- Email: [email protected]
Concreteness effects in the processing of Chinese words
Brain and Language 96 (2006) 59–68 www.elsevier.com/locate/b&l
Concreteness eVects in the processing of Chinese words Qin Zhang ¤, Chun-yan Guo, Jin-hong Ding, Zheng-yan Wang Department of Psychology, Capital Normal University, Beijing, PR China Accepted 11 April 2005 Available online 23 May 2005
Abstract The present study examined the relationship between word concreteness and word frequency using event-related potential (ERP) measurements during a lexical decision task. Potential eVects of concreteness in the processing of verbs were also examined. ERPs were recorded from 119 scalp electrodes in 23 right-handed participants. The results showed that concrete nouns were associated with a more negative ERP than abstract nouns at 200–300 and 300–500 ms after stimulus onset, regardless of word frequency. Between 300 and 500 ms, concrete nouns and abstract nouns produced diVerentiated scalp distributions, respectively. In terms of verbs, concreteness only produced small diVerence in ERP primarily in the central–parietal sites of the left hemisphere. 2005 Elsevier Inc. All rights reserved. Keywords: Concreteness eVect; Event-related potentials; Word frequency
1. Introduction A number of studies using various tasks such as lexical decision, word naming, recognition and recall, and sentence veriWcation (de Groot, 1989; Kroll & Merves, 1986; Paivio, 1986; Ransdell & Fischler, 1987; SchwanenXugel & Shoben, 1983; SchwanenXugel, Harnishfeger, & Stowe, 1988; SchwanenXugel & Stowe, 1989) have found that words representing concrete concepts (e.g., bicycle) are processed more quickly and accurately than words representing abstract concepts (e.g., honesty). This is often referred to as the concreteness eVect (e.g., Holcomb, Kounios, Anderson, & West, 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000; Zhang & Zhang, 1997). Presently there are two major theories that have attempted to explain this eVect, namely, the dualcoding theory (Paivio, 1986, 1991) and the context-availability model (SchwanenXugel, 1991). The dual-coding theory (Paivio, 1986, 1991), which held a multiple semantic-systems view, suggested that there are two separate semantic systems: one is verbal*
Corresponding author. Fax: +86 10 63193364. E-mail address: [email protected] (Q. Zhang).
0093-934X/$ - see front matter 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.bandl.2005.04.004
based and the other is image-based. These two hypothesized systems are functionally distinct, and yet, interconnected. According to this theory, all verbal stimuli initially activate representations in the verbal or “linguistic” semantic system. Concrete words, but not abstract words, however, are able to activate information in the image-based or “imagistic” system through referential connections to the system. Therefore, concrete words gain advantages over abstract words by having multiple processing resources and forms of representations. The context-availability model (SchwanenXugel, 1991), in contrast, rejected the idea that diVerent types of information codes or processing systems are available for concrete words and abstract words. Instead, this model argued that there might be only a single semantic system available for both concrete and abstract words, and comprehension relies on contextual information provided by preceding discourse or is inXuenced by the reader’s knowledge base (semantic memory). Compared to abstract words, concrete words may have greater associations in semantic memory, which would lead to more eYcient processing of concrete words, especially when little contextual information is provided. In this
60
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
case, the diVerences in the quantity of information, rather than in the type of information as speciWed by the dual-coding model, would contribute to the observed eVect of concreteness. In recent years, event-related brain potentials (ERPs) have been used to examine the source of concreteness eVects. Some studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000) reported that concrete words elicit a more negative ERP than abstract words between 300 and 500 ms after stimulus onset. This negativity coincided temporally with the classic N400 component (e.g., Kutas & Van Petten, 1988). It has been suggested that N400 reXects a process in which semantic information is integrated with preceding context (e.g., Brown & Hagoort, 1993; Holcomb, 1993). These results have seemingly provided support for the context-availability model. In these studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000), however, a signiWcant interaction between word concreteness and scalp distribution was also found, suggesting that concrete and abstract words were accessing diVerent cognitive and neural processing structures. Therefore, the results at the same time argued against the single-code context-availability model, which would predict a diVerence in the amplitude but not in the scalpdistribution of ERPs for concrete and abstract words. Holcomb et al. (1999) recently proposed a modiWed version of the dual-coding theory to better account for the results obtained in the ERP studies. According to the extended dual-coding theory, both superior associative connections and the use of mental imagery, instead of one or the other, contribute to advantages for processing concrete words over abstract words. An fMRI study (Jessen et al., 2000) reported that diVerent levels of activation were found in the encoding of concrete versus abstract nouns. SpeciWcally, a greater amount of activation in the lower right and left parietal lobes, the left inferior frontal lobe, and the precuneus regions occurred during the encoding of concrete nouns than during the encoding of abstract nouns. This additional activation associated with concrete nouns, especially in the parietal and frontal regions, may indicate a greater level of involvement for both verbalbased and image-based systems. The right parietal lobes have been linked to non-verbal information processing. Holcomb et al. (1999) further pointed out that concreteness and context might be separate independent factors that each inXuences N400 amplitude. Contextual factors can, in certain instances, mask or supersede the added beneWt available to concrete words through referential connections to the imagistic system. Although the modiWed dual-coding theory may account for the pattern seen in ERP results, it cannot explain the interactions between word frequency and concreteness reported by some response time studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; James, 1975; Zhang & Zhang, 1997) which suggested that the
eVect of concreteness was only seen in low-frequency words. The modiWed dual-coding theory would not predict any inXuence of word frequency on word concreteness. Frequency has been demonstrated as an important parameter of word recognition in many lexical processing tasks, such as lexical decision (Rubenstein, GarWeld, & Millikan, 1970), oral reading (Monsell, Doyle, & Haggard, 1989), and eye-Wxation durations (Just & Carpenter, 1980). These researches have found out that the high-frequency words are processed more quickly and accurately than the low-frequency words. Then, what is the relation between word concreteness and word frequency? James (1975) used a lexical decision task to show that the eVect of concreteness was only seen in lowfrequency words. Some other studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; Zhang & Zhang, 1997) obtained similar results. One possible explanation for the eVect of concreteness only seen in low-frequency words in some response time studies was that response time measurement may not be sensitive enough to detect potential diVerences between concrete and abstract words when they are all high-frequency words. High-frequency words often result in shorter response times. Subtle diVerences in response times to concrete words versus abstract words might be diYcult to detect due to truncated range. Therefore, the primary purpose of the current study was to use a more sensitive measure (i.e., ERPs) to determine the possible diVerence between abstract and concrete words, especially among high-frequency words. Although the current study also employed a lexical decision task as the behavior measure, participants were not asked to give explicit response to words in order to avoid explicit response’s inXuence on ERP measures. Participants were instructed to press a button only when he or she saw a non-word. Much of the research interested in the eVect of word concreteness was done on nouns. However, we think that verbs could also be classiWed by concreteness. Concrete verbs can refer to motion or action that can be observed by people (e.g., walk). Abstract verbs refer to mental actions that are not as visible (e.g., think). It is not clear whether the concreteness eVect can also be seen during the processing of verbs. Therefore, the second goal of the present study was to investigate the eVects of concreteness in verbs. The results from the current study may address whether the present theories interpreting the source of concreteness eVects can be expanded to verbs. Much of the above research on concreteness eVects was conducted in Western languages, especially English. Several researches utilizing the Chinese (e.g., Zhang & Zhang, 1997) have employed classical methods of reaction time, which found that the eVect of concreteness was only seen in low-frequency words. Few ERP research, however, has been conducted using the Chinese. It should be noted that Chinese is very diVerent from the Western languages (e.g.,
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
English). English uses a phonetic script, in which the basic unit represented by a grapheme is essentially a phoneme. But Chinese uses an ideographic script. Each symbol is equivalent to a morpheme. Although Chinese words may be formed by one, two, or more characters, common Chinese words consist of two characters. Each character is like a two-dimension picture. It has pronunciation and may have its meaning. However, unlike English, there is not grapheme–phoneme correspondence for Chinese. So it is very diYcult to pronounce a Chinese word according to its grapheme. These diVerences between Chinese and English may aVect lexical decision processes. In the lexical decision task the participant must decide whether a stimulus is a word or non-word. Stone and Van Orden (1993) argued that there are two routes for deciding whether a visual stimulus is a word or non-word: lexical route and non-lexical route. The lexical route takes a participant directly to a word’s meaning in the lexicon and he is then able to decide whether a stimulus is a word. The non-lexical route does not involve lexical access at all. After grapheme-to-phoneme conversion, the participant is able to decide whether a stimulus is a word. However, there is not a grapheme-to-phoneme conversion route in Chinese because of no grapheme–phoneme correspondence. So lexical decision in response to Chinese words may be diVerent from lexical decision in response to English words. Because many Chinese characters are derived from pictures representing meaning, a lot of modern Chinese characters still include components expressing meaning. And we can see that the Chinese characters’ connections with meanings are more direct. However, it is not clear whether these diVerences between Chinese and the Western languages aVect ERP concreteness eVects. Therefore, studying whether the ERP concreteness eVects of Chinese words are diVerent from that of the Western languages is also a goal of the present study.
