Brain & Language 117 (2011) 77–87

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Brain & Language

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Sublexical ambiguity effect in reading Chinese disyllabic compounds

Hsu-Wen Huang a,⇑, Chia-Ying Lee b,c,d,e, Jie-Li Tsai e,f, Ovid J.-L. Tzeng b,c

aDepartment of Psychology, University of Illinois at Urbana-Champaign, USAb Institute of Linguistics, Academia Sinica, Taiwanc Institute of Neuroscience, National Yang-Ming University, Taiwand Institute of Cognitive Neuroscience, National Central University, TaiwaneResearch Center for Mind, Brain, and Learning, National Chengchi University, TaiwanfDepartment of Psychology, National Chengchi University, Taiwan

a r t i c l e i n f o

Article history:Accepted 23 January 2011Available online 24 February 2011

Keywords:Chinese compoundsSublexical semantic ambiguityERPsN400Laterality

a b s t r a c t

For Chinese compounds, neighbors can share either both orthographic forms and meanings, or ortho-graphic forms only. In this study, central presentation and visual half-field (VF) presentation methodswere used in conjunction with ERP measures to investigate how readers solve the sublexical semanticambiguity of the first constituent character in reading a disyllabic compound. The sublexical ambiguityof the first character was manipulated while the orthographic neighborhood sizes of the first and secondcharacter (NS1, NS2) were controlled. Subjective rating of number of meanings corresponding to a char-acter was used as an index of sublexical ambiguity. Results showed that low sublexical ambiguity wordselicited a more negative N400 than high sublexical ambiguity words when words were centrally pre-sented. Similar patterns were found when words were presented to the left VF. Interestingly, differentpatterns were observed for pseudowords. With left VF presentation, high sublexical ambiguity psudo-words showed a more negative N400 than low sublexical ambiguity pseudowords. In contrast, with rightVF presentation, low sublexical ambiguity pseudowords showed a more negative N400 than high sublex-ical ambiguity pseudowords. These findings indicate that a level of morphological representationbetween form and meaning needs to be established and refined in Chinese. In addition, hemisphericasymmetries in the use of word information in ambiguity resolution should be taken into account, evenat sublexical level.

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1. Introduction

Reading is one of the most important acquired skills for humanbeings. How we recognize words has been the focus of a myriad ofinvestigations employing a range of different methodologies. Re-search in alphabetic languages has shown that the speed or accu-racy of identifying a word is affected by the existence of otherorthographically similar words, the so-called orthographic neigh-borhood size effect. These experiments have adopted Coltheart,Davelaar, Jonasson, and Besner’s (1977) definition of an ortho-graphic neighbor: any word that can be created by changing oneletter of the stimulus word with the preservation of letter positions(e.g., house and horde are orthographic neighbors of horse).

Models of visual word recognition assume that the lexical rep-resentations of the orthographic neighbors of a presented wordwill be activated and will play an important role in lexical access.According to the serial-search model (Forster, 1976), a series of

0093-934X/$ - see front matter � 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.bandl.2011.01.003

⇑ Corresponding author. Address: Department of Psychology, University ofIllinois, 603 E. Daniel St. Champaign, IL 61820, USA.

E-mail address: hwhuang@illinois.edu (H.-W. Huang).

comparison processes will result in an inhibitory neighborhoodsize effect: words with more neighbors would be processed slowerthan those with fewer neighbors. In contrast, according to theinteractive-activation framework (McClelland & Rumelhart,1981), the neighborhood size effect is facilitatory. Words withmore neighbors would receive more reciprocal activation fromtheir sublexical constituents, and reach the activation thresholdsmore quickly than those with fewer neighbors. To date, however,behavioral evidence of the neighborhood size effect has beeninconsistent. Snodgrass and Mintzer (1993) found an inhibitoryneighborhood size effect in the perceptual identification task- toidentify a fragmented word as it was gradually clarified- wordswith a large neighborhood size needed more time to be identified,which supported the serial-search model (Forster, 1976). On thecontrary, Andrews (1989) found that lexical decision and naminglatencies for low frequency words with large neighborhoods wereshorter than those with small neighborhoods. Facilitatory neigh-borhood size effects have also been reported by Forster and Shen(1996) and Sears, Hino, and Lupker (1995), which support theinteractive-activation model (McClelland & Rumelhart, 1981). Itseems that whether a facilitatory neighborhood size effect can be

78 H.-W. Huang et al. / Brain & Language 117 (2011) 77–87

obtained in the lexical decision task depends on the type of non-words used as distracters, and whether task instructions stress re-sponse speed or accuracy (Andrews, 1989; Grainger & Jacobs,1996). For example, a stronger facilitatory neighborhood size effectwas found when task difficulty was reduced by using less word-like nonwords (Andrews, 1989).

