decomposition, lookup, and recombination: meg evidence for the

Decomposition, lookup, and recombination: MEG evidence for the FullDecomposition model of complex visual word recognition

Joseph Fruchter a,⇑, Alec Marantz a,b,c

a Department of Psychology, New York University, New York, NY, USAb Department of Linguistics, New York University, New York, NY, USAc NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

a r t i c l e i n f o

Article history:Received 7 August 2014Accepted 1 March 2015

Keywords:MEGMorphological decompositionLexical frequencyVisual word recognition

a b s t r a c t

There is much evidence that visual recognition of morphologically complex words (e.g., teacher) proceedsvia a decompositional route, first involving recognition of their component morphemes (teach + -er).According to the Full Decomposition model, after the visual decomposition stage, followed by morphemelookup, there is a final ‘‘recombination’’ stage, in which the decomposed morphemes are combined andthe well-formedness of the complex form is evaluated. Here, we use MEG to provide evidence for thetemporally-differentiated stages of this model. First, we demonstrate an early effect of derivational familyentropy, corresponding to the stem lookup stage; this is followed by a surface frequency effect,corresponding to the later recombination stage. We also demonstrate a late effect of a novel statisticalmeasure, semantic coherence, which quantifies the gradient semantic well-formedness of complexwords. Our findings illustrate the usefulness of corpus measures in investigating the component pro-cesses within visual word recognition.

! 2015 Elsevier Inc. All rights reserved.

1. Introduction

1.1. Theories of morphologically complex visual word recognition

According to the Full Decomposition model of morphologicallycomplex visual word recognition (Taft, 1979, 2004; Taft & Forster,1975), complex visual words, composed of a stem and affix, arerecognized via a multi-stage process of decomposition, lookup,and recombination. In the first stage, a complex visual word isdecomposed into its component morphemes, based on the visualform of these morphemes. In the second stage, the lexical entriesfor the component morphemes are accessed from their form.Finally, after the lexical entries for the morphemes have beenlooked up, the separate morphemes are then recombined intothe complex form. For example, according to the FullDecomposition model, a complex visual word like cats would firstbe decomposed into cat + -s, the lexical entries for the stem cat andthe suffix -s would then be consulted, and finally, the meanings ofthe stem (i.e., a particular type of furry animal) and suffix (i.e., plu-ral) would be combined to obtain the meaning of the whole formcats (i.e., several of a particular type of furry animal). A more recent

version of a decompositional model was proposed by Crepaldi,Rastle, Coltheart, and Nickels (2010), in which they used a behav-ioral masked morphological priming effect for irregular verbs toargue for an initial morpho-orthographic stage of decomposition(for regular and pseudo-regular words), followed by a lemma-levelstage of lexical access for the component morphemes of a word(irrespective of orthographic regularity); notably, this theory pre-dicts differential effects for regular and irregular verbs, since theformer should benefit from priming at both the orthographic leveland the lemma level, while the latter should only benefit frompriming at the lemma level. Following up on this study, Fruchter,Stockall, and Marantz (2013) found an early (i.e., !170 ms) MEGmasked morphological priming effect for irregular verbs, which,contrary to Crepaldi et al. (2010), they used to argue for an initialstage of form-based decomposition for both regular and irregularverbs.1

In contrast to the decompositional view of complex visual wordrecognition, some models allow for recognition of complex wordsvia their entire surface forms without decomposition (i.e., without

http://dx.doi.org/10.1016/j.bandl.2015.03.0010093-934X/! 2015 Elsevier Inc. All rights reserved.

⇑ Corresponding author at: Department of Psychology, New York University, 6Washington Place, 2nd Floor, New York, NY 10003, USA.

E-mail address: [email protected] (J. Fruchter).

1 It is worth noting that the behavioral results from Fruchter et al. (2013) wereconsistent with the predictions of Crepaldi et al. (2010), namely that the regular verbsdisplayed a greater degree of RT priming (23 ms) relative to the irregular verbs(14 ms), although the statistical significance of this difference was not assessed in thestudy.

Brain & Language 143 (2015) 81–96

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access to the component morphemes of the word). In particular,Giraudo and Grainger’s (2000) supralexical model argues for an ini-tial stage of whole-word processing, followed by a later stage ofdecomposition. Other models that fall under this general umbrellainclude Pinker and Prince’s (1988) dual-route model, which arguesfor non-decompositional processing for irregular verbs, andBaayen, Milin, Ður -devic, Hendrix, and Marelli’s (2011) amorphousmodel, which explains seemingly morphological effects duringvisual word recognition without recourse to a specifically morpho-logical level of representation (but see Marantz, 2013, for an inter-pretation of Baayen et al., 2011, that is in fact consistent withcontemporary linguistic theories of morphology). Thus, therearises a crucial dividing line in the various theoretical accountsof morphologically complex visual word processing: is morpho-logical decomposition taken as a prerequisite of lexical access forcomplex words? In the present study, we consider the theorieswith the clearest predictions on this issue, namely the FullDecomposition model and the supralexical model, in order to askthe question: does morphological decomposition takes place priorto, or subsequent to, lexical access? We use MEG data from a visuallexical decision experiment to provide new evidence that, as pre-dicted by the Full Decomposition model, there is an initial stageof morphological decomposition, a later stage of lexical access forthe component morphemes of a complex word, as well as a finalrecombination stage.

1.2. Findings from experimental studies

In previous psycholinguistic and neurolingusitic work, the pro-cessing of morphologically complex words has been shown toinvolve a number of properties crucially tied to the identity,derivational and inflectional morphological family, and combina-toric potential of their component morphemes. At 80–100 ms aftervisual presentation of a complex word, there is an early MEGevoked response known as the M100 (associated with the Type Iresponse of Tarkiainen, Helenius, Hansen, Cornelissen, &Salmelin, 1999), localized to the occipital lobe, which is sensitiveto visual features of the stimulus. At 150–200 ms post-stimulusonset, the transition probability from stem to derivational affix(e.g., p(teacher|teach)) modulates an MEG evoked response knownas the M170, localized to the Visual Word Form Area in the leftfusiform gyrus (Solomyak & Marantz, 2010). Behavioral work hasshown that even pseudo-morphological structure plays a role invisual word recognition: in a masked priming paradigm, both gen-uinely affixed (e.g., teacher-TEACH) and pseudo-affixed (e.g., cor-ner-CORN) pairs exhibited significant priming effects in lexicaldecision, while comparable orthographic controls (e.g., brothel-BROTH) failed to show such behavioral facilitation (Rastle, Davis,& New, 2004). Following up on this work, Lewis, Solomyak, andMarantz (2011) found that the transition probability frompseudo-stem to pseudo-affix (e.g., p(corner|corn)) also modulatesthe M170, suggesting that this evoked response represents a brainindex of an early stage of visual form-based morphologicaldecomposition. Several EEG masked priming studies have demon-strated morphological priming effects at the N250 response, bothin the case of derived forms (Lavric, Clapp, & Rastle, 2007;Morris, Frank, Grainger, & Holcomb, 2007; Morris, Grainger, &Holcomb, 2008), as well as inflected forms (Morris & Stockall,2012; Royle, Drury, Bourguignon, & Steinhauer, 2012). Recently,Beyersmann, Iakimova, Ziegler, and Colé (2014) demonstratedEEG effects of morphological priming at 100–250 ms, while effectsof semantic priming were not yet visible at that latency. UsingMEG, Lehtonen, Monahan, and Poeppel (2011) found similar effectsfor regular derived words at a latency of !225 ms, and Fruchteret al. (2013) demonstrated effects of morphological priming forirregular past tense verbs at an even earlier latency (!170 ms). In

summary, there is extensive evidence for an early stage of ortho-graphic decomposition into the component morphemes of a com-plex visual word, regardless of whether the word is derived orinflected, regular or irregular, or even if the word is only apparentlycomplex (as in the case of pseudo-affixed items, such as corner).