61
experiment. All participants were native Chinese speakers and had normal or corrected-to-normal vision. 2.2. Stimuli and procedure
2. Methods
The stimuli in this study consisted of nouns and verbs with diVerent word frequency and concreteness. Each noun or verb included two Chinese characters. The following table summarizes characteristics of the stimuli (see Table 1). Mean frequency for words, Wrst and second characters formed words were calculated according to Modern Chinese Frequency Dictionary (Beijing Language College, 1986). The frequency of high-frequency nouns was above 60 per million, and that of low-frequency nouns was below 30 per million. In addition to these words, there were 91 non-words and 21 Wller words. The non-words were formed by combining two actual Chinese characters, but they all are meaningless. The Wller words were actual Chinese words. These words were assigned to seven lists. Each list contained 10 words from each type of noun or verb category plus 13 non-words and 3 Wller words. Therefore, there were 76 items in each list. The Wrst three items in each list were Wller words and other items were presented in pseudo-random order. The word lists were also presented in pseudo-random order. The seven lists of words were administered to each participant. Each trial began with a Wxation point that was presented for 1600–1800 ms. Following the Wxation, a stimulus was displayed on the screen for 300 ms, and then it was replaced by the Wxation point for the next trial. Participants were required to press a button as quickly and as accurately as possible if the displayed stimulus was not a Chinese word. During the presentation of the word lists, ERPs were recorded from the participants. Participants were asked to refrain from moving except for making responses or blinking. Prior to testing, 24 practice trials were administered to the participants to ensure that the testing procedure was well understood.
2.1. Subjects
2.3. Recording of ERPs
Twenty-three right-handed students (10 females) from Capital Normal University and ranging in age from 18 to 23 years (mean D 19) participated in the
Electroencephalogram (EEG) was recorded from 119 scalp electrodes using an electrode cap with Ag/AgCl inserts (see Fig. 1). All scalp electrodes were referenced to
Table 1 Number of each type of nouns and verbs and the corresponding mean concreteness, mean frequency, and mean stroke Type of words
Number of Mean concreteness Mean frequency for Mean frequency for Mean frequency for Mean stroke words (a 1–7 point scale) words (per million) Wrst characters (per million) second characters (per million) for words
Abstract nouns Abstract nouns Concrete nouns Concrete nouns Abstract verbs Concrete verbs
70 70 70 70 70 70
2.8 2.9 6.1 5.9 3.0 6.1
11.9 169.2 11.9 169.3 29.1 28.5
575.6 1108.3 576.7 1108.4 452.8 453.7
544.6 895.9 544.8 888.6 573.0 571.0
17.0 15.7 17.0 16.1 19.0 18.7
62
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
(F3/4, F5/6), fronto-temporal (FT7/8), fronto-central (FC3/4), central (C3/4), temporal (T7/8), parietal (108/115, 109/114, P3/4), temporo-parietal (TP7/8), and occipital (O1/2)] and a hemisphere factor (left vs. right). Therefore, the following ANOVAs on amplitude values were conducted for nouns, a 2 (frequency) £ 2 (concreteness) £ 2 (hemisphere) £ 12 (electrode-site) for lateral site and a 2 (frequency) £ 2 (concreteness) £ 5 (electrode-site) for midline site and for verbs, a 2 (concreteness) £ 2 (hemisphere) £ 12 (electrode-site) for lateral site and a 2 (concreteness) £ 5 (electrode-site) for midline site. The p values were corrected using the Greenhouse–Geisser method.
3. Results 3.1. Performance data Fig. 1. The approximate locations of electrode sites.
an electrode placed on the mastoid bone behind the left ear. Vertical electrooculogram (VEOG) and horizontal electrooculogram (HEOG) were recorded with two pairs of electrodes, one placed above and below left eye, and another placed beside the two eyes. Band pass was set from 0.1 to 30 Hz and the sampling rate was 250 Hz. Impedances of all electrodes were kept below 8 K. Average ERPs were formed oZine from correct-response trials free of ocular and movement artifacts. This resulted in six separate ERPs for each scalp site. The mean number of individual trials per waveform was 49. 2.4. Statistical analysis The averaged ERPs were quantiWed by calculating mean amplitude values (relative to a 200 ms prestimulus baseline) for the voltage points in three latency windows (nouns—200–300, 300–500, and 500–800 ms; verbs— 200–300, 300–400, and 400–500 ms). These windows were determined by a visual inspection of grand average waveforms (see Figs. 2–4) and previous reports (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000). The approach to statistical analysis involved the use of a repeated measure analysis of variance (ANOVA) followed by post hoc tests in cases of signiWcant interactions. There were two levels of word concreteness (concrete vs. abstract) and two levels of word frequency (high vs. low) for nouns. There were two levels of word concreteness (concrete vs. abstract) for verbs. ERPs at midline and lateral sites were analyzed in separate ANOVAs so that laterality eVects could be assessed. In addition to the aforementioned factors, midline site analyses included a factor of electrode-site (Fz vs. Fcz vs. Cz vs. Pz vs. Oz). Lateral site analyses included an electrode-site factor [anterior frontal (AF3/4), frontal
Participants were very accurate in making the lexical decisions. For all conditions error rate was lower than 2%. The following table presents the accuracy scores for each condition (see Table 2). The 2 (word frequency) £ 2 (word concreteness) ANOVA on response accuracies for nouns revealed a signiWcant main eVect of word frequency, F (1,22) D 16.09, p < .005. Together with the mean accuracy scores for low- and high-frequency words, the result indicated that participants were signiWcantly more accurate in making lexical decisions for high-frequency nouns than for low-frequency nouns. The ANOVA also revealed a signiWcant main eVect of word concreteness, F (1,22) D 6.22, p < .05. Similarly, based on mean accuracy scores for abstract and concrete nouns, this result revealed that participants were more accurate in making lexical decisions for abstract nouns than for concrete nouns. However, a t test failed to reveal any signiWcant diVerences in accuracies for abstract and concrete verbs. 3.2. ERP data The grand mean ERPs elicited by abstract and concrete nouns of high- and low-frequency are shown in Figs. 2 and 3. The grand mean ERPs elicited by abstract and concrete verbs are shown in Fig. 4. The ERPs in these Wgures show that several early (less than 400 ms) components were elicited by concrete and abstract words. They included a broadly distributed early negativity (N1) that peaked around 100 ms at all but the occipital sites. At these sites, there was an early positivity peaking at about 135 ms (P1) followed by a later N1 with a peak about 156 ms. At most sites, the N1 component was followed by a positivity peaking between 160 and 170 ms (P2). None of these early components diVered by word concreteness.
