Electroencephalography theta differences between

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Electroencephalography theta differences between object nouns and action verbs when identifying semantic relations a

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Mandy J. Maguire , Alyson D. Abel , Julie M. Schneider , Anna Fitzhugh , Jagger McCord & c

Vivek Jeevakumar a

Behavioural and Brain Sciences, University of Texas at Dallas, Rm A126, 1966 Inwood Road, Dallas, TX 75235, USA b

SLHS, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA

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Behavioural and Brain Sciences, University of Texas at Dallas, Rm A118, 1966 Inwood Road, Dallas, TX 75235, USA Published online: 23 Jan 2015.

To cite this article: Mandy J. Maguire, Alyson D. Abel, Julie M. Schneider, Anna Fitzhugh, Jagger McCord & Vivek Jeevakumar (2015): Electroencephalography theta differences between object nouns and action verbs when identifying semantic relations, Language, Cognition and Neuroscience, DOI: 10.1080/23273798.2014.1000344 To link to this article: http://dx.doi.org/10.1080/23273798.2014.1000344

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Language, Cognition and Neuroscience, 2015 http://dx.doi.org/10.1080/23273798.2014.1000344

Electroencephalography theta differences between object nouns and action verbs when identifying semantic relations Mandy J. Maguirea*, Alyson D. Abelb, Julie M. Schneiderc, Anna Fitzhughc, Jagger McCordc and Vivek Jeevakumarc a

Behavioural and Brain Sciences, University of Texas at Dallas, Rm A126, 1966 Inwood Road, Dallas, TX 75235, USA; bSLHS, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA; cBehavioural and Brain Sciences, University of Texas at Dallas, Rm A118, 1966 Inwood Road, Dallas, TX 75235, USA

Downloaded by [The University of Texas at Dallas] at 10:50 26 January 2015

(Received 17 July 2014; accepted 11 December 2014) Nouns and verbs differ in their semantic properties and relationships with other words in the lexicon. This study uses electroencephalography (EEG) to investigate how word class differences influence semantic retrieval and identification of semantic relationships. Participants identified whether the final word in a triplet (noun or verb) was related to the first two words (both nouns). Time frequency analysis of the EEG revealed word class and relatedness effects in theta, beta and gamma. Decreases in beta power were found during action verbs, which may be related to motor processing. Theta exhibited a word class by relatedness interaction in which unrelated verbs elicited a greater power increase compared to other conditions. These data indicate that action verbs require more neural activity when identifying the absence of relationships than object nouns, likely due to the abstract, flexible nature of verb meanings and the shallow structure of verb organisation. Keywords: EEG; time frequency analysis; theta; beta; gamma; semantic processing

Word classes, such as nouns and verbs, differ in both meaning and use in a way that seems to reflect fundamental conceptual categories. Nouns most often refer to entities in the world, while verbs refer to relationships between these entities (Kable, Lease-Spellmeyer, & Chatterjee, 2002). The conceptual differences between nouns and verbs contribute to differences in their use. For instance, verbs describe the relationships between nouns or states of being of nouns; thus, verbs are more abstract and flexible in use because the same verb can describe relationships across a range of nouns. Nouns, on the other hand, stay relatively consistent in their meaning, regardless of the verbs in the surrounding context. As such, word classes differ in how they relate to other words in the lexicon (Masterson, Druks, & Gallienne, 2008; Vigliocco, Vinson, Druks, Barber, & Cappa, 2011; Vigliocco, Vinson, Lewis, & Garrett, 2004). Whether differences in meaning and use translate to how the brain processes nouns and verbs is a long-standing debate. Here we address this question by using time frequency analysis of the electroencephalography (EEG) to study the semantic organisation and retrieval of object and agent nouns and action verbs. Because verbs denote relationships or states of being related to nouns, the representation of even concrete action verbs (e.g., fall) can differ based on the verb’s context (i.e., the polar bear falls on ice, the man falls in love or the stock prices fall). As such, the abstract nature of verb meanings is reflected in how verbs relate to other *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