These inconsistent behavioral findings might be due to the lim-itations of using reaction times in isolation when investigating cog-nitive processes that necessarily invoke parallel and overlappingstages. More generally, some aspects of semantic processing maynot be easy to capture with discrete measures such as responsetime or accuracy. In contrast, the event-related brain potential(ERP) methodology provides data that reflects processing at eachmillisecond from the onset of language stimuli, and multiple, dif-ferentiable cognitive operations. Previous electrophysiologicalstudies have described many ERP components associated with dif-ferent stages of lexical processing. The N400, for example, is a neg-ative polarity ERP component that is maximal over centro-parietalelectrode sites and reaches its maximal amplitude at around400 ms. after the onset of word stimulation. It is widely acceptedthat the N400 elicited by words is particularly sensitive to the pro-cessing of semantics, and is relatively insensitive to the decision orresponse strategies that frequently influence behavioral responses(Kutas & Hillyard, 1980). Holcomb, Grainger, and O’Rourke (2002)investigated the neighborhood size effect in reading English usingERPs. They showed that the larger the neighborhood size (usingeither words or pseudowords) the more negative the N400 ampli-tudes. Behavioral lexical decision responses, on the other hand,showed a facilitatory neighborhood size effect for words but aninhibitory neighborhood size effect for pseudowords which consis-tent with previous studies (Andrews, 1989, 1992; Carreiras, Perea,& Grainger, 1997; Grainger & Jacobs, 1996; Sears et al., 1995). Thisindicated that the opposed behavioral effects of words and pseudo-words were led by the same mechanism. Lexical activation washeightened with an increasing number of word neighbors; and thisactivation is used to set positive or negative response criteria forthe lexical decision task. The more negative N400 amplitudes forwords with large neighborhoods were interpreted as reflecting in-creased global lexical activity, which facilitated behavioralresponses.

The investigation of neighborhood size effects in Chinese canshed light on Chinese lexical processing. Chinese words are differ-ent from English in two aspects. First, more than 80% of Chinesewords are disyllabic compounds. Second—and more impor-tantly—in contrast to English, where words are composed of asmall set of meaningless letters, Chinese words usually consist ofcharacters that can be mapped onto syllables and morphemes withclear boundaries. The constituent Chinese characters of compoundwords can be regarded as perceptual units at the orthographic le-vel, but also as semantic units at the morphemic level (Hoosain,1991).

In previous studies, neighbors of a Chinese compound havebeen defined as words that share the same constituent characterwith preservation of the character position, regardless of the char-acter meaning (H. W. Huang et al., 2006; Tsai, Lee, Lin, Tzeng, &Hung, 2006). For instance, a two-character word (e.g., 花市, hua1shi4, flower market), has the neighbors sharing the first constituentcharacter, termed as NS1 (such as 花園, hua1 yuan2; flower garden)or the second constituent character, termed as NS2 (such as 都市,du1 shi4, city). The regression analyses in H. W. Huang et al.(2006) have demonstrated that the neighborhood size of the firstcharacter constituent played a more important role in lexical pro-cessing than the neighborhood size of the second character constit-uent did. Similarly, Tsai et al. (2006) have shown that the lexicaldecision latencies of words with large neighborhoods were shorterthan words with small neighborhoods, and the authors suggested

that the neighborhood of a word sharing the initial constituentcharacter plays a supportive role in lexical access. Evidence fromeye movement studies also found that words with many neighborsshowed higher skipping rate and shorter gaze durations thanwords with fewer neighbors (Tsai et al., 2006). These findingspointed to that: (1) neighborhood size of the first character beingimportant in lexical processing and (2) the facilitatory NS1 effectin Chinese may be similar to findings from languages with alpha-betic writing systems.