After the initial decomposition stage, lexical access for thedecomposed morphemes arguably takes place in the left superiorand middle temporal regions at 300–400 ms post-stimulus onset,as indexed by the M350 evoked response (Pylkkänen & Marantz,2003). The localization of lexical access to the left temporal lobe,and specifically the left middle temporal gyrus, is supported bythe findings of many previous studies (e.g., Binder et al., 1997;Friederici, 2012; Hickok & Poeppel, 2007; Indefrey & Levelt,2004). In the fMRI domain, Devlin, Jamison, Matthews, andGonnerman (2004) observed effects of masked morphologicaland semantic priming in the left middle temporal gyrus; similarly,Gold and Rastle (2007) found effects of masked semantic primingin the same region. In the MEG literature, the relationship betweenthe M350 and lexical access is supported by the fact that measuresrelated to the lexical identity and morphological family of the stemmodulate the M350; in particular, Solomyak and Marantz (2010)showed an effect of lemma frequency on the M350, andPylkkänen, Feintuch, Hopkins, and Marantz (2004) showed thatfamily frequency and family size modulate the M350, albeit inways that were not entirely predicted. In the EEG domain, manystudies have demonstrated lexical effects at the ERP analogue ofthe M350, namely the N400 response (e.g., Van Petten & Kutas,1990; see Lau, Phillips, & Poeppel, 2008, for a review of findingsrelating the N400 to lexical access). Recently, Laszlo andFedermeier (2014), using a single-trial correlational analysis ofERPs, demonstrated a separate time course of effects for variablesthat track relatively perceptual features (e.g., bigram frequency)as compared to variables that track relatively semantic features(e.g., number of lexical associates), with the former variablesbecoming relevant as early as !130–150 ms, while the latter vari-ables became relevant only at !300–340 ms.

Finally, after visual morphological decomposition, as well aslexical access for the decomposed morphemes, the last stage ofprocessing for complex words arguably involves the recombinationof stem and affix (Taft, 1979, 2004). Several previous studies haveprovided evidence suggestive of the presence of such a recombina-tion stage for complex words. In particular, Domínguez, de Vega,and Barber (2004) compared morphological and pseudo-morpho-logical overt (i.e., unmasked) priming effects in Spanish, and foundthat the former led to a sustained attenuation of the N400, whilethe latter produced a similar attenuation initially (i.e., at 250–350 ms), but which differentiated itself over time to form a delayedN400. Lavric, Rastle, and Clapp (2011) found comparable effects inEnglish for morphological (e.g., teacher-TEACH) and pseudo-mor-phological (e.g., corner-CORN) pairs. Extending these findings toa non-priming paradigm, Lavric, Elchlepp, and Rastle (2012)demonstrated similar effects for single word recognition of affixed(e.g., teacher) and pseudo-affixed (e.g., corner) words, providingfurther support for the notion of a two-stage process of complexvisual word recognition, in which affixed and pseudo-affixedwords are initially processed in a similar fashion but diverge fromeach other over time. Although these studies did not specificallyrefer to Taft’s (1979, 2004) recombination stage as an explanationof their results, they nevertheless provide initial empirical supportfor the presence of an early morpho-orthographic decompositionstage, followed by a later stage in which the semantics of the com-plex word become relevant.

In the present study, we report findings from an MEG visuallexical decision experiment with suffixed words. Our results buildon the prior experimental findings from the literature in a numberof important ways. First, we investigate whether a statistical

82 J. Fruchter, A. Marantz / Brain & Language 143 (2015) 81–96

measure derived from the lexical frequencies of the morphologicalfamily members of a stem, termed the derivational family entropy,plays a role in modulating the brain activity associated with lexicalaccess (i.e., the M350). Our use of this variable is inspired by amodel of morphological processing (Moscoso del Prado Martín,Kostic, & Baayen, 2004) that argues that complex word recognitionis most affected by the statistical relations among members of amorphological family, rather than mere stem frequencies. Here,we test the applicability of this model of morphological processingto neural activity, as measured with MEG: in particular, we exam-ine the role of derivational family entropy in modulating activitywithin the cortical regions and time windows associated with lexi-cal access for the decomposed morphemes.

In addition to investigating the lexical access stage of complexword recognition, we also aim to provide neural evidence for thesubsequent recombination stage. In particular, derivational familyentropy, as an index of stem lookup, should modulate brain activ-ity prior to the effects of surface frequency, as an index ofrecombination of stem and affix. Furthermore, we argue that therecombination stage must involve a semantic evaluation of thecombination of the component morphemes of a given complexword. In other words, regardless of the fact that a particular com-plex word (e.g., predictable) has been previously encountered, theFull Decomposition model predicts that during recognition, onemust nevertheless evaluate the well-formedness of the combina-tion of its constituent parts (e.g., predict + -able). In the presentstudy, we develop a novel methodology for quantifying the degreeof semantic well-formedness, which we call semantic coherence.Given the timing of the various stages within the FullDecomposition model, we expect that the effects of semanticcoherence should occur at the later stages of the visual recognitionprocess: in particular, the semantic effects, like the effects of sur-face frequency, should occur after the effects of derivational familyentropy, which relate to stem lookup.

1.3. Measures of information content

In our analysis of the morpheme lookup stage of the FullDecomposition model, we examine the effects of the informationcontent of a word’s morphological families upon lexical accessfor its previously decomposed stem. We use the derivational fam-ily entropy measure to capture this information content.Derivational family entropy, as will be defined formally withinSection 2.1.1.1, is a statistical measure of the distribution of lexicalfrequencies within the derivational family of the stem, such thatmore balanced distributions of frequencies correlate with higherentropy, and conversely, distributions of frequencies dominatedby a few family members have lower associated entropy.2 Ouruse of this measure follows the work of Moscoso del Prado Martínet al. (2004), who used a measure of morphological family entropywhich also included the inflectional families nested within thederivational families; here, we simplify the measure to only modelthe distribution of frequencies within the derivational families.

Moscoso del Prado Martín et al. (2004) studied the entropy ofmorphological families primarily as a component of the total infor-mation residual of a word, which is simply the sum of the surfacefrequency and family entropy measures. Thus, the informationresidual measure encapsulates both the information content ofthe word itself, as well as the support it receives from its morpho-logical families. Taking advantage of the temporal resolution of

MEG, here we separately examine both the hypothesized earlierderivational family entropy effect on lexical access for a stem, aswell as the hypothesized later surface frequency effect onrecombination of stem and affix.

1.4. Neural basis of semantic composition

In our analysis of the recombination stage of the FullDecomposition model, we also examine the effects of semanticcomposition, that is to say the building of complex meanings fromthe meanings of individual parts, within morphologically complexwords. Previous work on semantic composition within words hasshown that the semantic constraints with respect to combining aparticular prefix with a stem play a role in modulating an MEGresponse at around the same latency as the M350 evoked response,but from a medial frontal area shown to be sensitive to variablesassociated with semantic composition during sentence compre-hension (Pylkkänen, Oliveri, & Smart, 2009). The characteristicsof this ‘‘Anterior Midline Field’’ (AMF) in the study, namely itslatency and its modulation by the semantic relationship betweenmorphemes, are consistent with the properties of the recombina-tion stage of complex word recognition. However, the notion ofsemantic selection involved in the study was categorical:Pylkkänen et al. (2009) studied the reversative prefix un-, whichcan be attached to verbs whose action can be undone in a particu-lar way, but which produces unacceptable forms when attached toverbs of the wrong semantic type. For reasons elaborated uponwithin their article, phrases like the toilet was being unclogged weretaken as examples of acceptable semantic forms, while phrases likethe toilet was being unflushed were taken as examples of semanticviolations. Thus the Pylkkänen et al. (2009) study used a violationparadigm and a binary comparison between acceptable and unac-ceptable forms: the unacceptable forms yielded increased activa-tion in the brain region and in the time window associated withsemantic composition from previous studies (though these priorstudies focused on semantic composition between words, as willbe discussed further below). Their work thus leaves open the ques-tion of whether the semantic AMF effects for derivationally com-plex words are only obtained within a pure violation paradigm,or whether comparable effects would also be obtained during amore ecologically valid paradigm with fully acceptable complexwords, distributed over a range of semantic well-formedness.

Here, we are interested in exploring precisely this question,namely whether the relevant semantic variable modulating theAMF is more general and would be sensitive to a gradient ofsemantic coherence between stem and affix for fully acceptable(and existing) words. Varying semantic well-formedness isexpected even for productive affixation since the affixes them-selves are associated with semantic properties that make themmore or less compatible with different stems. Taking one illustra-tive example from the theoretical literature on derivational mor-phology, consider Riddle’s (1985) observation that wordscontaining the suffix -ness (as opposed to -ity) tend to denoteembodied traits; this may lead to the prediction that words suchas acuteness, which refers to a mental ability rather than an embod-ied trait, have a lower semantic coherence of stem and affix thanwords such as awkwardness, a canonical embodied trait. Riddlepoints to pairs of words built on the same stem like brutalnessand brutality as evidence for the claim that -ness carries semanticimplications that make it ‘‘cohere’’ better to some stems thanother. The word brutality is used to describe a behavior (‘‘his bru-tality in that situation was shocking’’) while the word brutalnessdescribes an embodied state (‘‘He’s got a brutalness the likes ofwhich I’ve never seen,’’ Riddle p. 439). Semantic coherence in thissense could be expected to modulate the frequency of word use; ifacuteness is less semantically coherent than awkwardness, we may

2 For example, the derivational family of the complex word teacher would be: teach,teacher, teaching, and teachable. The family would have a high associated entropy ifthe frequencies of each word were in the same general range; conversely, the familywould have a low associated entropy if the frequency of, say, teach were significantlyhigher than the frequencies of the other family members.

