The role of semantic and phonological factors in word recognition: An ERP cross-modal priming study...

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Neuropsychologia xxx (2010) xxx–xxx

Contents lists available at ScienceDirect

Neuropsychologia

journa l homepage: www.e lsev ier .com/ locate /neuropsychologia

he role of semantic and phonological factors in word recognition: An ERPross-modal priming study of derivational morphology

neta Kielar, Marc F. Joanisse ∗

epartment of Psychology, The University of Western Ontario, London, Ontario, Canada N6A 5C2

r t i c l e i n f o

rticle history:eceived 14 April 2010eceived in revised form 4 November 2010ccepted 24 November 2010vailable online xxx

eywords:vent related potentials (ERP)400 priming

a b s t r a c t

Theories of morphological processing differ on the issue of how lexical and grammatical information arestored and accessed. A key point of contention is whether complex forms are decomposed during recog-nition (e.g., establish + ment), compared to forms that cannot be analyzed into constituent morphemes(e.g., apartment). In the present study, we examined these issues with respect to English derivational mor-phology by measuring ERP responses during a cross-modal priming lexical decision task. ERP primingeffects for semantically and phonologically transparent derived words (government–govern) were com-pared to those of semantically opaque derived words (apartment–apart) as well as “quasi-regular” itemsthat represent intermediate cases of morphological transparency (dresser–dress). Additional conditionsindependently manipulated semantic and phonological relatedness in non-derived words (semantics:
erivational morphologycouch–sofa; phonology: panel–pan). The degree of N400 ERP priming to morphological forms varieddepending on the amount of semantic and phonological overlap between word types, rather than respect-ing a bivariate distinction between derived and opaque forms. Moreover, these effects could not beaccounted for by semantic or phonological relatedness alone. The findings support the theory that mor-phological relatedness is graded rather than absolute, and depend on the joint contribution of form and meaning overlap.
. Introduction

Recent studies have examined whether grammatical morphol-gy represents an independent mechanism of language separaterom lexical organization and processing (Allen & Badecker, 2002;ongtin, Segui, & Halle, 2003; Marslen-Wilson, Bozic, & Randall,008; Morris, Grainger, & Holcomb, 2008; Plaut & Gonnerman,000; Rastle & Davis, 2008; Rueckl & Aicher, 2008). Of specific

nterest is whether morphologically complex words (e.g., debat-ble) are treated differently from morphologically simple wordse.g., debate). Morphemes provide structure to the generally arbi-rary mapping between the phonological and semantic forms ofords because sequences of phonemes (or letter strings) corre-

ponding to stems or affixes occur repeatedly and in a predictableashion in words that have similar meaning (e.g., debatable, walka-le, beatable). Many such forms are also productive, such that theyan apply widely to both familiar and novel forms (e.g., the neolo-

Please cite this article in press as: Kielar, A., & Joanisse, M.F. The role of semanpriming study of derivational morphology. Neuropsychologia (2010), doi:10

ism to blog can be used to create the derived form bloggable). Suchomplex forms therefore provide a unique opportunity to study thetructure of language representations.

∗ Corresponding author. Tel.: +1 519 661 2111x86582.E-mail address: [email protected] (M.F. Joanisse).

028-3932/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.neuropsychologia.2010.11.027

© 2010 Elsevier Ltd. All rights reserved.

That said, much of what is known about morphological com-plexity comes from studies of inflectional morphology, perhapsmost famously past tense in English. The interest in these casesstems from the observation that they tend to respect a strong dis-tinction between regular and irregular forms, which is reflectedin interesting dissociations in how regular and irregular formsare learned by children and processed in adults (McClelland &Patterson, 2002; Pinker & Ullman, 2002). In contrast, derivationalmorphology arguably does not have as strong a distinction betweenregular and irregular forms. Instead, there are more subtle differ-ences in the degree of semantic and phonological relatedness ofderived forms and stems. That is, some derived words have mean-ings that are consistently related to the meaning of their stems(e.g., happiness–happy, government–govern, beauty–beautiful). Inaddition, these forms are phonologically transparent: they donot change the pronunciation and the stress pattern of the stemupon affixation. Not all forms are equally transparent, however.The meaning of the stem apart is not completely preserved inthe word apartment; likewise, serene is related in meaning toserenity, however the stem is phonologically altered. Such dif-

tic and phonological factors in word recognition: An ERP cross-modal.1016/j.neuropsychologia.2010.11.027

ferences in morphological transparency are of interest becausethey might address theories that propose a categorical distinctionin the neurocognitive systems engaged in processing regular (or“productive”) morphemes, vs. irregular (or “unproductive”) mor-phemes.

dx.doi.org/10.1016/j.neuropsychologia.2010.11.027


http://www.sciencedirect.com/science/journal/00283932

http://www.elsevier.com/locate/neuropsychologia

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A. Kielar, M.F. Joanisse / Neu

English derivational morphology also has a number of otherharacteristics that make it better suited to psycholinguistic inquiryhan past tense. Regular and irregular derived forms are superfi-ially similar in several respects: both involve affixes, even if theyre not purported to be segmentable (e.g., apartment and dresserre not argued to be derived from apart and dress). In addition,egular and irregular derived forms do not tend to differ markedlyith respect to frequency, allowing us to more directly compare the

wo types of forms. Thus, derived words appear to be well suitedo studying the functional and neural structure of human languageystem.

.1. Theories of morphological representation

A range of theories have been brought forward arguing thatanguage users decompose and store morphologically complex

ords as constituents (Taft, 1988; Taft & Forster, 1975), and thatuch processes engage a distinct type of linguistic knowledgehat extends beyond semantics and phonology (Feldman, 2000;eldman & Prostko, 2002; Forster & Azuma, 2000; Rastle, Davis,arslen-Wilson, & Tyler, 2000; Stolz & Besner, 1998). Such theo-

ies further suggest that production is the reverse of this processuch that rules are used to combine roots and affixes.

One consequence of strict decomposition is that it might predicthat monomorphemic words such as diaper, naked and cluster arelso treated as complex. This has led to the development of dual-echanism models that handle systematic and transparent forms

eparately from idiosyncratic opaque words (Laudanna, Badecker,Caramazza, 1989; Marslen-Wilson, Tyler, Waksler, & Older, 1994;

chreuder & Baayen, 1995). On this theory whether a word isecomposed or processed as a whole depends on the semanticelationship between a word and its putative stem. While semanti-ally transparent words are decomposed into stems and affixes andepresented and processed compositionally (e.g., govern + ment),emantically opaque words are stored as unanalyzed wholes (e.g.,apart + ment). Several recent studies reported facilitation effectsor opaque derivations in the context of masked priming. Thesendings suggest the presence of an orthographic-based morpho-

ogical decomposition process operating at early stages of wordecognition (Marslen-Wilson et al., 2008; Rastle & Davis, 2008;astle, Davis, & New, 2004; but see also Feldman, O’Connor, &oscoso del Prado Martin, 2009, for evidence that such an effect is

lso modulated by semantic factors).

.2. Morphology as a convergence of codes

Decompositional accounts work well for cases in which formsan be unambiguously categorized as morphologically simple oromplex. However, they have more difficulty with intermediateases that are neither completely transparent nor opaque. Englisherivational morphology seems to be rife with these graded effects,s it exhibits many partial regularities. For instance, Gonnerman,eidenberg, and Andersen (2007) point out that a word like dressers neither fully transparent nor opaque. In modern English, it refersot to someone who dresses but to a piece of furniture used to storelothes; on the other hand, it is a noun like all transparent derivedords ending in -er, and it is related to the activity of dressing. Thus

ress and dresser do share some degree of semantic overlap, albeito a lesser extent than transparently derived pairs. Many such casesxhibit this quasi-regular character.

According to the convergence of codes view, morphology does


ot depend on a unique set of lexical and/or rule representations,ut is instead a learned mapping between orthography, phonol-gy and semantics (Gonnerman et al., 2007; Joanisse & Seidenberg,999; Plunkett & Marchman, 1993; Rumelhart & McClelland, 1986).n this theory morphology is not an all-or-none phenomenon such

PRESShologia xxx (2010) xxx–xxx

that some words are complex and others are not; instead mor-phological effects vary continuously as a function of the degree ofsemantic and phonological similarity among words. It builds onthe observation that, by definition, morphology is correlated withother types of lexical information such as orthography, phonologyand semantics. All words are hypothesized to be represented usinga single mechanism in terms of partially overlapping activationpatterns among associatively linked phonological, orthographicand semantic codes. As a result of this, morphology is not adistinct level of knowledge but a special case of lexical knowl-edge in which statistical regularities capture systematic relationsbetween form and meaning information. This theory also assumesa nonlinear interaction between semantic and formal (phonologi-cal and/or orthographic) similarity; the joint contribution of formaland semantic similarity tends to be greater than the simple effectof one or the other.

This theory takes a different view of lexical processing tothe decomposition view, which assumes that any form that isnot transparently derived should be lexicalized; according to thedecomposition view, quasi-regular pairs like dress–dresser arestored separately in the same way as fully opaque pairs (e.g.,corn–corner). A key characteristic of the convergence of codes viewis its ability to accommodate different degrees of morphologicalrelatedness. For instance some words consist of morphemes thatseem to contribute to their meaning but in a less transparent way.Thus, whereas discover might be analyzed into two morphemesdis- and cover, the contribution of morpheme cover to the mean-ing of discover is less than in uncover. Also words like grocer arestructurally similar to baker in the sense that the -er provides acue that the word refers to a person’s occupation, even though*groc is not itself a valid word stem in English. Other partial reg-ularities involve bound morphemes such as -mit and -duce thatenter into word formation but have little meaning. Words likepermit, submit and commit, or reduce and induce, are related onlybecause they contain similar morphemes that are nevertheless notfully productive (e.g., *unmit; Gonnerman et al., 2007). Finally, thistheory also distinguishes different degrees of phonological simi-larity depending on the amount of formal overlap between words.Words like pirate–piracy are more phonologically transparent thanvain–vanity, and words like sign–signal are phonologically opaque.

1.3. Priming studies of morphological processing

A key source of data about morphological processing comesfrom studies of morphological priming, across a number of dif-ferent languages (Dutch and German: Drews & Zwitserlood, 1995;Italian: Laudanna et al., 1989; Orsoloni & Marslen-Wilson, 1997;Hebrew: Bentin & Felman, 1990; Frost, Deutsch, & Forster, 2000;Serbo-Croatian: Feldman & Fowler, 1987; English: Devlin, Jamison,Matthews, & Gonnerman, 2004; Gonnerman et al., 2007; Rastleet al., 2000; Spanish: De Diego Balaguer, Sebastian-Galles, Diaz,& Rodriguez-Fornells, 2005; French: Longtin et al., 2003). Thecommon finding among these studies is that prior exposure toa morphologically complex word (e.g., government) can facili-tate processing of the target word from which it is derived(e.g., govern). Facilitation occurs under auditory (Frost, Deutsch,Gilboa, Tannenbaum, & Marslen-Wilson, 2000; Marslen-Wilson &Tyler, 1997; Marslen-Wilson & Zhou, 1999), visual (Forster, Davis,Schoknecht, & Carter, 1987; Frost, Forster, & Deutsch, 1997) andcross-modal presentation (Marslen-Wilson et al., 1994), and whenprimes and targets are separated by a number of intervening items


(Bentin & Felman, 1990; Stanners, Neiser, Hernon, & Hall, 1979;Stolz & Feldman, 1995).