However, Chinese words are composed of characters that can bemapped onto morphemes with clear boundaries. In terms of thischaracteristic, neighbors could share the same orthographic formand the same morpheme, such as 花市 (hua1 shi4, flower market)and 花園 (hua1 yuan2, flower garden), or neighbors could sharethe same orthographic form but different morphemes, such as 花

市 (hua1 shi4, flower market) and 花錢 (hua1 qian2, spend money).Therefore, in addition to the supportive role of neighbors at theorthographic level, whether the constituent morphemes affectword recognition is an unclear issue. One way to test this possibil-ity is to manipulate the relationship between the whole wordmeaning and the meaning of its constituent morphemes which isdescribed as semantic transparency. At one end, there are idio-matic compounds, like 和尚 (he2 shang4, monk), in which themeaning of the word has nothing to do with the meaning of theconstituent elements, and hence are termed as opaque words. Onthe other hand, there are compositional compounds, such as 書店

(shu1 dian4, bookstore), in which the meaning of the word can becomputed from the meanings of the constituent elements, andhence are termed as transparent words. To our knowledge, thereis only one study that addresses this question. The NS1, NS2 andthe semantic transparency were manipulated in a study by C. M.Huang (2005). The hypothesis was that if the facilitative NS1 effectwas mainly originated at the level of orthographic form, bothsemantically transparent and semantically opaque words wouldshow similar NS1 effects. On the other hand, if the neighborhoodsize effect also reflected semantic activation at the morphemic le-vel, neighborhood effects would interact with word types. The re-sults showed a clear interaction between neighborhood size andsemantic transparency in lexical decision response times—specifi-cally, a facilitative NS1 effect for transparent words but an inhibi-tory NS1 effect for opaque words. This suggests that neighborhoodsize effects in Chinese may not purely originate at the orthographiclevel; instead, the morphemes of constituent characters must addi-tionally be considered.

Many studies have examined the role of morphological struc-ture in the lexical representations of compound words (Taft, Liu,& Zhu, 1999; Taft & Zhu, 1995; Zhou & Marslen-Wilson, 1994;Zhou & Marslen-Wilson, 1995; Zhou et al., 1999). It has been pro-posed that whereas each constituent character in a Chinese com-pound has its own orthographic, phonological, and semanticrepresentations, the whole word representation exists only at thesemantic level (Zhou & Marslen-Wilson, 1994, 1995). When a vi-sual compound is presented, the semantic representations are ac-cessed through the connections from the orthographic andphonological representations of its constituent characters. Theactivated semantic representations include both the whole wordand its constituent morphemes. Taft (2006) proposed a connec-tionist framework for the representation of poly-morphemic wordsby contrasting free morphemes, bound morphemes, and bindingcharacters (for example, neither character of 蚯蚓 (qiu1 yin3 earth-worm) ever occurs in any other context). In this framework, there isa level of representation that mediates between word forms andsemantics called ‘‘lemma”. Morphemic information is primarilycaptured in lemmas, with lemma distinctiveness depending onthe correlation between form and meaning. Hence free-standingwords and bound morphemes have representations at lemma

H.-W. Huang et al. / Brain & Language 117 (2011) 77–87 79

level, but binding characters have no representation of their own.What is unknown from Taft’s study (2006) is how to representmultiple morphemes of a character—specifically, one orthographicform conveys more than one meaning (e.g. ‘‘花” not only meansflower, but also spend and trick related)at lemma level.

Based on the previous finding that the first character will play akey role in the whole word reading (H. W. Huang et al., 2006; Tsaiet al., 2006), this study is primarily focused on the ambiguity of thefirst constituent character, which we term sublexical ambiguity. Weaim to understand that in addition to the neighborhood size effectsat orthographic level whether sublexical semantic ambiguity af-fects the processing of compounds. The investigation of sublexicalambiguity effects of Chinese words can shed light on Chinese lex-ical processing.