J. Fruchter, A. Marantz / Brain & Language 143 (2015) 81–96 83

also expect it to be used less often by English speakers, and thushave a lower surface frequency, after controlling for other relevantdifferences between the two words. This prediction leads us todevelop a new model for the semantic coherence of a derivedword, based on the deviation of the surface frequency from itsexpected value (given the stem frequency and a phonological mea-sure of the transition from stem to suffix). Our statistical estima-tion of semantic coherence (described formally in Section 2.1.1.4)offers a novel methodology for evaluating the semantic well-formedness of complex words, as compared to other possible tech-niques, including the corpus-based measure of Latent SemanticAnalysis (Landauer, Foltz, & Laham, 1998), behavioral judgmentsof the decomposability of complex words (Hay, 2000), as well asthe corpus-derived semantic coherence of a word’s morphologicalfamily used by Hauk, Davis, Ford, Pulvermüller, and Marslen-Wilson (2006). In contrast with the latter measure, which relatesmore directly to the consistency of meanings within the morpho-logical family of a stem, our instantiation of the semantic coher-ence measure instead utilizes a lexical frequency-based statisticalmodel to estimate the semantic well-formedness of the combina-tion of stem and affix.

Our study of semantic composition within words also buildsupon previous studies of semantic composition between words.For example, a previous MEG study (Pylkkänen & McElree, 2007)looked at sentence-level ‘‘complement coercion’’ effects; theseeffects relate to the repair of an initial conflict between a verband its complement. In sentences such as The author began thebook, in which the verb began selects for an event-denoting object(e.g., writing the book), the noun phrase the book is regarded asbeing coerced from an entity to an event, in order to satisfy thesemantic constraints of the verb. The sentences within the coercioncondition were compared to control sentences, such as The authorwrote the book, in which the verb does not conflict with its comple-ment. A second study (Brennan & Pylkkänen, 2008) examined sen-tence-level aspectual coercion effects, in which a punctual verbconflicts with a durative modifier, such as The clown jumped forten minutes (which yields a repetitive reading of the verb). In bothof these studies, an AMF was observed as a neural correlate of thecoercion condition. These experiments focused on coercion effects,since they arguably represent a purely semantic manipulation,without a corresponding change in the syntax (Pylkkänen &McElree, 2007); consequently, these paradigms enabled initialinvestigation into the neural basis of sentence-level semanticcomposition. Subsequent studies have examined the neural basisof semantic composition more directly; for example, Bemis andPylkkänen (2011) analyzed the neural response to adjective-nounpairs, such as red boat, thus confirming an AMF effect at !400 msfor cases of semantic composition in minimal linguistic phrases.The present study explores the notion of a gradient intra-wordsemantic coherence, in the framework of our current understand-ing of the recognition of morphologically complex words. Withinthis framework, the brain activity around 300–500 ms post-stimu-lus presentation associated with the AMF indexes the stage of pro-cessing when the constituent morphemes of a complex word,having been identified as word forms and having their lexicalentries activated, are recombined to determine the meaning andwell-formedness of the combination. In other words, the semanticcoherence of stem and affix should not be relevant for complexword recognition until this later recombination stage.

The neural generator of the AMF in the aforementioned studiesis typically taken to be ventromedial prefrontal cortex (or orbito-frontal cortex). This localization for prefrontal semantic effects dif-fers from what might be expected given the fMRI literature. Forexample, in their reviews of the neuroimaging findings on lan-guage processing, Friederici (2012) and Price (2012) both identifiedthe left inferior frontal gyrus, and not orbitofrontal cortex, with

semantic processing. Additionally, Thompson-Schill, D’Esposito,Aguirre, and Farah (1997) demonstrated effects of semantic selec-tion in the left inferior frontal gyrus. Lehtonen, Vorobyev, Hugdahl,Tuokkola, and Laine (2006) argued that the ventral portion of theleft inferior frontal gyrus (i.e., pars orbitalis) is responsible forsemantic integration during processing of morphologically com-plex words (but see Tyler, Stamatakis, Post, Randall, & Marslen-Wilson, 2005, in which the authors use fMRI data on the Englishpast tense to argue that left inferior frontal cortex subserves mor-pho-phonological segmentation). In a particularly relevant study,Vannest, Newport, Newman, and Bavelier (2011) observed effectsof morphological complexity in the left superior temporal gyrusand the left inferior frontal gyrus, which they interpreted in lightof Taft’s (1979, 2004) stages of decomposition and recombination.Nevertheless, while there is indeed conflicting evidence regardingthe prefrontal areas most associated with semantic (and morpho-logical) processing, given the extensive MEG literature on semanticcomposition, here we decided to investigate the orbitofrontal cor-tex as our prefrontal semantic region of interest.

In summary, our study extends the prior research on the neuralbasis of semantic composition in several important ways: (i) wedevelop a novel statistical measure of semantic well-formednessfor morphologically complex words (semantic coherence); (ii) weuse this measure to examine the gradient effect of semanticwell-formedness for fully acceptable items, as opposed to the bin-ary comparison of acceptable and unacceptable items; and (iii) wefocus on semantic composition within words (e.g., teach + -er), asopposed to the more traditional examples of semantic compositionbetween words (e.g., red + boat; Bemis & Pylkkänen, 2011).

1.5. Neural predictions for MEG experiment

Our experiment consists of an MEG visual lexical decision task,with the stimuli of interest being 200 bimorphemic suffixed words,distributed over a range of frequencies and semantic coherence,though all are existing words. For the analysis of the MEG data,the anatomical regions of interest are the left superior and middletemporal regions, associated with the M350 (Pylkkänen & Marantz,2003; Pylkkänen, Stringfellow, & Marantz, 2002; Pylkkänen et al.,2004; Solomyak & Marantz, 2010), and the orbitofrontal cortex,associated with the AMF (Brennan & Pylkkänen, 2008; Pylkkänen& McElree, 2007; Pylkkänen et al., 2009). We will investigate therole of derivational family entropy and surface frequency in modu-lating activity in the left temporal lobe during the post-M170 timewindow. In accordance with the results of a previous behavioralexperiment (Moscoso del Prado Martín et al., 2004), we expect tofind facilitatory effects of entropy and surface frequency; in thecontext of an MEG experiment, this means that we expect to finda decrease in neural activity correlated with an increase in thesevariables. Additionally, we expect the entropy effect to occur dur-ing lexical access for the stem, which should precede the surfacefrequency effect, since that is hypothesized to occur during thelater recombination stage. These findings would be consistent withthe predictions of the Full Decomposition model (Taft, 1979, 2004;Taft & Forster, 1975).

We will also investigate the role of semantic coherence inmodulating activity in the orbitofrontal cortex, the brain regionassociated with the AMF, during a later time window (i.e., duringand after the M350 response). We expect to find a facilitatoryeffect of semantic coherence, due to the previous MEG study ofun-prefixation (Pylkkänen et al., 2009), in which the anterior mid-line region showed increased amplitude for the semantic violationcondition. Finally, we expect to find a positive correlation betweensemantic coherence and the alternative corpus-based measure ofLatent Semantic Analysis, as well as facilitatory effects of the lattermeasure in orbitofrontal cortex.


2. Material and methods

2.1. Design and stimuli

The experiment consisted of a visual lexical decision task, withsimultaneous MEG recording of the magnetic fields induced byelectrical activity in the brain. The target stimuli were words cho-sen from four suffix families: -er, -ness, -ly, and -able. Membershipin these suffix families was determined by using the morphologicalparsing of words in the CELEX database (Baayen, Piepenbrock, &Gulikers, 1995). We excluded all compound words from our analy-sis. We also excluded words if any of the following conditions weremet: (i) the word contained at least one non-letter character; (ii)the stem appeared more than once as a CELEX lemma; (iii) the fre-quency of the word or its stem was zero; (iv) the stem containedmore than one morpheme, as determined by CELEX; (v) the accu-racy rate in lexical decision tasks was below 0.70 for the stem, asmeasured in the English Lexicon Project (Balota et al., 2007); or(vi) we predicted that the subjects would be unlikely to recognizethe meaning of the word.