Morphologically related words are also related in both form andmeaning. Thus, investigators have sought to isolate pure morpho-logical effects by contrasting effects of shared morphology with the


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ffects of shared orthographic and phonological form overlap in thebsence of morphological relationship (e.g., cars–car vs. card–car,ibbed–rib vs. ribbon–rib; Kempley & Morton, 1982; Murrell &orton, 1974), or effects resulting from semantic similarity (e.g.,

eflected–reflecting vs. held–holding; vowed–vow vs. pledge–vow;eldman, 2000; Kempley & Morton, 1982). In general it has beenound that lexical decisions are facilitated more by morphologi-ally related primes than by unrelated primes matched on formalNapps, 1989; Napps & Fowler, 1987; Stolz & Feldman, 1995: Expa) or semantic similarity to targets (Bentin & Felman, 1990; Napps,989; Stolz & Feldman, 1995).

The convergence of codes theory predicts that morphologi-al relatedness is a graded rather than absolute factor, such thatffects will tend to depend on the degree of semantic and for-al relatedness. Likewise, these effects should be interactive, such

hat joint effects of semantic and formal factors exceed what isbserved for each in isolation (Feldman, 2000; Kielar, Joanisse &are, 2008; Pastizzo & Feldman, 2009). Thus, priming effects forords that are morphologically related are explained as stem-ing from the joint contribution of formal and semantic similarity.

here is some support for this view. For example, Stanners et al.1979) found that although not significant, the difference betweendentity and morphological priming was larger in orthographi-ally dissimilar word pairs (e.g., hung–hang) than in similar wordairs (shook–shake); indeed, across a range of morphological prim-

ng studies (Basnight-Brown, Chen, Hua, Kostic, & Feldman, 2007;eldman & Basnight-Brown, 2008; Fowler, Napps, & Feldman, 1985;apps, 1989; Rueckl, Mikolinski, Raveh, Miner, & Mars, 1997; StolzFeldman, 1995; Tsapkini, Kehaya, & Jarema, 1999), there has beentrend towards less priming when formal similarity is reduced.

ikewise, in a series of cross-modal priming studies Gonnermant al. (2007) observed that the magnitude of priming was relatedo the degree of semantic and phonological transparency. Sim-larly, Morris, Frank, Grainger, and Holcomb (2007) reported araded effect of morphological structure on priming, with trans-arent items showing the greatest effect, orthographic items themallest, and opaque items showed intermediate effect. In anothertudy, Diependaele, Sandra, and Grainger (2005) found that mor-hological priming effects in Dutch and French were modulatedy the degree of meaning overlap between words. In this exper-

ment semantically transparent primes showed more facilitationhan semantically opaque primes and their orthographic controls.

Pastizzo and Feldman (2009) investigated the influence ofhared form and meaning on word recognition in word pairs thato not share morphemes (e.g., boat–float). In this study boat–floatype word pairs were compared with word pairs that shared only

eaning (e.g., swim–float), and pairs that shared only form (e.g.,oat–float) when primes were unmasked at 116 and 250 ms SOA,nd when they were forward masked and presented for 48 ms.n all three experiments they found superadditive effects of formnd meaning, such that the magnitude of facilitation for boat–floattems was greater than what could be predicted from the linearombination of the swim–float and coat–float effects alone. Thesendings indicate that readers are sensitive to the degree of for-al and semantic similarity between words, and support the idea

hat morphological structure is graded and emerges as a result ofhe convergence of semantic and ortho/phonological information.urther these data suggest that the influence of form and meaningverlap on the magnitude of morphological facilitation is interac-ive and that these effects combine in a nonlinear manner.


.4. Present study

As discussed above, much of what is known about derivationalorphology comes from behavioral priming studies. However itas not always clear how these effects are influenced by phono-

PRESShologia xxx (2010) xxx–xxx 3

logical and semantic factors, and by task parameters such asprime/target modality and SOA (for further discussion, see Kielaret al., 2008). Likewise, behavioral responses such as lexical deci-sion reflect the endpoint of multiple stages of word recognitionleaving open the possibility that the time course of processing isdifferent for different types of words; for instance it could be thatmorphological priming is different from purely form and meaningpriming even if similar-sized effects are observed in all cases. Thepresent work takes a different approach: event related potentials(ERPs) are used to provide precise information on the time courseand scalp distribution of morphological priming effects. Such anapproach might allow us to distinguish processes related to phonol-ogy, semantics and morphology by examining the relative timecourse and scalp distribution of priming effects due to each of thesefactors.

In our study, ERPs were measured to lexical decisions in the con-text of a cross-modal priming paradigm. Cross-modal priming hasbeen shown in prior studies to be sensitive to the morphologicaloverlap between words (Kielar et al., 2008; Longtin et al., 2003;Marslen-Wilson et al., 1994). Moreover, presenting prime and tar-get words in different modalities minimizes effects that are due tothe orthographic similarity of the two items. Thus, it is less likelythat any observed facilitation derives exclusively from low levelacoustic, phonetic or visual overlap between words. While ortho-graphic effects are clearly interesting (Lavric, Clap, & Rastle, 2007;Rastle & Davis, 2008), they also tend to abstract away from ques-tions regarding how morphology is encoded in spoken languageirrespective of orthography. The design of the study capitalizes onsome key characteristics of derivational morphology in English,where different processes vary with respect to their productivityand semantic transparency. Specifically, prior behavioral studiesof morphological processing have attempted to isolate effects ofmorphological structure by separately comparing effects of sharedform and shared meaning (e.g., car–cars vs. car–card, or vowed–vowvs. pledge–vow; Feldman & Soltano, 1999; Marslen-Wilson et al.,1994). As described below, we took a somewhat different approachin which the degree of shared form and shared meaning wasalso manipulated while holding morphological relatedness con-stant.

Visually presented stem forms were used as target stimuli,preceded by either their derived forms (government–govern), orby unrelated words (apartment–govern). The priming effects wereassessed by comparing the amplitude of the ERPs to related andunrelated stems. Of specific interest was the N400 ERP compo-nent, which prior studies have shown is attenuated for primedvs. unprimed targets (Bentin, McCarthy, & Wood, 1985; Kielar &Joanisse, 2010; Lavric et al., 2007; Münte, Say, Clahsen, Schiltz, &Kutas, 1999; Rodriguez-Fornells, Münte, & Clahsen, 2002). N400amplitude is thought to reflect the lexical–semantic aspects oflanguage processing (Kutas & Federmeier, 2000) such that itsamplitude reflects the ease accessing or activating a word in mem-ory (Holcomb, 1988; Holcomb & Neville, 1990).

To more closely examine the locus of ERP priming effects,semantic and phonological relationships between prime and targetpairs were manipulated concurrently with morphological relat-edness. First, semantic relatedness was examined by comparingsemantically transparent pairs (government–govern) to semanti-cally intermediate (dresser–dress) and semantically opaque pairs(apartment–apart). Second, phonological transparency was investi-gated by varying the phonological similarity of semantically similarprime-target pairs (e.g., government–govern, where the stem form is


preserved vs. serenity–serene, which changes the stress pattern andvowel of the stem). Third, the independent contributions of phonol-ogy and semantics were tested by using words that were relatedonly in phonology (e.g., dollar–doll) or meaning (e.g., jacket–coat),but were morphologically unrelated.


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Table 1Stimulus characteristics for the word items.

Transparent (+M+P+S) Quasi-regular (+M+P∼S) Opaque (+M+P−S)

M SD M SD M SD

LengthTarget 5.40 1.20 4.90 1.10 4.30 1.10Prime 8.40 1.70 8.00 1.30 7.00 1.40

Frequencya

Target 3.70 1.00 3.80 1.60 3.20 1.50Prime 2.30 1.30 1.40 1.80 2.40 1.90

Neighborhood (N)b

Target 3.10 3.60 5.20 6.00 8.10 5.80Prime 1.00 1.30 0.40 0.80 1.30 2.00

Prime-target overlap (%)Orthographyc 62 11 58 11 60 11Phonemesd 64 10 59 13 60 12Steme 100 2 94 12 96 10Sem Relf 8 0 6 2 2 2

Transparent (+M−P+S) Phonological (−M+P−S) Semantic (−M−P+S)

M SD M SD M SD

LengthTarget 6.00 1.10 3.90 1.00 5.02 1.20Prime 9.40 1.50 5.80 1.50 6.42 1.60

Frequencya

Target 2.80 1.20 3.80 1.60 3.60 1.00Prime 1.30 1.40 2.40 1.50 3.20 1.60

Neighborhood (N)b

Target 1.30 2.80 10.5 6.10 4.70 4.10Prime 0.20 0.40 3.60 4.40 1.56 2.80

Prime-target overlap (%)Orthographyc 55 9 66.10 10.10 1 5Phonemesd 51 8 67.50 9.90 4 9Steme 77 12 96 9 - -Sem Relf 8 1 2 2 8 1

a Log frequency of values from CELEX.b Number of orthographic neighbors from N-watch.c Number of letters shared in the same position between prime and target.d

ootatpptpsoadwhtdblptof(st

Number of phonemes shared between prime and targets.e Number of shared stem phonemes between prime and target.f Semantic relatedness scores.

If there is a categorical distinction between transparent andpaque words based on their morphological structure, we shouldbserve a clear distinction in the magnitude, timing or distribu-ion of ERP priming effects for transparent and opaque cases. Inddition, the quasi-regular cases should produce priming effectshat are similar to those of the opaque cases. Likewise, the decom-osition theory predicts that partially transparent prime targetairs (serenity–serene) should not produce N400 priming becausehey cannot be easily decomposed. If on the other hand, mor-hological effects arise from the correlation between formal andemantic overlap as predicted by the convergence of codes the-ry, then a modulation of N400 priming effects is expected forll morphologically related words, but the size of this effect willepend on the degree of formal and semantic overlap betweenords. Transparent derived forms (e.g., driver–drive), which areighly similar in form and meaning, are expected to attenuatehe amplitude of the N400 component more strongly than opaqueerivations (e.g., department–depart), which are related in formut not meaning. Further, quasi-regular cases (e.g., dresser–dress,

ovely–love) should produce intermediate effects relative to trans-arent and opaque forms. It is also expected that the size ofhe N400 priming effects will be affected by the degree of form


verlap. Greater priming-related attenuation of the N400 wave-orm is expected for words that are phonologically transparente.g., illness–ill), compared to pairs that are less similar (e.g.,erenity–serene), even when semantic similarity is matched acrosshe two list types.

