2).Q11

Results

Regression analyses were performed on the meanitem latencies and accuracies across subjects and(naming and lexical-decision) tasks. Each time,a three-step hierarchical regression analysis wasperformed that included phonological onsets inthe first step, lexical variables in the secondstep, and imageability and AoA in the thirdstep. The standardized regression coefficientsfrom these steps and the corresponding(adjusted) R2s are presented in Table 3. Theoverall pattern of results was consistent withthat reported by Balota et al. (2004). In addition,AoA affected both reading aloud and lexical-decision latencies, but its effect was larger forlexical-decision performance than for naming.Also, we note that although the increase in R2

in naming latencies between Steps 2 and 3 wasquite small (i.e., less than 1%), the increase wassignificant, F(2, 2318) ¼ 8.91, p , .01. AoAdid not significantly predict naming accuracy,but it did predict lexical-decision accuracy.Notably, imageability, which was related tonaming latencies in the Balota et al. (2004)study was no longer significant once AoA wasincluded as a predictor. However, it remained asignificant predictor of lexical-decision latenciesand accuracy. Both objective frequency—that is,log (frequency þ 1), based on Zeno et al., 1995—and subjective frequency (Balota et al., 2004)were significant predictors of naming latencies,lexical-decision latencies, and lexical-decision

Table 1. Correlation matrix for the predictor variables used in Steps 2 and 3 of the regression analyses

Variable 1 2 3 4 5 6 7 8 9 10

1 Length 1.0 2.654�� 2.158�� 2.162�� 2.005 2.023 .019 .009 2.067�� .261��

2 Neighbourhood size 1.0 .122�� .140�� .114�� .011 .090�� .124�� .063�� 2.206��

3 Subjective frequency 1.0 .780�� 2.072�� 2.103�� 2.005 2.042� 2.020 2.721��

4 Objective frequency 1.0 2.059�� 2.119�� .002 2.063�� .013 2.689��

5 Feedforward onset consistency 1.0 .023 .218�� .059�� .065�� 2.005

6 Feedforward rime consistency 1.0 .070�� .223�� .037 .080��

7 Feedback onset consistency 1.0 .056�� .040 2.034

8 Feedback rime consistency 1.0 .079�� 2.019

9 Imageability 1.0 2.381��

10 AoA 1.0

��p,.01. Q11

Table 2. Standardized regression coefficients in

which AoA rating serves as the dependent variable

Predictor variable

Regression

coefficient

Initial phoneme characteristics

Affricative .050

Alveolar 2 .175

Bilabial 2.179

Dental 2.065

Fricative .108

Glottal 2.065

Labiodental 2.057

Liquid .110

Nasal .081

Palatal 2.123

Stop .143

Velar 2.122

Voice .022

Length .137��

Neighbourhood size .009

Subjective frequency 2.486��

Objective frequency 2.289��

Feedforward onset consistency 2.040��

Feedforward rime consistency .022�

Feedback onset consistency 2.009

Feedback rime consistency 2.031��

Imageability 2.373��

Adjusted R2 .725

Note: AoA¼age of acquisition.�p ¼ .052. ��p, .01.



error rates. Neither frequency measure reliably pre-dicted naming accuracy.2Q12

Discussion

Our predictor set of variables accounted for 73%of the variance in AoA ratings, but AoA ratingspredicted naming and lexical-decision latenciesabove and beyond these variables. Imageability,which previously had been shown to accountfor unique variance in both naming and lexicaldecision (e.g., Balota et al., 2004), onlyaccounted for unique variance in lexical decisiononce AoA was considered. Therefore, imageabil-ity remains a significant predictor of lexicaldecision performance. However, its role onnaming performance is tenuous. We note thatour results are consistent with those reportedfrom a smaller scale study on naming andlexical-decision performance for 220 Englishwords by Morrison and Ellis (2000)Q17 .

The observation that AoA is significantlyrelated to performance on both tasks above andbeyond the predictor set is newsworthy. As pre-viously noted, Balota et al. (2004) were unable toassess the relationship of AoA with naming andlexical-decision performance because AoA normswere not available for the majority of their wordcorpus. In addition, the availability of these

norms allows researchers to control or manipulateAoA in ways that were not previously possible.