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
63
Fig. 2. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to high-frequency abstract nouns and high-frequency concrete nouns. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
64
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
Fig. 3. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to low-frequency abstract nouns and low-frequency concrete nouns. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
65
Fig. 4. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to abstract verbs and concrete verbs. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
66
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
Table 2 Mean percentage of response accuracy for each condition Word type
Percentage of accuracy
High-frequency abstract nouns High-frequency concrete nouns Low-frequency abstract nouns Low-frequency concrete nouns Abstract verbs Concrete verbs
99.9 99.7 99.0 98.1 99.6 99.3
analyses revealed that the signiWcant ERP diVerences between concrete and abstract verbs were only at centroparietal sites (C3 and 109) of the left hemisphere. 400–500 ms: analyses of this epoch did not reveal a signiWcant main eVect of concreteness. The interactions between concreteness and other variables were also not signiWcant.
4. Discussion There were also several later ERP components visible in the waveforms. Following the P2, there was a widespread negative-going wave that peaked around 400 ms (N400). Following the N400, there was a positive wave (P3) that peaked at about 500 ms. Beginning at about 200–300 ms after stimulus onset, ERPs elicited by concrete and abstract words diverged. 3.2.1. Nouns 200–300 ms: an omnibus ANOVA of the mean amplitudes for this epoch revealed that overall, concrete words were associated with a more negative-going wave than abstract words [main eVect of word concreteness, midline—F (1,22) D 16.90, p < .001; lateral—F (1,22) D 22.23, p < .001]. 300–500 ms: analyses of this epoch showed that voltages to concrete words were more negative than those to abstract words [main eVect of word concreteness, midline—F (1,22) D 28.55, p < .001; lateral—F (1,22) D 43.47, p < .001]. This eVect was widespread across the scalp. However, it was largest at left anterior sites [word concreteness £ electrode site £ hemisphere interaction, F (11,242) D 5.38, p < .005]. In addition, there was a signiWcant interaction between frequency and electrode sites in this time window [F(11,242) D 3.54, p < .05]. The interaction between frequency and concreteness, however, was not signiWcant. 500–800 ms: the omnibus ANOVA for this epoch revealed that low-frequency words were associated with a more positive-going wave than high-frequency words [midline—F (1,22) D 6.07, p < .05; lateral—F (1,22) D 5.88, p < .05]. The interaction between concreteness and frequency was not signiWcant. 3.2.2. Verbs 200–300 ms: the omnibus ANOVA for this epoch revealed that there were neither signiWcant main eVect for concreteness nor interactions between concreteness and other variables. 300–400 ms: analyses of this epoch showed that there was a trend toward signiWcance in midline sites [F (1,22) D 3.53, p D .074], suggesting that concrete verbs tended to associate with a more negative-going wave than abstract verbs. In lateral sites, the interaction between word concreteness and hemisphere also showed a trend toward signiWcance [F (1,22) D 4.26, p D .051]. Post hoc
The current study made use of a lexical decision task and ERP measures to investigate how word frequency inXuence the eVect of concreteness. The behavioral results showed a clear frequency eVect. Participants were signiWcantly more accurate in making lexical decisions for high-frequency nouns than for low-frequency ones. However, the behavioral results also revealed that participants were more, not less, accurate in making lexical decisions for abstract nouns than for concrete ones. Perhaps this result is due to speed-accuracy trade-oVs, but it has not yet been conWrmed by the current study. In contrast to some earlier RT studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; James, 1975; Zhang & Zhang, 1997), in which the eVect of concreteness was only found in low-frequency words but not in high-frequency words, the current results showed that word frequency did not interact with concreteness in 200–300 and 300–500 ms after the onset of stimulus in ERPs. Instead, both low-frequency words and high-frequency words were signiWcantly aVected by word concreteness. Consistent with some other studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000), the current study also revealed that concrete nouns were associated with a more negative ERP than abstract nouns in 300–500 ms following the onset of stimulus. This negative ERP may exhibit a typical N400 concreteness eVect. Our results also revealed that this typical N400 concreteness eVect was not inXuenced by word frequency. Therefore, in contrary to what was shown by RT studies, the current Wndings may suggest that word concreteness and word frequency are two independent factors, and there is no interaction between concreteness and frequency. Such inconsistencies between RT studies and ERP studies were also reported by other researchers. For example, Kounios and Holcomb (1992) showed that RTs and N400 amplitudes were not correlated in a sentence veriWcation task. They argued that RTs and ERPs may not necessarily tap into the same set of underlying cognitive operations. In particular, RT might be much more sensitive to participants’ decision-making processes and task-dependent strategies than N400 amplitude. We also assume that the N400 in the current study was sensitive primarily to changes in the semantic integration process rather than the changes in the lexical decision process. Consistent with this interpretation, the
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
current results found that N400 amplitudes were larger in concrete words than in abstract words, irrespective of word frequency, suggesting that compared to abstract words, concrete words of both low- and high-frequency have relatively more semantic information (linguistic and imagistic) that needs to be integrated. RT measures, however, may not be sensitive to this integration process. In addition to semantic information integration, usage of strategies in lexical decision tasks may also be reXected diVerently in RT studies and ERP studies. RT measures, but not N400 amplitude, may be sensitive to task-dependent strategies. In lexical decision tasks, participants may rely on word familiarity instead of integrating semantic information to make lexical decisions. A “Yes” response may be more easily made to familiar words than to unfamiliar words. High-frequency words are familiar words regardless of concreteness. In this case, responses can be made equally fast for both highfrequency concrete and abstract words. In mean RTs, there should be no diVerences between abstract and concrete words when the words are all high-frequency. Similarly, a “No” response may be more easily made to unfamiliar words than to familiar words. Unfamiliar words are often low-frequency words. However, if participants were to make a “Yes” response for a low-frequency word, semantic information must be activated. Concrete words have relatively more semantic information than abstract words. Therefore, a “Yes” response may be easier to make for a concrete word than for an abstract word. This additional reliance on semantic information besides familiarity may account for the diVerence in mean RTs between abstract and concrete words in low-frequency words. The present study showed that concrete nouns were associated with a more negative ERP than abstract nouns in 200–300 and 300–500 ms after stimulus onset. Of particular importance, between 300 and 500 ms after stimulus onset, a signiWcant interaction between word concreteness, electrode sites, and hemisphere indicates that there might be diVerential scalp distribution of eVects for concrete and abstract nouns. Together with results from other similar studies (Holcomb et al., 1999; Kounios & Holcomb, 1994), these Wndings may provide support for the extended dual-coding hypothesis, which suggests that both superior associative connections and the use of mental imagery contribute to the processing advantages of concrete words over abstract words. However, some previous studies failed to Wnd signiWcant diVerences in the amplitude between abstract and concrete nouns at shorter intervals after stimulus onset (i.e., 150–300 ms). Our research, in contrast, found a signiWcant main eVect of word concreteness in the 200– 300 ms interval, suggesting that semantic information was activated earlier in the current experiment than in previous studies. One possible interpretation for this diVerence is that the concreteness eVects of Chinese
67