2. Methods

2.1. Participants

Sixteen right-handed native speakers of English gave informed consent to par-ticipate in this study. All procedures were approved by a local institutional researchethics board. All participants were students at the University of Western Ontario(age range 17–32 years, M = 24, SD = 5), had normal or corrected-to normal vision,and reported no hearing impairment and no history of neurological or psychiatricillness. Participants received two course credits or $20 for participating in the study.

2.2. Materials

Six sets of prime-target pairs were constructed, in which we manipulated therelationship along the three dimensions of interest: morphology (M), phonology (P)and semantics (S). The first four sets consisted of pairs in which a word ending in aderivational suffix primed a corresponding base word. The fully transparent condi-tion (+M+P+S) consisted of 49 semantically and phonologically transparent primetarget pairs (e.g., government–govern). The partially transparent (+M−P+S) conditionconsisted of 45 prime target pairs that were semantically transparent but whichinvolved a phonological change (e.g., serenity–serene). The third condition consistedof 47 “quasi-regular” forms (+M+P∼S, e.g., dresser–dress); these represented inter-mediate cases with respect to semantic transparency, such that although the primewas not derived from the target word, there was some semantic and phonologicalrelationship. The +M+P−S condition consisted of 47 semantically opaque prime tar-get pairs (e.g., apartment–apart); in this case the prime consisted of the target plus a


derivational affix, however the two were not closely related with respect to seman-tics (i.e., they are not considered derived on a decomposition account). Finally, therewere two morphologically unrelated conditions: 50 semantically related pairs thatwere phonologically and morphologically unrelated (−M−P+S; e.g., carton–box);42 phonologically related but semantically unrelated word pairs (−M+P−S; e.g.,fairy–fair).




A. Kielar, M.F. Joanisse / Neuropsychologia xxx (2010) xxx–xxx 5

F ions:a (C2,M datao 1, FC5

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ig. 1. The 64 channel montage representing grouping of the electrodes into 11 regnalyses: CC (Cz, CPz), FC (Fz, FCz), PC: (Pz, POz), LC (C5, C3, C1, CP5, CP3, CP1), RCidline regions: CC, FC, PC; central regions: RC, LC, and parietal regions: LP, RP. The

f the effect using isovoltage maps: RF (F2, F4, F6, F8, FC2, FC4, FC6); LF (F7, F5, F3, F

A list of unrelated prime-target pairs was created for each stimulus list by par-ng each target in the set with an unrelated prime word from the same set (e.g.,llness–bright; happiness–clean). To avoid repeating items, two lists were constructeduch that each target was presented only once, half with a related prime and halfith an unrelated prime, with targets counterbalanced across lists. Each participantas tested on only a single list, so that they never saw a prime or target more than

nce. Priming effects were then identified by comparing responses to the relatednd unrelated prime lists within each condition.

The nonword filler condition consisted of orthographically legal and pronounce-ble nonword targets created by changing one or two letters of a familiar Englishord, paired with an unrelated real-word English prime (e.g., basket–KAND). Theseere included for the purpose of providing “no” response trials in the lexical decision

ask. Including nonword targets that are orthographically and phonologically legalequired participants to attend to all characteristics of items rather than employingmore surface metric such as word-likeness. In addition, a portion of the nonword

rials included a morphologically complex unrelated prime (e.g., brightness–TENCH;anity–SMOP), such that nonword trials were not predictable from the morpholog-cal status of the prime.

All item characteristics are presented in Table 1 and Appendix A, and includeeasures of word frequency (Baayen, Piepenbrock, & Gulikers, 1995), length (num-

er of letters), orthographic neighborhood (Coltheart’s N; Coltheart, Davelaar,onasson, & Besner, 1977), semantic similarity (see below), and phonological andrthographic overlap (number of phonemes or letters shared in the same positionetween prime and target, divided by the number of letters in the longer word;astizzo & Feldman, 2002).

Semantic similarity of word pairs was calculated using similarity ratingsbtained in a separate norming study, as follows: A list of 526 English word pairs wasreated, consisting of items that fit the description of the conditions listed above,long with 140 filler pairs selected at random from among English words of a sim-lar frequency to the experimental items. Word pair order was randomized andivided into two lists with the sequence of items counterbalanced. Ratings were thenbtained from 44 undergraduate students who were native speakers of English, andho did not participate in the ERP study. Raters were presented with a written ques-

ionnaire in which the prime-target word pairs were listed. They were instructed toate the similarity of meaning of the two words in each pair using a numerical scalef 1 (not at all related) to 9 (extremely related). They were encouraged to use theull scale, and were reminded in the instructions that some words sound the same


r have similar spelling but have different meaning (e.g., irony–iron). This served tomphasize that judgments should focus on how strongly word pairs are related ineaning, and that they should ignore similarity in spelling and sound.

The semantic similarity ratings were calculated by averaging responses for eachtem across raters. These were then used to select the final items for each of theix priming conditions on the basis of seeing responses in each range, and being

The data from seven electrode regions indicated by the solid line were used in theC4, C6, CP2, CP4, CP6), RP (P2, P4, P6, P8, PO4, PO6), LP (P1, P3, P5, P7, PO3, PO5).from the other electrodes indicated by the broken line were used for visualization, FC3, FC1); RT (FT8, T8, TP8); LT (FT7, T7, TP7).

careful not to let the range of responses overlap across conditions. Items in thesemantically transparent conditions (+M+P+S and +M−P+S) had ratings from 7.11 to8.10 (M = 7.70, SD = 0.20). Items in the opaque set (+M+P−S) had ratings of 1.30–2.71(M = 1.96, SD = 0.40) and items in the quasi-regular set (+M+P∼S) contained itemswith intermediate ratings between 3.60 and 6.90 (M = 5.99, SD = 0.90). In additionitems in the form only set (−M+P–S) contained word pairs with ratings of 1.30 to3.30 (M = 1.73, SD = 0.47) and items in the semantic only set (−M−P+S) containedwords with ratings of 7.00–8.42 (M = 7.59, SD = 0.48).

2.3. Behavioral task and EEG recording

On each trial participants saw a fixation cross displayed at the center of a 19′′

computer monitor and simultaneously heard an auditory prime over earphones. Thevisual target was presented 500 ms after the end of the auditory prime, in uppercaseletters using a black font on white background. Participants made a lexical decisionto the target via a keypress.

EEGs were recorded from 64 scalp sites according to the international 10–20system using Ag/AgCl sintered electrodes embedded in a cap and a reference elec-trode placed on the nose tip. Vertical and horizontal eye movements were recordedfor later off-line rejection by placing electrodes above and below the left eye andover the outer canthi of each eye. Signals were recorded with a bandpass filter of0.01–100 Hz and sampled at 500 Hz, with impedances kept at or below 5 k�. ERPswere obtained by dividing trials into epochs from −100 to 800 ms relative to tar-get onset, baseline corrected to the pre-stimulus interval, and low pass filtered at20 Hz (24 dB/oct zero phase-shift digital filter). Trials with EOG activity greater than±75 �V were excluded from further analysis, as were trials containing incorrectresponses.

To reduce the amount of data to be submitted to statistical analyses, groupsof electrode channels were averaged to into 11 scalp regions (Fig. 1). Data fromall individual electrodes were used to visualize the effects via isovoltage maps.Condition-wise ERPs were compared using repeated measures analyses of variance(ANOVAs) with prime type (related vs. unrelated) and region (CC, FC, PC, LP, RP, LC,RC) as within-subjects factors, with separate analyses performed for each word type.The N400 amplitude of each trial type was quantified by computing mean voltagesat two time intervals (early: 324–400 ms; late: 400–476 ms). Follow-up analysescompared mean primed vs. unprimed amplitudes for each condition at each region.Difference waves were also computed for the primed minus unprimed condition


for each word type. Follow-up analyses compared priming effects for morphologi-cally related words as follows: mean amplitude measures were calculated for eachcondition’s difference wave (related minus unrelated prime) and compared usingrepeated measures ANOVAs at each midline (CC, FC, PC), lateral–parietal (LP, RP) andlateral–central regions (LC, RC). The effects of semantic (+M+P: +M+P+S, +M+P−S,+M+P∼S) and phonological (+M+S: +M+P+S, +M−P+S) relatedness were evaluated


ARTICLE ING Model


6 A. Kielar, M.F. Joanisse / Neuropsyc

Table 2Mean latency (ms) and accuracy (%) data for primed and unprimed targets in eachword condition.

Condition RT Accuracy

M SD M SD

Transparent (+M+P+S)Related 529 79 99 2Unrelated 579 80 96 4Difference +50** 3

Quasi-Reg (+M+P∼S)Related 545 90 99 3Unrelated 581 70 94 6Difference +36** 5

Opaque (+M+P−S)Primed 560 83 97 4Unprimed 604 75 88 1Difference +45** 9

Transparent (+M−P+S)Primed 530 91 98 3Unprimed 596 73 95 6Difference +66** 3

Phonological (−M+P−S)Primed 566 66 97 4Unprimed 592 76 91 8Difference +26 6

Semantic (−M−P+S)Primed 578 78 97 3Unprimed 595 73 98 3

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. Results

.1. Behavioral results

Mean response latencies and error rates are presented in Table 2.ncorrect responses and reaction times more extreme than +-3SDrom the mean were removed and treated as errors. The phonolog-cal target save, quasi-regular target defy, and opaque target dam

ere also excluded based on error rates greater than 40% acrossarticipants.

The RT data were submitted to two-way repeated measuresNOVA (relatedness, word type), which revealed significant mainffects of relatedness, F1(1, 15) = 39.07, p < .01, F2(1, 270) = 113.55,< .01, word type, F1(5, 75) = 7.19, p < .01, F2(5, 270) = 3.97, p < .01,nd relatedness × word type interaction, F1(5, 75) = 3.19, p < .05,2(5, 270) = 3.49, p < .01. The results suggested stronger behavioralriming for some conditions than for others. Planned compar-

sons revealed significant facilitation for all morphologically relatedrime target pairs (fully transparent (+M+P+S), F1(1, 15) = 15.79,< .01, F2(1, 48) = 31.92, p < .01; partially transparent (+M−P+S),

1(1, 15) = 29.76, p < .01, F2(1, 44) = 50.96, p < .01; quasi-regular+M+P∼S) F1(1, 15) = 16.76, p < .01, F2(1, 44) = 20.66, p < .01, andpaque (+M+P–S), F1(1, 15) = 18.35, p < .01, F2(1, 45) = 35.66, p < .01).he priming effect for phonologically related (−M−S+P) words wasarginal by subjects and not significant by items, F1(1, 15) = 4.26,= .06, F2(1, 40) = 3.13, p > .05; for the semantic condition (−M+S−P)

he priming effect was marginal by subjects and significant bytems, F1(1, 15) = 4.24, p = .06; F2(1, 49) = 5.04, p < .05.

