d912f507d8058c6273

Brain and Language 78, 143–168 (2001)doi:10.1006/brln.2000.2390, available online at http://www.idealibrary.com on

Walking or Talking?: Behavioral andNeurophysiological Correlates

of Action Verb Processing

Friedemann Pulvermuller,* Markus Harle,† and Friedhelm Hummel‡

*Cognition and Brain Sciences Unit, Medical Research Council, Cambridge, England; †FachgruppePsychologie, Universitat Konstanz, Konstanz, Germany; and ‡Neurologische Klinik,

Universitat Tubingen, Tubingen, Germany

Brain activity elicited by visually presented words was investigated using behavioral mea-sures and current source densities calculated from high-resolution EEG recordings. Verbs re-ferring to actions usually performed with different body parts were compared. Behavioral dataindicated faster processing of verbs referring to actions performed with the face muscles andarticulators (face-related words) compared to verbs referring to movements involving the lowerhalf of the body (leg-related words). Significant topographical differences in brain activityelicited by verb types were found starting ,250 ms after word onset. Differences were seenat recording sites located over the motor strip and adjacent frontal cortex. At the vertex, closeto the cortical representation of the leg, leg-related verbs (for example, to walk) producedstrongest in-going currents, whereas for face-related verbs (for example, to talk) the most in-going activity was seen at more lateral electrodes placed over the left Sylvian fissure, closeto the representation of the articulators. Thus, action words caused differential activation alongthe motor strip, with strongest in-going activity occurring close to the cortical representationof the body parts primarily used for carrying out the actions the verbs refer to. Topographicallyspecific physiological signs of word processing started earlier for face-related words and lastedlonger for verbs referring to leg movements. We conclude that verb types can differ in theirprocessing speed and can elicit neurophysiological activity with different cortical topographies.These behavioral and physiological differences can be related to cognitive processes, in partic-ular to lexical semantic access. Our results are consistent with associative theories postulatingthat words are organized in the brain as distributed cell assemblies whose cortical distributionsreflect the words’ meanings. 2001 Academic Press

Key Words: action; frontal lobe; language; lexical access; neurophysiology; semantics;word-category-specific brain processes.

INTRODUCTION

Neurological models from the late 19th century suggest that language is processedin the two language areas of Broca and Wernicke located close to the Sylvian fissureof the dominant hemisphere, usually the left in right-handers (Lichtheim, 1885;Geschwind, 1970). However, this view has been challenged both by modern neuro-psychological studies in brain-lesioned patients and by brain imaging studies. Al-

We thank Siggi Haug, Katrin Zohsel, and Hans Schleichert for their help in performing the experi-ments. Supported by Grants Pu 97/5-2 and Pu 97/11-1 of the Deutsche Forschungsgemeinschaft (DFG).

Address correspondence and reprint requests to Friedemann Pulvermuller, Cognition and Brain Sci-ences Unit, Medical Research Council, 15 Chaucer Road, Cambridge, CB2 2EF, England. E-mail:[email protected].

1430093-934X/01 $35.00

Copyright 2001 by Academic PressAll rights of reproduction in any form reserved.

144 PULVERMULLER, HARLE, AND HUMMEL

though organic language disturbances primarily arise from left-perisylvian lesions,numerous areas outside these classic ‘‘language centers’’ become active in manylanguage tasks. In addition, using fine-grained neuropsychological testing, lesionsoutside the perisylvian core could be shown to elicit well-defined linguistic deficits,many of which are category-specific (Warrington & McCarthy, 1984, 1987; War-rington & Shallice, 1985; Daniele et al., 1994; Humphreys & Forde, 2001). Theseresults suggest that the old neurological models of language processing proposedsome 100 years ago are incorrect: Language is not housed in two small areas in theleft hemisphere, but may involve various cortical areas (Freud, 1891). It might bethat the areas involved depend on the type of language-related semantic informationbeing processed (for review, see Pulvermuller, 1999).

Neurobiological theories of associative learning and memory suggest that cognitiverepresentations are distributed cortical neuron populations including neurons boundtogether by associative learning (Hebb, 1949; Braitenberg, 1978; Fuster, 1995, 1997).Therefore, if a word has frequently been copresented with nonlinguistic stimuli, suchas objects, faces, or sounds, its neuronal representation will include the coactivatedneurons into its representation so that a mental image can later be immediatelyaroused whenever the word form is perceived. The neuronal representation, then,would be a cell assembly distributed over language areas and additional areas relatedto the word’s meaning (Braitenberg, 1980, 1997; Braitenberg & Pulvermuller, 1992;Braitenberg & Schuz, 1992).

This proposal receives support from recent neuroimaging experiments. Grammati-cal function words, for example, have been found to elicit brain activity lateralizedto the left and focused to left-perisylvian recording sites (Neville et al., 1992; Pulver-muller et al., 1995). In contrast, contentful words such as many nouns and verbs ledto more widely distributed signs of activity. Comparing content words of differenttypes, it was found that nouns primarily invoking visual associations and verbs mak-ing reference to actions of the own body-produced cortical responses with distincttopographies as well: The differences in psychological associations of body move-ments vs visual images corresponded to physiological differences recorded aboveprimary and/or higher order motor and visual cortices (Pulvermuller et al., 1999a).These physiological word category differences can be related to the meaning ofwords. Remarkably, neurophysiological recording techniques with high temporal res-olution (EEG and MEG) indicated that word category differences were presentshortly after stimulus onset. The relevant delays were in the order of 200 ms andthus occurred as early as (or even earlier than) physiological differences betweenwords and meaningless but phonologically regular pseudowords. This suggests thatinformation about the form and meaning of a word is cortically accessed almostsimultaneously (cf. Marslen-Wilson & Tyler, 1975). These data support the view thatwords are stored by distributed cell assemblies with defined cortical topographiesreflecting semantic word properties (Pulvermuller, 1999). Stimulation of these puta-tive word representations leads to an early activation process (‘‘ignition’’ of the as-sembly; Braitenberg, 1978) affecting all parts of the network near-simultaneously.

However, more evidence for this idea is needed. For example, one may argue thattopographical differences in brain responses to action verbs and visually related nouns(Pulvermuller et al., 1999a) might be due to the difference in lexical categories (verbvs noun) rather than to semantic factors. It thus appears necessary to investigatewords from the same lexical category that diverge in their semantic associations.Such work gave evidence of differential cortical activation primarily in the temporallobe, inferior to Wernicke’s area, and in occipital areas. Color words were found to‘‘light up’’ visual areas involved in color processing (Martin et al., 1995), nounsreferring to animals specifically aroused inferior temporal or occipital cortices (Da-

BRAIN CORRELATES OF ACTION VERBS 145

masio et al., 1996; Martin et al., 1996), and tool names induced activity enhancementin a temporo-occipital area likely involved in the perception of motion (Martin etal., 1996)—possibly reflecting the dynamic aspects of tools which are frequentlyperceived when being manipulated. This led Damasio and colleagues (1996) to con-clude that ‘‘the retrieval of words denoting entities belonging to . . . distinct concep-tual categories . . . depends on separable regions in higher-order cortices of the lefttemporal lobe.’’

However, from the perspective of a theory of associative learning, there is noreason for postulating a special role of temporal or occipital lobes in processing wordmeaning. The cortex has been proposed to be a huge associative memory in whichcorrelated neuronal activity is the driving force of the build-up of representations(Braitenberg & Schuz, 1998; Fuster, 1995). This may be the reason why associationof visual perceptions and word forms involve temporal and occipital visual areas inaddition to classic language areas. Visual associations are obvious for most nouns,and nouns represent the largest word category—at least in the Germanic languages,which are frequently investigated in brain imaging experiments. However, there areother word categories for which visual associations are less important. Action verbs,for example, are much less numerous compared to nouns, but they have an importantrole in most languages. Verbs are the primary tools for talking about actions. Eachaction verb can be characterized by the actions and body movements it refers to. Forstoring contingencies between words and actions, the frontal lobes may be of utmostimportance in addition to perisylvian language areas. Results from lesion studies andfrom imaging experiments are consistent with this: Lesions in the frontal lobe canlead to specific deficits in action verb processing (Daniele et al., 1994), and actionverbs and tool names have been found to activate frontal areas (Martin et al., 1996;Pulvermuller et al., 1999a, 1999b). This is evidence that the frontal lobe is of particu-lar relevance for the processing and representation of words related to actions.