words are diVerent from that of the Western languages (e.g., English) because of the linguistic diVerences between Chinese and the Western languages. As stated in Section 1, English adopts phonetic script, but Chinese uses an ideographic script. In Chinese there is not a grapheme-to-phoneme correspondence. So lexical decision in response to Chinese words may rely more on meaning in the lexicon than it does to English words. Originally, Chinese characters are derived from pictures representing meaning. So the connection of form and meaning of Chinese characters is closer. It is possible, therefore, that semantic information is activated earlier in the processing of Chinese words, and signiWcant diVerences between abstract and concrete nouns were found at shorter intervals after stimulus onset. Although the verbal and imagery systems have been linked to the left and right hemispheres of the brain, respectively (Jessen et al., 2000; Paivio, 1986), it may not imply that diVerences between concrete and abstract nouns should be larger in the right hemisphere than in the left hemisphere based on some previous theories. That is, according to the dual-coding theory, larger diVerences in amplitude between concrete and abstract nouns should be in the right hemisphere because concrete words were processed in both the imagery system and the verbal system, whereas abstract words were processed only in the verbal system. Thus, activation diVerence should lie in the imagery system, consequently, in the right hemisphere. However, according to the extended dual-coding theory, diVerences between concrete and abstract words can originate from both the imagery system and the verbal system. It is diYcult to say in which hemisphere one would see greater diVerences between concrete and abstract nouns. In contrast to the predictions made by the dual-coding and extended dual-coding theories, the current study found that a greater diVerence in the activation pattern between concrete and abstract words was observed in the left hemisphere than in the right hemisphere. This may be in part due to the fact that our task did not encourage participants to use imagery. However, there still might be some inadvertent or implicit image generation. Therefore, the task itself may determine whether greater diVerences between concrete and abstract words can be observed in the verbal system or in the imagery-related system. In the present study, processing of verbs showed a diVerent pattern of activation in ERPs compared to what we found in the processing of nouns. Although in several sites concrete verbs were associated with a more negative ERP than abstract verbs in 300–400 ms, ERP diVerences between concrete and abstract verbs were very small. The concreteness eVect of nouns was widespread across the scalp, with the greatest eVect at left anterior sites. Concreteness eVects of verbs, however, were primarily in the centro-parietal sites of the left
68
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
hemisphere. One possible interpretation for the diVerences in concreteness eVect between verbs and nouns can be that the source of concreteness eVects for verbs is diVerent from that for nouns. Some studies (Pulvermüller, 1999; Warrington & McCarthy, 1987) proposed that nouns and verbs have diVerent mental representation. The meaning of most verbs is related to the motor modality, whereas the meaning of concrete nouns is related to the visual modality. Any diVerences between concrete and abstract verbs may be due to the fact that concrete verbs are more associated with motor programs than abstract verbs. Another possible interpretation is that concreteness eVects of nouns and verbs may be from the same sources. But the diVerence in concreteness ratings between abstract and concrete words might be smaller for verbs than for nouns. This latter interpretation is supported by the Wnding that ERP diVerences between concrete and abstract verbs were smaller than those between concrete and abstract nouns. Further study is needed to determine which interpretation can better explain the data. In sum, the present study showed that concrete nouns were associated with a more negative ERP than abstract nouns in 200–300 and 300–500 ms, regardless of word frequency. This Wnding implies that frequency and concreteness might be two independent factors. In 300–500 ms, there were diVerential scalp distributions of eVects for concrete and abstract words, a result that is consistent with the extended dual-coding hypothesis. Finally, the ERP diVerences between concrete and abstract verbs were smaller than the diVerences between concrete and abstract nouns, and the diVerences are located primarily at centro-parietal sites of the left hemisphere.
Acknowledgments We thank Dr. Jing Chen, Dr. Adam Lawson, and Prof. Zhi Wang for revising this paper. This work was supported by Ministry of Education Grant 20040028001 of China, Ministry of Science and Technology Grant 95-special-09 of China, and Learning and Cognition Key Lab of Bejing.
References Brown, C., & Hagoort, P. (1993). The processing nature of the N400: Evidence from masked priming. Journal of Cognitive Neuroscience, 5, 34–44. de Groot, A. M. B. (1989). Representational aspects of word imageability and word frequency assessed through word association. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 824–845. de Mornay Davies, P., & Funnell, E. (2000). Semantic representation and ease of predication. Brain and Language, 73, 92–119. Holcomb, P. J. (1993). Semantic priming and stimulus degradation: Implications for the role of the N400 in language processing. Psychophysiology, 30, 47–61.
Holcomb, P. J., Kounios, J., Anderson, J. E., & West, W. C. (1999). Dual-coding, context-availability, and concreteness eVects in sentence comprehension: An electrophysiological investigation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 721–742. James, C. (1975). The role of semantic information in lexical decisions. Journal of Experimental Psychology: Human Perception and Performance, 104, 130–136. Jessen, F., Heun, R., Erb, M., Granath, D. O., Klose, U., Papassotiropoulos, A., & Grodd, W. (2000). The concreteness eVect: Evidence for dual coding and context availability. Brain and Language, 74, 103–112. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye Wxations to comprehension. Psychological Review, 87, 329–354. Kounios, J., & Holcomb, P. J. (1992). Structure and process in semantic memory: Evidence from event-related brain potentials and reaction times. Journal of Experimental Psychology: General, 121, 459–479. Kounios, J., & Holcomb, P. J. (1994). Concreteness eVects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 804–823. Kroll, J. F., & Merves, J. S. (1986). Lexical access for concrete and abstract words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 92–107. Kutas, M., & Van Petten, C. (1988). Event-related brain potential studies of language. In P. K. Ackles, J. R. Jennings, & M. G. H. Coles (Eds.), Advances in psychophysiology (pp. 139–187). Greenwich, CT: JAI Press. Monsell, S., Doyle, M. C., & Haggard, P. N. (1989). EVects of frequency on visual word recognition tasks: Where are they? Journal of Experimental Psychology: General, 118, 43–71. Paivio, A. (1986). Mental representations: A dual coding approach. New York: Oxford University Press. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287. Pulvermüller, F. (1999). Words in the brain’s language. Behavioral and Brain Sciences, 22, 253–336. Ransdell, S. E., & Fischler, I. (1987). Memory in a monolingual mode: When are bilinguals at a disadvantage? Journal of Memory and Language, 26, 392–405. Rubenstein, H., GarWeld, L., & Millikan, J. A. (1970). Homographic entries in the internal lexicon. Journal of Verbal Learning and Verbal Behavior, 9, 487–494. SchwanenXugel, P. (1991). Why are abstract concepts hard to understand. In P. J. SchwanenXugel (Ed.), The psychology of word meanings (pp. 223–250). Hillsdale, NJ: Erlbaum. SchwanenXugel, P. J., & Shoben, E. J. (1983). DiVerential context eVects in the comprehension of abstract and concrete verbal materials. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 82–102. SchwanenXugel, P. J., Harnishfeger, K. K., & Stowe, R. W. (1988). Context availability and lexical decisions for abstract and concrete words. Journal of Memory and Language, 27, 499–520. SchwanenXugel, P. J., & Stowe, R. W. (1989). Context availability and the processing of abstract and concrete words in sentences. Reading Research Quarterly, 24, 114–126. Stone, G., & Van Orden, G. C. (1993). Strategic control of processing in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 19, 744–774. Warrington, E. K., & McCarthy, R. A. (1987). Categories of knowledge: Further fractionations and an attempted integration. Brain, 110, 1273–1296. West, W. C., & Holcomb, P. J. (2000). Imaginal,semantic,and surfacelevel processing of concrete and abstract words: An electrophysiological investigation. Journal of Cognitive Neuroscience, 12, 1024–1038. Zhang, Q., & Zhang, B. Y. (1997). Research on concreteness eVects of Chinese two-character words. Acta Psychologica Sinica, 29, 216– 224 (in Chinese, abstract in English).