The fact that AoA is more strongly related tolexical-decision performance than naming per-formance suggests that it emerges when a seman-tic code is computed. This is consistent with theperformance of connectionist models thatproduce an effect of AoA when the mappingbetween input and output is arbitrary. InEnglish, neither letter strings nor phonemes sys-tematically map onto meaning. Thus, one mightexpect an effect of AoA to emerge when asemantic code is computed as is likely to be thecase when making a lexical decision. In contrast,one would expect either no effect or a smallereffect to emerge during reading aloud becausethe mapping between orthography and phonol-ogy is more systematic (i.e., “quasiregular”,Seidenberg & McClelland, 1989, p. 525). Incontrast to this prediction, we found a cleareffect of AoA on reading-aloud latencies. It isnot clear how PDP models might accommodatethis result. In the dual-route cascadic (i.e.,DRC) model (Coltheart, Rastle, Perry,Langdon, & Ziegler, 2001), baseline activationlevels for word representations are determinedby frequency. It is possible that baseline acti-vation levels could also be determined by AoAin order to account for these results.

2 Based on a recent paper by Baayen, Feldman, and Shreuder (2006) Q13, log frequency’s effect on processing speed seems to level off

at the very high end of the frequency scale. We addressed this issue by replacing our log frequency value, log (frequency þ 1), with

[log (frequency þ 1)]2. This modification did not change the results reported here in any meaningful way. Specifically, in all cases,R2

was either the same or higher with the original measure of log frequency. More importantly, this change did not change the predictive

power of AoA at all, and only in one case did the predictive power of another factor change. That is, in the re-analysis of naming

accuracy, subjective frequency was now a significant predictor (B ¼ .073 Q14, p , .05) Also, we note that frequency trajectory correlates

with AoA, r ¼ –.50, p , .01. Therefore, it is possible that the variance attributable to AoA in the first set of analyses would be

eliminated once frequency trajectory was added to the model. We conducted another set of analyses in which the objective frequency

measure employed by Balota et al. (2004) Q15was replaced by the cumulative frequency measure and frequency trajectory measure defined

by Zevin and Seidenberg. Q16There were 2,046 words in our data set for which there were values available for this new set of predictors.

In this analysis, AoA remained a significant predictor of performance in both of the latency analyses, but the inclusion of frequency

trajectory suggested the presence of a suppressor variable. Specifically, while the signs of beta did not change for AoA whether fre-

quency trajectory was included or not, the signs of beta for frequency trajectory in both of the lexical-decision analyses change

depending on the presence or absence of AoA in the equation. Furthermore, the beta signs corresponding to frequency trajectory

when AoA is included in the equation make little theoretical sense. When AoA is included in the equation, frequency trajectory

is positively related to response latencies (i.e., words that have higher frequencies early in development are associated with slower

response latencies than are words that have lower frequencies early in development) and negatively related to decision accuracy

(i.e., words that have higher frequencies early in development are associated with lower accuracies than are words that have lower

frequencies early in development).


CORTESE AND KHANNA

In general, the results presented here are con-sistent with the semantic-locus hypothesis pro-posed by Brysbaert et al. (2000), Van Loon-Vervoon (1989), and Steyvers and Tenenbaum(2005)Q18 . Assuming that AoA effects arise when asemantic code is computed, we hypothesize thatan effect of AoAmay surface in naming from feed-back connections between semantics and phonol-ogy. However, based on the current data, we

cannot rule out the hypothesis that AoA directlyaffects the mapping between orthography andphonology.

Finally, we note that Brysbaert andGhyselinck (2006) have demonstrated that, inaddition to AoA, word frequency also accountsfor more variance in lexical decision than innaming in a variety of languages. Of course, wecannot conclude that every variable that affects

Table 3. Standardized regression coefficients for naming latencies and accuracy and for lexical-decision