To further illuminate the possible role of the frontal cortex in action word pro-cessing, it may be fruitful to investigate additional predictions of a Hebbian approachto language. Association learning implies fine-grained distinctions between subtypesof action verbs. Verbs can refer to different types of actions, one important distinctionbeing based on the body parts primarily involved: There are verbs referring to move-ments involving the hands, legs, or face. Body muscles, the physical machinery ac-tions are based on, are well known to be topographically represented in the motorcortex, and one may therefore hypothesize that verbs referring to actions involvingdifferent parts of the body activate distinct cortical areas. This follows from Hebb’slearning rule because during language acquisition, action verbs are frequently usedshortly before, during, or after the relevant actions are being performed (Tomasello &Kruger, 1992) and, thus, simultaneous or near-simultaneous action potentials occurin neurons in perisylvian language areas and, in addition, in neurons in the relevantmotor, premotor, and possibly prefrontal areas. Thus, the Hebbian principle of corre-lation learning predicts that the neuronal representation of a word referring to move-ments of the legs (to walk) will include neurons in dorsal motor areas, while anaction word related to arm movements (to write) may have an overlapping but distinctrepresentation in language and lateral frontal areas, and finally a face- or speech act-verb (to talk) may have most of its neurons localized in perisylvian sites because itssemantic neurons have a role in controlling the face and articulatory muscles. Thus,differential cortical activation along the motor strip induced by action verbs would beconsistent with what is known about the cortical representation of body movements(Penfield & Rassmussen, 1950; Ghez, 1991) and would lend support to associativemodels of word representation and processing. Figure 1 illustrates cell assembly-types proposed for three different types of action verbs.


Three experiments were carried out to elucidate physiological, behavioral, andcognitive correlates of action verb processing. The words under investigation wererelated to movements of the face and articulators (face-related words) of the handsand arms (arm-related words) or of the feet and legs (leg-related words). In Experi-ment 1, neurophysiological responses were recorded through 60 EEG channels whilesubjects had to process action verbs from the three subcategories. Experiment 2 fo-cused on behavioral responses, reaction times, and errors related to the processingof action words. In addition, cognitive processes elicited by the stimuli under investi-gation were scrutinized in a rating study. In the third experiment, subjects had tojudge each stimulus word along different cognitive dimensions.

In two experiments (1 and 2), subjects were instructed to make speeded lexicaldecisions; that is, they had to press a button to words but not to pronounceablepseudowords intermixed with the words. This task, lexical decision, was applied toforce subjects to attend to and to process the word stimuli beyond the level ofmere visual perception of their shape. A further advantage of the lexical decisiontask is that it does not force subjects to attend to the words’ meanings. In fact, thistask can be solved without any knowledge about word meaning at all. If semanticallyrelated effects, both physiological and behavioral, emerge in a task not requiringaccess to word meanings, one may argue that the respective knowledge is automati-cally activated and accessed in a condition where only word forms need to be pro-cessed.

Collecting behavioral responses—in addition to physiological recordings—wasimportant for testing another prediction of the neurobiological model of action wordprocessing (Fig. 1). Wider cortical distributions (as proposed for leg-related words)and more narrow distributions (as in the case of face-related items) predict a differ-ence in processing times because longer cortico-cortical connections imply longertraveling times for action potentials when the network ignites. Thus, in addition toa physiological distinction, we expected differences in response times related to verbsemantics, that is, faster responses to face-related words than to leg-related words.Since Experiment 1 yielded such differences, and because these were somewhat incontrast with the predictions one may want to make based on the psychological litera-ture, we set out to replicate these results in Experiment 2, under somewhat optimizedconditions. In the third experiment, the rating study, we used an elaborate question-naire in which subjects were asked to specify for each word the cognitive processesit normally arouses. This latter study was performed to obtain data about cognitive

FIG. 1. Associative learning principles imply that action verbs are cortically represented as cellassemblies binding together word form and meaning. A word referring to movements with a particularpart of the body (leg, arm, or face) should include neurons involved in programming the respectiveactions. Based on the somatotopic organization of the motor cortex, different cortical topographies canbe proposed for the neural correlates of verbs referring to movements primarily involving the legs, arms,and face, respectively.


correlates of putative physiological and behavioral differences between the wordsunder investigation.

METHODS

Experiment 1: Neurophysiology

Subjects. 20 subjects (7 males) participated in the experiment. Their age varied between 18 and 32years (mean 5 23.3; standard deviation 5 3.6). All participants were undergraduate or graduate students.All had normal or corrected-to-normal vision and no history of neurological illness or drug abuse. Theywere all monolingual native speakers of German. Neuropsychological testing (Oldfield, 1971) revealedthat all were strongly right-handed with no familial left-handedness. Informed consent was obtainedfrom all subjects, and they were paid for their participation (15 DM/hour).

Stimuli. The stimuli were visually presented German words and pseudowords. Words included threedifferent types of action verbs: arm-related, face-related, and leg-related verbs (see Appendix). All wordswere two syllables long and consisted of 5 to 10 letters. Word length did not significantly differ betweenthe three word categories (average values 5 6.8, 6.9, and 6.9 letters for face-, arm-, and leg-relatedwords, respectively). The normative lexical frequencies of the words (briefly ‘‘word frequencies’’) weretaken from Baayan et al. (1993). They did not differ between face- and arm-related verbs (averages:3.8 vs 5.7 occurrences per million), but were significantly higher for leg-related words (37.6 occurrencesper million). This was confirmed by a one-way analysis of variance with three levels, one for each wordcategory [F(2, 62) 5 4.6, p 5 .01] and by planned comparisons [leg vs face: F(1, 32) 5 4.6, p 5 .04;leg vs arm: F(1, 32) 5 4.7, p 5 .04; arm vs face: F , 1, n.s.]. Analyses of lemma frequencies led tosimilar results, that is, higher scores for leg-related action verbs compared to both other word categories.Because lexical decisions are speeded when word frequencies are high (Scarborough et al., 1977), thesedifferences predict faster response times for the leg-related words compared to both arm- and face-relateditems.

Bisyllabic pseudowords were generated by exchanging letters within and between the words and weretherefore matched to the words by number of letters. All pseudowords were in accord with the phonologi-cal and orthographic rules of German. Pseudowords similar to French or English words were not in thesample. All stimuli were presented on a computer screen, written in uppercase letters, each word orpseudoword subtending a horizontal visual angle of 5° or less. Stimuli were presented in black on agray background in the middle of a video screen.

Procedure. Subjects were instructed to perform speeded lexical decisions. They had to decide, asfast and accurately as possible, whether they saw a word or a pseudoword and press a button with theindex finger to words. The responding hand was counterbalanced across subjects. Presentation was donein two blocks containing 96 stimuli each. The stimuli were presented for 100 ms in pseudorandom orderwith an SOA randomly varying between 3.5 and 4.5 s. For each subject, a new stimulus sequence wasgenerated with not more than 4 words or pseudowords in direct succession. At the beginning of eachblock and during interstimulus intervals, subjects focused on a fixation-cross appearing in the middleof the screen. Subjects were instructed to avoid eye blinks and movements during stimulus presentationand to restrict blinking to self-initiated pauses. Throughout the experiment, subjects could initiate pausesby not releasing the button used for responding.