Concreteness eVects in the processing of Chinese words Qin Zhang ¤, Chun-yan Guo, Jin-hong Ding, Zheng-yan Wang Department of Psychology, Capital Normal University, Beijing, PR China Accepted 11 April 2005 Available online 23 May 2005
Abstract The present study examined the relationship between word concreteness and word frequency using event-related potential (ERP) measurements during a lexical decision task. Potential eVects of concreteness in the processing of verbs were also examined. ERPs were recorded from 119 scalp electrodes in 23 right-handed participants. The results showed that concrete nouns were associated with a more negative ERP than abstract nouns at 200–300 and 300–500 ms after stimulus onset, regardless of word frequency. Between 300 and 500 ms, concrete nouns and abstract nouns produced diVerentiated scalp distributions, respectively. In terms of verbs, concreteness only produced small diVerence in ERP primarily in the central–parietal sites of the left hemisphere. 2005 Elsevier Inc. All rights reserved. Keywords: Concreteness eVect; Event-related potentials; Word frequency
1. Introduction A number of studies using various tasks such as lexical decision, word naming, recognition and recall, and sentence veriWcation (de Groot, 1989; Kroll & Merves, 1986; Paivio, 1986; Ransdell & Fischler, 1987; SchwanenXugel & Shoben, 1983; SchwanenXugel, Harnishfeger, & Stowe, 1988; SchwanenXugel & Stowe, 1989) have found that words representing concrete concepts (e.g., bicycle) are processed more quickly and accurately than words representing abstract concepts (e.g., honesty). This is often referred to as the concreteness eVect (e.g., Holcomb, Kounios, Anderson, & West, 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000; Zhang & Zhang, 1997). Presently there are two major theories that have attempted to explain this eVect, namely, the dualcoding theory (Paivio, 1986, 1991) and the context-availability model (SchwanenXugel, 1991). The dual-coding theory (Paivio, 1986, 1991), which held a multiple semantic-systems view, suggested that there are two separate semantic systems: one is verbal*
Corresponding author. Fax: +86 10 63193364. E-mail address: [email protected] (Q. Zhang).
0093-934X/$ - see front matter 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.bandl.2005.04.004
based and the other is image-based. These two hypothesized systems are functionally distinct, and yet, interconnected. According to this theory, all verbal stimuli initially activate representations in the verbal or “linguistic” semantic system. Concrete words, but not abstract words, however, are able to activate information in the image-based or “imagistic” system through referential connections to the system. Therefore, concrete words gain advantages over abstract words by having multiple processing resources and forms of representations. The context-availability model (SchwanenXugel, 1991), in contrast, rejected the idea that diVerent types of information codes or processing systems are available for concrete words and abstract words. Instead, this model argued that there might be only a single semantic system available for both concrete and abstract words, and comprehension relies on contextual information provided by preceding discourse or is inXuenced by the reader’s knowledge base (semantic memory). Compared to abstract words, concrete words may have greater associations in semantic memory, which would lead to more eYcient processing of concrete words, especially when little contextual information is provided. In this
60
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
case, the diVerences in the quantity of information, rather than in the type of information as speciWed by the dual-coding model, would contribute to the observed eVect of concreteness. In recent years, event-related brain potentials (ERPs) have been used to examine the source of concreteness eVects. Some studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000) reported that concrete words elicit a more negative ERP than abstract words between 300 and 500 ms after stimulus onset. This negativity coincided temporally with the classic N400 component (e.g., Kutas & Van Petten, 1988). It has been suggested that N400 reXects a process in which semantic information is integrated with preceding context (e.g., Brown & Hagoort, 1993; Holcomb, 1993). These results have seemingly provided support for the context-availability model. In these studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000), however, a signiWcant interaction between word concreteness and scalp distribution was also found, suggesting that concrete and abstract words were accessing diVerent cognitive and neural processing structures. Therefore, the results at the same time argued against the single-code context-availability model, which would predict a diVerence in the amplitude but not in the scalpdistribution of ERPs for concrete and abstract words. Holcomb et al. (1999) recently proposed a modiWed version of the dual-coding theory to better account for the results obtained in the ERP studies. According to the extended dual-coding theory, both superior associative connections and the use of mental imagery, instead of one or the other, contribute to advantages for processing concrete words over abstract words. An fMRI study (Jessen et al., 2000) reported that diVerent levels of activation were found in the encoding of concrete versus abstract nouns. SpeciWcally, a greater amount of activation in the lower right and left parietal lobes, the left inferior frontal lobe, and the precuneus regions occurred during the encoding of concrete nouns than during the encoding of abstract nouns. This additional activation associated with concrete nouns, especially in the parietal and frontal regions, may indicate a greater level of involvement for both verbalbased and image-based systems. The right parietal lobes have been linked to non-verbal information processing. Holcomb et al. (1999) further pointed out that concreteness and context might be separate independent factors that each inXuences N400 amplitude. Contextual factors can, in certain instances, mask or supersede the added beneWt available to concrete words through referential connections to the imagistic system. Although the modiWed dual-coding theory may account for the pattern seen in ERP results, it cannot explain the interactions between word frequency and concreteness reported by some response time studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; James, 1975; Zhang & Zhang, 1997) which suggested that the
eVect of concreteness was only seen in low-frequency words. The modiWed dual-coding theory would not predict any inXuence of word frequency on word concreteness. Frequency has been demonstrated as an important parameter of word recognition in many lexical processing tasks, such as lexical decision (Rubenstein, GarWeld, & Millikan, 1970), oral reading (Monsell, Doyle, & Haggard, 1989), and eye-Wxation durations (Just & Carpenter, 1980). These researches have found out that the high-frequency words are processed more quickly and accurately than the low-frequency words. Then, what is the relation between word concreteness and word frequency? James (1975) used a lexical decision task to show that the eVect of concreteness was only seen in lowfrequency words. Some other studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; Zhang & Zhang, 1997) obtained similar results. One possible explanation for the eVect of concreteness only seen in low-frequency words in some response time studies was that response time measurement may not be sensitive enough to detect potential diVerences between concrete and abstract words when they are all high-frequency words. High-frequency words often result in shorter response times. Subtle diVerences in response times to concrete words versus abstract words might be diYcult to detect due to truncated range. Therefore, the primary purpose of the current study was to use a more sensitive measure (i.e., ERPs) to determine the possible diVerence between abstract and concrete words, especially among high-frequency words. Although the current study also employed a lexical decision task as the behavior measure, participants were not asked to give explicit response to words in order to avoid explicit response’s inXuence on ERP measures. Participants were instructed to press a button only when he or she saw a non-word. Much of the research interested in the eVect of word concreteness was done on nouns. However, we think that verbs could also be classiWed by concreteness. Concrete verbs can refer to motion or action that can be observed by people (e.g., walk). Abstract verbs refer to mental actions that are not as visible (e.g., think). It is not clear whether the concreteness eVect can also be seen during the processing of verbs. Therefore, the second goal of the present study was to investigate the eVects of concreteness in verbs. The results from the current study may address whether the present theories interpreting the source of concreteness eVects can be expanded to verbs. Much of the above research on concreteness eVects was conducted in Western languages, especially English. Several researches utilizing the Chinese (e.g., Zhang & Zhang, 1997) have employed classical methods of reaction time, which found that the eVect of concreteness was only seen in low-frequency words. Few ERP research, however, has been conducted using the Chinese. It should be noted that Chinese is very diVerent from the Western languages (e.g.,
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
English). English uses a phonetic script, in which the basic unit represented by a grapheme is essentially a phoneme. But Chinese uses an ideographic script. Each symbol is equivalent to a morpheme. Although Chinese words may be formed by one, two, or more characters, common Chinese words consist of two characters. Each character is like a two-dimension picture. It has pronunciation and may have its meaning. However, unlike English, there is not grapheme–phoneme correspondence for Chinese. So it is very diYcult to pronounce a Chinese word according to its grapheme. These diVerences between Chinese and English may aVect lexical decision processes. In the lexical decision task the participant must decide whether a stimulus is a word or non-word. Stone and Van Orden (1993) argued that there are two routes for deciding whether a visual stimulus is a word or non-word: lexical route and non-lexical route. The lexical route takes a participant directly to a word’s meaning in the lexicon and he is then able to decide whether a stimulus is a word. The non-lexical route does not involve lexical access at all. After grapheme-to-phoneme conversion, the participant is able to decide whether a stimulus is a word. However, there is not a grapheme-to-phoneme conversion route in Chinese because of no grapheme–phoneme correspondence. So lexical decision in response to Chinese words may be diVerent from lexical decision in response to English words. Because many Chinese characters are derived from pictures representing meaning, a lot of modern Chinese characters still include components expressing meaning. And we can see that the Chinese characters’ connections with meanings are more direct. However, it is not clear whether these diVerences between Chinese and the Western languages aVect ERP concreteness eVects. Therefore, studying whether the ERP concreteness eVects of Chinese words are diVerent from that of the Western languages is also a goal of the present study.