words in the lexicon. In the previous example, the word fall semantically relates to ice, love and stock prices, three conceptually distinct concepts. The corresponding nouns, on the other hand, often fall into groupings of words that can often be hierarchically organised, such that polar bear is likely to have many close taxonomic (e.g., animal, bear, grizzly bear) or thematic relationships (e.g., ice, snow, winter, penguins; Barsalou, 1999; Carey, 1988, 1991; Hashimoto, McGregor, & Graham, 2007; Lin & Murphy, 2001; Maguire, Brier, & Ferree, 2010; Markman, 1989; Schank, Abelson, & Scripts, 1977). There are exceptions to these generalisations. For example, some subclasses of nouns such as nouns that refer to actions and events (e.g., a kick or a walk) share properties that are more similar to verbs than object nouns. Similarly, there are small groupings of action verbs that can be closely related in semantic space (e.g., run, jog, sprint). Despite these exceptions, commonly the overall semantic organisation of nouns and verbs in relation to other words in the lexicon differs. Nouns, especially object nouns, exhibit clear categories with clusters of close relationships whereas verbs exhibit an abstract, shallow, matrix-like organisation with many distant semantic relationships (Masterson et al., 2008; Vigliocco et al., 2004, 2011). The N400 event-related potential (ERP) response has been informative in studying the conscious and subconscious processing of semantic relationships. A commonly used task in ERP studies of semantic relationships


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involves participants seeing or hearing word pairs and judging whether the two words are related. The N400 amplitude is significantly larger to the second word in the pair when the word pairs are unrelated compared with when they are related (Kutas & Hillyard, 1983). Further, the N400 attenuates reliably based on the strength of relationships (Federmeier & Kutas, 1999; Grose-Fifer & Deacon, 2004; Kutas & Hillyard, 1984, 1989). For example, the N400 response to the word polar bear following ice may be smaller than the N400 response to the word polar bear following a somewhat less-related item like giraffe, which would still be smaller than the difference between unrelated words like polar bear and desk. In this way, the N400 has provided a great deal of information about how words relate to one another in the lexicon. Although the attenuation of the N400 response corresponding to semantic relatedness, irrespective of word class, is well documented, findings regarding how different word classes (namely nouns and verbs) influence the N400 relatedness effect are inconsistent (Gomes, Ritter, Tartter, Vaughan Jr, & Rosen, 1997; Khader, Scherag, Streb, & Rösler, 2003; Rösler, Streb, & Haan, 2001). Rösler et al. (2001) found that the N400 amplitude for strongly related noun pairs was smaller than the amplitude for strongly related verb pairs, indicating that identifying relationships between verbs may be more difficult to identify than relationships between nouns. On the other hand, Gomes et al. (1997) found no amplitude differences for primed or unprimed nouns and verbs. Applying time frequency analysis to the EEG as opposed to traditional ERP analyses may clarify some of contradictory findings and offer new information about noun and verb processing. Time frequency analysis allows one to analyse the same portion of EEG as an ERP analysis; however, using time frequency analysis, the EEG is decomposed to identify changes in neural oscillations within various frequency ranges that make up the EEG signal (e.g., alpha, theta, beta). One of the most common measures within time frequency analysis is to identify changes in the amplitude, or power, of the response within each frequency range of interest. For example, an increase in theta power may occur concurrently with a decrease in beta power during the same cognitive task. An increase in power is thought to relate to the activation of additional neural assemblies firing at the same frequency, also referred to as neuronal synchrony (Maguire & Abel, 2013; Nunez & Srinivasan, 2005). Similarly, a decrease in power is thought to correspond to decreased activation of neural assemblies within a frequency or neuronal desynchrony. Neural synchrony can also be exhibited as coherence between sites, meaning that correlated changes within or between frequencies are identified across scalp locations. In this paper we focus on synchrony within a neural area. Using this technique, time frequency analysis