Fifty words were chosen from each of the four suffix families,resulting in 200 total words (listed in Appendix A). An equal-sizedgroup of 200 non-words was selected from the English LexiconProject; they were chosen such that equal quantities of the non-words ended with the same letters as each of the four suffix cate-gories. The non-words were also matched pair-wise in length withthe 200 real words.

2.1.1. Derivation of statistical measuresWe computed several basic traits of the 200 suffixed words

directly from the CELEX corpus data: biphone transition probabil-ity, surface frequency of the suffixed form, stem frequency, andfamily frequency (i.e., the summed frequency of all words in agiven morphological family). We also calculated several complexstatistical measures (derived from the more basic measures):derivational family entropy, information residual, and semanticcoherence. We obtained values for the Latent Semantic Analysis(LSA) measure from the pairwise comparison application on theLSA website (Landauer, n.d.).

2.1.1.1. Derivational family entropy. Following the model from aprevious study (Moscoso del Prado Martín et al., 2004), we definethe entropy of a morphological family as:

HðPÞ ¼ %X

x2P

pðxjPÞlog2pðxjPÞ ¼ %X

x2P

FðxÞFðPÞ

log2FðxÞFðPÞ

; ð1Þ

where P is a given morphological family, x ranges over all words inthe family P, F(x) is the frequency of word x, and F(P) is the summedfrequency of all words in family P. Note that Moscoso del PradoMartín et al. (2004) applied Eq. (1) to the inflectional families ofwords, and also modeled the nested structure of inflectional fami-lies within derivational families. However, in the present study,we simplify the definition of the entropy model to apply only tothe distribution of frequencies within the derivational family of aword (i.e., ‘‘derivational family entropy’’).

2.1.1.2. Information residual. Moscoso del Prado Martín et al. (2004)studied the entropy of morphological families primarily as a com-ponent of the total information residual of a word:

IRðwÞ ¼ %log2ðFðwÞÞ % HtotðwÞ; ð2Þ

where IRðwÞ is the information residual of word w, F(w) is the fre-quency of word w, and HtotðwÞ is the total entropy of the familiesto which word w belongs. Thus, the information residual measure

encapsulates both the information content of the word itself, aswell as the support it receives from its morphological families.

2.1.1.3. Biphone transition probability. In order to control for thephonological well-formedness of the combination of stem and suf-fix, we utilized a measure called biphone transition probability(BTP). This was defined as the probability of encountering the firsttwo phonemes of the suffix, given the preceding two phonemes, ormore formally:

BTPðY jXÞ ¼ pðY1Y2jXN%1XNÞ; ð3Þ

where (Y1, Y2, . . ., YN) are the phonemes of suffix Y, and (X1, X2, . . .,XN) are the phonemes of stem X. The use of this measure was moti-vated by a prior study of the differential effects of high-probabilityphonological transitions (e.g., bowlful) vs. low-probability phono-logical transitions (e.g., pipeful) on morphological parsing (Hay,2000).

2.1.1.4. Semantic coherence. Our definition of the semantic coher-ence measure relies on a linear regression model, which predictssurface frequency, for the members of a given suffix family, as afunction of stem frequency, biphone transition probability andsemantic coherence. For example, suppose we are considering aparticular suffix Y. Then we have the following linear regressionmodel for predicting surface frequency (ranging over all stems Xthat combine with Y):

logðFðX þ YÞÞ ¼ aþ b1 logðFðXÞÞ þ b2BTPðYjXÞ þ b3SCðX;YÞ þ e;ð4Þ

where F(X + Y) represents the surface frequency of the complexword composed of stem X and suffix Y, F(X) represents the fre-quency of stem X (as a whole word), BTP(Y|X) represents thebiphone transition probability of the suffix Y given the stem X (cal-culated via Eq. (3)), and SC(X, Y) represents the semantic coherenceof stem X and suffix Y.

Taking a more concrete example, consider the word hunter.Given all stems that combine with the suffix -er, we can modelthe surface frequency of hunter using Eqs. (3), (4) as follows:

logðFðhunt þ -erÞÞ ¼ aþ b1 logðFðhuntÞÞ þ b2pð0er0j0nt0Þþ b3SCðhunt; -erÞ þ e

Since we can obtain values for surface frequency, stem fre-quency, and biphone transition probability from CELEX corpus data(Baayen et al., 1995), we can fit a linear regression model for thefirst variable as a function of the latter two variables:

logðFðX þ YÞÞ ¼ aþ b1 logðFðXÞÞ þ b2BTPðYjXÞ þ e2 ð5Þ

Setting Eq. (4) to be equal to Eq. (5), we obtain:

e2 ¼ b3SCðX;YÞ þ e; ð6Þ

implying that e2, the error from the regression model in Eq. (5), isdirectly correlated with SC(X, Y); thus, we can utilize e2 as our bestestimate of semantic coherence. In other words, we derive thesemantic coherence measure by estimating the intercept a, andcoefficients b1 and b2, for the linear regression model in Eq. (5), sub-stituting the values of surface frequency, stem frequency, andbiphone transition probability for each word in the suffix familyinto the equation, and extracting the associated error term for eachword.

2.1.1.5. Latent semantic analysis. Finally, we looked at the LatentSemantic Analysis (Landauer et al., 1998) measure as yet anotherway of determining the semantic fit of stem and suffix. In particu-lar, we performed pairwise comparisons between the suffixedforms and their respective stems in the LSA space of ‘‘General


Reading up to 1st year college.’’ This provided us with similarityscores between %1 and 1 for 162 of the 200 words; 38 words werenot available in the LSA database.

2.2. Experimental procedures

Twelve right-handed native English speakers participated in theMEG experiment. All subjects provided written informed consentto participate in the study. Two of the subjects were eliminatedfrom analysis; one subject had excessive eye motion (causing sig-nificant artifactual signal at the front sensors), and the other sub-ject had an accuracy rate for the lexical decision task that wastwo standard deviations below the mean accuracy rate across sub-jects. The remaining subjects ranged in age from 19 to 32 years old(median age: 25). Eight of the subjects were enrolled as students atNew York University. The gender composition was 4 males and 6females. The ethnic composition was 7 Caucasians, 1 Asian, and 2participants with more than one ethnicity.

PsyScope X (Cohen, MacWhinney, Flatt, & Provost, 1993) wasused as the presentation platform for the experiment. The stimuliwere projected onto a screen that was located approximately50 cm away from the participant. The stimuli were presented inlowercase Courier font, size 32. Each trial consisted of a fixationcross (‘‘+’’) appearing for 500 ms, followed by the stimulus appear-ing for 300 ms. Subjects were instructed to respond to the stimulusby pressing one of two buttons with their left hand, to signifywhether or not they recognized the stimulus as a valid word.

A 157-channel axial gradiometer whole-head MEG system(Kanazawa Institute of Technology, Kanazawa, Japan) recordedthe MEG data at a sampling frequency of 1000 Hz. The data was fil-tered between DC and 500 Hz, with a band elimination filter of60 Hz. The subjects’ heads were digitized prior to entering themagnetically shielded room. The head positions during the experi-ment were determined via coils attached to anatomical landmarks.Structural MRIs were also obtained for all the subjects, and the coillocations were used to translate from the MEG spatial coordinatesto the MRI coordinates.

2.3. Analysis

2.3.1. Behavioral analysisReaction times (RTs) and accuracy data were recorded for each

trial of the experiment. In order to analyze the correlation of RTwith the linguistic variables of interest, we used independent lin-ear mixed-effect models (Baayen, Davidson, & Bates, 2008) withRT as the dependent variable, the linguistic variable as the fixedeffect, and subject and item as random effects. The linear mixedeffects models were constructed using the lmer function of thelme4 package in R (Bates & Maechler, 2009). The p-values werecomputed via Monte Carlo simulation with 10,000 iterations each.