Data recording. Behavioral and physiological data were recorded. Behavioral responses were accu-racies of lexical decisions and response times. For recording neurophysiological data, a cap with 60 tinelectrodes (ElectroCap International), SynAmps amplifiers (NeuroScan), and a Pentium computer wereused. EEG electrodes were placed according to the extended 10–20 system (Jasper, 1958). Additionalelectrodes were used for monitoring eye movements. They were placed above and below the left eye(for recording the vertical electrooculogram, EOG) and at the outer canthi (for recording the horizontalEOG). Two more electrodes were fixed at the ear lobes. Data were digitally stored for off-line analysis.They were recorded with a 0- to 100-Hz bandpass filter and sampled at 500 Hz. All EEG recordingsand the recordings from ear lobes were against Cz as a reference. Signals were converted off-line tolinked ear lobe reference and subsequently submitted to current source density analysis as describedbelow. Note that data recorded in this way allow for the computation of any alternative reference (Pictonet al., 1995) and for the estimation of local current source densities (Perrin et al., 1989).

Data analysis. Behavioral and neurophysiological data elicited by words were analyzed. Accuraciesand latencies of lexical decisions were averaged for each subject and word category. For statisticalevaluation of behavioral responses, one-way analyses of variance with the three-level factor Verb Classwere performed.

Epochs of 1024 ms length were cut out from the continuously recorded data. These epochs started100 ms before onset of stimuli. After baseline correction, trials with potentials greater than 100 µV were


rejected and a blink-correction algorithm (Scan software package) was applied. In addition, all trialswere visually inspected and obvious artifacts were rejected. Only artifact-free trials with correct responseswere included into all subsequent analyses.

Averages of event-related potentials (ERPs) were computed for every subject, electrode, and wordcategory. For each time point of these average curves, current source density (CSD) solutions werecomputed using the spherical spline method (Perrin et al., 1989; Perrin, 1992). Current source densitieswere calculated because this allows for minimizing the contribution of distant generators to the signals.Thus the CSD map primarily reflects the contribution of cortical sources close to the respective recordingsites (Perrin et al., 1989; Law et al., 1993; Junghofer et al., 1997). Mean CSDs were then calculatedfor defined time windows (see also Results) for each subject, electrode, and verb class. Based on grandaverage curves, seven time windows were defined for statistical analyses of possible differences of CSDsbetween verb classes. These windows were 120–140 ms (I), 220–240 ms (II), 240–260 ms (III), 260–300 ms (IV), 300–340 ms (V), 340–380 ms (VI), and 380–460 (VII) (see also Fig. 4). Additional timeintervals were analyzed as indicated under Results. Further, data from the baseline and the first 100 msafter stimulus onset were analyzed. The latter intervals failed to reveal any significant differences betweenword categories.

ERPs and CSD estimates were submitted to analyses of variance all including the factors verb class,plus topographical variables. Topographical analyses of brain responses included overall evaluation basedon values from all 60 recording sites. Additional statistical tests were performed for regions of interest(ROIs). The model presented in the discussion led us to choose recording sites placed above or closeto the motor strip (e.g., the C and FC lines of the extended 10–20 system). Additional analyses wereperformed on data recorded from selected sites over the frontal, parietal, and occipital cortex, respec-tively. If the analysis of ERPs or CSDs revealed significant topographical differences, we used the methodproposed by McCarthy and Wood (1985) to normalize the data for topographically nonspecific variationsin amplitude and recalculated the statistical analyses. Means over all 60 recording sites were calculatedfor each subject and condition, and each value had the mean subtracted and then was divided by thevariance. This yielded normalized z-score values. Additional statistical tests were performed on thesenormalized values which are reported below except indicated otherwise. In all analyses, the Greenhouse–Geisser correction of degrees of freedom was applied when appropriate (Greenhouse & Geisser, 1959).Adjusted p values are reported throughout.

Experiment 2: Behavioral Replication

The second experiment was performed to confirm the behavioral data obtained in the first. As isdescribed in detail below, Experiment 1 revealed faster responses to face-related words compared to legwords, although the normative lexical frequency of the latter was higher than that of the former. Weinvestigated the reliability of this unexpected finding in a replication study where the experiment wasadjusted to achieve optimal behavioral responses.

A different group of 17 volunteers (8 males) was employed for this study. They satisfied the samecriteria as the participants of Experiment 1. Their age was between 20 and 29 years (mean 5 23.2).The stimuli were the same as in Experiment 1 and the same procedure was applied, except for thefollowing difference: Subjects had to press a button with their right index finger to words only. Thiswas done to allow for faster responses. In addition, subjects were instructed to respond as fast as possible.Response times and accuracies were analyzed using analyses of variance. Data from subjects who mademore than 10% errors were excluded from these analyses. The response to each stimulus word wasrecorded with a separate trigger so that a detailed item analysis could be performed.

Experiment 3: Ratings of Cognitive Processes

To obtain direct information about human cognitive processes related to action verb processing, thestimuli used in Experiments 1 and 2 were included in a questionnaire. Following Experiment 2, the 17participants were asked to answer six questions about each word. These covered (1) the subjective wordfrequency (Do you use this word frequently?), (2) the word’s concreteness, (3) its visual imagibility,and three action-related issues. They had to indicate whether a word reminded them of an action theywould usually perform with their face or articulators (4), with their hands and arms (5), or with theirfeet and legs (6).

The sequence of words was randomized individually for each subject. Questions were presented ona computer screen and graded responses had to be made by pressing a number key between 1 (no) and5 (yes). Ratings were analyzed using analyses of variance and t or F tests were used for post hoc testing.


RESULTS

Behavioral Data: Ratings

According to the raters’ judgements, the three categories of verbs did not differin their frequency of occurrence. Average values on the 5-point rating scale were3.2, 3.1, and 3.2 for the three item groups. This contrasts with the objective dataobtained from a word frequency count (Baayan et al., 1993) where leg-related verbsreceived higher frequency scores. Ratings of concreteness of word meanings did notreveal significant between-category differences either. Average values were 4.1, 4.3,and 4.2 points for the classes of face, arm, and leg words, respectively. There was asignificant difference in the ratings of visual associations. Leg and arm words elicitedstronger visual association ratings (3.3 and 3.4 points) than face-related words [2.6points; F(2, 32) 5 15.1, p , .0001].

The most pronounced difference in semantic meaning between the three verb typeswas revealed by the ratings of movement associations (Fig. 2, diagram at the bottom).When the subjects were asked about the body movements the words reminded themof, they consistently reported that strongest associations of face and articulator move-ments were elicited by the face words, strongest associations of arm and hand move-ment were elicited by the arm words, and strongest leg- and foot-movement associa-tions were evoked by the leg words. The interaction of association type and verbtype revealed by the ratings is displayed in Fig. 2. An analysis of variance left nodoubt that this interaction was significant [F(4, 64) 5 563.9, p , .0001]. These datawere consistent with the classification into three semantic groups of verbs.

Behavioral Data: Reaction Times and Accuracy of Lexical Decisions

In Experiment 1, accuracies were high for all words and pseudowords (95% andup) and did not reveal any difference between stimulus classes. The analysis of re-sponse times revealed a main effect of the factor Verb Class [F(2, 38) 5 11.8, ε 50.82, p 5 .0003]. On average, lexical decisions were faster to face-related words(676 ms after stimulus onset) compared to arm-related verbs (688 ms), which were,in turn, faster than those to leg-related items (708 ms). Statistical analysis confirmedall of these differences, the face/leg comparison producing the most impressive re-sults [arm vs face, t(19) 5 6.7, p 5 .02; arm vs leg, t (19) 5 6.3, p 5 .02; face vsleg, t(19) 5 20.6, p 5 .0002].