can reveal changes in the EEG that would remain undetected with traditional ERP analysis alone. For example, using both ERP and time frequency analysis, Maguire et al. (2010) studied semantic relationship differences between thematically related noun pairs (dog–bone) and taxonomically related noun pairs (dog– horse). Similar to previous studies, ERP differences were not identified between taxonomic and thematic relationships. However, time frequency analysis revealed two findings: (1) a theta (4–8 Hz) power increase over right frontal areas for thematic compared to taxonomic relationships, which the authors attributed to increased engagement of memory processes, and (2) an alpha power (8–12 Hz) increase over parietal areas for thematic compared to taxonomic relationships, which supports claims that taxonomic relationships require greater attentional processes. These findings show that time frequency analysis is sensitive to aspects of semantic relationships not captured by the ERPs; thus, applying this technique to the study of object noun and action verb semantic relationships may provide new insight into their semantic organisation. Previous studies using time frequency analysis have implicated theta (4–8 Hz), beta (12–30 Hz) and gamma (30–80 Hz) frequency bands in semantic processing, particularly in identifying semantic relationships and processing differences between object noun and action verb retrieval more generally. Theta power has been consistently shown to increase in response to semantically unrelated words in word pairs or in an ongoing sentence (Davidson & Indefrey, 2007; Hagoort, Hald, Bastiaansen, & Petersson, 2004; Hald, Bastiaansen, & Hagoort, 2006; Maguire et al., 2010; Roehm, Schlesewsky, Bornkessel, Frisch, & Haider, 2004; Wang, Zhu, & Bastiaansen, 2012). These theta changes relate to the traditional N400 response (e.g., Davidson & Indefrey, 2007) and, like the N400 response, increased theta power is thought to represent increased semantic processing demands indexed by increased neural activation due to the difficulty of retrieving unprimed semantic information compared to primed information. Thus, if verbs exhibit more distant and shallow relationships to other words in the lexicon compared to nouns, one would expect them to also exhibit greater theta power related to increased processing demands during semantic integration. In addition to changes in the theta band, the beta and gamma frequency bands exhibit changes associated with the identification of semantic relationships as well as differential activity related to the retrieval of object nouns and action verbs. Beta power decreases are observed in response to action verbs compared to nouns and as well as during verb generation tasks (Fisher et al., 2008; Moreno, de Vega, & León, 2013; Van Elk, Van Schie, Zwaan, & Bekkering, 2010; see Weiss & Mueller, 2012 for a review). The beta decreases associated with verb processing are of particular interest because they are is similar to


Language, Cognition and Neuroscience the changes observed in beta when performing actions and watching others perform actions (Weiss & Mueller, 2012). As a result, beta decreases may index sensory-perceptual semantic processing of action verbs. In terms of semantic relationships, beta decreases are elicited by syntactically and semantically unrelated words that occur in an ongoing sentence (Bastiaansen, Magyari, & Hagoort, 2010; Luo, Zhang, Feng, & Zhou, 2010). Given the association of beta with both verb processing and semantic relationships, it seems that beta may be sensitive to differences in the processing of noun and verb semantic relationships. Like beta, gamma also relates to action verb processing and to semantic relatedness. Specifically, gamma power increases more in response to action verbs than object nouns over motor cortices (Pulvermüller et al., 1996) and increases over both left and right inferior frontal areas during verb generation tasks (Doesburg, Vinette, Cheung, & Pang, 2012). Conversely, gamma power decreases are observed in response to incongruent or unrelated information in ongoing sentences (Bastiaansen et al., 2010; Hagoort et al., 2004; Hald et al., 2006; Penolazzi, Angrilli, & Job, 2009; Wang et al., 2012). Notably, decreases in gamma power in response to unrelated information differ from theta, which increases at incongruencies, suggesting that theta and gamma index different processes – gamma indexes semantic predictability and theta indexes semantic integration (Wang et al., 2012). The varying responses of gamma to stimuli of interest in this study (power increases during verb processing and power decreases to semantic incongruency) set up interesting questions as to how gamma may respond to semantic relationships of verbs versus nouns. The current study uses time frequency analysis to investigate potential differences in EEG-related activity while processing object nouns and action verbs and identifying relationships between those words and others. Participants were asked to attend to the last word in a triplet in which the first two words were semantically related nouns (i.e., bat–ball) and the target word was a related or unrelated noun (i.e., glove or cow) or a related or unrelated verb (i.e., hit or eat). They indicated whether the third word (target) did or did not relate to the first two words. This study addresses two questions. First, are there differences in the neural oscillations related to identifying object noun compared to action verb semantic relationships? We predict that identifying relationships will require greater neural engagement for action verbs, given their abstract, shallow organisation compared with object nouns, as shown by greater theta power increases for target verbs. This would suggest more effortful semantic retrieval processes in identifying relationships between verbs and other words in one’s lexicon compared to nouns. Second, are there differences in semantic retrieval for nouns compared to verbs? We expect beta power decreases and gamma power increases for verbs compared