2.3.2. MEG analysis2.3.2.1. Data analysis. The MEG data were noise reduced via theContinuously Adjusted Least-Squares Method (Adachi,Shimogawara, Higuchi, Haruta, & Ochiai, 2001), in the MEG160software (Yokogawa Electric Corporation and Eagle TechnologyCorporation, Tokyo, Japan). Cortically constrained minimum-normestimates were calculated via MNE (MGH/HMS/MIT Athinoula A.Martinos Center for Biomedical Imaging, Charleston, MA). The cor-tical reconstructions were obtained using FreeSurfer (CorTechsLabs Inc., La Jolla, CA and MGH/HMS/MIT Athinoula A. MartinosCenter for Biomedical Imaging, Charleston, MA). A source spaceof 5124 points was generated for each reconstructed surface, andthe BEM (boundary-element model) method was employed onactivity at each source to calculate the forward solution. Usingthe grand average of all trials for a particular subject, after baseline

correction with the time interval [%100 ms, 0 ms], and low pass fil-tering at 40 Hz, the inverse solution was computed from the for-ward solution, in order to determine the most likely distributionof neural activity. The inverse solution was computed utilizing afree orientation for the source estimates (i.e., the estimates wereunconstrained with respect to the cortical surface). In addition tothe selection of the free orientation methodology, we analyzedthe signed minimum norm source estimates, with positive valuesindicating an upward directionality, and negative values indicatinga downward directionality, in the coordinate space defined by thehead. For a discussion of the reasoning behind our selection ofthese particular parameters, as well as a reanalysis of the datausing two alternate sets of orientation-related parameters, pleasesee Appendix B. The signed source estimates were transformedinto (signed) noise-normalized dynamic statistical parametermaps (dSPMs; Dale et al., 2000). Noise covariance estimates werecalculated from the entirety of each epoch in the experiment(i.e., starting at the beginning of the baseline period and extendingto the end of each trial). The SNR parameter, which controls theregularization of the source estimates, was set to 2. FreeSurfer’sautomatically-parcellated anatomical regions of interest (ROIs)were used to obtain estimates of the average signed noise-normal-ized neural activity within the left temporal and orbitofrontal cor-tical regions. In order to analyze the grand-averaged evokedactivity across all subjects, we morphed each individual subject’sbrain to the common space of a single representative subject’sbrain. Sensor space data was obtained via the MEG160 software.

Outlier trials were removed based on the total number of datapoints that were more than two standard deviations away fromthe mean; in particular, an epoch was removed if its total numberof outlier points was more than three standard deviations abovethe mean.

2.3.2.2. Anatomical ROI analysis. We examined two general areas ofinterest within the left hemisphere: the temporal lobe and orbito-frontal cortex (Fig. 1). To analyze the temporal lobe, we used boththe superior and middle temporal anatomical ROIs, since they havebeen previously associated with the M350 response and lexicalaccess (Pylkkänen & Marantz, 2003; Pylkkänen et al., 2002;Pylkkänen et al., 2004; Solomyak & Marantz, 2010). We also ana-lyzed orbitofrontal cortex, which has been associated with theAMF and semantic composition (Brennan & Pylkkänen, 2008;Pylkkänen & McElree, 2007; Pylkkänen et al., 2009). SinceFreesurfer separates orbitofrontal cortex into both medial and lat-eral regions, we analyzed both the lateral and medial orbitofrontalanatomical ROIs in our experiment.

In our analysis of the left temporal ROIs, we first investigatedwhether there is an effect of derivational family entropy. Giventhe connection between the M350 and lexical access, we expectedthe effect of derivational family entropy to occur in the rough timewindow surrounding the M350 response, a slow negative declinebeginning at !150 ms and ending at !500 ms. Accordingly, ourtime window of interest was specified as the general late intervalof 150–500 ms post-stimulus onset. Secondly, we investigatedwhether there is an effect of surface frequency, occurring afterthe effect of derivational family entropy. We expected both effectsto be facilitatory, which given the negative-going direction of theM350 response, predicts a positive correlation between the meanactivity in the left temporal ROIs and the variables of interest.

In our analysis of the left orbitofrontal ROIs, we investigatedwhether there is an effect of semantic coherence. Given our under-standing of the recombination stage of complex visual word recog-nition, we predicted that this effect should take place at a relativelylate time interval. Thus, our time window of interest was the lateinterval 300–500 ms post-stimulus onset. We also expected thatthis effect should be facilitatory in nature. However, since we did


not yet know the direction of the AMF as measured via our particu-lar analysis pipeline with the left orbitofrontal ROIs, we did notknow in advance whether the correlation between orbitofrontalactivity and semantic coherence should be positive or negative.Finally, we investigated whether semantic coherence was posi-tively correlated with the alternative LSA measure, as well aswhether the LSA measure itself correlated with orbitofrontalactivity.

2.3.2.3. Statistical methodology. In order to analyze the effects of thelinguistic variables of interest on the neural response to thevisually presented words, linear mixed effects models (Baayenet al., 2008) were employed millisecond-by-millisecond (i.e.,independently at each time point) within a given time window,with the average neural activity in the ROI as the dependent vari-able, a linguistic variable as the fixed effect, and subject and itemas random effects. Afterwards, the resulting t-values for the fixedeffect3 were corrected for multiple comparisons over the time win-dow of interest. The linear mixed effects models were constructedusing the lmer function of the lme4 package in R (Bates &Maechler, 2009). The technique that we used for multiple compar-isons correction is based on the method of Maris and Oostenveld(2007), as adapted by Solomyak and Marantz (2009). Specifically,we computed

Pt, the sum of all t-values within a single temporal

cluster of consecutive significant effects in the same direction(where significant is defined by |t| > 1.96, p < 0.05 uncorrected).

The highest absolute value ofP

t, for any cluster within the wholetime window, was then compared to the results of the same proce-dure repeated on 10,000 random permutations of the independentvariable (i.e., the linguistic measure). A Monte Carlo p-value wasthus computed, based on the percentage of times a random per-mutation of the independent variable led to a larger maximum abso-lute value of

Pt than the original maximum absolute value of

Pt (as

computed on the actual data).In order to verify the temporal precedence of the derivational

family entropy effect, relative to the surface frequency effect, weemployed the following procedure. First, in order to reduce thewithin-subject noise, we created a smoothed representation of themean activity for the left temporal ROIs, by averaging within50 ms time windows (for each trial and each subject); we then nor-malized the activity for each time window, over all the trials for agiven subject. Across our hypothesized time window of interest(150–500 ms), for each subject, we fit a linear regression model pre-dicting the mean activity over a given 50 ms time window as a func-tion of the linguistic variable of interest (i.e., derivational familyentropy or surface frequency), and then we extracted the coefficientfrom the model. For each subject, we recorded the latency of thepositive (i.e., facilitatory) peak of the coefficient. Finally, we per-formed a paired t-test of the peak latencies for the derivational fam-ily entropy and surface frequency effects, across all subjects.

3. Results

3.1. Behavioral results

The mean accuracy rate across all subjects was 89.7% (±4.2%).The mean RT across all subjects was 699.2 ms (±241.7 ms). RT

Fig. 1. Location of anatomical ROIs, highlighted in green on a representative subject’s inflated cortical surface. The top row, a lateral view of the left hemisphere, displays: (A)the middle temporal ROI, and (B) the superior temporal ROI. The bottom row, a ventral view of the left hemisphere, displays: (C) the lateral orbitofrontal ROI, and (D) themedial orbitofrontal ROI.

3 No degrees of freedom are provided for the t-values generated by the linearmixed effects models; due to the large number of observations, the t-distributioneffectively converges to the standard normal distribution (Baayen et al., 2008: Note1).


was found to be inversely correlated with surface frequency(t = -7.06, p = 0.0001), stem frequency (t = %7.25, p = 0.0001),derivational family entropy (t = %2.48, p = 0.0076), and semanticcoherence (t = %3.47, p = 0.0002), indicating that higher valuesfor these frequency-based variables correlated with shorter RTs.As expected, the information residual variable was positively cor-related with RT (t = 6.37, p = 0.0001); this result is straightfor-wardly the consequence of the information residual being thenegative sum of two other variables that were inversely correlatedwith RT. When stem frequency is orthogonalized with respect tosurface frequency, it remains significantly correlated with RT(t = %3.77, p = 0.0001). However, when derivational family entropyis orthogonalized with respect to surface frequency, the variable isno longer significantly correlated with RT (t = %0.37, p = 0.702).The correlation of RT with the LSA measure was in the expecteddirection, though it did not reach statistical significance(t = %1.02, p = 0.311).4

3.2. MEG results

3.2.1. Average activityThe average activation across all subjects and all trials, obtained

via the source-space analysis, is shown from lateral and ventralperspectives in Fig. 2. Note that the both the left temporal lobe(Fig. 2A) and the left orbitofrontal cortex (Fig. 2B) contain signifi-cant patches of activity at 380 ms post-stimulus onset. Withinthe left inferior frontal lobe, there appears to be no significantactivity in the inferior frontal gyrus (Fig. 2A); instead, all of theactivity seems to localize to orbitofrontal cortex, validating itschoice as our prefrontal region of interest. Fig. 3(A and B) displaysthe time-course of average activation within the left middle tem-poral and lateral orbitofrontal ROIs, respectively, over a 500 mswindow post-stimulus onset. Fig. 4 displays the grand-averagedsensor space data, averaged over the seven sensors with the stron-gest negative signal at the peak of the M100 (i.e., 90 ms post-stimulus onset; Fig. 4A), as well as the seven sensors with thestrongest negative signals at the peak of the M350 (i.e., 350 mspost-stimulus onset; Fig. 4B). Note that the M100 and M170evoked responses display sharp peaks (Fig. 4A), while the M350evoked response displays a protracted decline over the time win-dow !150–350 ms (Fig. 4B).