A congruent analysis of accuracy data revealed a main effect of


rime, F1(1, 15) = 21.32, p < .01, F2(1, 270) = 41.14, p < .01, word type,1(5, 75) = 5.93, p < .01, F2(5, 270) = 3.84, p < .01 and prime by con-ition interaction, F1(5, 75) = 5.50, p < .01, F2(5, 270) = 4.39, p < .01.he investigation of this interaction revealed that responses wereore accurate for related than unrelated targets in fully trans-


parent (+M+P+S), F1(1, 15) = 6.82, p < .05, F2(1, 48) = 3.42, p > .05;opaque (+M+P−S), F1(1, 15) = 16.05, p < .01, F2(1, 45) = 20.67, p < .01;quasi-regular (+M+P∼S), F1(1, 15) = 8.41, p < 05, F2(1, 44) = 10.37,p < .01, and phonological conditions, F1(1, 15) = 20.08, p < .01 F2(1,40) = 8.43, p < .01; but were equally accurate for the semantic, [bothF < 1] and partially transparent (+M−P+S) conditions F1(1,15) = 2.24,p > .05, F2(1, 44) = 3.03, p > .05.

3.2. ERP results

The grand average ERPs to the related and unrelated verbs ineach condition at the midline frontal, central and parietal regions(FC, CC, PC) are shown in Fig. 2. The magnitude of the N400 prim-ing effects (unrelated–related) is plotted for each condition at themidline central region (CC) in Fig. 3A and B; isovoltage maps showthe distribution of the early and late N400 priming effects acrossthe whole scalp in Fig. 3C.

3.2.1. Early N400 primingWe first examined priming for each condition at the early N400

time window (324–400 ms); the analysis for the fully transpar-ent (+M+P+S) targets revealed significant main effects of region,F(6, 90) = 3.47, p < .01, relatedness, F(1, 15) = 12.54, p < .01, and arelatedness × region interaction, F(6, 90) = 4.45, p < .05. Follow-upanalyses revealed significant priming at all seven electrode regions(Table 3). The partially transparent (+M−P+S) condition also yieldedsignificant main effects of relatedness, F(1, 15) = 6.89, p < .05 andregion, F(6, 90) = 3.86, p < .01 and an interaction, F(6, 90) = 3.43,p < .01. Significant priming was found at the midline regions andat the right central and right parietal regions. The quasi-regularcondition (+M+P∼S) showed significant main effects of region, F(6,90) = 4.48, p < .01 and relatedness, F(1, 15) = 9.10, p < .01, but nointeraction, F(6, 90) = 2.50, p > .05. The follow-up analyses revealedsignificant priming effects across all seven regions. For the opaquecondition (+M+P−S) there was a significant main effect of region,F(6, 90) = 5.76, p < .01, but not relatedness, F(1, 15) = 1.58, p > .05and no interaction [F < 1]. The phonological condition (−M+P−S)revealed a significant main effect of region, F(6, 90) = 8.62, p < .01,but not relatedness, F(1, 15) = 3.02, p > .05, and no interaction, F(6,90) = 2.57, p > .05. Finally, the semantic condition showed a maineffect of region, F(6, 90) = 4.35, p < .01, but not relatedness [F < 1],and no interaction, F(6, 90) = 1.52, p > .05.

Planned comparisons of the N400 priming effects acrossmorphologically related conditions +M+P (+M+P+S: fullytransparent, +M+P∼S: quasi-regular, +M+P−S: opaque), usingunprimed–primed difference waves, revealed a greater prim-ing effect for fully transparent words compared to opaquetargets at the lateral–central (LC, F(1, 15) = 5.75, p < .05; RC,F(1,15) = 10.79, p < .01), lateral–parietal (LP, F(1, 15) = 5.23, p < .05,RP, F(1, 15) = 17.31, p < .01), and midline regions (CC, F(1, 15) = 11.20,p < .01; PC, F(1, 15) = 13.71, p < .01). Priming effects for quasi-regulartargets were significantly greater than for opaque targets at themidline regions (CC, F(1, 15) = 4.81, p < .05, and PC, F(1, 15) = 7.20,p < .05). In contrast, there were no significant differences betweenfully transparent and quasi-regular words at any of the regions(all Fs < 1). Analogous analyses comparing difference waveformsfor +M+S word pairs (+M+P+S: fully transparent vs. +M−P+S:partially transparent), revealed greater priming effects for fullytransparent targets compared partially transparent targets at thelateral–parietal and midline regions (LP, F(1, 15) = 4.76, p < .05; RP,F(1, 15) = 5.28, p < .05), PC, F(1, 15) = 4.42, p < .05).


3.2.1.1. Effects of semantic and phonological overlap. Effects ofsemantic and phonological relatedness were evaluated by com-paring amplitudes of the difference waves (unrelated–related)across morphologically related conditions. The first repeated





d and

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Fig. 2. Grand average ERPs (n = 16) elicited by relate

easures ANOVA, evaluated effects of semantic overlap


+M+P: +M+P+S, +M+P∼S, +M+P−S) and phonological over-ap (+M+S: +M+P+S, +M−P+S) across at all seven electrodeegions (LC, RC, LP, RP, FC, CC, PC). This analysis revealedain effect of region, F(6, 90) = 6.36, p < 01, relatedness con-

ition, F(3, 45) = 4.94, p < .01, and region x relatedness condition

unrelated words at the midline regions (FC, CC, PC).

interaction, F(18, 270) = 2.04, p < .05, indicating that effects


of semantic and phonological relatedness differed at eachregion.

To investigate effect of semantic relatedness analyses of vari-ance with three levels of +M+P conditions (+M+P+S; +M+P∼S;+M+P−S) as the dependent variable were conducted at each elec-





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ig. 3. N400 priming results. (A) Difference waves (unrelated–related) at the midlinach morphologically related condition. (B) Mean differences in amplitude (unrelatime intervals. (C) Topographical distribution of priming effects across the scalp ba

rode region. These analyses revealed significant effect of semanticelatedness at the lateral–central (LC, F(2, 30) = 5.85, p < .01; RC: F(2,0) = 4.28, p < .05), lateral–parietal (LP, F(2, 30) = 4.89, p < .05; RP,(2, 30) = 6.08, p < .01), and at the midline regions (CC, F(2, 30) = 7.07,< .01; PC, F(2, 30) = 7.86, p < .01). At these regions the analyses

evealed graded effects of semantic relatedness (see Fig. 3A and


), such that the amplitude of priming effect was greater for fullyransparent (+M+P+S) and quasi-regular (+M+P∼S) words com-ared to opaque (+M+P−S) targets, (transparent vs. opaque: LC,(1, 15) = 10.69, p < .01, RC, F(1, 15) = 10.29, p < 01, LP, F(1, 15) = 8.80,< 05, RP, F(1, 15) = 15.09, p < .01, CC, F(1,15) = 15.40, p < .01, PC,

n (CC). The difference waves illustrate the electrophysiological effect of priming forlated) for all conditions at the midline region (CC) in 324–400 ms and 400–476 msthe difference waveforms (unrelated–related) using isovoltage mapping.

F(1, 15) = 17.14, p < .01; quasi-regular vs. opaque, LC, F(1, 15) = 8.39,p < .05, LP, F(1, 15) = 7.56, p < 05, CC, F(1, 15) = 8.26, p < .05, PC, F(1,15) = 9.76, p < .01). However, there was no significant difference inthe amplitude of the priming effects between fully transparent andquasi-regular targets at any of the regions (all Fs < 1).

The effect of phonological relatedness was evaluated at each


electrode region by entering two +M+S conditions (+M+P+S;+M−P+S) as the dependent variable into a repeated measuresANOVA. This analysis showed a significant effect of phonolog-ical relatedness at the left parietal (LP) region, F(1, 15) = 4.81,p < .05, revealing greater amplitude priming for fully transpar-





Table 3Analysis of N400 priming effects across conditions and electrode regions. F-values are listed, for the comparison of primed vs. unprimed ERPs.

Word type Electrode region

CC FC PC LC RC LP RP

Early N400 (324–400 ms)Trans (+M+P+S) 12.85** 10.57* 14.05** 7.19* 14.75** 8.69* 17.55**

Quasi (+M+P∼S) 9.82* 13.18** 9.69** 6.10* 11.32** 5.80* 7.12*

Opaque (+M+P−S) ns ns ns ns ns ns nsTrans-p (+M−P+S) 7.67* 6.77* 6.65* ns 12.60** ns 7.21*

Phon (−M+P−S) 5.37* ns ns ns ns ns nsSem (−M−P+S) ns ns ns ns ns ns ns

Late N400 (400-476 ms)Trans (+M+P+S) 5.11* ns 8.50* 5.64* 9.12** 7.80* 11.16**

Quasi (+M+P∼S) 5.25* ns 5.93* 8.13* 7.23* 8.37* 5.25*

Opaque (+M+P−S) ns ns ns ns ns 10.86** nsTrans-p (+M−P+S) ns ns ns ns ns ns nsPhon (−M+P−S) ns ns ns ns ns ns ns

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* p < .05.** p < .01.

nt (+M+P+S) words compared to partially transparent targets+M−P+S).

.2.2. Late N400 primingWithin the 400–475 time window, the analysis of the fully

ransparent (+M+P+S) condition revealed significant main effects ofelatedness, F(1, 15) = 7.44, p < .05, region, F(6, 90) = 5.80, p < .01, andelatedness by region interaction, F(6, 90) = 3.63, p < .05. The follow-p analyses indicated significant priming at all midline and lateralegions except FC (Table 3, bottom). For the partially transparent+M−P+S) condition there was a significant main effect of region,(6, 90) = 6.78, p < .01, but not relatedness, F(1, 15) = 1.61, p > .05,nd no interaction [F < 1]. For the quasi-regular targets there wereignificant main effects of region, F(6, 90) = 7.12, p < .01 and related-ess, F(1, 15) = 6.82, p < .05 but no interaction F(6, 90) = 1.98, p > .05.ollow up analyses revealed significant facilitation at all regionsxcept FC. The opaque condition yielded a significant main effectf region, F(6, 90) = 5.69, p < .01, but not relatedness, F(1, 15) = 1.82,> .05; the relatedness x region interaction was significant, F(6,0) = 3.53, p < .05. The follow-up analyses indicated a significantriming effect at the LP region, F(1, 15) = 10.86, p < .01. The semanticondition showed a significant main effect of region, F(6, 90) = 4.48,< .01, but not relatedness, [F < 1] and no interaction F(6, 90) = 1.45,> .05). Similarly, the phonological condition showed a significantain effect of region, F(6, 90) = 2.69, p < .05, but not relatedness, F(1,

5) = 1.67, p > .05, and no interaction F(6, 90) = 1.12, p > .05.As in the early time window, planned comparisons of the wave-

orms in the three +M+P conditions (+M+P+S: fully transparent,M+P∼S: quasi-regular, +M+P−S: opaque) revealed no significantifferences in mean amplitudes among conditions at this time

nterval (Fs < 1).