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to nouns, similar to what has been reported in previous papers. This may indicate an increase in motor activation related to processing of the semantic information in action verbs compared to object nouns. Methods Participants Eighteen native English speaking college students between the ages of 18 and 30 and with at least 12 years of education participated in this research. Exclusion criteria included a history of traumatic brain injury and other significant neurological issues (CVA, seizure disorders, history of high fevers, tumours or learning disabilities), left-handedness, use of alcohol or other controlled substances within 24 hours of EEG administration and medications other than over-the-counter analgesics. These criteria were selected because each can potentially affect electrophysiological responses in unpredictable ways. Two participants were excluded due to an inability to preprocess their EEG data using the guidelines presented below. Usable EEG data from 16 participants entered into the final analyses. Stimuli Following Khader et al. (2003) we created a robust semantic context by presenting word triplets: two semantically related nouns (word pair prime) followed by either a noun or a verb (target word). Target words were either semantically related or semantically unrelated to their preceding word pair prime. Four sets of 58 word triplets (232 total triplets) were developed for this study, each included two nouns followed by a: (1) semantically related noun, (2) semantically unrelated noun, (3) semantically related verb and (4) semantically unrelated verb. Semantic context was created using two nouns as opposed to words from other word classes (verbs, adjectives, adverbs) in order to create a robust semantic context without an overt syntactic frame. Object nouns are more concrete and semantically stable across situations than other word classes and, therefore, are ideal for the goal of providing a semantic context without syntactic priming. Studies of semantic relationships vary to some degree in defining what constitutes a relationship. In this case, we excluded any word pairs that created a compound word (e.g., traffic jam). Within nouns, there were two types of relationships across the word triplets: (1) categorical, meaning all three words were from the same category of items (e.g., cookie, cake, fudge or table, chair, desk) or (2) thematically related, in that the items are often encountered in the same space (e.g., broom, trash, dirt or kitchen, food, stove). The verbs fell into three groups: common action relationships (e.g., cow, pig, feed or cookie, cake, bake), thematic relationships (e.g., baby, stroller, push or


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table, chair, sit), and what we called ‘one does to the other’, meaning that one noun in the pair performed the action on the other noun in the pair (e.g., zebra, grass, graze or bat, ball, hit). This last set of triplets denoted a specific kind of thematic relationship. To test that noun and verb target words were equivalently similar (semantically related) or dissimilar (semantically unrelated) to their preceding word pair primes, we collected semantic relatedness ratings for the 232 triplets. College students (N = 158) were asked to rate on a scale of 1 (not at all related) to 5 (highly related) how closely related the third word in the triplet was to the preceding two. A repeated-measures analysis of variance (ANOVA) revealed a word class by relatedness interaction, F(1, 157) = 18.81, p < 0.05 and a significant main effect of relatedness, F(1, 157) = 2169.67, p < 0.05, but no main effect of word class. The interaction was driven by the fact that for the unrelated words there were no differences between nouns (M = 1.54, SD = 0.57) and verbs (M = 1.58, SD = 0.6), t(157) = 1.80, p = 0.075; however, for related words there were differences, with nouns being slightly more related (M = 4.40, SD = 0.54) than verbs (M = 4.31, SD = 0.55), t(157) = 3.64, p < 0.05. All words in the study were early acquired highly imageable, high-frequency words. As seen in Table 1, the 58 word pair primes (words 1 and 2) were identical across all four conditions, thus each word served as its own control for factors known to affect semantic retrieval (i.e., age of acquisition, frequency, familiarity, imageability and concreteness). Similarly, the target words were each used twice, once as a related item and once as an unrelated item so that differences in semantic congruency within a word class could not be attributed to the specific word. Participants were randomly assigned to one of four randomised orders of the 232 triplets.

Table 1. Examples of each triplet type.

Noun related Noun unrelated Verb related Verb unrelated

Word 1

Word 2

Target

Milk Milk Milk Milk

Bowl Bowl Bowl Bowl

Cereal Purse Drink Pull

Figure 1. Example of study sequence.