3.2.2. Left temporal ROI resultsUpon examination of the left middle temporal ROI (Fig. 3C), we

found a highly significant positive (i.e., facilitatory)5 effect ofderivational family entropy (p < 0.0001 for the cluster at 241–387 ms, corrected for 150–500 ms), as well as a later effect of surfacefrequency (p = 0.0034 for the cluster at 431–500 ms, corrected for150–500 ms). The temporal precedence of the derivational familyentropy effect, relative to the surface frequency effect, was con-firmed to be significant via a paired t-test of the peak effect latencyacross subjects (t(9) = 2.41, p = 0.039). The timing of these effects isconsistent with the pronounced increase in negative (i.e., down-ward-directed) activity within the ROI at !150 ms (Fig. 3A); inparticular, the pronounced negative activity suggests a later (i.e.,post-M170) stage of neural processing within the left temporal lobe,consistent with its modulation by linguistic variables related to thelexical access and recombination stages.

The left superior temporal ROI (not shown) displayed a similarpattern of results as the middle temporal ROI, but with weaker sig-nificance: there was a significant effect of derivational familyentropy (p = 0.0019 for the cluster at 242–326 ms, corrected for150–500 ms), a trend toward significance for surface frequency(p = 0.0586 for the cluster at 446–477 ms, corrected for150–500 ms), and a trend toward significance for the temporalprecedence of the derivational family entropy effect, relative tothe surface frequency effect (t(9) = 2.15, p = 0.059).

3.2.3. Left orbitofrontal ROI resultsUpon examination of the left lateral orbitofrontal ROI (Fig. 3D),

we found a highly significant negative (i.e., facilitatory)6 effect ofsemantic coherence (p = 0.0004 for the cluster at 354–500 ms, cor-rected for 300–500 ms). The timing of this effect is consistent withthe observed peak in positive (i.e., upward-directed) mean activitywithin the orbitofrontal ROI at !380 ms (Fig. 3B). Additionally, inthe left medial orbitofrontal ROI (not shown), there was a significanteffect of semantic coherence (p = 0.001 for the cluster at 379–500 ms, corrected for 300–500 ms).

3.2.4. LSA resultsWe found a positive linear relationship between semantic

coherence and the alternative corpus-based measure LSA(r = 0.2475, p = 0.0015). We attempted to use the LSA measure asa predictor for the average activity in the left lateral orbitofrontalROI, in a similar manner to our analysis of semantic coherence.While the LSA measure displayed a trend in the predicted direc-tion, it did not reach statistical significance during the time win-dow of interest (300–500 ms); the time point with the peakuncorrected effect (469 ms) was just below significance(t = %1.90, p = 0.053).7

4. Discussion

The results of our experiment show that statistical measuresbased on lexical frequency can provide a window into the spa-tiotemporally-differentiated stages of neural processing duringcomplex visual word recognition.

The behavioral analysis confirmed the role of our linguistic vari-ables of interest in the visual word recognition process. In particu-lar, surface frequency, stem frequency, derivational family entropy,information residual, and semantic coherence were all shown tosignificantly modulate RT. The significance of derivational familyentropy, surface frequency, and their combination (i.e., the infor-mation residual measure) replicates the findings of Moscoso delPrado Martín et al. (2004).

The MEG analysis provided a more fine-grained temporal pic-ture of the role that these variables play in the various stages ofthe visual recognition process for morphologically complex words.Derivational family entropy was shown to facilitate left temporalactivity beginning around 240 ms, while surface frequency wasshown to facilitate left temporal activity primarily at a later timewindow (!430–500 ms). The significance of a statistical measureof the distribution of frequencies within a morphological family,for a neural response associated with lexical access (i.e., theM350), supports the notion that suffixed words are decomposedinto stems and affixes for recognition. Furthermore, the distincttime-courses of the correlations with entropy and surface

4 It should be noted that the LSA analysis necessarily required eliminating 19% ofthe data, due to the lack of LSA values for some of the stimuli; thus, some degree ofstatistical power was lost as a consequence of this smaller data set.

5 Given the negative sign of the average activity within the left middle temporalROI during the time window of interest, note that a positively signed correlationindicates a facilitatory effect, while a negatively signed correlation indicates aninhibitory effect.

6 Given the positive sign of the average activity within the left lateral orbitofrontalROI during the time window of interest, note that a positively signed correlationindicates an inhibitory effect, while a negatively signed correlation indicates afacilitatory effect.

7 As mentioned earlier, it is worth noting that the LSA analysis required eliminating19% of the data, which may have impacted the significance of the neural effects.


frequency elaborates upon the findings of Moscoso del PradoMartín et al. (2004), showing that modulation of behavioral RTsby these two measures may in fact be due to temporally dif-ferentiable cognitive processes during visual recognition of mor-phologically complex words. More specifically, the earlier effectof derivational family entropy may be due to the attempt at lexicalaccess for the decomposed stem, while the significance of surfacefrequency during the time window 430–500 ms may be due tothe later recombination stage, during which the previously decom-posed stems and affixes are recombined (Taft, 1979, 2004).

The semantic coherence measure was shown to facilitate leftorbitofrontal activity in the time window !350–500 ms. The sig-nificance of our novel statistical measure of semantic coherencesuggests that orbitofrontal cortex contains a neural signature ofthe gradient semantic fit of stems and affixes. This notion of a gra-dient semantic fit elaborates upon the work of Pylkkänen et al.(2009) with regard to categorical semantic selection in reversativeun-prefixation; here we use a continuous measure of semanticcoherence within existing words, and we show that neural activitywithin the brain area associated with the AMF response correlateswith this statistical measure. However, it remains possible that theorbitofrontal effect of semantic coherence may, in fact, be due to asurface frequency effect of recombination; in particular, our for-mulation of semantic coherence necessarily involves a correlationwith surface frequency, since the semantic coherence measure isderived from the residuals of a linear regression model predictingsurface frequency as a function of stem frequency and biphonetransition probability. Future work may be useful in providingmore conclusive evidence differentiating the semantic nature ofthe orbitofrontal effect from the more general effects ofrecombination in the left temporal lobe. Finally, while the effect

of the LSA measure on orbitofrontal activity did not reach sta-tistical significance during the time window of interest, it did cor-relate significantly with semantic coherence, providing additionalsupport for the latter measure as a genuine index of the semanticfit between stem and affix.

Interestingly, the prefrontal effects reported here localized toorbitofrontal cortex, rather than the left inferior frontal gyrus,the neighboring region that is more traditionally associated withlanguage processing, including semantic processing (e.g.,Friederici, 2012; Price, 2012). It is possible that this is due to asource localization error; however, prior MEG work (e.g., Brennan& Pylkkänen, 2008; Pylkkänen & McElree, 2007; Pylkkänen et al.,2009) has consistently implicated medial orbitofrontal regions insemantic composition. Moreover, the localization of our peaksemantic coherence effect to left lateral orbitofrontal cortex is con-sistent with the fMRI findings of Lehtonen et al. (2006), in whichthe ventral portion of the left inferior frontal gyrus (i.e., pars orbi-talis) was argued to subserve the semantic integration of stem andaffix within morphologically complex words.