2. Experiment 1

In lexical ambiguity literatures, two types of ambiguity are dis-tinguished. In homonymy, a single word form has two (or more)semantically unrelated meanings. For example, the word bankhas the meanings ‘‘river side” and ‘‘financial institution”. In poly-semy, on the other hand, a single word form is associated withtwo or more semantically related senses. For instance, the wordpaper can be used to refer to a material, as in ‘‘shredded paper”,or to the content of a publication, as in ‘‘Today’s paper was inter-esting”. Prior work examining these two types of ambiguity in lex-ical decision has demonstrated an ambiguity disadvantage forhomonymous words, and a sense advantage for polysemous words(Rodd, Gaskell, & Marslen-Wilson, 2002). Similar results were alsoobtained on an MEG study: the latency of the M350 component, anindex of lexical activation, was slower for homonyms than for non-homonyms, and also faster for words with many senses than forwords with few senses (Beretta, Fiorentino, & Poeppel, 2005).These findings imply that unrelated meanings were listed in sepa-rate entries, whereas related senses are stored as a single coremeaning.

To examine how sublexical ambiguity affects Chinese word pro-cessing, in the current study the sublexical ambiguity was manip-ulated while orthographic neighborhood sizes (NS1 and NS2) werematched (see Fig. 1, described below). Assuming that characters

Fig. 1. Examples of high sublexical ambiguity and low sublexical ambiguity words whrefers number of neighbors sharing the first constituent character. The number followi

with more than one meaning do have separate representations atthe morphemic level, a sublexical ambiguity disadvantage is pre-dicted. ERPs were recorded as participants were doing a lexicaldecision task. The N400 component was used as an index, sincethis component is particularly sensitive to the semantic process.

2.1. Methods

2.1.1. ParticipantsTwenty-one healthy undergraduate or graduate students (age

range 19–25, mean age 21.9 years) participated for cash in thisexperiment. Allwere native speakers ofMandarin Chinesewith nor-mal or corrected-to-normal vision. Participants were right-handed,as determined by a handedness scale (Chapman & Chapman,1987); three reported having left-handed family members.

2.1.2. Materials2.1.2.1. Index for number of meanings. The number of meanings cor-responding to each character was accessed from a norming studydone by 270 university students (age range 18–28) in an unpub-lished database of 5640 Chinese characters. They were all nativespeakers of Chinese. 5640 characters were selected from the Aca-demia Sinica balanced corpus (C. R. Huang & Chen, 1998), and thenrandomly divided into 15 lists; within each, 376 characters wereincluded. During norming, one character was presented at a timeon the center of the screen and remained there until the partici-pant made a response by pressing a number key (1–5). Participantswere asked to think of as many two-character words as possible byusing the target character, no matter whether the character was inthe first or second position. Then, participants used a 1–5 ratingscale to estimate the number of different meanings correspondingto the presented character. Rating 1 represented that the meaningof the character is explicit and only one meaning is available, or thecharacter is always bound with the other character even thoughthere is no explicit meaning when it stands alone. Rating 5 repre-sented that the meaning of the character is highly diverse and canhave more than five different meanings.

A corpus analysis has shown that number of meaningscorresponding to a character is highly correlated with its neighbor-hood size (R = 0.6, p < .0001). Given that characters with lager

ile controlling the neighborhood sizes. Underlined words are target examples. NS1ng the written pronunciation denotes the tone of the word in Mandarin.

80 H.-W. Huang et al. / Brain & Language 117 (2011) 77–87

neighborhoods also tend to have more meanings, to better under-stand the mappings from orthographic forms to morphemes is tomatch the neighborhood sizes.