Procedure Participants sat in a chair 1 m from a computer monitor. They were told that they would hear pairs of words followed by a third word and to indicate by button press whether the last word did or did not ‘go together’ with the first two words. The participants heard each word with a fixation cross presented on the screen in between each word (see Figure 1). The fixation cross was used to limit eye and head movements. Following the presentation of the last word, a grey box was displayed which cued the participants to provide their response. Words were recorded by a male with a standard American accent. EEG acquisition EEG was collected from 64 silver/silver-chloride electrodes mounted within an elastic cap (Neuroscan Quickcap), which are placed according to the International 10–20 electrode placement standard (Compumedics, Inc.). EEG data were recorded continuously using a Neuroscan SynAmps2 amplifier and Scan 4.3.2 software sampled at 1 kHz with impedances typically below 5 kΩ. EEG pre-processing Data were recorded with the ground at AFz and the reference electrode located near the vertex, resulting in small amplitudes over the top of the head. In order to eliminate this effect, the data were re-referenced off-line to the average potential over the entire head, which approximates the voltages relative to infinity (Nunez, 1981). To interpolate missing electrodes, a spline-based estimate of the average scalp potential (Ferree, 2006) was computed using spherical splines (Perrin, Pernier, Bertrand, & Echallier, 1989). Placing the electrode cap on a realistic phantom head, the electrode coordinates were digitised (Polhemus, Inc.), and these coordinates were used to fit the splines for each subject. In participants with a small number of bad electrodes, the splines were used to interpolate those electrodes, yielding a total of 62 data channels for every subject. The validity of this method of interpolation is supported theoretically for 64 or more electrodes (Srinivasan, Tucker, & Murias, 1998). Blinks and eye movement were monitored via two electrodes, one mounted above the left eyebrow and one mounted below the left eye. The data were processed to remove ocular and muscle artefacts in the following way.


Language, Cognition and Neuroscience First, poorly functioning electrodes were identified visually and removed. Second, eye blink artefacts were removed by a spatial filtering algorithm in the Neuroscan Edit software using the option to preserve the background EEG. Third, time segments containing significant muscle artefacts or eye movements were rejected based on visual inspection. After artefact removal, EEG data were segmented into epochs spanning 500 ms before to 1500 ms after the presentation of the target word. A semi-automatic artefact rejection procedure followed two rejection criteria: (1) amplitudes ±75 µV and (2) voltage differences between two adjacent time points > 50 µV. The average number of trials retained across all conditions was 39.5 (SD = 8.1), and a one-way ANOVA revealed no difference across conditions in the number of trials retained; F(3, 60) = 0.85, p = 0.47. Time frequency perturbations

analysis:

event-related

spectral

Time frequency analysis was used to quantify eventrelated spectral perturbations (ERSPs). Fourier power spectra were computed using an adaptation of the pwelch function implemented in MATLAB (Mathworks, Inc.), applied to 0.5 s windows. In each epoch and time window, the time series was linearly detrended to reduce spectral leakage from the zero-frequency bin, cosine tapered to reduce spectral leakage, and zero-padded to 1 s duration to achieve 1-Hz frequency resolution. Each window was then Fourier transformed, magnitude squared and suitably normalised to obtain the power spectral density (PSD) in units µV2/Hz. The result was averaged across trials to estimate the PSD in each window. By keeping the raw power values, rather than the log power values minus the baseline, our use of the term ERSP differs slightly from that of Delorme and Makeig (2004). Throughout the peri-stimulus interval, the 0.5 s wide window was moved in 0.05 s steps. The time of each window was defined as the centre of the nonzero data in that window. The earliest time window was –0.75 s and the latest time was 1.25 s because the centres of 0.5 s windows cannot reach the ends of the epoch. To calculate the baseline spectrum from both conditions combined, the 1-s baseline interval was divided into three 0.5 s windows with 50% overlap (Welch, 1967). Baseline subtraction was not used because analyses focused on the difference between task conditions (see below). We used the EEGLAB toolbox, an open-source, interactive Matlab toolbox for processing continuous and event-related EEG, to examine potential word class (noun versus verb) and condition (related versus unrelated) ERSP differences. Using EEGLAB, we performed random permutation statistical analysis of the EEG data, computing p values for every time/frequency point for each