In the replication study, Experiment 2, response times were slightly faster. Face-related words elicited the fastest average response (665 ms), followed by arm-relatedwords (670 ms) and leg-related words (681 ms). The difference between face andleg words was again significant [F(1, 16) 5 4.4, p 5 .05]. Accuracy of responseswas also slightly higher for face items compared to leg items (97.5% vs 95.9% cor-rect), but this difference did not reach significance. Behavioral results are summarizedin Fig. 2 (upper diagram).

One may argue that the difference in response times between semantic verb catego-ries may be partially due to a few exceptional words in one or more of the groups,producing a few latency outliers. Therefore, an item analysis was performed on thedata obtained in Experiment 2. This item analysis revealed that, within each verbcategory, the well-known effect of word frequency on reaction times was apparent(e.g., Bardley, 1983). Response times fell off with increasing logarithms of wordfrequencies. Words whose response times deviated more than 2 standard deviationsfrom the overall mean were excluded from the statistical analysis. In addition, weexcluded those words from the three categories with exceptionally high word frequen-cies. This resulted in three word groups matched for word length (means 5 6.7, 6.8,


FIG. 2. Behavioral results obtained in Experiments 1–3. Top: The two lexical decision experimentsconsistently revealed longer latencies of responses to leg-related verbs compared to face-related items.Arm-related words were in between. Bottom: The three verb subcategories differed in their semanticassociations reported by experiment participants. Consistent with their classification as face-, arm-, andleg-related words, they primarily elicited associations of face, arm, and leg movements, respectively.

and 7.2 letters) and frequency (means 5 3.4, 3.7, and 3.3 occurrences/Mio.). Theitem analysis performed on these now well-matched word groups again confirmedthe significant word category difference. Face words elicited faster responses com-pared to leg words [F(1, 47) 5 4.0, p 5 .05; latency difference 5 25 ms]. Armwords were also slower than leg words, but did not differ from face items. The sameresults were obtained when response times to words that had orthographic neighborsin the stimulus set [minimal pairs such as wischen (to wipe) and waschen (to wash)]were removed from the sample.


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In summary, the behavioral results indicated significantly faster processing of face-related words compared to leg-related words, with arm-related items in between. Theaverage latency difference between face and leg words was between 16 and 32 ms,indicating that processing of leg words needed more time than the processing ofwords referring to face and articulator movements.

Physiological Responses

Event-Related Potentials. Thirty-two words per word category were presentedto each participant in Experiment 1. From the 32 resulting trials, a few had to beexcluded because of incorrect behavioral responses or artifacts in the physiologicaldata. On average, 27.1 trials per category and person were accepted. There were nosignificant differences between word categories regarding the number of acceptedtrials (face 5 27.7; arm 5 26.9; leg 5 26.6).

Figure 3 shows grand average event-related potentials elicited by the three wordcategories. Scalp recordings from nine selected loci are shown. These indicated dif-ferences between word categories. The traces of face-related words slightly divergedfrom the rest between 150 and 200 ms after stimulus onset. However, this differencecould not be substantiated by an overall analysis of variance, which failed to reveala Recording Site 3 Word Category interaction for this interval. Analyses of regionsof interest at the C and FC lines also failed to reveal significant word category differ-ences. The additional analyses only indicated a significant interaction of the factorsWord Category and Topography at parietal recordings [F(4, 76) 5 4.05, ε 5 0.77,p 5 .01], which, however, did not survive normalization for global amplitude differ-ences. These results failed to provide strong evidence for an early (,200 ms) wordcategory difference in standard ERPs. Analyses of time windows between 200 and300 ms also did not reveal significant effects.

Visual inspection of a second negative-going peak between 300 and 400 ms afterstimulus onset, however, indicated another word category difference. In contrast tothe N400 component, which is maximal at posterior, usually parietal, recording sites(Curran et al., 1993), this wave was most negative-going at sites over the frontallobe. Statistical analyses revealed a significant Word Category 3 Topography inter-action in the interval of 340–380 ms (which we will call time slice VI below) [F(4,76) 5 3.14, ε 5 0.80, p 5 .03]. For normalized data, this interaction just failed toreach significance [F(4, 76) 5 2.46, ε 5 0.83, p 5 .06].

The late positive-going wave, which peaked between 400 and 500 ms after stimulusonset, also indicated a word category difference. This P3-like wave was highest inamplitude for face-related words and smallest for leg words, the difference beingmost pronounced at left-lateral sites. An analyses of variance of data from selectedelectrodes indicated that this was a reliable difference. A significant Word Category3 Topography interaction emerged for the data from parietal recordings [not signifi-cant before normalization, but thereafter; 340–380 ms: F(4, 76) 5 4.25, ε 5 0.85,p 5 .006; 380–460 ms: F(4, 76) 5 5.17, ε 5 0.73, p 5 .002] and for recordingsfrom the frontal lobes (F and FC lines) there was a main effect of word categorywith highest peaks for face-related items [F(2, 38) 5 4.16, p 5 .03], which, however,did not survive data normalization. Thus, there was evidence for a late differencebetween P3-like components elicited by the three word groups. This late differenceis likely related to the difference in latencies of lexical decisions found for the threeword groups (longest reaction times and smallest P300 ERPs for leg words). A re-duced P300 ERP has earlier been found for words whose lexical decision latencieswere slower (Polich & Donchin, 1988).

Thus, we conclude that stimulus-triggered standard event-related potentials re-


vealed differences between verb categories around 350 ms and later. This contrastswith the results of the analyses of current source densities calculated from thesesame event-related potential curves. As reported below, the latter revealed earliersignificant topographical differences starting around 250 ms poststimulus onset—where standard ERPs did not significantly differ between word categories.

We should note that the present data did not indicate a clear word group differencein peak latencies of event-related potentials. Osterhout et al. (1997) have recentlyreported that log word frequency, which falls off with increasing word length, corre-lates with the latency of a negative-going word-evoked component, more frequentwords eliciting earlier peaks. A similar difference was absent in our present data.The early ERP components elicited by the more frequent subcategory of verbs, leg-related items, even tended to peak later in some of the recordings (see, e.g., the peakaround 150–200 ms in recordings from Cz displayed in the middle of Fig. 3). It maybe that the effect reported earlier only arises if word frequencies vary more substan-tially than in the present stimulus set.

Grand-Average Current Source Density Curves. Figure 4 shows current sourcedensity curves obtained from central electrodes placed over the motor strip. The timecourse of CSDs exhibited an in-going (negative) deflection peeking 100–150 ms afterstimulus onset which was more pronounced and earlier at more lateral electrodes, e.g.,C5 and C6. Subsequently, currents became more out-going (150–200 ms) and eventu-ally became in-going again (200–250 ms). Another in-going peak was visible around300–400 ms after stimulus onset, after which curves descended toward the baseline.

Grand average curves first indicated possible differences between verb classesabout 130 ms after stimulus onset, particularly at the lateral electrode sites C5 andC6, which are placed approximately above the Sylvian fissure and thus close to thecortical representation of the face (Towle et al., 1993; Lagerlund et al., 1994). Overthe left hemisphere, the face-related words tended to elicit the most in-going currents.These early putative differences could not be confirmed statistically. Curves differedmore substantially starting around 200 ms after stimulus onset. Above the articulator/face representation, at left-lateral recording site C5 (uppermost traces in Fig. 4), theface-related items again were those with the most in-going currents. In contrast, abovethe cortical representation of the legs, which is partly hidden in the interhemisphericsulcus but also occupies the precentral gyrus at the vertex (recording site Cz; tracesin the middle), the leg-related items started to produce the most in-going currents1

around 250 ms. This difference appeared to persist until ,350 ms after stimulusonset. Thus, the curves suggest an earlier divergence of face word-traces from therest at C5 (250 ms), while traces of leg words diverged from the other two categoriesat Cz somewhat later. The yellow areas in Fig. 4 highlight the respective differencein CSD responses between face- or leg-related verbs and the arm-related words,which, as we argue below, can be considered as baseline in this experiment.