61
experiment. All participants were native Chinese speakers and had normal or corrected-to-normal vision. 2.2. Stimuli and procedure
2. Methods
The stimuli in this study consisted of nouns and verbs with diVerent word frequency and concreteness. Each noun or verb included two Chinese characters. The following table summarizes characteristics of the stimuli (see Table 1). Mean frequency for words, Wrst and second characters formed words were calculated according to Modern Chinese Frequency Dictionary (Beijing Language College, 1986). The frequency of high-frequency nouns was above 60 per million, and that of low-frequency nouns was below 30 per million. In addition to these words, there were 91 non-words and 21 Wller words. The non-words were formed by combining two actual Chinese characters, but they all are meaningless. The Wller words were actual Chinese words. These words were assigned to seven lists. Each list contained 10 words from each type of noun or verb category plus 13 non-words and 3 Wller words. Therefore, there were 76 items in each list. The Wrst three items in each list were Wller words and other items were presented in pseudo-random order. The word lists were also presented in pseudo-random order. The seven lists of words were administered to each participant. Each trial began with a Wxation point that was presented for 1600–1800 ms. Following the Wxation, a stimulus was displayed on the screen for 300 ms, and then it was replaced by the Wxation point for the next trial. Participants were required to press a button as quickly and as accurately as possible if the displayed stimulus was not a Chinese word. During the presentation of the word lists, ERPs were recorded from the participants. Participants were asked to refrain from moving except for making responses or blinking. Prior to testing, 24 practice trials were administered to the participants to ensure that the testing procedure was well understood.
2.1. Subjects
2.3. Recording of ERPs
Twenty-three right-handed students (10 females) from Capital Normal University and ranging in age from 18 to 23 years (mean D 19) participated in the
Electroencephalogram (EEG) was recorded from 119 scalp electrodes using an electrode cap with Ag/AgCl inserts (see Fig. 1). All scalp electrodes were referenced to
Table 1 Number of each type of nouns and verbs and the corresponding mean concreteness, mean frequency, and mean stroke Type of words
Number of Mean concreteness Mean frequency for Mean frequency for Mean frequency for Mean stroke words (a 1–7 point scale) words (per million) Wrst characters (per million) second characters (per million) for words
Abstract nouns Abstract nouns Concrete nouns Concrete nouns Abstract verbs Concrete verbs
70 70 70 70 70 70
2.8 2.9 6.1 5.9 3.0 6.1
11.9 169.2 11.9 169.3 29.1 28.5
575.6 1108.3 576.7 1108.4 452.8 453.7
544.6 895.9 544.8 888.6 573.0 571.0
17.0 15.7 17.0 16.1 19.0 18.7
62
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
(F3/4, F5/6), fronto-temporal (FT7/8), fronto-central (FC3/4), central (C3/4), temporal (T7/8), parietal (108/115, 109/114, P3/4), temporo-parietal (TP7/8), and occipital (O1/2)] and a hemisphere factor (left vs. right). Therefore, the following ANOVAs on amplitude values were conducted for nouns, a 2 (frequency) £ 2 (concreteness) £ 2 (hemisphere) £ 12 (electrode-site) for lateral site and a 2 (frequency) £ 2 (concreteness) £ 5 (electrode-site) for midline site and for verbs, a 2 (concreteness) £ 2 (hemisphere) £ 12 (electrode-site) for lateral site and a 2 (concreteness) £ 5 (electrode-site) for midline site. The p values were corrected using the Greenhouse–Geisser method.
3. Results 3.1. Performance data Fig. 1. The approximate locations of electrode sites.
an electrode placed on the mastoid bone behind the left ear. Vertical electrooculogram (VEOG) and horizontal electrooculogram (HEOG) were recorded with two pairs of electrodes, one placed above and below left eye, and another placed beside the two eyes. Band pass was set from 0.1 to 30 Hz and the sampling rate was 250 Hz. Impedances of all electrodes were kept below 8 K. Average ERPs were formed oZine from correct-response trials free of ocular and movement artifacts. This resulted in six separate ERPs for each scalp site. The mean number of individual trials per waveform was 49. 2.4. Statistical analysis The averaged ERPs were quantiWed by calculating mean amplitude values (relative to a 200 ms prestimulus baseline) for the voltage points in three latency windows (nouns—200–300, 300–500, and 500–800 ms; verbs— 200–300, 300–400, and 400–500 ms). These windows were determined by a visual inspection of grand average waveforms (see Figs. 2–4) and previous reports (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000). The approach to statistical analysis involved the use of a repeated measure analysis of variance (ANOVA) followed by post hoc tests in cases of signiWcant interactions. There were two levels of word concreteness (concrete vs. abstract) and two levels of word frequency (high vs. low) for nouns. There were two levels of word concreteness (concrete vs. abstract) for verbs. ERPs at midline and lateral sites were analyzed in separate ANOVAs so that laterality eVects could be assessed. In addition to the aforementioned factors, midline site analyses included a factor of electrode-site (Fz vs. Fcz vs. Cz vs. Pz vs. Oz). Lateral site analyses included an electrode-site factor [anterior frontal (AF3/4), frontal
Participants were very accurate in making the lexical decisions. For all conditions error rate was lower than 2%. The following table presents the accuracy scores for each condition (see Table 2). The 2 (word frequency) £ 2 (word concreteness) ANOVA on response accuracies for nouns revealed a signiWcant main eVect of word frequency, F (1,22) D 16.09, p < .005. Together with the mean accuracy scores for low- and high-frequency words, the result indicated that participants were signiWcantly more accurate in making lexical decisions for high-frequency nouns than for low-frequency nouns. The ANOVA also revealed a signiWcant main eVect of word concreteness, F (1,22) D 6.22, p < .05. Similarly, based on mean accuracy scores for abstract and concrete nouns, this result revealed that participants were more accurate in making lexical decisions for abstract nouns than for concrete nouns. However, a t test failed to reveal any signiWcant diVerences in accuracies for abstract and concrete verbs. 3.2. ERP data The grand mean ERPs elicited by abstract and concrete nouns of high- and low-frequency are shown in Figs. 2 and 3. The grand mean ERPs elicited by abstract and concrete verbs are shown in Fig. 4. The ERPs in these Wgures show that several early (less than 400 ms) components were elicited by concrete and abstract words. They included a broadly distributed early negativity (N1) that peaked around 100 ms at all but the occipital sites. At these sites, there was an early positivity peaking at about 135 ms (P1) followed by a later N1 with a peak about 156 ms. At most sites, the N1 component was followed by a positivity peaking between 160 and 170 ms (P2). None of these early components diVered by word concreteness.