.2.2.1. Effects of semantic and phonological overlap. As in thearlier time window, effects of semantic and phonological relat-dness were evaluated by comparing amplitudes of priming effectsunrelated–related) across morphologically related conditions. Therst ANOVA evaluated effects of semantic overlap (+M+P: +M+P+S,M+P∼S, +M+P−S) and phonological overlap (+M+S: +M+P+S,M−P+S) across all seven electrode regions (LC, RC, LP, RP, FC,C, PC). This analysis revealed a significant main effect of region,(6, 90) = 7.13, p < .01, and a region x relatedness interaction, F(18,


70) = 2.57, p < .05, indicating that effects of semantic and phono-ogical relatedness differed at each region. The effect of semanticelatedness was investigated by conducting analyses of varianceith three levels of +M+P conditions (+M+P+S; +M+P∼S, +M+P−S)

ntered as the dependent variable. This analysis revealed signif-

ns ns ns ns

icant effect of semantic relatedness at PC region, F(2, 30) = 3.53,p < .05, indicating that the amplitude of priming effects for fullytransparent targets was significantly larger than those of theopaque targets, F(1, 15) = 5.87, p < .05. There were no significantdifferences between fully transparent and quasi-regular words atany of the regions (all Fs < 1). Similarly, the effect of phonologicalrelatedness was evaluated by entering two levels of +M+S condi-tions (M+P+S and +M−P+S) as dependent variables in a repeatedmeasures ANOVA. There were no significant differences amongconditions in these comparisons (LC, F(1, 15) = 1.09, p > .05; LP, F(1,15) = 3.32, p >. 05; RP, F(1, 15) = 2.02, p > .05; PC, F(1, 15) = 1.91,p > .05, RC, FC, CC: all Fs < 1).

4. Discussion

There is increasing interest in using English derivational mor-phology to provide insights into the cognitive and brain basesof language representation. This includes a number of behav-ioral priming investigations (Feldman & Basnight-Brown, 2008;Gonnerman et al., 2007; Marslen-Wilson et al., 1994; Marslen-Wilson et al., 2008; Rastle et al., 2000, 2004), patient studies(Badecker & Caramazza, 1991; Ito, Sugioka, & Hagiwara 1996)and functional neuroimaging (Bozic, Marslen-Wilson, Stamatakis,Davis, & Tyler, 2007; Devlin et al., 2004; Gold & Rastle, 2007;Lehtonen, Vorobyev, Hugdahl, Tuokkola, & Laine, 2006; Lehtonen etal., 2007; Vannest, Polk, & Levis, 2005). Many of these studies havereported dissociations between morphologically decomposableand opaque forms. These results have been taken as an indicationof a specialized mechanism for processing the morphological struc-ture of words. On this view, morphologically transparent forms(e.g., engagement) are decomposed into a base word (engage) andbound morphemes (-ment); in contrast, opaque forms (e.g., apart-ment) are stored as whole words in the lexicon such that theyare lexically separate from the base forms. Such decompositiontheories also assume that morphological effects in priming aredriven by morphology-specific knowledge that it is distinct fromphonological and semantic relatedness (Kempley & Morton, 1982;Marslen-Wilson et al., 1994, 2008; Napps, 1989; Stolz & Besner,1998).

In the present study, we examined the alternative interpretationthat these dissociations are due to differences in form and meaning


relationships among words. Priming effects between derived wordsand stems might reflect the systematic phonological, orthographicand semantic similarity between primes and targets (Devlin etal., 2004; Feldman & Prostko, 2002; Feldman & Basnight-Brown,2008; Gonnerman et al., 2007; Plaut & Gonnerman, 2000; Rueckl


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Aicher, 2008; Rueckl et al., 1997). An interesting aspect of thisccount is that the magnitude of priming varies with the degree oform or meaning relatedness, similar to what has previously beenbserved in past tense (Kielar et al., 2008; Kielar & Joanisse, 2010;astizzo & Feldman, 2002). On this view, morphological regularitieseflect cases where the mapping between sound and meaning is thetrongest and most consistent. However, the effects are quasireg-lar such that they can also occur for putatively non-derived casess well.

The contribution of the ERP measures used in the present studyas the addition of a continuous measure of changes in mental

omputation over time, which reflects pre-decision stages of wordnalysis. This contrasts with behavioral priming studies, in whichT data reflects the final output of multiple processes. This maybscure the full scope of processing and lead to a failure to observeifferences in processing that resolve prior to response generation.hus, it is possible that pairs of words that are formally related willield a similar magnitude of priming as morphologically relatedairs, but due to different underlying processes. ERP studies helpvercome this by allowing separable cognitive mechanisms to beevealed either temporally or spatially.

In this study, the separate effects of shared form and sharedeaning were contrasted with effects occurring when the prime

nd target overlapped in both form and meaning. We capitalizedn a key characteristic of derivational morphology in English, thaterived forms vary with respect to their phonological and semanticransparency. The semantic dimension was examined by compar-ng ERP priming effects of semantically transparent derived words+M+P+S: government–govern) to those of semantically opaqueord pairs (+M+P−S: apartment–apart) and quasi-regular cases

hat are not derived, but which are typically rated as sharing someegree of meaning nevertheless (+M+P∼S: dresser–dress). Likewise,he effect of formal relatedness was investigated for transparentorms by holding semantic relatedness constant and varying onlyhonological similarity (e.g., +M+P+S: dancer–dance; vs. +M−P+S:erenity–serene). In addition, strictly meaning- and form-basedriming was assessed to investigate the degree to which observedorphological effects are the consequence of simple phonological

r semantic similarity. Including stimuli on the continuum of formnd meaning overlap allowed us to observe a graded contributionf this information to word recognition.

The results showed that both phonological and semantic fac-ors modulated the amplitude of N400 priming. Significant N400riming effects were observed for the two sets of transparent pairs+M+P+S and +M−P+S) and for quasi-regular pairs (+M+P∼S). More-ver, we found that morphological priming effects were gradedn nature and modulated by phonological and semantic factors.he largest priming effect was obtained for words that are closelyelated in both meaning and sound (+M+P+S: government–govern),ompared to intermediate cases that overlapped more weaklyith respect to semantics (+M+P∼S: dresser–dress) or phonology

+M−P+S: obscene–obscenity). These in turn produced greater N400riming effects than items that were only semantically associatedbox–carton) or word pairs that were phonologically similar butemantically unrelated (i.e., opaque +M+P−S: apartment–apart, andhonological −M+P−S: dollar–doll).

The results are consistent with earlier reports of graded effects oforphological structure in an ERP masked priming study (Morris

t al., 2007). This earlier study found the largest priming effectsor morphologically transparent forms (teacher–teach), muchmaller effects for an orthographic control condition (nickel–nick),


nd an intermediate effect for morphologically opaque wordscenter–cent). The influence of form and meaning on morphologicalrocessing also has been reflected in the pattern of fMRI activation.evlin et al. (2004) measured fMRI suppression during maskedresentation of morphologically, semantically and orthographi-


cally related prime target pairs. The priming related reductionin activation for morphologically related words overlapped withorthographic effects in the posterior occipital–temporal cortex, andwith semantic effects in the middle temporal cortex, indicating thatmorphological effects reflect the contribution of orthographic andsemantic similarity. Similarly, a masked priming study conductedby Gold and Rastle (2007) found attenuated fMRI responses in thefusiform and posterior-occipital gyri that were equivalent for mor-phologically and orthographically related prime target pairs. Takentogether these results suggest that brain regions involved in pro-cessing of orthographic and semantic input make an equivalentcontribution to encoding this information in both morphologicaland non-morphological contexts.

The time-varying pattern of the ERP priming effects in thepresent study suggests that the exact pattern of facilitation changeswith the time course of word processing. In the early N400 timewindow significant priming effects were observed for morpholog-ically related words and to a lesser extent also for phonologicalrelatedness. The effect was the greatest for the fully transparentword pairs (+M+P+S), followed by the quasi-regular (+M+P∼S),and partially transparent targets (+M−P+S), which shared weakersemantic and phonological overlap, respectively. For the seman-tically opaque condition, similarity in form was not consistentlycorrelated with similarity in meaning and thus these items did notshow significant facilitation.

In the later time interval the N400 priming effects persisted fortransparent (+M+P+S) and quasi-regular targets, but were greatlyattenuated for all other conditions. These results indicate thatincreased processing time strengthened the joint influence of for-mal and semantic overlap. The fully transparent and quasi-regularwords (+M+P+S, +M+P∼S) consistently overlapped on both seman-tic and formal dimensions, while the remaining prime targetpairs closely overlapped on only one dimension, either meaning(+M−P+S, −M−P+S) or form (+M+P−S, −M+P−S), but not both.Thus, morphological effects are most pronounced when both formand meaning are strongly and consistently correlated, supportingthe view that the word recognition system is sensitive to the degreeof interaction between semantic and formal codes.

Some recent masked priming studies have observed prim-ing effects for both morphologically related pairs (breaker–break)as well as for morphologically opaque forms that neverthelessend in morpheme-like segments (brother–broth). This includesbehavioral priming (Marslen-Wilson et al., 2008; McCormick,Rastle, & Davis, 2008; Rastle et al., 2004), as well as ERP prim-ing studies examining either N400 or earlier-going components(Lavric et al., 2007; Morris et al., 2008). These findings havebeen interpreted as reflecting a morpheme decomposition mech-anism that occurs at early stages of visual word recognition,and which is not sensitive to the semantic properties of words.Such effects appear inconsistent with what was found in thepresent study. That said, priming of morphologically opaqueforms appears to be isolated to the orthographic (i.e., visual)domain. Thus, we failed to find similar effects in the presentstudy when using cross-modal priming; instead we observedsignificantly stronger priming effects for semantically transpar-ent pairs compared to opaque pairs. We suggest this is dueto the fact that primes were presented auditorily, which min-imized the influence of visual or orthographic mechanisms onprocessing. This procedure also involved relatively long SOAs,which was necessarily the case since auditory words unfoldover several hundred milliseconds. Overall, the fact that we


observed greater priming for morphologically transparent pairsthan for opaque pairs seems consistent with the interpretationthat early-going morphological effects like the ones discussedby Rastle and Davis (2008) reflect orthographic mechanisms,rather than a modality-general morphological decomposition


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echanism. Consistent with this interpretation, Longtin et al.2003) found facilitation of both transparent and opaque tar-ets using masked visual priming paradigm, but only transparentords primed their stems under cross-modal presentation. Sim-

lar results were reported by Diependaele et al. (2005) in Dutch,ho again found that opaque derivations produced facilitation

nly in the visual modality, and not in a similar cross-modalondition.