Overall analyses of variance computed on data from all 60 EEG electrodes revealedsignificant interactions of the factors Verb Type and Topography for all time windowsexcept for I, II, and VII [240–260 ms (III): F(118, 2242) 5 2.3, ε 5 0.08; p 5 .02;260–300 ms (IV): F(118, 2242) 5 2.42, ε 5 0.08, p 5 .01; 300–340 ms (V): F(118,2242) 5 3.08, ε 5 0.07, p 5 .003; 340–380 ms (VI): F(118, 2242) 5 2.91, ε 5

1 In physical terms, outgoing activity revealed by current source density analysis means that electronsleave the head. If one condition causes more in-going signals at a particular site than another condition,this means that this particular condition produces stronger (denser) outward electron flows at the sitein question. These electron flows are likely generated by nearby cortical sources (Perrin et al., 1989;Law et al., 1993; Junghofer et al., 1997), that is, large numbers of simultaneous excitatory postsynapticpotentials in apical dendrites of cortical pyramidal cells close to the recording electrode (Mitzdorf, 1985).


FIG. 4. Time course of event-related current source densities (CSDs) recorded over the motor strip.Significant differences between the traces representing brain responses to subcategories of action verbsstarted around 250 ms after word onset. Yellow areas indicate more in-going CSDs for arm-relatedwords at C5 and for leg-words at Cz relative to the arm-word condition. C5 is approximately above therepresentation of the right half of the face; Cz is above the leg representations.

0.07, p 5 .004]. Thus, brain topographies of physiological responses significantlydiffered between verb classes starting around 250 ms after stimulus onset.

Difference maps. To obtain a more complete picture of the topographical differ-ences in brain responses between word categories, difference maps were calculated.Figure 5 shows topographical differences between arm- and face-related items (upperdiagrams) and between arm- and leg-related items (lower diagrams). The diagramsshow CSDs obtained for time slice III (240–260 ms), the earliest window where wordcategory topographies significantly differed. Circles on the left represent the head seen


FIG. 5. Difference maps of CSDs calculated for time slice III (240–260 ms). Values obtained forface- and leg-related words are subtracted from those of arm-related words. The circles on the leftrepresent the head seen from above (the nose is up and left is left). The circles on the right representlateral views on the left half of the head (the nose is on the left). Red foci indicate stronger in-goingcurrents for face-related verbs (upper diagrams) and leg-related verbs (lower diagrams). Blue foci indicatestronger in-going activity for arm-related words. Notice CSD enhancement at left-lateral sites for facewords and at central sites for leg words.

FIG. 6. Direct comparison of CSD topographies elicited by face- and leg-related verbs in time sliceIII (240–260 ms). Again the view from the top is displayed on the left and a lateral view on the lefthemisphere is presented on the right. Stronger in-going currents for face (leg) words are indicated inblue (red). Again, more in-going currents are present at left-lateral recordings for face-related items andat central recordings for leg-related items.

from above, the nose pointing upward, whereas those on the right show a lateral viewon the left half of the head. Red areas indicate stronger in-going currents for face- andleg-related verbs, whereas blue areas indicate more in-going activity for arm words.

The map on the upper left shows a red focus on its left. This focus is approximatelyabove the representation of the face and articulators in motor cortex. In contrast, the


map on the lower left shows a central red focus whose approximate location is abovecortical leg representations. The same foci can be identified in the diagrams on theright presenting difference maps calculated for the recordings from the left hemi-sphere. Additional differences are indicated by the blue foci in the upper half of thediagrams on the left reflecting most in-going activity for arm-related items recordedabove prefrontal cortex. Foci at the bottom of the diagrams on the left indicate addi-tional differences at occipital recording sites. Here, arm and leg words produced morein-going activity compared to face items.

When topographies of current source densities were directly compared betweenface- and leg-related items, electrophysiological differences above the motor stripwere again evident. Figure 6 presents difference maps in which brain responses toleg-related items were subtracted from face words. The focus in the middle of theleft diagram again indicates strongest in-going CSDs for leg-related items, whereasthe left-lateral blue focus now represents most in-going currents for face-related verbs.An additional blue focus is present over the right hemisphere which is more posteriorthan the one on the left. These maps show that, on average, face- and leg-related verbselicit stronger in-going currents—that is, they cause more electrons to leave thehead—close to the cortical representations of their respective referent actions.

Difference maps calculated for later time windows (IV–VI) gave evidence of similarbut less focused topographical verb category differences of CSDs over the motor strip.

Statistical Analyses of CSD Data from Electrodes of Interest Close to the MotorStrip (C and FC Lines)

As a next step, more detailed analyses were performed to test the hypothesis aboutdifferential activation above the motor strip and other regions of interest. To thisend, data from recordings sites C5 (left hemisphere, face representation), Cz (legrepresentations), and C6 (right hemisphere, face representation) were entered intoanother set of analyses with the design Recording Site (three levels) 3 Verb Class(three levels). Similar analyses were conducted for the slightly more anterior sitesF5, F2, and F6. The right hemisphere’s possible contribution to word category-specificprocesses has recently been discussed controversially (see Pulvermuller, 1999), andwe were interested in finding out about possible indicators for verb category-specificneurophysiological responses above the hemisphere not dominant for language.Therefore, a recording site over the right cortical hemisphere, C6, was included intothe analyses. The analyses revealed significant effects in time windows III–V.

Time Slice III (240–260 ms)

Before normalization of the data (see Methods), there was a significant interactionof the factors Verb Class and Topography of CSDs [F(4, 76) 5 3.7, ε 5 0.89, p 5.01] and a main effect of Verb Class [F(2, 38) 5 3.44, ε 5 0.92, p 5 .05]. Afternormalization, which erased the global main effect, the Verb Class 3 Topographyinteraction appeared even more reliable [F(4, 76) 5 4.42, ε 5 0.88, p 5 .004]. Plannedcomparisons performed for each electrode separately evidenced that leg-related verbsproduced more in-going currents compared to arm-related items at Cz [F(1, 19) 58.18, p 5 .01]. In contrast, the face-related items elicited more in-going activity at C5compared to arm-related verbs [F(1, 19) 5 7.28, p 5 .01]. Direct comparison of arm-and leg-related items revealed a significant effect when data from C5 were analyzed[F(1, 19) 5 11.61, p 5 .003]. Thus, if the arm condition is considered as a baselineagainst which the other two verb categories are compared (see discussion), significantlyenhanced in-going activity was present above the cortical areas involved in controllingactions the verbs refer to. The significant interaction is displayed in Fig. 7.


FIG. 7. The interaction of Verb Class and Topography in time slice III is displayed for electrodesplaced above the motor strip. The y axis shows CSDs (in microvolts per square centimeter). Significantdifferences are indicated by asterisks. Differences between face-related items and other categories aresignificant at C5. At Cz, the only significant difference was between leg- and arm-related items. Later,the differences at Cz between leg-related items and the other categories dominated (cf. Fig. 4).

The additional analysis of data from FC5, FCz, and FC6 placed over slightly moreanterior prefrontal areas also revealed a significant interaction of Word Category andTopography [F(4, 76) 5 3.36, ε 5 0.73, p 5 .03] for time range III (240–260 ms),which could be confirmed after normalization of data for global amplitude differences[F(4, 76) 5 3.87, ε 5 0.86, p 5 .01]. Consistent with the results from the C line,planned comparisons revealed that at FC5 face-related verbs produced more in-goingsignals than leg-related items [F(1, 19) 5 8.15, p 5 .01]. Over the right hemisphere,at FC6, the arm-condition was more in-going than the leg-condition [F(1, 19) 55.10, p 5 .03]. The difference at FCz between leg- and arm-related verbs was closeto significance [F(1, 19) 5 4.00, p 5 .06].