latencies and accuracy

Naming Lexical decision

Predictor variable Latencies Accuracy Latencies Accuracy

Step 1a

Initial phoneme characteristics

Affricative 2 .376�� .936�� 2.196 .209

Alveolar 1.175�� 22.581�� .453 2.386

Bilabial 1.022�� 22.301�� .400 2.322

Dental .354�� 2.739�� .114 2.117

Fricative 2 .744�� 2.383�� 2.282 .411

Glottal .261�� 21.096�� .196 2.178

Labiodental .628�� 21.327�� .264 2.226

Liquid 2 .888�� 1.895�� 2.335 .371

Nasal 2 .712�� 1.277�� 2.198 .215

Palatal .566�� 21.691�� .373 2.320

Stop 2 1.113�� 2.589�� 2.377 .439

Velar 1.035�� 21.903�� .404 2.356

Voice 2 .116�� 2.004 .104�� 2.092��

Adjusted R2 .348 .043 .011 .010

Step 2

Length .153�� 2.083�� 2.005 .080��

Neighbourhood size 2 .107�� .006 .010 2.024

Subjective frequency 2 .136�� .042 2.374�� .324��

Objective frequency 2 .125�� .079� 2.313�� .196��

FF onset consistency 2 .021 .167�� 2.064�� .061��

FF rime consistency 2 .077�� .157�� 2.057�� .077��

FB onset consistency 2 .076�� .097�� .003 .019

FB rime consistency 2 .074�� .078�� 2.017 .016

Adjusted R2 .495 .135 .414 .238

Step 3

Imageability .002 .043� 2.178�� .239��

AoA .099�� 2.052 .256�� 2.189��

Adjusted R2 .498 .139 .504 .340

aInitial phoneme characteristics.�p ¼ .074. ��p, .01. �� .01 , p, .05. Q12



lexical decision more than naming is a “semantic”variable. However, variables that are clearlysemantic in nature (e.g., imageability) do affectlexical decision more than naming. Thus, thisleaves open the possibility that word frequencyaffects the computation of a semantic code fromprint more than it affects the computation of aphonological code from print. In a PDP model,we would expect a larger effect of frequency onthe computation of semantics from frequencythan on the computation of phonology from fre-quency. Connections corresponding to individualwords will be strengthened by exposure to otherwords sharing similar structure. As previouslynoted, the mapping between orthography andphonology is fairly systematic, and so the connec-tions corresponding to each specific word arestrengthened when it is encountered, but alsowhenever words with similar structures are pro-cessed. For example, the connections correspond-ing to the /int/ pronunciation will bestrengthened by exposure to mint, tint, lint, andso on. In other words, the pronunciation ofmint will benefit not only when it is encounteredduring learning, but also when these other wordswith similar pronunciations are encountered. Incontrast, the mapping between orthography andsemantics is mostly arbitrary, and so the connec-tions corresponding to specific words will mainlybe strengthened only when they themselves areprocessed. For example, because mint, tint, andlint all mean different things, the computationof the semantic code for one word does notreceive support from words containing similarorthographies. In such a situation, frequencywould be expected to have a relatively largeeffect. We also note that, in the Plaut et al.(1996) simulations of surface dyslexia, word fre-quency was implemented as a semantic variable.

In summary, we found that AoA influencedboth naming and lexical-decision performance,but it affected lexical-decision performance more.The previously reported effect of imageability onnaming performance was not obtained with thepredictor set employed in the current study,which included AoA. These findings can beaccommodated by the semantic-locus hypothesis

and by a version of the PDP model that predictsAoA effects when there are arbitrary mappingsbetween input and output.

Original manuscript received 21 November 2006

Accepted revision received 21 February 2007

First published online day month year

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PQJE231447Queries

Michael J. Cortese

Q1 Juhasz, B. J. (2005). in refs, 2006 in text.

Q2 Brysbaert, Van Wijnendaele, & DeDeyne, 2000. Not in refs.

Q3 Zevin & Balota, 2002. Not in refs.

Q4 Steyvers & Tanenbaum, 2005, changed to Tenenbaum, as in refs, OK?

Q5 Are italics needed if the instructions are displayed?

Q6 Stadthagen-Gonzalez & Davis (in press). Still in press?

Q7 “Stadthagen-Gonzalez &” inserted, OK?

Q8 Morrison, Chappell, & Ellis. No year given. Presume this is 1997? Otherwise not in text.

Q9 Balota, Yap, Cortese, Hutchison, Kessler, Loftus, et al. (in press). Still in press? If so, delete vol.and page nos. If not, insert year.

Q10 Spieler & Balota, 1997. Not in refs.

Q11 p value footnote OK for Table 1?

Q12 Table 3: p values: � changed to ��, and �� changed to � [largest probability receives the fewestasterisks, APA5: 3.70, p. 170]

Q13 Baayen, Feldman, and Shreuder (2006). Not in refs.

Q14 (B ¼ .073): B ok here? Or should it be beta, which is referred to later?

Q15 2004 OK for Balota et al.?

Q16 Is date 2002 or 2004 for Zevin and Seidenberg?

Q17 Morrison and Ellis (2000). Not in refs.

Q18 Steyvers and Tennenbaum (2005) changed to Tenenbaum, as in refs, OK?

Q19 van Loon-Vervoorn (1989). Please give English translation of title of book in brackets (APA 5,4.16, p. 251).

Age of acquisition predicts naming and lexical-decision performance above and beyond 22 other...

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