The finding that semantic similarity modulated ERP primingf morphologically related forms is also consistent with a recenttudy by Feldman et al. (2009), who compared forward maskedriming effects for semantically transparent (e.g., coolant–cool) andemantically opaque word pairs (e.g., rampant–ramp). As in theresent study, they found that morphological facilitation was sig-ificantly greater for transparent than opaque prime target pairs.he present results support their interpretation that semanticimilarity can influence the processing of derivationally suffixedords.

A critical finding of the present study is the failure to iden-ify effects that can be solely attributed to morphology. Effects of

orphological relatedness were clearly modulated by the degreef semantic and/or phonological relatedness. Similarly, we failedo identify effects that occurred for all and only all words end-ng in a derivational suffix (regardless of transparency). Finally,o priming effects occurred solely for transparent forms to thexclusion of all others. This seems consistent with the theoryhat morphology reflects not a unique representational mode ofanguage, but rather a special case in which there exists a sys-ematic mapping between form and meaning information. Fornstance, the convergence of codes theory of morphology pro-oses that morphological regularities emerge from overlappingctivation patterns in the associatively linked phonological, ortho-raphic and semantic codes (Joanisse & Seidenberg, 1999; Plaut &onnerman, 2000). This is a single mechanism in the sense that

t encodes both transparent and opaque words. The transparentnd opaque derivations, as well as regular and irregular forms, dif-er because they draw on meaning and form to different degrees.he theory also assumes nonlinear effects of semantic and for-al (orthographic and/or phonological) overlap, such that form

imilarity will have the greatest effect for words that also sharesemantic relationship (Pastizzo & Feldman, 2009). For exam-

le, lock–sock are formally similar but differ significantly in termsf meaning, whereas lock and key overlap in terms of seman-ics but have minimal formal overlap. Accordingly, lock shouldroduce less facilitation for the target sock than for the morpho-

ogically related word locker, since the latter overlaps with lockn both form and meaning. Thus, morphological priming arises

hen these overlapping cues lead to facilitation in accessing a wordRueckl et al., 1997). Perhaps the most striking demonstration ofhis comes from the recent finding of superadditive priming effectsn morphologically unrelated word pairs that nevertheless shareverlapping form and meaning (e.g., boat–float, Pastizzo & Feldman,009).

Another aspect of the present results is that words in the seman-ic −M−P+S condition did not produce significant N400 effects. Atrst glance, the failure to observe semantic priming seems prob-

ematic given previous studies that reported a sensitivity of N400o semantically related word pairs in visual lexical decision tasksBrown & Hagoort, 1993; Deacon, Hewitt, Yang, & Nagata, 2000;olcomb & Neville, 1990). However, it is also known that the N400ffect can be influenced by stimulus list characteristics, such as


he proportion of semantically related and unrelated word pairsn the stimulus set (Brown, Hagoort, & Chwilla, 2000; Holcomb,988). The present results may also reflect this. In the stimuluset used in this study, the proportion of prime target pairs shar-ng a formal relationship was greater than those sharing a strictly


semantic overlap (82% vs. 18% of related trials). This may have ledthe word recognition system to tune more to the formal character-istics of items and less to the semantic relationship between words(Bodner & Mason, 2003; de Groot, 1984; Holcomb, 1988; Napps& Fowler, 1987; Tweedy, Lapinsky, & Schvaneveldt, 1997). Similarresults were found in our previous priming study of English pasttense where the proportion of formally related trials was greaterthan proportion of words sharing semantic relationship (Kielar &Joanisse, 2010).

We also noted that RT results differed from ERP results; thatis, significant priming was found for the opaque condition in RTs,but not in the ERP data. A similar dissociation was obtained inour previous N400 priming study (Kielar & Joanisse, 2010), andalso by other authors (Brown & Hagoort, 1993; Hamberger &Friedman, 1992; Jescheniak, Schriefers, Garrett, & Friederici, 2002;Kellenbach, Wijers, & Mulder, 2000; Münte et al., 1999; Rodriguez-Fornells et al., 2002). The data suggest that N400 amplitude andlexical decision response latencies reflect partially independentcognitive processes that may not be sensitive to the same stagesof word analysis. The N400 effects examined in the present studyrepresent an intermediate stage of word analysis occurring prior toresponse selection. In contrast, the reaction time effects reflect theoutcome of information processing that extends beyond the ERPcomponents of interest here. Therefore, it is possible that ERPs aremore sensitive to the interactive effects between form and mean-ing, leading to larger effects for words that overlap in both, relativeto those word pairs that only share a single dimension of related-ness.

5. Conclusions

Processing of morphologically complex words was studied usingERPs and priming. The results provide a measure of how form andmeaning of words are computed over time and how these factorscontribute to the processing of morphological structure. Of inter-est was whether the differences in the degree of form and meaningoverlap between derivationally related words would be reflected inthe ERP responses. Decomposing the N400 into early and late-goingeffects revealed time-varying patterns of facilitation. The resultsindicated that processing differences between derived and non-derived forms are not strictly due to morphological complexity butare instead tied to differences in the basic modes of representationused in processing words. Thus, the results are consistent with theview that morphology is processed as a function of statistical reg-ularities in sounds and meanings. Because morphologically relatedwords are similar in both respects, they are connected in a sys-tematic and structured way that influences the word recognitionprocess.

Acknowledgments

AK was supported by a Postgraduate Scholarship from the Nat-ural Sciences and Engineering Research Council (NSERC). MFJ wassupported by a Discovery Grant from NSERC and a New Investiga-tor Award from the Canadian Institutes for Health Research. ERPinfrastructure was provided by the Canada Foundation for Innova-tion. We are grateful to Randy Lynn Newman for helpful commentsin the early stages of this study.

Appendix A.


Prime and target pairs used in the experiment. Itemsmatched for natural log frequency values from CELEX, ortho-graphic neighborhood (N-watch; Davis, 2005), orthographic length,phonological and orthographic overlap and semantic relatednessrating.


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ppendix A (Continued)

Prime Target Phon overlap Ortho overlap Sem Rel

Item Freq Len N Item Freq Len N (%phonemes) (%letters)

Fully transparent (+M+P+S)Illness 3.5 7 0 Ill 4.1 3 2 40.0 42.9 7.8Happiness 3.3 9 0 Happy 4.9 5 2 57.1 44.4 7.8Toughness 0.8 9 1 Tough 3.6 5 5 50.0 55.6 7.7Talker 0.7 6 4 Talk 5.6 4 8 60.0 66.7 7.3Sickness 2.8 8 0 Sick 4.2 4 15 50.0 50.0 7.7Weakness 3.1 8 0 Weak 3.8 4 8 50.0 50.0 7.9Thickness 1.3 9 0 Thick 4.2 5 3 50.0 55.6 7.8Farmer 3.4 6 4 Farm 4.2 4 6 80.0 66.7 7.7Prisoner 2.8 8 1 Prison 4.2 6 1 71.4 75.0 7.8Discovery 3.5 9 1 Discover 3.8 8 0 88.9 88.9 7.8Painter 2.9 7 4 Paint 3.7 5 6 66.7 71.4 7.5Selection 3.4 9 0 Select 3.0 6 0 71.4 66.7 7.6Dirty 3.7 5 1 Dirt 3.0 4 5 80.0 80.0 7.8Hunter 2.4 6 2 Hunt 3.3 4 8 66.7 66.7 7.8Cleaner 2.2 7 4 Clean 4.5 5 3 66.7 71.4 7.6Swimmer 0.8 7 3 Swim 3.2 4 5 66.7 57.1 7.7Acceptable 3.4 10 1 Accept 4.6 6 1 75.0 60.0 7.8Dependable 0.6 10 0 Depend 3.5 6 1 75.0 60.0 7.4Nicely 2.3 6 1 Nice 5.0 4 7 60.0 66.7 7.2Messy 1.4 5 2 Mess 3.3 4 7 75.0 80.0 7.8Predictable 0.0 11 0 Predict 2.3 7 0 77.8 63.6 7.4Brightness 1.7 10 0 Bright 4.3 6 4 57.1 60.0 7.9Bitterness 2.3 10 0 Bitter 3.6 6 8 71.4 60.0 7.9Comfortable 0.0 11 0 Comfort 3.7 7 1 71.4 63.6 7.8Judgment 3.2 8 0 Judge 4.0 5 3 42.9 50.0 7.3Harmful 2.1 7 0 Harm 3.6 4 7 66.7 57.1 7.4Dancer 1.8 6 4 Dance 3.8 5 1 66.7 83.3 7.8Friendly 3.9 8 0 Friend 5.2 6 0 71.4 75.0 7.2Debatable 0.2 9 0 Debate 3.7 6 2 71.4 55.6 7.1Delightful 2.6 10 0 Delight 3.3 7 0 62.5 70.0 7.8Loneliness 2.4 10 1 Lonely 3.3 6 1 62.5 50.0 7.9Successful 4.4 10 0 Success 4.6 7 0 66.7 70.0 8.0Slowly 4.9 6 0 Slow 4.4 4 12 66.7 66.7 7.8Rigidity 1.4 8 0 Rigid 3.2 5 0 62.5 62.5 7.6Taxation 3.0 8 0 Tax 4.7 3 9 42.9 37.5 7.5Starvation 2.0 10 0 Starve 1.9 6 0 55.6 50.0 7.8Complexity 2.5 10 0 Complex 4.2 7 0 70.0 70.0 7.9Density 2.5 7 1 Dense 2.6 5 2 57.1 57.1 7.5Examination 0.0 11 0 Examine 3.4 7 0 60.0 54.5 7.7Imagination 0.0 11 0 Imagine 4.5 7 1 60.0 54.5 7.8Immunity 1.2 8 1 Immune 1.8 6 0 57.1 62.5 7.8Intensity 3.1 9 1 Intense 3.6 7 1 66.7 66.7 7.8Alteration 1.1 10 0 Alter 2.9 5 3 55.6 50.0 7.5Relaxation 2.2 10 0 Relax 2.9 5 1 55.6 50.0 7.8Election 4.3 8 3 Elect 1.6 5 2 57.1 62.5 7.6Attraction 2.6 10 0 Attract 3.0 7 0 62.5 70.0 7.3Security 4.8 8 0 Secure 3.5 6 0 62.5 62.5 7.6Modernity −0.1 9 0 Modern 5.1 6 0 66.7 66.7 7.2Stupidity 2.0 9 0 Stupid 3.6 6 0 66.7 66.7 8.0Mean 2.3 8.4 0.8 3.7 5.4 3.1 63.6 62.1 7.7SD 1.3 1.7 1.3 0.9 1.2 3.6 10.1 10.6 0.2