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comparison of interest. Random permutation statistics create a null distribution which assumes no condition differences by randomly assigning conditions across subjects and averaging the assigned values. The observed test statistics are compared to the null distribution and comparisons that fall in the distribution tails (±2.5%) are considered significant. Results Behavioural findings Averages were calculated for related/unrelated judgement accuracy and reaction time. All participants did well very well on this task, averaging 96.3% accuracy across all conditions. A 2 × 2 ANOVA revealed a main effect of word class, F(1, 15) = 6.99, p = 0.018, such that triplets that ended with a noun (M = 97.09%, SD = 2.46) were responded to more accurately than triplets that ended with a verb (M = 95.42%, SD = 2.99). For reaction times, a 2 × 2 repeated-measures ANOVA revealed a word class by relatedness interaction, F(1, 15) = 5.75, p = 0.03 with no significant main effects. Follow-up analyses indicate that this interaction was driven by a significant difference in noun and verb reaction times, t(16) = 2.81, p = 0.013, when the target word was related to the primes. Specifically, verb targets (M = 119 ms; SD = 24) were identified as relating to the preceding nouns more quickly than noun targets (M = 126 ms; SD = 30). For unrelated triplets, there were no differences in reaction times between verbs (M = 128 ms; SD = 35) and nouns (M = 132 ms; SD = 42). This small but somewhat surprising reaction time difference may be related to verbs being somewhat more frequent in a typical corpus compared to nouns. To test this hypothesis, we compared frequency ratings for the nouns and verbs in our sample based on the MRC Psycholinguistic Database (http://www.psych.rl.ac.uk/ User_Manual_v1_0.html). Of the words used in this study that were found in the MRC (42 of 58 nouns and 39 of 58 verbs), verbs were more frequent (M = 125.03, SD = 182.79) than verbs (M = 78.79, SD = 137.03), but not statistically so, likely do to the large variability in the frequency ratings, t(79) = 1.29, p = 0.199. Event-related spectral perturbation The ERSP representations of the variables of interest for each comparison as well as their contrast and electrodes that show significant effects are presented in Figures 2–4. Interaction The goal of this study was to examine differences in semantic retrieval between nouns and verbs and differences in how one identifies semantic relationships between object nouns and action verbs. Towards this goal, we performed a 2 (word class) × 2 (relatedness)


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comparison in the frequencies of interest, specifically theta (4–8 Hz), beta (13–30 Hz) and gamma (30–50 Hz), across the time period of interest (300–800 ms). This time period was used because it matches the traditional N400 relatedness effect and because it is the time period most often implicated in semantic retrieval and integration studies. Related to the interaction, we expected to find differences primarily for unrelated verbs, indicating deeper and more prolonged semantic integration activity. Following Wang et al. (2012), we only report on findings that were significant at more than one electrode site to limit possible type I error.

increase for verbs compared to nouns but no effect of word class on theta. Theta. No significant differences in theta were observed. Beta. As shown in Figure 3, noun and verb processing differs for beta at fronto-central and posterior locations Specifically, at posterior locations, beta power decreased more for verbs than nouns from 600–800 ms. Gamma. There were significant effects in the gamma range over right central areas (see Figure 3). Specifically, gamma power increased for verbs versus nouns variably across the 300–800 ms time window.

Theta. An interaction between word class and relatedness was found at points along the midline between 450 and 700 ms (Figure 2). This interaction appears to be driven by greater theta power for unrelated verb targets versus the comparison conditions (related verbs, related nouns, unrelated nouns).

Relatedness To identify differences between related and unrelated targets regardless of word class, we performed a oneway ANOVA on relatedness for the frequencies of interest (theta, beta and gamma) for the time period of interest (300–800 ms). We expected to find increased theta power, along with decreased beta and gamma power for unrelated targets versus related targets. Theta. Figure 4 shows widespread differences between related and unrelated words in the theta range between 400 and 800 ms post-target word with a greater theta power increase for unrelated versus related words. Beta. Figure 4 shows widespread differences in beta, clustering primarily between 12–25 Hz across the 300– 800 ms time window. Surprisingly, this is in the opposite direction of what we expected. At all points, this effect

Beta and gamma. No significant interactions were found in the gamma or beta ranges. Word class To identify differences between nouns and verbs outside of relatedness effects, we performed a one-way ANOVA on word class for the frequencies of interest (theta, beta and gamma), for the time period of interest (300–800 ms). We anticipated a beta power decrease and a gamma power

Figure 2. Results of the ERSP interaction analysis for theta (300–800 ms, 4–8 Hz). Note: Power increases are indicated by yellow/red and power decreases are indicated by light blue/navy. (A) Electrodes that show significant interaction effects. The circle around FCz demonstrates that all other analyses were based upon that electrode; (B) ERSP representations of each of the four conditions (noun related, noun unrelated, verb related and verb unrelated) at electrode FCz, and (C) ERSP representation of the significant interaction at FCz, as made evident by the colour brown (green is caused by masking of non-significant effects). Boxes around (B) and (C) are highlighting the theta range (4–8 Hz).