2.1.2.2. Stimuli. Word frequency and neighborhood size are avail-able from the Academia Sinica balanced corpus (C. R. Huang &Chen, 1998), 120 Chinese two-character words were selected.Words were divided into four subsets by manipulating word fre-quency and sublexical ambiguity (number of meanings corre-sponds to the first character of a word). High frequency wordshad a frequency of occurrence at least 150 per 10 million (range150–2000, mean 687), and low frequency words had a frequencyof occurrence less than 30 per 10 million (range 1–30, mean 13).Low sublexical ambiguity words were those whose first characterhad a meaning rating smaller than 1.7 (range 1.1–1.7, mean 1.5)and high sublexical ambiguity words were those whose first char-acter had a meaning rating larger than 2.8 (range 2.8–3.8, mean 3).The neighborhood size for first (NS1) and second (NS2) characterswere matched across conditions (high frequency low sublexicalambiguity words: 15.1 vs. 15; high frequency high sublexical ambi-guity words: 15.1 vs. 14.8; low frequency low sublexical ambiguitywords: 15; 15.3; low frequency high sublexical ambiguity words:14.8; 14.3). Other possible confounding factors such as log trans-formed character frequency, subjective familiarity rating of thecharacters and thenumberofmeanings corresponding to the secondcharacter were controlled. The subjective familiarity is based on anunpublished corpus of 5640 Chinese characters. The data werecollected from 160 college students by using a 7-point scale forfamiliarity rating (see Table 1).

A total of 120 pseudowords were created for the lexical decisiontask by concatenations of two characters that do not occur in theword corpus. Characters used for pseudowords were not over-lapped with characters used for real words. Sublexical ambiguityfor pseudowords was matched with those of real words. Half ofthem had low sublexical ambiguity (range 1.1–1.8, mean 1.5)and the other half of them had high sublexical ambiguity (range2.8–3.6, mean 3). NS1 and NS2 were matched for two pseudowordconditions (low sublexical ambiguity pseurdowords: 14.3 vs. 14.4;high sublexical ambiguity words: 13 vs. 13.8). Pronunciation ofpseudowords was controlled so as not to resemble that of realwords. Practice trials contained 10 words and 10 pseudowords.

2.2. Procedure

Participants were tested in a single experimental session con-ducted in a sound-proof, electrically-shielded chamber. They wereseated in a comfortable chair 70 cm in front of a monitor, and wereinstructed to read the words for comprehension. They first partic-ipated in a practice run in order to familiar themselves with theprocedure; this was followed by a formal run. Each participant re-ceived 240 randomized experimental trials in four test blocks. Atwo-to-three minute break was given to prevent eyestrain andfatigue.

Each trial began with a white cross presented centrally for500 ms. Then, the stimulus was presented and remained onscreenfor a maximum of 1300 ms, or until the participant made a lexicaldecision response by pressing a mouse button. At the end of each

Table 1Example stimuli.

High frequency

Low sublexical ambiguity High sublexical ambiguity

糖漿 科技

tang2 jiang1 molasses ke1 ji4 technology

trial a capital B was presented in the center of the screen for1500 ms, which indicated to participants that blinking was now al-lowed. Participants were asked not to blink their eyes until theappearance of the capital B to minimize the interference of eyemovement artifacts. The inter-trial interval was 1500 ms.

Participants were instructed to make a lexical decision judg-ment. If they judged the stimulus to be a word, they were in-structed to press the right mouse button; otherwise they were topress the left mouse button. Speed and accuracy were stressedequally. Response time, accuracy, and event-related potential datawere collected during the process.

2.3. EEG data acquisition and preprocessing

The electroencephalogram (EEG) was recorded from 64 sinteredAg/AgCl electrodes mounted on an electrode cap (QuickCap, Neu-romedical Supplies, Sterling, USA) and referenced to the bilateralmastoid. The EEG was continuously recorded and digitized at a rateof 500 Hz and the signal was amplified by SYNAMPS2� (Neuro-scan, Inc.) amplifiers with the band-pass at 0.1–100 Hz for off-lineanalysis. Eye movements and blinks were monitored with supra-and infra-orbital electrodes and with electrodes in the externalcanthi. Electrode impedances remained below 5 kX.

Epochs started 100 ms pre-stimulus, lasted to 922 ms, and usedthe 100 ms pre-stimulus for baseline correction. Artifact rejectionwas performed in two stages. The first stage was eye-movementrejection in which trials with voltage variations larger than±100 lV in either VEOG or HEOG were rejected. In the secondstage, trials with voltage variations larger than ±60 lV in at leastone of the rest of the channels were rejected. The data were thenband-pass filtered between 0.01–30 Hz (zero phase shift mode,12 dB/oct). By averaging over corresponding trials, ERPs were com-puted for every subject, electrode and experimental condition forcorrect trials only. Statistical analyses were performed on meanamplitudes in the N400 window after Geisser–Greenhousecorrection.