Against the background of the hypothesis under investigation, the most importantresult is the following: Compared to arm-related items, there were more in-goingCSDs for face-related verbs at C5 and for leg-related verbs there was more in-goingactivity at Cz. Direct comparisons of face and leg items yielded a significant differ-ence at C5 but not at Cz.

Time Slices IV (260–300 ms) and V (300–340 ms)

The comparison at the central electrodes did not produce a significant interaction,but the enhancement of in-going CSDs at Cz elicited by leg-related items comparedto arm-related verbs was still reliable in time window IV [F(1, 19) 5 8.05, p 5 .01],and slice V revealed such enhanced in-going activity compared to both other condi-tions [F(1, 19) . 4.49, p , .05]. As can be seen by observation of Cz recordingsdisplayed in the middle of Fig. 4, grand-average CSD traces of leg-related items wereabove those of both other categories during these time windows.

In time window V, another significant interaction of the factors Verb Class andTopography was obtained for the normalized data from FC electrodes [F(4, 76) 5


2.98, ε 5 0.82, p 5 .03]. Planned comparisons only revealed one significant difference:between face- and arm-related verbs at recording site FC5 [F(1, 19) 5 4.48, p 5 .05].

In summary, the differences between verb categories obtained in the early timewindow around 250 ms could, in part, be confirmed by the analyses of later timeslices around 300 ms. While in the earlier window the left-lateral focus for arm-related items was most pronounced, the later intervals uncovered the most clear-cutdifferences at Cz between leg-related items and the other two categories.

Analyses of CSDs Recorded from Additional Sites

One may ask whether similar topographic differences between action verbs werepresent not only at recording sites placed over motor and premotor areas, but, inaddition, at more anterior and more posterior sites. To test for this, analyses wereperformed on normalized data from an electrode triples over prefrontal cortex, fortwo triplets at centro-parietal and parietal recording sites, and for an additional tripletover occipital cortex.

The analyses of data from prefrontal electrodes (sites F5, Fz, and F6) revealed asignificant main effect of Verb Class in time slices III [240–260 ms: F(2, 38) 5 3.67,ε 5 0.91, p 5 .04] and IV [260–300 ms: F(2, 38) 5 3.44, ε 5 0.97, p 5 .04]. Thesemain effects were due to more in-going activity elicited by arm-related words comparedto both other categories (cf. the blue anterior foci in the difference maps of Fig. 5).

The centro-parietal and parietal triplets did not reveal statistically significant ef-fects. For occipital electrodes (O1, Oz, and O2), statistical analysis showed anothersignificant main effect of Verb Type for time windows I [120–140 ms: F(2, 38) 55.92, ε 5 0.94, p 5 .007] and IV [260–300 ms: F(2, 38) 5 3.44, ε 5 0.98, p 5.04]. More in-going activity was present for arm- and for leg-related items comparedto face verbs. One may therefore consider the hypothesis that face-related items onthe one side and arm- and leg-related words on the other may elicit differential visualassociations or imagery (see Discussion below).

DISCUSSION

A series of experiments revealed cognitive, behavioral, and neurophysiologicaldifferences between verbs referring to actions performed with different body parts.The four main results were the following:

1. Latencies of lexical decisions were shorter for face-related words comparedto leg words, the mean differences ranging between 16 and 32 ms (depending onexperimental setting and evaluation procedure).

2. There were topographical differences in neurophysiological responses betweenverb types. Currents above the cortical representation of leg movements were mostin-going2 for leg-related verbs, while recordings close to the representation of faceand articulators revealed most in-going signals for face-related verbs. Additional sig-nificant differences were seen over prefrontal and occipital areas.

3. The time course of neurophysiological responses indicated that specific activityto face- and leg-related items appeared in overlapping but distinct time ranges. Alreadyaround 200 ms after stimulus onset, recordings above the face representation suggestedparticularly strong in-going activity following face-related verbs (compared to bothother verb types) and this between-word-class difference was largest at 250 ms. Incontrast, the more pronounced in-going activity to leg-related items over cortical legrepresentations started somewhat later and was clearest at ,300 ms.

4. At the cognitive level, the verb types were distinguished by their semantic asso-ciations. In particular, a reflection of the fact that they refer to movements with differ-

2 This means stronger outward flow of electrons at the site in question; cf. footnote 1.


ent body parts could be objectively documented and quantified in the subjects’ rat-ings. In addition, there was evidence that word stimuli also differed in the visualassociations they elicited.

We now discuss each result in more detail.

Latency Differences in Behavior

In two experiments, latencies of lexical decisions were shorter for face-relatedverbs compared to verbs referring to leg movements. This result was obtained whenwords exactly matched for both word length and frequency were included in theanalysis, and it could even be obtained when comparing leg-related words with higheraverage lexical frequencies to relatively rare face-related words. This was unexpectedbecause word frequency is well known to be inversely related to lexical decisionlatencies: The more common an item is, the faster it is classified in the lexical decisiontask (Scarborough et al., 1977; Gernsbacher, 1984), predicting faster responses tothe (on average) more common leg-related verbs. Obviously, the slower processingof leg-related words compared to face-related items is in need of an explanation.

One may argue that various psycholinguistic and psychological variables could pos-sibly underlie this latency difference. However, by analyzing our word material, whichis listed in the Appendix, we were unable to detect obvious between-group differences,such as, for example, in the complexity of the syllables making up the words or inthe number of complements they would require in sentence contexts. While we cannotexclude alternative explanations of the latency difference, our proposal for a putativeexplanation is the following: Words are processed by strongly connected neuron en-sembles. Full activation of such a cell assembly depends on the cortical distributionof its neurons. If these neurons are localized exclusively in the perisylvian areas, aspostulated for face-related words (see Fig. 1, diagram on the right), activity spreadingwithin the network can therefore be fast. However, if the neurons of a similar assemblyare more dispersed over distant cortical areas, as postulated for leg-related verbs (Fig.1, diagram on the left), the amount of time needed for a full ignition of the assemblywill likely be higher due to the longer delays caused by axonal conduction. Neuroana-tomical and neurophysiological studies revealed that (1) most myelinated axons inthe human corpus callosum have fiber calibers of 0.5 to 1 µm (Aboitiz et al., 1992)and (2) conduction velocities in these fibers are 5–10 m/s (Miller, 1996; Swadlow &Waxman, 1976). Assuming similar properties for long-distance cortico-cortical fiberswithin one hemisphere, this predicts 20- to 40-ms conduction delays for activity travel-ling the ,10 cm from perisylvian sites to dorsal motor areas and back (Pulvermuller,2000). This additional delay involved in the activation of the postulated cell assembliesrepresenting leg-related words may underlie the differences in latencies observed forthe three verb classes. The estimate based on neuroanatomical and neurophysiologicalknowledge (20–40 ms) and the actual difference in average latencies of behavioralresponses (ranging between 16 and 32 ms, depending on experimental procedure andanalysis technique) were compatible.

Average latencies of lexical decisions on arm-related words were between of thoseof the other two groups (Fig. 2, upper diagram). As is explained in more detail below,we consider it problematic to interpret the data from arm-related words in the presentexperiment because responses had to be carried out with the hand. Therefore, the hand-motor systemmayhave been inan activeorpreparatorystate throughout theexperiment.

Topographical Differences in Event-Related Brain Responses

Action verbs of different semantic types elicited distinct patterns of neurophys-iological responses. Current source density distributions calculated from high-


resolution event-related EEG responses revealed focal differences between verb sub-categories in the time range between 240 and 340 ms after onset of visual stimuli.Apart from physiological differences recorded over prefrontal and occipital areas,there was differential activation along the motor strip. Verbs referring to actionsperformed with the face muscles and articulators produced more in-going activity atlateral recordings, above the cortical representation of the face and articulators,whereas verbs related to actions performed with the legs produced the highest ampli-tudes of in-going current source densities over the cortical leg representation (record-ings at the vertex, Cz). These results are consistent with the view that aspects of themeaning of action verbs are reflected in topographies of early event-related neuro-physiological responses. Additional focal verb-type differences in neurophysiologicalresponses were present over prefrontal areas and over the occipital lobes.