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Figure 3. Results of the ERSP analysis for the word class comparison (nouns and verbs). Note: Power increases are indicated by yellow/red and power decreases are indicated by light blue/navy. (A) Electrodes that show significant effects between 300 and 800 ms for beta (12–30 Hz), the circle around P4 indicates ERSP representations for beta are based upon that electrode. (B) Electrodes that show significant effects between 300 and 800 ms for gamma (30–80 Hz), the circle around C6 indicates ERSP representations for gamma are based upon that electrode. (C) ERSP representations of nouns and verbs and significant differences between word classes (p < 0.05) at electrodes P4 for beta and C6 for gamma, as made evident by the colour brown (green is caused by masking of non-significant effects). Boxes around (B) are highlighting the beta range (top; 12–30 Hz) and gamma range (bottom; 30–80 Hz).

indicates a larger decrease in beta power for related versus unrelated items. Gamma. Within the gamma band, no differences were significant for two or more adjacent electrodes.

Discussion The goal of this paper was to investigate whether EEG can identify differences in the neural engagement underlying the processing of noun and verb semantic relationships. We predicted two main effects related to theta as a measure of semantic processing. The first was that, as in previous work (Davidson & Indefrey, 2007; Hagoort et al., 2004; Hald et al., 2006; Wang et al., 2012), unrelated words would elicit greater power increases than related words. The second was a main effect of word class in which verbs would elicit increases in theta power compared to nouns because of their abstract nature and

shallow, matrix-like semantic organisation. Interestingly, the data revealed an interaction in which unrelated verbs exhibited more theta synchrony than the other conditions. Additionally, the results supported previous research suggesting differences in gamma and beta relate to action verbs compared to object nouns. These findings provide important information about identifying semantic relationships and semantic retrieval. The larger, more prolonged increase in theta power for unrelated target verbs versus all three other categories (related verbs, unrelated nouns and related nouns) is especially interesting because it indicates that correctly identifying a relationship did not result in different levels of neural engagement for nouns and verbs. Instead, the difference between nouns and verbs occurs when the participants fail to identify a relationship. Specifically, it seems that, even when the target verb did not clearly relate to the proceeding prime words, participants exhibited an


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Figure 4. Results of the ERSP analysis for the relatedness comparison (related and unrelated). Note: Power increases are indicated by yellow/red and power decreases are indicated by light blue/navy. (A) Electrodes that show significant effects between 300 and 800 ms for theta (4–8 Hz), the circle around CP5 indicates ERSP representations for theta are based upon that electrode. (B) Electrodes that show significant effects between 300 and 800 ms for beta (12–30 Hz), the circle around FC3 indicates ERSP representations for beta are based upon that electrode. (C) ERSP representations of related and unrelated targets and significant differences between conditions (p < 0.05) at electrode CP5 for theta and FC3 for beta, as made evident by the colour brown (green is caused by masking of non-significant effects). Boxes around (B) are highlighting the theta range (top; 4–8 Hz) and beta range (bottom; 12–30 Hz).

increase in theta power when determining if a relationship was possible more so than for a noun in a similar situation. Because this increase in theta power is generally believed to be related to more neurons firing within the theta frequency, such an increase may be interpreted as indicative of more difficult semantic processing. This is likely due to the fact that verbs are more abstract, containing more semantic relationships that have a shallower semantic distribution (Vinson & Vigliocco, 2002). Maguire, Magnon, Ogiela, Egbert, and Sides (2013) discussed a similar phenomenon in a word-to-picture matching task in which participants took longer to indicate when an action verb was semantically incongruent with a picture compared to an object noun. They argued that because action verbs are more abstract and less perceptually specific, participants sorted through a wider range of conceptual interpretations of a verb before discounting it in relation to the picture (e.g., although eventually

discounted, there are situations in which a man sitting in a chair could relate to the verb carry). Interestingly, in the current study with word triplets, the theta power increase indicates that unrelated verbs seem to have similarly required more neural resources and perhaps more cognitive effort to identify. This is especially interesting because although unrelated nouns and verbs did not differ from one another in our pilot data, the means indicated that the verbs were actually slight more related to their preceding nouns than the nouns were. Main effects of semantic relatedness resulted in widespread theta and beta changes but no effects were found in gamma. Widespread theta increases to unrelated or incongruent words are also reported in a number of other studies (Davidson & Indefrey, 2007; Hagoort et al., 2004; Hald et al., 2006; Maguire et al., 2010; Wang et al., 2012). Thus, this study adds to the considerable literature indicating that theta functions as an index of semantic