2.4. Results

2.4.1. BehaviorFor words, analyses of variance (ANOVAs) with the within-sub-

ject factors of word frequency and sublexical ambiguity were per-formed on reaction times and accuracy. Trials with incorrectresponses or reaction times that exceeded two SD from the sub-ject’s mean were removed from the analysis.

The main effect of word frequency was significant: high fre-quency words were responded to more rapidly than low frequencywords [F(1, 20) = 51, p < .0001]. The main effect of sublexical ambi-guity was not significant [F < 1]. The interaction between two fac-tors was not significant [F(1, 20) = 2.7, p = .13]. Since there were nobaseline differences, pairwise comparisons of sublexical ambiguityeffects for each word frequency were then conducted separately,given the apriori goal of determining sublexical ambiguity effectpatterns in two frequency types. Results revealed that sublexicalambiguity effect for low frequency words were marginally signifi-cant, with slower response times to high sublexical ambiguitywords (597.8 ms) than to low sublexical ambiguity words

Low frequency

Low sublexical ambiguity High sublexical ambiguity

鞋店 量表

xie2 dian4 shoe store liang4 biao3 inventory

(588.8 ms) [t(20) = �1.4, p = .08]. There was no such tendency inhigh frequency words (low sublexical ambiguity words: 557 ms;high sublexical ambiguity words: 553 ms) [t(20) = 1.1, p = .27].

The accuracy analysis revealed a significant main effect of wordfrequency: low frequency words were responded less accuratelythan high frequency words (low frequency words: 95%; high fre-quency words: 98%) [F(1, 20) = 20.5, p < .001]. Additionally, theinteraction between word frequency and sublexical ambiguitywas significant [F(1, 20) = 9.6, p < .01]. Post-hoc analysis showedthat there is a significant sublexical ambiguity effect for low fre-quency words: accuracy for high sublexical ambiguity words(93%) was lower than those low sublexical ambiguity words(98%) [F(1, 40) = 21.7, p < .001]. There is no sublexical ambiguity ef-fect for high frequency words (low sublexical ambiguity words:98%; high sublexical ambiguity words: 98%) [F < 1].

Paired t-tests of reaction time and accuracy for pseudowords re-vealed no differences between two groups (low sublexical ambigu-ity pseudowords: 630.7 ms, 96%; high sublexical ambiguitypseudowords: 630.6 ms, 97%).

2.4.2. ERPsTypically, N400 effect is somewhat larger over the right than

left hemisphere (Kutas & Van petten, 1994). In order to accessthe full pattern of results, we separated N400 (250–550 ms) anal-yses on midline and lateral data for both words and pseudowords.For words, word frequency, sublexical ambiguity and electrode (FZ,FCZ, CZ, CPZ and PZ) were included as within-subject factors in themidline analysis. In the lateral analysis, word frequency, sublexicalambiguity, laterality (left and right), and electrode (F3/4, FC3/4, C3/4, and CP3/4) were used as within-subject factors. As for pseudo-words, sublexical ambiguity and electrode sites were used as with-in-subject factors in the midline analysis. In the lateral analysis,sublexical ambiguity, laterality and electrodes were used as with-in-subject factors. For each ANOVA, the Geisser–Greenhouseadjustment to the degrees of freedom was applied to correct forviolations of sphericity associated with repeated measures. Forall F tests with more than one degree of freedom in the numerator,the corrected p-value is reported.

The midline analysis revealed a significant effect of word fre-quency [F(1, 20) = 21.29; p < .001]; low frequency words elicitedlarger N400s than high frequency words. The two-way interactionbetween word frequency and sublexical ambiguity was significant[F(1, 20) = 6.04; p < .05]. Post-hoc comparison revealed that thesublexical ambiguity effect was significant in the low frequencywords [F(1, 40) = 4.95; p < .05] but not in the high frequency words[F(1, 40) = 2.39; p = .14]. Low frequency, low sublexical ambiguitywords elicited larger N400s than low frequency, high sublexicalambiguity words (Fig. 2).