CSD analysis of EEG signals with high spatial resolution is a method for increasingthe contribution of local sources to the signal while attenuating the contribution ofmore global sources (Law et al., 1993; Perrin et al., 1989; Picton et al., 1995). Forverbs referring to different body parts, the model summarized in the Introductionpredicted differences in local sources. This motivated the choice of the CSD method.

In contrast to CSD data, event-related potentials calculated relative to a standardreference only gave evidence of more global and later differences between verb subcat-egories. These ERP differences were present around the peak of a P300-like wave,400 ms after stimulus onset. We should emphasize that the main result obtained fromCSD analysis was present in time intervals where standard ERPs failed to indicatebetween-category differences. On the other hand, CSD signals did not reproduce theeffects seen in standard ERPs. The two measures revealed two distinct effects separatedin time. Thus, it is clear that the present CSD effects are not the by-product of a differ-ence also apparent from standard ERPs. No differences in latencies of ERP componentsrelated to word frequency or stimulus category were found in the present data set.

It is possible to relate the late effect revealed by standard ERPs to the behavioraldifferences discussed above: Leg words elicited the slowest responses in the lexicaldecision task and the smallest P300-like peaks. Polich and Donchin (1988) also foundthat the stimulus words followed by slower lexical decisions elicited smaller P300 com-ponents. In contrast to this late P300 modulation, it appears less likely that the earlyfocal verb-type differences in CSDs around 250 ms relate to the variation in responselatencies. Why should a difference in response latencies shift the focus of current sourcedensities along the motor strip? At least, according to the authors’ knowledge, there isno evidence for such response time-related topographical shifts from earlier studies.We propose that the early topographical difference found in CSDs reflects word mean-ing while the later modulation of P300 amplitude has to do with response preparation.

It is critical to clarify why no differences were seen between arm-hand-relatedverbs and the other two categories over motor cortices, that is, at central recordingsites. Most likely, this was due to the task applied in this experiment. Subjects hadto perform a button press with one of their hands, thereby classifying stimuli asmeaningful words or meaningless pseudowords. Therefore, subjects were likely ina state of preparation for hand movements throughout the experiment. This may haveled to a continuously enhanced level of neuronal activity in hand-motor areas, mask-ing possible additional activity related to word semantics. It is well known that prepa-ration of motor activity leads to focal activity signs at central EEG recordings (Korn-huber & Deecke, 1965; Rockstroh et al., 1989). A ceiling effect may thereforeunderlie the absence of any pronounced divergence of the event-related traces ofhand-related verbs from both other word categories over the motor strip.

Assuming a ceiling effect for arm-related words, this condition can be used as abaseline against which brain responses to other word categories can be interpreted.


Topographies of these comparisons are presented in Fig. 5. In these comparisons,the more lateral focus of in-going activity (red) elicited by face-related words (upperdiagrams) contrasts with the central focus elicited by leg words (lower diagrams).Word category differences were most focused in an earlier time window (240–260ms) and more spread out and somewhat shifted in later intervals, for example, be-tween 300 and 340 ms. Direct comparison of topographies elicited by leg- vs face-related verbs (Fig. 6) again indicated a central focal enhancement of in-going activityfor leg words (in red) and a left-lateral enhancement focus (in blue) for face words.

One may ask whether high-resolution noninvasive neurophysiological recordingscan, in principle, reliably reflect distinct cortical sources along the motor strip andwhich topographies would be expected in the case of movements of the articulatorsand legs. In this context, it is essential to emphasize that the homuncular organizationof the motor cortex is well known to become manifest in EEG and MEG recordings.For example, a late component of the readiness potential (or Bereitschaftspotential)proceeding toe movements is maximal at the top of the head, at recording site Cz,roughly above the respective motor cortex, and slightly stronger over the ipsilateralhemisphere (Boschert et al., 1983). In contrast, finger movements are preceded by aBereitschaftspotential maximal over the hand representation of the contralateral hemi-sphere. Speech onset is preceded by an even more strongly lateralized Bereitschafts-potential (Deecke et al., 1986), and tongue movements produce still more lateralizedsources than face movements (Cheyne et al., 1991). In summary, there is clear evi-dence that the homuncular organization of the motor cortex is reflected in EEG andMEG recordings. Based on these data, the activation patterns obtained for face- andleg-related verbs are consistent with a differential activation of areas involved in pro-gramming movements of the articulators and lower extremities, respectively.

Although the present differences in CSDs recorded over the motor strip likelyreflect neuronal activity in underlying cortical areas, they do not allow for inferenceson whether the actual generators were localized in primary motor, premotor, or pre-frontal areas. The less reliable word category differences seen at more anterior elec-trodes (FC recordings) may be used to rule out a too anterior source, and the absenceof significant effects at parietal recordings speaks against a much more posteriorsource. Thus, the present data may suggest a difference generated in motor or premo-tor areas. However, it should be kept in mind that activation foci possibly related toword category-specific processes slightly differed between the direct comparison offace- vs. leg-related words (Fig. 6) and the comparison of each of these conditionsto what arguably represents the baseline condition (Fig. 5). This slight divergenceadds to the uncertainty about the exact cortical localization of the generators. Never-theless, the present data converge on activity generators producing in-going currentslocalized close to the cortical leg-representation in the case of leg-related words, butclose to the mouth and articulator representations in the case of face and speech actverbs. Clearly, metabolic imaging studies are necessary for a more exact localization.

The present data do not have strong implications regarding the possible role ofthe right hemisphere in word category-specific processes (Pulvermuller, 1999). Overthe motor strip, the predicted topographical differences were only seen at central andleft-hemispheric recording sites, but not at lateral right-hemispheric loci. Note thatonly right handers without left-handed family members were included in this study.In these subjects, language processes can be assumed to be strongly lateralized tothe left hemisphere. The predicted differences were thus seen over the dominanthemisphere. However, it should be kept in mind that verb-class differences were alsopresent at right-sided recordings, although not at the predicted loci. For example,hand-related items produced stronger in-going signals at left and right fronto-centralrecordings. In addition, the difference map on the left in Fig. 6 shows a blue focus


over frontal and right temporo-occipital areas. At this site, face-related items pro-duced stronger in-going currents than leg-related verbs. Because of their posteriorand lateral localizations, it appears unlikely that the underlying sources were in motorcortices. Future studies are necessary to confirm and more narrowly define right-hemispheric sources involved in word category-specific processing.

In summary, the differences in verb-elicited CSD topographies are consistent withthe view that action verbs activate cortical networks including neurons in motor and/or premotor areas of the dominant hemisphere involved in controlling the actions theverbs refer to.

Latency Differences in CSDs

In left-lateral recordings, there was an early divergence of face-related items fromthe rest (at 200 ms, see uppermost traces in Fig. 3), while at central recordings, thespecific enhancement of in-going currents for leg-related verbs was seen not beforethe time slice of the significant Verb Class 3 Topography interaction, 240–260 msafter stimulus onset, and was most pronounced after 300 ms (see Fig. 4, traces inthe middle). For the time slice around 250 ms, where the significant interaction wasfound, post hoc tests failed to confirm that the traces of leg words separated fromthose of face items at vertex recordings, although the comparison to the baseline,responses to arm-related items, yielded significant results only for leg words. Onlyin a later time window did leg word traces stick out significantly against those ofboth other verb categories at Cz, over the cortical representation of the legs. Verbclass-specific increases of focal in-going currents were thus present at a slightlylonger latency for leg words compared to face words.