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
63
Fig. 2. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to high-frequency abstract nouns and high-frequency concrete nouns. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
64
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
Fig. 3. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to low-frequency abstract nouns and low-frequency concrete nouns. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
65
Fig. 4. Grand mean event-related brain potentials (ERPs) from 20 scalp sites to abstract verbs and concrete verbs. The approximate locations of these sites can be seen in Fig. 1. The x-axes display time on a millisecond scale with the vertical calibration bar placed at the time of the onset of the stimulus. The y-axes represent voltages on a microvolt scale, with negative voltages plotted up, according to convention.
66
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
Table 2 Mean percentage of response accuracy for each condition Word type
Percentage of accuracy
High-frequency abstract nouns High-frequency concrete nouns Low-frequency abstract nouns Low-frequency concrete nouns Abstract verbs Concrete verbs
99.9 99.7 99.0 98.1 99.6 99.3
analyses revealed that the signiWcant ERP diVerences between concrete and abstract verbs were only at centroparietal sites (C3 and 109) of the left hemisphere. 400–500 ms: analyses of this epoch did not reveal a signiWcant main eVect of concreteness. The interactions between concreteness and other variables were also not signiWcant.
4. Discussion There were also several later ERP components visible in the waveforms. Following the P2, there was a widespread negative-going wave that peaked around 400 ms (N400). Following the N400, there was a positive wave (P3) that peaked at about 500 ms. Beginning at about 200–300 ms after stimulus onset, ERPs elicited by concrete and abstract words diverged. 3.2.1. Nouns 200–300 ms: an omnibus ANOVA of the mean amplitudes for this epoch revealed that overall, concrete words were associated with a more negative-going wave than abstract words [main eVect of word concreteness, midline—F (1,22) D 16.90, p < .001; lateral—F (1,22) D 22.23, p < .001]. 300–500 ms: analyses of this epoch showed that voltages to concrete words were more negative than those to abstract words [main eVect of word concreteness, midline—F (1,22) D 28.55, p < .001; lateral—F (1,22) D 43.47, p < .001]. This eVect was widespread across the scalp. However, it was largest at left anterior sites [word concreteness £ electrode site £ hemisphere interaction, F (11,242) D 5.38, p < .005]. In addition, there was a signiWcant interaction between frequency and electrode sites in this time window [F(11,242) D 3.54, p < .05]. The interaction between frequency and concreteness, however, was not signiWcant. 500–800 ms: the omnibus ANOVA for this epoch revealed that low-frequency words were associated with a more positive-going wave than high-frequency words [midline—F (1,22) D 6.07, p < .05; lateral—F (1,22) D 5.88, p < .05]. The interaction between concreteness and frequency was not signiWcant. 3.2.2. Verbs 200–300 ms: the omnibus ANOVA for this epoch revealed that there were neither signiWcant main eVect for concreteness nor interactions between concreteness and other variables. 300–400 ms: analyses of this epoch showed that there was a trend toward signiWcance in midline sites [F (1,22) D 3.53, p D .074], suggesting that concrete verbs tended to associate with a more negative-going wave than abstract verbs. In lateral sites, the interaction between word concreteness and hemisphere also showed a trend toward signiWcance [F (1,22) D 4.26, p D .051]. Post hoc
The current study made use of a lexical decision task and ERP measures to investigate how word frequency inXuence the eVect of concreteness. The behavioral results showed a clear frequency eVect. Participants were signiWcantly more accurate in making lexical decisions for high-frequency nouns than for low-frequency ones. However, the behavioral results also revealed that participants were more, not less, accurate in making lexical decisions for abstract nouns than for concrete ones. Perhaps this result is due to speed-accuracy trade-oVs, but it has not yet been conWrmed by the current study. In contrast to some earlier RT studies (de Groot, 1989; de Mornay Davies & Funnell, 2000; James, 1975; Zhang & Zhang, 1997), in which the eVect of concreteness was only found in low-frequency words but not in high-frequency words, the current results showed that word frequency did not interact with concreteness in 200–300 and 300–500 ms after the onset of stimulus in ERPs. Instead, both low-frequency words and high-frequency words were signiWcantly aVected by word concreteness. Consistent with some other studies (Holcomb et al., 1999; Kounios & Holcomb, 1994; West & Holcomb, 2000), the current study also revealed that concrete nouns were associated with a more negative ERP than abstract nouns in 300–500 ms following the onset of stimulus. This negative ERP may exhibit a typical N400 concreteness eVect. Our results also revealed that this typical N400 concreteness eVect was not inXuenced by word frequency. Therefore, in contrary to what was shown by RT studies, the current Wndings may suggest that word concreteness and word frequency are two independent factors, and there is no interaction between concreteness and frequency. Such inconsistencies between RT studies and ERP studies were also reported by other researchers. For example, Kounios and Holcomb (1992) showed that RTs and N400 amplitudes were not correlated in a sentence veriWcation task. They argued that RTs and ERPs may not necessarily tap into the same set of underlying cognitive operations. In particular, RT might be much more sensitive to participants’ decision-making processes and task-dependent strategies than N400 amplitude. We also assume that the N400 in the current study was sensitive primarily to changes in the semantic integration process rather than the changes in the lexical decision process. Consistent with this interpretation, the
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
current results found that N400 amplitudes were larger in concrete words than in abstract words, irrespective of word frequency, suggesting that compared to abstract words, concrete words of both low- and high-frequency have relatively more semantic information (linguistic and imagistic) that needs to be integrated. RT measures, however, may not be sensitive to this integration process. In addition to semantic information integration, usage of strategies in lexical decision tasks may also be reXected diVerently in RT studies and ERP studies. RT measures, but not N400 amplitude, may be sensitive to task-dependent strategies. In lexical decision tasks, participants may rely on word familiarity instead of integrating semantic information to make lexical decisions. A “Yes” response may be more easily made to familiar words than to unfamiliar words. High-frequency words are familiar words regardless of concreteness. In this case, responses can be made equally fast for both highfrequency concrete and abstract words. In mean RTs, there should be no diVerences between abstract and concrete words when the words are all high-frequency. Similarly, a “No” response may be more easily made to unfamiliar words than to familiar words. Unfamiliar words are often low-frequency words. However, if participants were to make a “Yes” response for a low-frequency word, semantic information must be activated. Concrete words have relatively more semantic information than abstract words. Therefore, a “Yes” response may be easier to make for a concrete word than for an abstract word. This additional reliance on semantic information besides familiarity may account for the diVerence in mean RTs between abstract and concrete words in low-frequency words. The present study showed that concrete nouns were associated with a more negative ERP than abstract nouns in 200–300 and 300–500 ms after stimulus onset. Of particular importance, between 300 and 500 ms after stimulus onset, a signiWcant interaction between word concreteness, electrode sites, and hemisphere indicates that there might be diVerential scalp distribution of eVects for concrete and abstract nouns. Together with results from other similar studies (Holcomb et al., 1999; Kounios & Holcomb, 1994), these Wndings may provide support for the extended dual-coding hypothesis, which suggests that both superior associative connections and the use of mental imagery contribute to the processing advantages of concrete words over abstract words. However, some previous studies failed to Wnd signiWcant diVerences in the amplitude between abstract and concrete nouns at shorter intervals after stimulus onset (i.e., 150–300 ms). Our research, in contrast, found a signiWcant main eVect of word concreteness in the 200– 300 ms interval, suggesting that semantic information was activated earlier in the current experiment than in previous studies. One possible interpretation for this diVerence is that the concreteness eVects of Chinese
67