The lateral analysis showed a significant main effect of wordfrequency [F(1, 20) = 22.5; p < .001]; low frequency words elicitedgreater N400 than high frequency words. The two-way interactionbetween word frequency and sublexical ambiguity was significant[F(1, 20) = 7.27; p < .05]. Post-hoc comparisons revealed that thesublexical ambiguity effect was significant in the low frequencywords [F(1, 40) = 4.95; p < .01] but not in the high frequency words[F(1, 40) = 0.76; p = .39]. Low frequency, low sublexical ambiguitywords elicited larger N400s than low frequency, high sublexicalambiguity words (Fig. 2).

For pseudowords, no sublexical ambiguity effect reached signif-icance in either analysis (midline: [F(1, 20) = 2.93; p = .10]; lateral:[F(1, 20) = 1.52; p = .23]), although numerically high sublexicalambiguity pseudowords (midline: 1.05 lV; lateral: 0.73 lV) wereslightly more negative than low sublexical ambiguity pseudo-words(midline: 1.67 lV; lateral: 1.12 lV). And the sublexicalambiguity did not interact with other factors in either analysis[Fs 6 2].

2.5. Discussion

In this study, we demonstrated that sublexical ambiguity affectsword recognition, which suggests that in reading a disyllabic com-pound, semantic representations are accessed through connectionsfrom the orthographic and semantic representations of constituentcharacters. For behavioral results, low frequency high sublexicalambiguity words showed a tendency towards longer reactiontimes and more errors than low frequency low sublexical ambigu-ity words. This is consistent with previous studies on semanticambiguity showing an ambiguity disadvantage (Rodd, Gaskell, &Marslen-Wilson, 2004; Rodd et al., 2002). The sublexical ambiguitydisadvantage may indicate difficulty in mapping a single ortho-graphic form to multiple morphemic representations. However,low sublexical ambiguity words also showed larger N400s.

Our N400 results seemed inconsistent with previous ambiguitystudies (Beretta et al., 2005) on the surface. However, most of thestudies addressed ambiguity effects at the word level instead ofsublexical level of a word. In Chinese, there is one study presentsthe initial character of a compound first and then presents thewhole word (H. W. Huang, 2009). For the epoch of initial character,high ambiguity characters elicited more negative N400s than lowambiguity characters these finding are in line with previous find-ings at lexical level (Beretta et al., 2005). But when a character isimbedded in a two-character word, whole word presentationmight provide contextual information that could help resolve sub-lexical ambiguity (among multiple meanings) very quickly. Inother words, at an earlier stage of processing, words with high sub-lexical ambiguity first character might be more difficult thanwords with low sublexical ambiguity first character at narrowingmultiple meanings to just one meaning. At the later stage of pro-cessing, to identify a unique word with the same meaning of theinitial character is easier for words with high sublexical ambiguity.Because when the orthographic neighborhood size is controlled,high sublexical ambiguity implies smaller number of words sharethe same initial meaning whereas low sublexical ambiguity im-plies larger number of words share the same initial meaning.Therefore, there are fewer competitors sharing the same initialmeaning for high sublexical words than low sublexical ambiguitywords (Fig. 1). For pseudowords, however, only processes for thefirst stage are applicable. Therefore, pseudowrds with high sublex-ical ambiguity showed a tendency of more negative N400 thanthose with low sublexical ambiguity.

Another possible explanation of our ERP results is based on theresults from studies for hemispheric asymmetries in processinglexical ambiguity stimuli (e.g. BANK) by using the visual half-field(VF) priming paradigm. This technique takes advantage of the factthat stimuli presented in the left side of the visual field are initiallyprocessed exclusively by the right hemisphere and vice versa.Although information presented in this manner can be later trans-mitted to both hemisphere, the interpretation of VF paradigmsrests on the assumption that responses to stimuli presented brieflyto one VF reflect mainly the processing of that stimulus by the con-tralateral hemisphere, so that responses to targets in the right vi-sual field (RVF) reflect left hemisphere (LH) processes andresponses to targets in the left visual field (LVF) reflect processesin the right hemisphere (RH).