Partially transparent (+M−P+S)Decision 4.6 8 1 Decide 4.2 6 2 42.9 50.0 7.9Deception 2.1 9 1 Deceive 1.7 7 1 37.5 44.4 7.8Serenity 1.2 8 0 Serene 1.6 6 0 50.0 62.5 7.1Criminal 3.5 8 0 Crime 3.9 5 4 37.5 50.0 7.6Observation 0.0 11 0 Observe 2.9 7 0 60.0 54.5 7.9Admiration 2.8 10 0 Admire 2.8 6 0 44.4 50.0 7.9Combination 0.0 11 0 Combine 2.8 7 1 50.0 54.5 7.6Condemnation 0.0 12 0 Condemn 2.0 7 0 54.5 58.3 7.6Consultation 0.0 12 0 Consult 2.7 7 1 63.6 58.3 7.7Curiosity 3.1 9 0 Curious 3.9 7 1 66.7 55.6 7.9Electricity 0.0 11 0 Electric 3.8 8 0 54.5 72.7 7.5Equality 3.4 8 0 Equal 4.2 5 0 50.0 62.5 7.7Expectation 0.0 11 0 Expect 4.7 6 1 55.6 54.5 7.6Explanation 0.0 11 0 Explain 4.4 7 0 50.0 45.5 8.1Hostility 2.8 9 0 Hostile 3.3 7 0 55.6 66.7 7.4Inspiration 0.0 11 0 Inspire 1.7 7 0 50.0 54.5 7.8

Please cite this article in press as: Kielar, A., & Joanisse, M.F. The role of semantic and phonological factors in word recognition: An ERP cross-modalpriming study of derivational morphology. Neuropsychologia (2010), doi:10.1016/j.neuropsychologia.2010.11.027

Invitation 3.0 10 0 Invite 2.5 6 1 44.4 50.0 7.8Maturation 0.2 10 1 Mature 2.9 6 2 55.6 50.0 7.4Morality 2.8 8 0 Moral 4.2 5 2 62.5 62.5 7.2Nobility 1.3 8 1 Noble 2.8 5 1 50.0 37.5 7.5Rapidity 0.7 8 0 Rapid 3.6 5 3 62.5 62.5 7.2


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Stability 2.7 9 0 Stable 3.3 6 1 44.4 44.4 7.7Validity 1.9 8 0 Valid 2.7 5 1 62.5 62.5 8.0Perfection 2.4 10 0 Perfect 4.2 7 0 66.7 70.0 8.0Introduction 0.0 12 0 Introduce 3.1 9 0 54.5 66.7 8.1Absorption 1.4 10 0 Absorb 2.5 6 0 50.0 50.0 7.9Ability 4.3 7 1 Able 5.8 4 3 28.6 28.6 7.1Severity 1.4 8 0 Severe 3.6 6 1 50.0 62.5 7.6Legality 0.2 8 0 Legal 4.2 5 1 62.5 62.5 7.2Derivation 0.2 10 0 Derive 2.1 6 0 40.0 50.0 7.2Divinity 0.9 8 0 Divine 2.9 6 2 50.0 62.5 7.3Solidity 0.5 8 1 Solid 3.8 5 0 50.0 62.5 7.3Accusation 1.8 10 0 Accuse 1.6 6 0 50.0 50.0 7.8Acidity 0.3 7 0 Acid 3.1 4 3 57.1 57.1 7.7Adaptation 2.1 10 0 Adapt 2.4 5 2 55.6 45.5 7.4Provocation 0.0 11 0 Provoke 2.0 7 0 60.0 45.5 7.1Brutality 1.8 9 0 Brutal 2.6 6 0 55.6 66.7 7.5Fatality −0.2 8 0 Fatal 2.7 5 1 50.0 62.5 7.7Deprivation 0.0 11 0 Deprive 0.0 7 0 50.0 50.0 7.6Adoration 1.0 9 0 Adore 1.3 5 3 50.0 44.4 7.8Sanity 1.8 6 1 Sane 2.1 4 18 33.3 50.0 7.8Vulgarity 0.7 9 0 Vulgar 2.2 6 0 55.6 66.7 8.0Expiration −0.6 10 0 Expire 0.3 6 2 44.4 50.0 8.0Compilation 0.0 11 0 Compile −0.3 7 0 50.0 54.5 7.4Aspiration 0.6 10 0 Aspire 1.0 6 0 44.4 50.0 7.3Mean 1.3 9.4 0.2 2.8 6.0 1.3 51.4 54.9 7.6SD 1.4 1.5 0.4 1.2 1.1 2.8 8.4 9.0 0.3

Opaque (+M+P−S)Apartment 3.8 9 0 Apart 4.7 5 0 55.6 55.6 2.0Organize 2.6 8 0 Organ 2.6 5 0 71.4 62.5 1.5Important 5.9 9 0 Import 2.5 6 1 66.7 66.7 2.0Customer 2.7 8 0 Custom 2.7 6 0 75.0 75.0 2.2Fasten 0.9 6 3 Fast 4.6 4 11 66.7 66.7 2.4Figment −0.4 7 1 Fig 1.7 3 15 42.9 42.9 1.9Penance 0.3 7 0 Pen 3.0 3 20 50.0 42.9 1.6Ponder 0.9 6 5 Pond 2.7 4 4 66.7 66.7 1.4Awful 4.1 5 0 Awe 2.1 3 10 50.0 40.0 2.3Authorize −0.1 9 0 Author 3.4 6 0 71.4 66.7 2.5Appliance 0.5 9 0 Apply 3.9 5 3 57.1 44.4 2.3Tenable 0.5 7 0 Ten 5.4 3 16 50.0 42.9 1.4Moment 5.8 6 0 Mom 1.7 3 9 50.0 50.0 1.3Damage 3.9 6 0 Dam 2.1 3 11 60.0 50.0 1.5Manage 3.6 6 1 Man 7.0 3 19 60.0 50.0 2.1Permit 2.9 6 1 Perm 0.0 4 4 80.0 66.7 1.4Peasant 3.0 7 0 Peas 2.1 4 11 33.3 57.1 1.8Rampant 0.3 7 1 Ramp 1.5 4 8 57.1 57.1 1.9Warrant 1.9 7 0 War 5.8 3 15 50.0 42.9 1.7Posterity 1.0 9 0 Poster 1.8 6 6 66.7 66.7 2.0Witness 3.1 7 3 Wit 2.5 3 12 50.0 42.9 1.5Fairy 2.4 5 4 Fair 4.5 4 4 75.0 80.0 1.9Army 4.7 4 2 Arm 4.7 3 5 75.0 75.0 1.9Bitter 3.6 6 8 Bits 3.5 4 15 60.0 50.0 1.6Drawer 2.7 6 1 Draw 4.1 4 5 60.0 66.7 1.9Belly 2.8 5 7 Bell 3.7 4 13 75.0 80.0 1.5Tapestry 0.8 8 0 Tape 3.3 4 10 37.5 50.0 2.3Corner 4.6 6 1 Corn 3.2 4 10 66.7 66.7 1.5Dentist 1.9 7 0 Dent 1.3 4 14 57.1 57.1 1.8Chapter 4.7 7 2 Chap 3.1 4 6 50.0 57.1 1.3Message 4.2 7 1 Mess 3.3 4 7 60.0 57.1 1.3Principal 3.5 9 0 Prince 3.5 6 1 62.5 55.6 1.7Beaker 0.2 6 3 Beak 1.6 4 11 60.0 66.7 1.9Party 5.9 5 6 Part 6.2 4 17 80.0 80.0 1.7Gravity 2.9 7 0 Grave 3.4 5 9 42.9 57.1 1.8University 5.3 10 0 Universe 3.5 8 0 70.0 70.0 1.9Passive 2.8 7 1 Pass 4.6 4 12 60.0 57.1 2.6Department 4.7 10 1 Depart 1.8 6 1 66.7 60.0 2.7Portable 1.9 8 0 Port 3.3 4 12 57.1 50.0 2.7Cranky 0.0 6 2 Crank 0.8 5 6 83.3 83.3 2.6Hostage 0.8 7 1 Host 3.0 4 8 66.7 57.1 2.6Designate −0.1 9 0 Design 4.4 6 1 50.0 66.7 2.5


Gradual 2.6 7 0 Grade 2.6 5 7 42.9 57.1 2.6Backer −0.4 6 4 Back 7.1 4 16 60.0 66.7 2.3Copious 1.0 7 0 Copy 3.6 4 3 50.0 42.9 2.4Accordion −0.2 9 0 Accord 2.0 6 0 71.4 66.7 2.3Lament 0.6 6 1 Lame 1.4 4 15 33.3 66.7 2.1


G

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ppendix A (Continued)Prime Target Phon overlap Ortho overlap Sem Rel


Mean 2.4 7.0 1.3 3.2 4.3 8.1 59.7 59.6 2.0SD 1.9 1.4 2.0 1.5 1.1 5.8 12.2 11.4 0.4

Quasi-regular (+M+P∼S)Careful 4.0 7 0 Care 5.2 4 22 60.0 57.1 6.4Coverage 2.4 8 0 Cover 4.7 5 10 83.3 62.5 6.7Various 4.9 7 0 Vary 3.1 4 3 50.0 42.9 6.9Deviant 1.7 7 1 Defy 1.5 4 3 28.6 28.6 6.9Publicity 3.1 9 0 Public 5.9 6 0 62.5 66.7 6.5Highness 0.6 8 0 High 5.9 4 2 40.0 50.0 6.7Lovely 4.1 6 2 Love 5.9 4 12 60.0 66.7 5.9Health 4.9 6 2 Heal 1.5 4 15 50.0 66.7 5.9Package 2.8 7 0 Pack 3.3 4 15 75.0 57.1 5.3Lately 2.5 6 1 Late 5.3 4 14 60.0 66.7 4.2Jointly 1.5 7 0 Joint 3.7 5 2 71.4 71.4 5.0Momentous 1.0 9 0 Moment 5.8 6 0 75.0 66.7 6.0Sensuous 1.0 8 0 Sense 5.7 5 2 57.1 50.0 5.9Useful 4.4 6 0 Use 6.2 3 0 40.0 50.0 6.3Meaningless 0.0 11 0 Meaning 4.3 7 3 75.0 63.6 6.0Tasteless 0.5 9 0 Taste 4.0 5 6 57.1 55.6 6.3Payable 1.3 7 1 Pay 5.2 3 22 40.0 42.9 6.4Coolness 1.0 8 0 Cool 4.0 4 9 50.0 50.0 6.7Sleepless 0.9 9 0 Sleep 4.8 5 5 57.1 55.6 6.6Reflector −0.4 9 0 Reflect 3.2 7 1 87.5 77.8 6.9Darken 0.1 6 1 Dark 5.2 4 10 80.0 57.1 7.0Breakage −0.2 8 0 Break 4.7 5 6 66.7 62.5 6.8Partial 2.4 7 1 Part 6.2 4 17 50.0 57.1 6.1Coloration 0.0 10 0 Color 0.0 5 1 62.5 50.0 6.0Vanity 1.9 6 1 Vain 2.6 4 7 33.3 33.3 6.5Centrality −0.5 10 0 Central 4.8 7 0 70.0 70.0 6.8Civility 0.1 8 0 Civil 4.2 5 2 62.5 62.5 6.5Convexity 0.0 9 0 Convex 0.1 6 1 66.7 66.7 6.7Recitation −0.6 10 0 Recite 1.0 6 1 44.4 50.0 6.9Formality 1.6 9 1 Formal 4.0 6 2 66.7 66.7 6.4Seniority 0.3 9 0 Senior 3.9 6 1 66.7 66.7 6.9Treatment 4.2 9 0 Treat 3.6 5 2 50.0 55.6 7.0Variety 4.1 7 0 Vary 3.1 4 3 42.9 42.9 6.6Pulsation −0.8 9 0 Pulse 2.2 5 1 50.0 44.4 6.8Booker 0.0 6 4 Book 5.6 4 12 60.0 66.7 3.7Jubilant 0.1 8 0 Jubilee 0.5 7 0 62.5 62.5 4.6Coolant −1.1 7 0 Cool 4.0 4 9 50.0 57.1 5.2Business 5.5 8 1 Busy 4.1 4 4 50.0 37.5 3.8Alienate −0.2 8 0 Alien 2.8 5 1 71.4 62.5 5.1Dresser 1.5 7 2 Dress 4.4 5 5 66.7 71.4 4.9Streamline −0.9 10 0 Stream 3.6 6 2 62.5 60.0 3.6Salutation 0.2 10 0 Salute 1.8 6 0 62.5 50.0 6.6Vitality 1.9 8 0 Vital 3.8 5 0 50.0 62.5 6.3Adversity −0.1 9 0 Adverse 2.0 7 0 66.7 66.7 6.7Mindless 1.3 8 1 Mind 5.9 4 12 57.1 50.0 5.6Fruitful 1.5 8 0 Fruit 4.0 5 0 57.1 62.5 4.5Fluidity 0.4 8 0 Fluid 2.7 5 0 62.5 62.5 7.0Mean 1.4 8.0 0.4 3.8 4.9 5.2 59.0 57.6 6.0SD 1.8 1.3 0.8 1.6 1.1 6.0 12.7 10.6 0.9