words are diVerent from that of the Western languages (e.g., English) because of the linguistic diVerences between Chinese and the Western languages. As stated in Section 1, English adopts phonetic script, but Chinese uses an ideographic script. In Chinese there is not a grapheme-to-phoneme correspondence. So lexical decision in response to Chinese words may rely more on meaning in the lexicon than it does to English words. Originally, Chinese characters are derived from pictures representing meaning. So the connection of form and meaning of Chinese characters is closer. It is possible, therefore, that semantic information is activated earlier in the processing of Chinese words, and signiWcant diVerences between abstract and concrete nouns were found at shorter intervals after stimulus onset. Although the verbal and imagery systems have been linked to the left and right hemispheres of the brain, respectively (Jessen et al., 2000; Paivio, 1986), it may not imply that diVerences between concrete and abstract nouns should be larger in the right hemisphere than in the left hemisphere based on some previous theories. That is, according to the dual-coding theory, larger diVerences in amplitude between concrete and abstract nouns should be in the right hemisphere because concrete words were processed in both the imagery system and the verbal system, whereas abstract words were processed only in the verbal system. Thus, activation diVerence should lie in the imagery system, consequently, in the right hemisphere. However, according to the extended dual-coding theory, diVerences between concrete and abstract words can originate from both the imagery system and the verbal system. It is diYcult to say in which hemisphere one would see greater diVerences between concrete and abstract nouns. In contrast to the predictions made by the dual-coding and extended dual-coding theories, the current study found that a greater diVerence in the activation pattern between concrete and abstract words was observed in the left hemisphere than in the right hemisphere. This may be in part due to the fact that our task did not encourage participants to use imagery. However, there still might be some inadvertent or implicit image generation. Therefore, the task itself may determine whether greater diVerences between concrete and abstract words can be observed in the verbal system or in the imagery-related system. In the present study, processing of verbs showed a diVerent pattern of activation in ERPs compared to what we found in the processing of nouns. Although in several sites concrete verbs were associated with a more negative ERP than abstract verbs in 300–400 ms, ERP diVerences between concrete and abstract verbs were very small. The concreteness eVect of nouns was widespread across the scalp, with the greatest eVect at left anterior sites. Concreteness eVects of verbs, however, were primarily in the centro-parietal sites of the left
68
Q. Zhang et al. / Brain and Language 96 (2006) 59–68
hemisphere. One possible interpretation for the diVerences in concreteness eVect between verbs and nouns can be that the source of concreteness eVects for verbs is diVerent from that for nouns. Some studies (Pulvermüller, 1999; Warrington & McCarthy, 1987) proposed that nouns and verbs have diVerent mental representation. The meaning of most verbs is related to the motor modality, whereas the meaning of concrete nouns is related to the visual modality. Any diVerences between concrete and abstract verbs may be due to the fact that concrete verbs are more associated with motor programs than abstract verbs. Another possible interpretation is that concreteness eVects of nouns and verbs may be from the same sources. But the diVerence in concreteness ratings between abstract and concrete words might be smaller for verbs than for nouns. This latter interpretation is supported by the Wnding that ERP diVerences between concrete and abstract verbs were smaller than those between concrete and abstract nouns. Further study is needed to determine which interpretation can better explain the data. In sum, the present study showed that concrete nouns were associated with a more negative ERP than abstract nouns in 200–300 and 300–500 ms, regardless of word frequency. This Wnding implies that frequency and concreteness might be two independent factors. In 300–500 ms, there were diVerential scalp distributions of eVects for concrete and abstract words, a result that is consistent with the extended dual-coding hypothesis. Finally, the ERP diVerences between concrete and abstract verbs were smaller than the diVerences between concrete and abstract nouns, and the diVerences are located primarily at centro-parietal sites of the left hemisphere.
Acknowledgments We thank Dr. Jing Chen, Dr. Adam Lawson, and Prof. Zhi Wang for revising this paper. This work was supported by Ministry of Education Grant 20040028001 of China, Ministry of Science and Technology Grant 95-special-09 of China, and Learning and Cognition Key Lab of Bejing.
References Brown, C., & Hagoort, P. (1993). The processing nature of the N400: Evidence from masked priming. Journal of Cognitive Neuroscience, 5, 34–44. de Groot, A. M. B. (1989). Representational aspects of word imageability and word frequency assessed through word association. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15, 824–845. de Mornay Davies, P., & Funnell, E. (2000). Semantic representation and ease of predication. Brain and Language, 73, 92–119. Holcomb, P. J. (1993). Semantic priming and stimulus degradation: Implications for the role of the N400 in language processing. Psychophysiology, 30, 47–61.
Holcomb, P. J., Kounios, J., Anderson, J. E., & West, W. C. (1999). Dual-coding, context-availability, and concreteness eVects in sentence comprehension: An electrophysiological investigation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 721–742. James, C. (1975). The role of semantic information in lexical decisions. Journal of Experimental Psychology: Human Perception and Performance, 104, 130–136. Jessen, F., Heun, R., Erb, M., Granath, D. O., Klose, U., Papassotiropoulos, A., & Grodd, W. (2000). The concreteness eVect: Evidence for dual coding and context availability. Brain and Language, 74, 103–112. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye Wxations to comprehension. Psychological Review, 87, 329–354. Kounios, J., & Holcomb, P. J. (1992). Structure and process in semantic memory: Evidence from event-related brain potentials and reaction times. Journal of Experimental Psychology: General, 121, 459–479. Kounios, J., & Holcomb, P. J. (1994). Concreteness eVects in semantic processing: ERP evidence supporting dual-coding theory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 804–823. Kroll, J. F., & Merves, J. S. (1986). Lexical access for concrete and abstract words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 92–107. Kutas, M., & Van Petten, C. (1988). Event-related brain potential studies of language. In P. K. Ackles, J. R. Jennings, & M. G. H. Coles (Eds.), Advances in psychophysiology (pp. 139–187). Greenwich, CT: JAI Press. Monsell, S., Doyle, M. C., & Haggard, P. N. (1989). EVects of frequency on visual word recognition tasks: Where are they? Journal of Experimental Psychology: General, 118, 43–71. Paivio, A. (1986). Mental representations: A dual coding approach. New York: Oxford University Press. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287. Pulvermüller, F. (1999). Words in the brain’s language. Behavioral and Brain Sciences, 22, 253–336. Ransdell, S. E., & Fischler, I. (1987). Memory in a monolingual mode: When are bilinguals at a disadvantage? Journal of Memory and Language, 26, 392–405. Rubenstein, H., GarWeld, L., & Millikan, J. A. (1970). Homographic entries in the internal lexicon. Journal of Verbal Learning and Verbal Behavior, 9, 487–494. SchwanenXugel, P. (1991). Why are abstract concepts hard to understand. In P. J. SchwanenXugel (Ed.), The psychology of word meanings (pp. 223–250). Hillsdale, NJ: Erlbaum. SchwanenXugel, P. J., & Shoben, E. J. (1983). DiVerential context eVects in the comprehension of abstract and concrete verbal materials. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 82–102. SchwanenXugel, P. J., Harnishfeger, K. K., & Stowe, R. W. (1988). Context availability and lexical decisions for abstract and concrete words. Journal of Memory and Language, 27, 499–520. SchwanenXugel, P. J., & Stowe, R. W. (1989). Context availability and the processing of abstract and concrete words in sentences. Reading Research Quarterly, 24, 114–126. Stone, G., & Van Orden, G. C. (1993). Strategic control of processing in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 19, 744–774. Warrington, E. K., & McCarthy, R. A. (1987). Categories of knowledge: Further fractionations and an attempted integration. Brain, 110, 1273–1296. West, W. C., & Holcomb, P. J. (2000). Imaginal,semantic,and surfacelevel processing of concrete and abstract words: An electrophysiological investigation. Journal of Cognitive Neuroscience, 12, 1024–1038. Zhang, Q., & Zhang, B. Y. (1997). Research on concreteness eVects of Chinese two-character words. Acta Psychologica Sinica, 29, 216– 224 (in Chinese, abstract in English).