One concern about the semantic effects in orbitofrontal cortex,specifically, is that the MEG signal may be contaminated by arti-facts due to eye blinks or eye movements. Orbitofrontal cortex isa location that is particularly problematic for signal detection(e.g., Hillebrand & Barnes, 2002); additionally, due to the proximityof the frontal MEG sensors to the eyes, it is possible that our sourcelocalization procedure may confuse eye movement with neuralactivity generated in the anterior sections of the frontal lobe.While outlier rejection should mitigate the effects of very largeartifacts in the MEG data, smaller artifacts, such as those causedby micro-saccades, would be expected to remain present in thedata. Bemis and Pylkkänen (2013) discussed the possibility that

432-3-4

dSPM

(A) (B)

(C) (D)

Fig. 2. Mean whole-brain activity (in dSPM units) across all subjects and all trials at 380 ms post-stimulus onset, illustrated on a representative subject’s inflated corticalsurface. The top row contains: (A) a lateral view of the left hemisphere, with downward activity in the temporal lobe displayed in blue, and (B) a ventral view of the lefthemisphere, with upward activity in the orbitofrontal cortex displayed in red/yellow. The bottom row contains: (C) a lateral view of the right hemisphere, and (D) a ventralview of the right hemisphere.


their ventromedial prefrontal effects might be due to subtle eyemovements, and they noted that while there may indeed be suchartifacts in the measured activity from regions in and around orbi-tofrontal cortex, these artifacts are not expected to be time-lockedto stimulus presentation, and are therefore likely to manifest asinduced rather than evoked activity.

5. Conclusion

In summary, the results of this experiment provide evidence ofthe effects of morphological decomposition on lexical access, asindexed by derivational family entropy, and they also illuminatethe recombination stage, as indexed by surface frequency and

Fig. 3. Source space analysis using free orientation and signed source estimates in MNE. (A) Mean activity within the left middle temporal ROI. (B) Mean activity within theleft lateral orbitofrontal ROI. (C) Effects of derivational family entropy (blue) and surface frequency (red) on mean activity within the left middle temporal ROI. (D) Effect ofsemantic coherence (blue) on mean activity within the left lateral orbitofrontal ROI. The dotted black lines in C and D represent the level of correlation needed to reachstatistical significance at t = ±1.96, p = 0.05 (uncorrected).

M170

M100 (A) (B)M350

Fig. 4. MEG response in sensor space, averaged over all subjects and all trials, shown for: (A) the sensors with the strongest negative signal at 90 ms post-stimulus onset(‘‘Peak M100 sensors’’), and (B) the sensors with the strongest negative signal at 350 ms post-stimulus onset (‘‘Peak M350 sensors’’). The peaks of the M100, M170, and M350evoked responses are indicated on the respective plots.


semantic coherence. With respect to the theoretical debate aboutvisual processing of morphologically complex words, our resultsfavor the Full Decomposition model (Taft, 1979, 2004; Taft &Forster, 1975) over non-decompositional models, such as thesupralexical model (Giraudo & Grainger, 2000); in particular, ourdemonstration of the temporal precedence of morphologicaldecomposition relative to lexical access provides confirmation ofthe predictions made by the Full Decomposition model, while con-tradicting the predictions of the supralexical model, in which lexi-cal access takes place prior to decomposition. With respect to theneural basis of complex visual word recognition, our results pro-vide additional support for the previously observed association ofthe left temporal lobe, and particularly, the left middle temporalgyrus, with the lexical access stage (e.g., Friederici, 2012; Gold &Rastle, 2007; Hickok & Poeppel, 2007; Indefrey & Levelt, 2004),as well as the later recombination stage. With respect to the neuralbasis of semantic composition, our results extend those of severalprevious MEG experiments, which have found effects of semanticcomposition in medial orbitofrontal regions (e.g., Pylkkänen &McElree, 2007; Pylkkänen et al., 2009); here, we demonstrate thatactivity in left orbitofrontal cortex is responsive to the gradientsemantic well-formedness of morphologically complex words.Finally, our results demonstrate the usefulness of corpus-derivedstatistical measures in investigating the various cognitive and neu-ral stages that occur during visual processing of morphologicallycomplex words. In particular, our statistical measure of semanticcoherence serves as a novel methodology for quantifying thesemantic well-formedness of complex words, which in turn, allowsus to predict the varying cognitive resources utilized by the seman-tic evaluation process during the recombination stage of complexvisual word recognition.

Acknowledgments

This work was supported by the National Science Foundationunder Grant No. BCS-0843969, and by the NYU Abu DhabiResearch Council under Grant No. G1001 from the NYUADInstitute, New York University Abu Dhabi. We thank GwynethLewis and Jeff Walker for their assistance in collecting the experi-mental data, Todd Gureckis for his comments on the original draftof the manuscript, David Poeppel, Liina Pylkkänen, AdamBuchwald, and Christian Brodbeck for discussion of the study,and several anonymous reviewers for their helpful feedback onprevious versions of this manuscript.

Appendix A. List of 200 suffixed words

Vitally Precipitousness Considerable DelivererImmaculately Cleverness Adaptable LoitererValiantly Awareness Teachable ManagerAmicably Stubbornness Understandable ObserverOrdinarily Meekness Alterable ComposerGently Strenuousness Eatable FlattererDecently Grumpiness Verifiable KnitterGladly Gladness Imaginable PoacherCursorily Giddiness Manageable DestroyerHeftily Uniqueness Commendable DispenserStringently Opaqueness Perishable RetainerTorridly Sensitiveness Adorable TempterCircumspectly Flabbiness Observable MeddlerSmugly Fierceness Preferable RefresherAdversely Briskness Predictable Robber

Loyally Tallness Inhabitable WielderRarely Clumsiness Persuadable EntertainerFragrantly Fondness Sufferable BloaterMerrily Recklessness Forgettable HealerCleverly Awkwardness Redeemable GrowerLudicrously Explicitness Reconcilable LaundererAstutely Softness Retractable ExplorerConcisely Gentleness Indictable PerisherRelevantly Nakedness Enjoyable ModifierDismally Conspicuousness Believable AdviserEminently Cuteness Advisable PropellerSplendidly Chubbiness Governable EarnerPaternally Deliciousness Expandable SeducerTastefully Ornateness Avoidable UpholstererMutually Remissness Excitable ScavengerTerribly Acuteness Extraditable EnchanterTemporarily Pensiveness Conceivable OffenderPlacidly Nimbleness Dispensable SolverAbsolutely Sullenness Retrievable ExplainerStrangely Boldness Attainable SenderFiercely Gruesomeness Detectable BlasphemerVainly Vastness Speakable BetrayerDelicately Seriousness Forgivable WandererDistinctly Bashfulness Definable DeceiverConsciously Innocuousness Recognizable ReckonerAccurately Fickleness Solvable MagnifierEloquently Vicariousness Deplorable RetrieverInscrutably Wistfulness Disposable SingerInsidiously Queasiness Inflatable RoamerContiguously Weirdness Expendable DwellerDiscretely Tardiness Obtainable SpeakerImplicitly Robustness Agreeable InformerPrecariously Dinginess Perceivable WriterCredibly Absoluteness Endurable TeacherSerenely Strictness Determinable Hearer

Appendix B. Alternate orientation constraints

B.1. Methods

For our primary MEG analysis, we used a free orientation set-ting for the computation of the inverse solution in MNE, whichmeans that the source estimates were unconstrained with respectto the cortical surface. Furthermore, we analyzed the signed esti-mates, with the sign indicating the vertical direction in the coordi-nate space defined by the head (i.e., MNE’s ‘‘MEG head coordinateframe’’), rather than the coordinate space defined by the corticalsurface; our primary analysis thus employs the methodology usedby several previous studies (Ettinger, Linzen, & Marantz, 2014;Fruchter et al., 2013; Hsu, Lee, & Marantz, 2011; Lewis &Poeppel, 2014; Lewis et al., 2011; Linzen, Marantz, & Pylkkänen,2013; Simon, Lewis, & Marantz, 2012; Solomyak & Marantz,2009; Solomyak & Marantz, 2010). This choice of parametersallows us to connect our analysis to the single dipole modelingliterature, where directionality of dipole sources is computed rela-tive to the head coordinate system and where sources are asso-ciated with characteristic field patterns over the head;furthermore, this choice of parameters yields a smoothly varyingdistribution of signed activity over the cortex. In order to verifythat our results can be obtained using more traditional settingsfor these parameters, we repeated our entire MEG analysis withtwo alternate methodologies. For the first alternative analysis, weused a combination of free orientation and unsigned source


estimates, which is a more widely used methodology. For the sec-ond alternative analysis, we selected a loose orientation constraintfor the computation of the inverse solution: this means that thecomponent of the current normal to the cortex is set to 1, whilethe transverse component is set to a specified amount (in our case,0.2). Additionally, the source estimates were signed, with the signindicating the direction relative to the cortical surface normal;specifically, positive values indicate a current directed outwardfrom the cortex, and negative values indicate a current directedinward toward the cortex. Since this methodology yields neighbor-ing patches of activity with opposite signs, we selected functionalROIs based on the peak negative patches of activity within the leftmiddle temporal and lateral orbitofrontal anatomical ROIs. Weselected the negative activity specifically, since it represented thestronger evoked response in the grand-averaged data; moreover,there is reason to believe that the MEG signal generally corre-sponds to current flowing from the cortical surface to the depthof the cortex (i.e., negative activity in the context of a loose ori-entation constraint; Lopes da Silva, 2010). The functional ROIswere drawn on the grand-average activity in the neuroanatomicalspace of the representative subject, and the ROIs were then mor-phed back to each of the individual subjects’ brains, in order to per-form the across-subjects analysis.