Language, Cognition and Neuroscience retrieval and integration. While this effect was also found in the interaction, in which unrelated verb targets elicited greater and more prolonged theta, the interaction effect was localised to midline central and parietal areas, whereas the relatedness theta effect was more widely distributed. The decrease in beta power observed for related target words in the current study was counter to our predictions. Previous studies reported beta power decreases in response to words that were syntactically or semantically incongruent within an ongoing sentence (Bastiaansen et al., 2010; Luo et al., 2010). Given that the current study used word triplets as opposed to sentences, beta appears to be sensitive to linguistic contexts and task demands, such that the syntactic frame is necessary to observe the beta decrease for unrelated items. Words in isolation may have a very different impact on beta engagement. Indeed, changes in beta have been observed in relation to a wide range of linguistic processes (see Weiss & Mueller, 2012 for a review). Thus, the findings regarding the beta response to differences in relatedness in this study serve to increase our knowledge about beta changes during language processing. Related to word retrieval, the time frequency analysis of potential differences underlying noun and verb processing revealed effects in beta and gamma, but not theta. For beta, we identified a decrease in power for verbs over posterior areas and for nouns over fronto-central areas. This topographical pattern is similar to reports by Weiss, Berghoff, Rappelsberger, and Müller (2001) who identified decreased beta power for action verbs at central electrodes, whereas at anterior regions, the effect was reversed. In general, the decrease in beta power for action verbs over posterior areas is consistent with a number of previous studies (Fisher et al., 2008; Moreno et al., 2013; Van Elk et al., 2010) and the same effect that has been linked to similar changes in beta related to performing actions and watching others perform actions (Weiss & Mueller, 2012). Similarities in the beta effects for action verbs and action execution/observation have driven the proposal that beta desynchrony indexes embodied semantic processing of action verbs or the processing of action verbs using mental simulation of actions (Weiss & Mueller, 2012). Word class effects were also identified as increases in gamma power for verbs over right central areas. These results are consistent with Pulvermüller et al.’s (1996) findings of increased gamma over central areas for verbs versus nouns when words were presented in isolation. In line with the semantic embodiment theory, gamma increases appearing broadly over motor areas in both this study and that of Pulvermüller et al. suggest that verb processing is influenced by motor associations with the verb at some level.

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The aim of the current study was to examine semantic processing differences between nouns and verbs. Towards this, participants made judgements of the semantic relatedness of target words with preceding word pair primes that included only nouns. The decision to limit the primes to object nouns was based on the goal of isolating semantic relationship processing by providing a robust semantic context that was relatively devoid of syntactic structure. Nouns provide a concrete semantic context that would be difficult to establish using verb pair primes given that most verbs are more flexible in use. Creating the semantic context in this way may introduce limitations in interpreting findings. For instance, as a result of this design, there were three times as many nouns as verbs. Because of this, results may reflect processing of an uncommon stimulus (verbs) rather than the intended effects, such as word class. We consider this possibility unlikely, however, because such an effect would be seen most clearly as a main effect of word class in the reaction times or in changes in theta power and neither of these effects was observed. Considering potential limitations of this design, an important future direction of this work is to vary the semantic context to identify how the use of different semantic primes, such as verb pair primes, may influence processes engaged during semantic relationship identification This goal of this study was to examine whether EEG can identify underlying differences in semantic relationships between nouns and verbs. Time frequency analysis of the EEG data showed that identifying a verb as semantically unrelated to the preceding context requires increased and more prolonged theta power between 450 and 700 ms after the word over midline central and parietal areas. Given that theta is considered to index semantic integration, it seems that the process of attempting to integrate an unrelated verb with two related nouns is more challenging than the integration of related verbs and both related and unrelated nouns. The shallow and less-structured semantic organisation of verbs likely contributes to the greater effort involved in identifying verb semantic relationships. Taken together with previous research, the current findings support more effortful processing of verbs compared to nouns. Acknowledgements The authors would like to thank Grant Magnon and Bambi Delarosa for their help. This work was supported by a University of Texas at Dallas faculty initiative grant awarded to the first author.

Disclosure statement No potential conflict of interest was reported by the authors.

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