Phonological (−M+P−S)Planet 3.2 6 2 Plan 4.6 4 3 66.7 66.7 1.4Funk 0.0 4 7 Fun 3.8 3 13 75.0 75.0 2.3Agent 3.8 5 0 Age 5.5 3 9 40.0 60.0 1.6Seat 4.4 4 15 Sea 5.1 3 10 66.7 75.0 1.4Pawn 0.8 4 7 Paw 1.2 3 16 66.7 75.0 1.4Paint 3.7 5 6 Pain 4.3 4 10 75.0 80.0 1.3Mast 0.9 4 16 Mass 4.7 4 14 75.0 75.0 1.4Lawn 3.0 4 7 Law 5.1 3 16 66.7 75.0 1.4Dollar 2.7 6 1 Doll 2.9 4 11 60.0 66.7 1.5Cellar 2.3 6 1 Cell 3.6 4 10 60.0 66.7 3.4Match 4.0 5 7 Mat 2.0 3 22 66.7 60.0 1.5Dragon 2.0 6 0 Drag 2.9 4 7 66.7 66.7 1.5Linen 2.9 5 4 Line 5.4 4 22 40.0 80.0 1.9Electrode 0.1 9 0 Elect 1.6 5 2 62.5 55.6 1.8Enterprise 3.4 10 0 Enter 3.9 5 2 55.6 50.0 1.9


Bulletin 1.4 8 0 Bullet 2.5 6 4 71.4 75.0 2.2Stampede 0.4 8 0 Stamp 2.6 5 3 71.4 62.5 2.9Socket 1.6 6 3 Sock 1.1 4 12 60.0 66.7 1.8Tentacle 0.0 8 0 Tent 3.6 4 15 66.7 50.0 1.4


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N

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Market 4.9 6 2 Mark 4.4 4 12 80.0 66.7 1.4Dialect 1.3 7 0 Dial 1.5 4 4 50.0 57.1 1.5Termite −0.3 7 0 Term 4.4 4 3 80.0 57.1 1.4Cardiac 1.0 7 0 Car 5.6 3 18 50.0 42.9 1.3Bullet 2.5 6 4 Bull 3.2 4 15 60.0 66.7 1.6Blanket 2.8 7 0 Blank 2.9 5 6 71.4 71.4 1.8Rocket 2.1 6 6 Rock 4.4 4 12 60.0 57.1 1.6Saddle 2.2 6 2 Sad 3.8 3 17 75.0 50.0 1.4Willow 1.3 6 3 Will 7.7 4 16 75.0 66.7 1.7Pillow 2.6 6 2 Pill 2.6 4 14 75.0 66.7 1.7Needle 2.4 6 0 Need 6.1 4 7 75.0 66.7 1.5Aspirin 1.1 7 0 Aspire 1.0 6 0 66.7 71.4 1.6Barn 2.3 4 12 Bar 4.2 3 16 75.0 75.0 1.9Freak 1.6 5 3 Free 5.3 4 3 75.0 60.0 1.5Keep 5.9 4 9 Key 4.3 3 5 66.7 50.0 1.8Panel 3.0 5 1 Pan 3.4 3 19 75.0 60.0 1.3Farm 4.2 4 6 Far 6.2 3 17 75.0 75.0 2.1Beef 2.8 4 6 Bee 1.9 3 13 66.7 75.0 1.7Tent 3.6 4 15 Ten 5.4 3 16 75.0 75.0 1.4Heaven 3.6 6 2 Heavy 4.9 5 2 75.0 66.7 1.4County 3.8 6 2 Count 4.4 5 3 80.0 83.3 1.7Tinsel 0.1 6 0 Tin 3.4 3 17 60.0 50.0 2.9Severe 3.6 6 1 Sever 0.7 5 6 80.0 83.3 2.5Mean 2.4 5.8 3.6 3.8 3.9 10.5 67.5 66.1 1.7SD 1.5 1.5 4.4 1.6 0.8 6.1 9.9 10.1 0.5

Semantic (−M−P+S)Fortune 3.4 7 0 Wealth 4.1 6 1 0.0 0.0 7.8Clean 4.5 5 3 Wash 3.7 4 12 0.0 0.0 7.4Construct 2.3 9 0 Build 4.3 5 2 0.0 0.0 8.2Jacket 3.5 6 2 Coat 4.0 4 9 20.0 0.0 8.2Battle 4.3 6 3 Fight 4.6 5 8 25.0 0.0 8.0Tremble 1.3 7 0 Shiver 1.5 6 2 0.0 0.0 7.2Honor 0.0 5 1 Glory 3.1 5 0 25.0 0.0 6.9Corridor 3.3 8 0 Passage 3.6 7 1 0.0 0.0 7.2Dominant 3.2 8 0 Supreme 3.2 7 0 0.0 0.0 7.4Detergent 1.6 9 1 Soap 3.0 4 6 0.0 0.0 7.8Evidence 5.0 8 0 Proof 3.4 5 0 0.0 0.0 8.3Pursue 2.8 6 0 Follow 4.5 6 3 0.0 0.0 6.8Gamble 1.6 6 2 Risk 4.2 4 5 0.0 0.0 7.0Stomach 3.7 7 0 Belly 2.8 5 7 0.0 0.0 8.1Sweet 3.8 5 5 Candy 1.8 5 7 0.0 0.0 7.2Professor 4.3 9 1 College 4.3 7 1 0.0 0.0 6.7Marriage 4.5 8 1 Wedding 3.5 7 4 0.0 12.5 7.8Doctor 4.9 6 0 Nurse 3.5 5 2 0.0 0.0 7.2Lemon 2.6 5 1 Sour 2.4 4 11 0.0 0.0 7.1Brother 4.4 7 1 Sister 4.4 6 2 16.7 0.0 7.0Disease 4.1 7 0 Cancer 4.3 6 4 0.0 0.0 7.6Nephew 2.0 6 0 Niece 1.6 5 1 25.0 16.7 7.1Ancient 4.4 7 0 Old 6.6 3 1 0.0 0.0 7.9Automobile 2.7 10 0 Car 5.6 3 18 0.0 0.0 8.4Breeze 2.4 6 2 Wind 4.7 4 12 0.0 0.0 7.5Carton 1.0 6 2 Box 4.4 3 11 0.0 0.0 7.1Conceal 2.5 7 0 Hide 3.5 4 10 0.0 0.0 8.0Forest 4.2 6 0 Woods 3.5 5 6 0.0 16.7 7.7Idea 5.6 4 0 Notion 3.6 6 4 0.0 0.0 7.6Imitate 1.7 7 0 Copy 3.6 4 3 0.0 0.0 7.8Large 5.9 5 2 Huge 4.7 4 1 25.0 0.0 8.3Injury 3.1 6 1 Hurt 4.1 4 4 16.7 0.0 8.0Intelligent 0.0 11 0 Smart 3.1 5 1 10.0 0.0 8.3Crooked 1.7 7 2 Bent 3.7 4 14 0.0 0.0 6.9Celebrity 0.9 9 0 Star 4.0 4 7 0.0 0.0 7.7Fast 4.6 4 11 Quick 4.2 5 2 0.0 0.0 8.2Rotate 1.2 6 0 Spin 2.1 4 5 0.0 0.0 8.0Aroma 1.1 5 0 Smell 4.1 5 5 0.0 0.0 8.1Trash 1.3 5 2 Rubbish 2.9 7 0 25.0 0.0 7.3Sofa 3.0 4 3 Couch 2.2 5 6 0.0 25.0 8.2Chair 4.7 5 2 Stool 2.2 5 3 0.0 0.0 7.2


Drench 0.0 6 2 Soak 1.9 4 3 0.0 0.0 7.9Corpse 2.3 6 0 Mummy 2.5 5 4 0.0 0.0 6.8Profit 3.6 6 0 Gain 3.9 4 8 0.0 0.0 7.7Blanket 2.8 7 0 Sheet 3.5 5 5 14.3 0.0 7.2Fire 5.0 4 15 Smoke 4.1 5 4 0.0 0.0 7.2Destroy 3.7 7 0 Break 4.7 5 6 0.0 0.0 7.2


IN PRESSG

N

1 ropsychologia xxx (2010) xxx–xxx

A

Phon overlap Ortho overlap Sem Rel

Len N (%phonemes) (%letters)

5 3 0.0 0.0 7.28851

R

A

B

B

B

B

B

B

B

B

B

C

D

D

d

D

D

D

D

F

F

F

F

F

19, 866–877.Lehtonen, M., Cunillera, T., Rodriguez-Fornells, A., Hulten, A., Tuomainen, J., & Laine,

M. (2007). Recognition of morphologically complex words in finish: Evidence

ARTICLEModel


6 A. Kielar, M.F. Joanisse / Neu


Prime Target

Item Freq Len N Item Freq

Water 6.1 5 7 Steam 2.9Place 6.3 5 4 Location 2.8Happy 4.9 5 2 Cheerful 2.9Mean 3.2 6.4 1.6 3.6SD 1.6 1.6 2.8 1.0

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