This finding has an obvious analogy in the behavior of the subjects tested. Specificphysiological indicators of face words were present earlier than those of leg words,and, correspondingly, latencies of lexical decisions were faster for face words com-pared to leg words. It should be noted that the difference varied somewhat, not onlybetween the two behavioral experiments (where latency differences were 16 and 32ms, respectively), but also between the behavioral data on the one side and the latencydifference of maximal divergence points of CSDs on the other, where it amountedto 50 ms. However, we note that the present behavioral and neurophysiological datasets consistently indicate faster processing of face-related words compared to leg-related words, the difference being 1/50th to 1/20th of a second.

It is possible that the physiological and behavioral latency difference have the sameunderlying cause. According to the model summarized in the Introduction (see Fig.1), the differences in ‘‘scatteredness’’ of the assemblies may lead to differences intheir ignition times and thereby to latency differences reflected in both behavior andbrain physiology.

Cognitive Factors Reflected in Behavioral and Physiological VerbClass Differences

It is important to define the range of possible cognitive counterparts of the behav-ioral and physiological differences between subcategories of verbs. In this respect, therating study performed on the stimulus words may be helpful. Our subjects’ ratings ofstimuli from the three groups did not reveal differences in their familiarity, and theywere all rated to be concrete and to similar degrees. The familiarity ratings somewhatcontrast with the data on normative lexical frequencies. As noted under Methods,lexical frequencies of leg-related words were higher compared to both other word


groups. Whereas the subjects’ ratings indicated that they used or perceived thesewords equally frequently.

A difference between the stimulus sets was evident in the ratings of visual associa-tions elicited by the words. Visual associations of arm and leg-related words wererated to be stronger compared to face verbs. Using the same logic as earlier whencomparing brain responses to nouns and verbs (Pulvermuller et al., 1999a) or seman-tic subclasses of nouns (Pulvermuller et al., 1999b), one would therefore expect thatphysiological recordings close to visual areas separate face words from the other twoclasses. Indeed, both arm- and leg-related items elicited stronger in-going CSDs atoccipital recording sites than face-related words. This rather unexpected result pro-vides further support for the idea that semantic properties of words can become mani-fest in the physiological brain responses they induce.

The verb-induced differential focal activation over the motor strip has been relatedto action associations linked to word forms. This idea was based on the fact that, inlanguage use, verbs are used to refer to actions performed with different body parts.However, one may argue that many movements involve not only the head or legmuscles most crucial for performing them, but, as, for example, in the case of ‘‘towalk,’’ may involve various additional body parts. However, the ratings indicatedthat the most substantial and consistent semantic difference between the three lexicalsubcategories was the fact that they primarily reminded subjects of movements withthe face, arms, and legs, respectively, and additional associations were rated to beless pronounced and are therefore probably less important. Thus, the most substantialand consistent difference between the ratings of the three word groups strongly con-firmed the three semantic subtypes of action verbs at the level of cognitive processesreported by the subjects. This difference relates to the difference in lexical semantics,which is obvious for the three subcategories of verbs. It appears that a particularlyrelevant semantic difference between these items is in their corresponding actionsand the body parts involved in them. We cannot be sure that it is exactly this differ-ence which becomes manifest in the physiological difference, but the fact that correctpredictions on topographical differences on high-resolution CSDs could be made mayincrease the trust in this view.

CONCLUSIONS

This study led to three unexpected results about action verbs referring to differentparts of the body: (i) Face- and leg-related words were responded to with differentlatencies. We found faster processing of face words and longer latencies for legwords. (ii) Event-related current source densities calculated from high-resolutionEEG recordings revealed significant topographical differences between verb typesas early as 250 ms after stimulus onset. Focal enhancement of in-going currents wasseen for leg-related words at the vertex, close to the leg representation, and for face-related words over perisylvian areas, close to the face/articulator representation. (iii)Word category-specific in-going current enhancement over the respective parts ofthe motor strip was present somewhat earlier for face words than for leg words,consistent with the behavioral data. We conclude that verbs referring to actions per-formed with different body parts are processed differently in the human brain. Differ-ences in latencies and brain activity patterns can be explained based on a neurobiolog-ical model postulating that words are cortically represented by cell assemblies whosetopographies reflect the words’ lexical meanings.


APPENDIX

Word Stimuli Used in the Experiment

For each item, its number of letters and its lemma frequency together with its wordfrequency are listed. In the rightmost column, English translations are given. Thereare 32 items in each semantic category.

Word Lemma Word No. of Englishstimuli frequency frequency letters translation

Face- or mouth-related wordsachzen 5 0 6 to moanbeissen 22 5 7 to biteblasen 15 3 6 to blowbrullen 12 2 7 to roarbrummen 9 2 7 to droneflustern 32 3 8 to whispergahnen 5 1 6 to yawngrinsen 12 2 7 to grinhauchen 0 0 7 to respirejammern 6 1 7 to lamentkauen 7 3 5 to chewknirschen 6 1 9 to croakknurren 9 0 7 to growlkussen 34 6 6 to kisslacheln 96 12 7 to smilelallen 0 0 6 to babblelecken 1 0 6 to lickmurmeln 1 1 7 to murmurnagen 0 0 5 to gnawpfeifen 23 5 7 to whistlepusten 2 1 6 to puffrufen 159 29 5 to shoutsaugen 7 2 6 to suckschlucken 9 0 9 to swallowschnalzen 0 0 9 to clickschreien 77 9 8 to cryseufzen 21 1 7 to sighsingen 73 25 6 to singstohnen 13 1 7 to groanstottern 0 0 8 to stuttersummen 40 7 6 to buzzzischen 9 1 7 to hiss

Averages 22.0 3.8 6.8Arm- or hand-related words

binden 58 10 6 to bindboxen 6 3 5 to boxdrucken 1 0 7 to pressfechten 3 1 7 to fencefeilen 1 1 6 to fileformen 15 4 6 to formfuchteln 0 0 8 to brandishgraben 10 2 6 to diggreifen 109 24 7 to seizehacken 0 0 6 to hackheben 124 19 5 to liftkammen 8 1 6 to combklatschen 10 2 9 to applaudkneten 0 0 6 to kneadkratzen 10 3 7 to scratch


Appendix—Continued

Word Lemma Word No. of Englishstimuli frequency frequency letters translation

lenken 37 13 6 to steerpacken 0 0 6 to grabreiben 14 2 6 to rubruhren 33 6 6 to stirschalten 11 1 8 to switchschlagen 235 46 8 to beatschopfen 10 4 8 to scoopschwenken 12 2 9 to swingstechen 13 2 7 to stingstopfen 11 2 7 to stuffstreicheln 12 3 10 to stroketippen 9 1 6 to tiptrommeln 25 7 8 to beat the drumwaschen 21 6 7 to washwischen 16 1 7 to wipezeichnen 58 15 8 to drawzerren 0 0 6 to tug

Averages 27.3 5.7 6.9Leg- or foot-related words

fliehen 25 9 7 to fleeflitzen 2 1 7 to flitfluchten 26 7 8 to run awayfolgen 201 60 6 to followgehen 1138 210 5 to gohinken 5 1 6 to walk with a limphumpeln 0 0 7 to hobblehupfen 9 3 6 to hopkicken 0 0 6 to kickknien 11 1 5 to kneekommen 1548 351 6 to comelaufen 202 53 6 to rushradeln 0 0 6 to ride a bicyclerennen 30 4 6 to runschleichen 17 2 10 to creepschreiten 26 7 9 to stepspringen 77 13 8 to jumpsprinten 0 0 8 to sprintstampfen 9 2 8 to stampstapfen 0 0 7 to plodstehen 1132 329 6 to standsteigen 218 39 7 to ascendstolpern 9 1 8 to stumblestrampeln 0 0 9 to kicksturmen 15 3 7 to stormtanzen 34 14 6 to dancetrampeln 2 1 8 to trampletreten 299 74 6 to treadtrippeln 0 0 8 to tripwandern 28 10 7 to wanderwandeln 22 8 7 to walkwaten 2 1 5 to wade

Averages 158.9 37.6 6.9


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