B.2. Results

B.2.1. Average activityThe average left hemisphere activation across all subjects and

all trials, obtained via the source-space analyses with alternate

settings for the orientation constraints, is shown from lateral andventral perspectives in Fig. B.1. The top row displays the averageactivation using a free orientation with unsigned source estimates,and the bottom row displays the average activation using a looseorientation with signed source estimates. Note that the both theleft temporal lobe (Fig. B.1:A and C) and the left orbitofrontal cor-tex (Fig. B.1:B and D) contain significant patches of activity at380 ms post-stimulus onset, for both alternative methodologies;however, the inverse solution with the loose orientation constraintyields a characteristic pattern of locally alternating positive andnegative patches of activity (Fig. B.1:C and D). Fig. B.2(A and B) dis-plays the time-course of average activation within the left middletemporal and lateral orbitofrontal ROIs, respectively, over a 500 mswindow post-stimulus onset, for the analysis with free orientationand unsigned estimates. Fig. B.3(A and B) displays the time-courseof average activation within the left middle temporal and lateralorbitofrontal functional ROIs, drawn over the peak patch of nega-tive activity within the respective anatomical ROIs on the repre-sentative subject’s brain (and morphed back to each individualsubject’s brain), for the analysis with a loose orientation constraintand signed estimates. Note that the latter time-courses (Fig. B.3:Aand B) match the general pattern of average activation obtained viaour primary analysis (Fig. 3:A and B); in particular, the middle tem-poral ROI shows a pronounced increase in negative activity startingat !150 ms, and the lateral orbitofrontal ROI shows a clear peak at!380 ms. Additionally, the general shape of the M350 evokedresponse in sensor space (Fig. 4B) matches the source space resultsfor the middle temporal ROI via these two methodologies(Figs. 3A and B.3A). However, the time-courses of average

(A) (B)

(C) (D)

Fig. B.1. Mean whole-brain activity (in dSPM units) across all subjects and all trials at 380 ms post-stimulus onset, illustrated on a representative subject’s inflated corticalsurface. The top row displays the average activity in the left hemisphere, using a free orientation and unsigned source estimates in MNE, from: (A) a lateral view, and (B) aventral view. The bottom row displays the average activity in the left hemisphere, using a loose orientation and signed source estimates in MNE, from: (C) a lateral view, witha functionally defined ROI highlighted in green, overlaid on the blue patch of negative activity within the middle temporal ROI, and (D) a ventral view, with a functionallydefined ROI highlighted in green, overlaid on the blue patch of negative activity within the lateral orbitofrontal ROI.


activation obtained via the analysis with free orientation andunsigned estimates (Fig. B.2:A and B) fail to show such clearevoked responses, in either the middle temporal or lateral orbito-frontal ROIs.

B.2.2. Left temporal ROI resultsB.2.2.1. Free orientation and unsigned estimates. Upon examinationof the left middle temporal ROI in the first alternative analysis(Fig. B.2C), we found a trend toward significance for derivationalfamily entropy (p = 0.093 for the cluster at 224–250 ms, correctedfor 150–500 ms), and a significant effect of surface frequency(p = 0.022 for the cluster at 452–500 ms, corrected for 150–500 ms). The temporal precedence of the derivational familyentropy effect, relative to the surface frequency effect, was con-firmed to be significant via a paired t-test of the peak effect latencyacross subjects (t(9) = 4.87, p = 0.0009).

B.2.2.2. Loose orientation and signed estimates. Upon examination ofthe left middle temporal functional ROI in the second alternativeanalysis (Fig. B.3C), we found a significant effect of derivationalfamily entropy (p = 0.018 for the cluster at 252–319 ms, correctedfor 150–500 ms), and a trend toward significance for surface fre-quency (p = 0.064 for the cluster at 460–500 ms, corrected for150–500 ms). The temporal precedence of the derivational familyentropy effect, relative to the surface frequency effect, was con-firmed to be significant via a paired t-test of the peak effect latencyacross subjects (t(9) = 2.86, p = 0.019).

B.2.3. Left orbitofrontal ROI resultsB.2.3.1. Free orientation and unsigned estimates. Upon examinationof the left lateral orbitofrontal ROI in the first alternative analysis(Fig. B.2D), we found a significant effect of semantic coherence(p = 0.041 for the cluster at 407–442 ms, corrected for 300–500 ms).

B.2.3.2. Loose orientation and signed estimates. Upon examination ofthe left lateral orbitofrontal functional ROI in the second alterna-tive analysis (Fig. B.3D), we found a significant effect of semanticcoherence (p = 0.042 for the cluster at 408–450 ms, corrected for300–500 ms).

B.3. Discussion

The analyses with alternate orientation constraints generallycorroborated the findings from our primary analysis. In the analy-sis with free orientation and unsigned source estimates, the leftmiddle temporal ROI showed a significant effect of surface fre-quency, and a trend toward significance for derivational familyentropy. In the analysis with a loose orientation constraint andsigned source estimates, the left middle temporal functional ROIshowed a significant effect of derivational family entropy, and atrend toward significance for surface frequency. The temporalprecedence of the derivational family entropy effect, relative tothe surface frequency effect, was shown to be significant underboth methodologies. Finally, the left lateral orbitofrontal ROI

Fig. B.2. Source space analysis using free orientation and unsigned source estimates in MNE. (A) Mean activity within the left middle temporal ROI. (B) Mean activity withinthe left lateral orbitofrontal ROI. (C) Effects of derivational family entropy (blue) and surface frequency (red) on mean activity within the left middle temporal ROI. (D) Effectof semantic coherence (blue) on mean activity within the left lateral orbitofrontal ROI. The dotted black lines in C and D represent the level of correlation needed to reachstatistical significance at t = ±1.96, p = 0.05 (uncorrected).


showed a significant effect of semantic coherence under bothmethodologies.

While it is encouraging that the effects reported via our primaryanalysis largely seem to replicate under these alternate method-ologies, there are a number of issues that render problematic theselection of either alternative methodology. In particular, thetime-courses of average activation obtained via the unsignedanalysis fail to show clear evoked responses, while both types ofsigned analysis yield evoked responses that display a similar shapeto the grand-averaged M350 response in sensor space. This sug-gests that the loose orientation constraint with signed estimatesmight be a suitable analysis methodology, especially since it moreclosely models the actual physiology of the neural activity thatyields the observed MEG signal (Lopes da Silva, 2010).Unfortunately, there is a significant difficulty with adopting eventhis methodology: the locally alternating patches of positive andnegative activity would lead to a reduction in statistical power,at the very least, and a complete elimination of effects, at theworst, since an average of the signed activity in an ROI with oppo-site-signed patches could lead to the effects in both parts of the ROIcancelling each other out. In the present study, we attempted toremedy this problem by drawing a functional ROI over the peaknegative activity in the grand-averaged whole-brain analysis.However, this is not an ideal solution, since there may still be someresidual effect of the nearby positive activity, which would serve toreduce the significance of any effects in the ROI. One advantage ofour primary analysis methodology is, therefore, that it yields amore smoothly varying distribution of signed activity over the

cortex; this allows for greater statistical power when averagingover a region such as the middle temporal ROI, since the activityin that ROI would tend to be uniformly signed (e.g., negativelysigned, in the case of the middle temporal ROI).

It is worth acknowledging, however, a significant drawback ofour primary analysis methodology: although the projection ofthe source estimates onto the vertical dimension of the head coor-dinate system is pragmatically motivated, as explained above, theselection of the vertical dimension is essentially arbitrary from thestandpoint of neurophysiology. In particular, this methodology willunderestimate the amplitude of neural activity not aligned withthe vertical dimension. It would be a mistake, therefore, to equateour measure of signed activation with any simple notion of regio-nal neural activity; consequently, our methodology, for example,cannot be used to compare the magnitude of activity across ROIs,since the respective ROIs might have different portions of their cor-tical surfaces orientated orthogonally to the relevant dimensionwithin the head coordinate system.

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