Semantic processes in the cerebral hemispheres have beenextensively explored using behavioral measures along (e.g.,Beeman et al., 1994; Burgess & Simpson, 1988), or with electro-physiological measures (e.g., Atchley & Kwasny, 2003; Meyer &Federmeier, 2007). There is an emerging understanding that thetwo hemispheres play complementary roles during meaningretrieval (Jung-Beeman, 2005). For instance, word priming studieshave shown that when ambiguous homonyms are presented in aneutral context, the LH selects the dominant meaning, whereas

H.-W. Huang et al. / Brain & Language 117 (2011) 77–87 81

the RH activates and maintains both the dominant and subordinatemeanings (Burgess & Simpson, 1988). Similarly, Faust and Chiarello(1998) have investigated hemispheric asymmetries in resolvinglexical ambiguity within sentence contexts. Sentences that bias to-wards a single meaning of sentence-final ambiguous words werefollowed by lateralized targets related to the sentence-congruentor –incongruent meaning of the ambiguous word, or unrelatedword. In the RVF/LH, sentence-congruent targets were facilitated,while incongruent targets were not primed. In contrast, related tar-gets were facilitated in the LVF/RH, regardless of sentence context.These studies seem to indicate that the LH controls meaning selec-tion processes, whereas RH maintains all possible meanings. How-ever, studies involving unilateral RH damage participants havesuggested that RH contributes to the meaning selection as well(Tompkins, Baumgaertner, Lehman, & Fassbinder, 2000). Finally,recent ERP evidence, has demonstrated that two hemispheres re-sponded similarly when contextual information is provided. Incontrast, when using a neutral context, LH tends to maintain multi-ple meanings, whereas RH activates one meaning (Meyer &Federmeier, 2007).

In the current experiment, all of the stimuli were centrally pre-sented; hence the first character was presented to the left of fixa-tion (right hemisphere). However, the presentation time of stimuliwas much longer than those used in the split visual field studies(500 ms vs. 150 ms). This might increase the crosstalk of the twohemispheres and thus the effect was diluted. In Experiment 2 wewill use a divided visual field paradigm to further investigatehow sublexical ambiguity affects word recognition in two

hemispheres. If whole word presentation did help ambiguity reso-lution very fast and there are hemispheric asymmetries in process-ing ambiguous stimuli, based on the hypothesis that LH selects onemeaning (Burgess & Simpson, 1988) we expect to see the low sub-lexical ambiguity words elicit more negative N400s than high sub-lexical words with RVF/LH presentation. In contrast, with LVF/RHpresentation, since RHmaintains all meanings (Burgess & Simpson,1988), we expect to see high sublexical ambiguity words elicitmore negative N400s than low sublexical ambiguity words. How-ever, if RH also involves in the selection process, we shall see theresults showing similar patterns as those with RVF/LH presenta-tion. As for pseudowords, if the absence of contextual informationdid affect meaning selection processes in two hemispheres (Meyer& Federmeier, 2007), we predict to see high sublexical ambiguitypseudowords elicit more negative N400s than low sublexical ambi-guity pseudowords in the LH. On the other hand, under this cir-cumstance, RH activates one meaning; therefore low sublexicalambiguity pseudowords will show more negative N400s than highsublexical ambiguity pseudowords in the RH.

3. Experiment 2

3.1. Materials and methods

3.1.1. ParticipantsTwenty-one healthy undergraduate or graduate male students

(age range 19–25, mean 22.1) participated in Experiment 2 for

F3

C3

CP3

FZ

CZ

CPZ

F4

C4

CP4

-5 uV

800 ms4000

Fig. 2. Grand average ERPs to low frequency, high sublexical ambiguity and low frequency, low sublexical ambiguity words are shown at nine electrode sites. Low sublexicalambiguity words elicited more negative N400 responses than high sublexical ambiguity words.

82 H.-W. Huang et al. / Brain & Language 117 (2011) 77–87

cash. They were all native speakers of Chinese with normal or cor-rected-to-normal vision. Participants were right-handed, as deter-mined by the handedness scale (Chapman & Chapman, 1987); tworeported having left-handed family members.

3.1.2. StimuliOne hundred and twenty low frequency Chinese two-character

words were selected from the Academia Sinica balanced corpus(C. R. Huang & Chen, 1998) for this experiment. The words weredivided into four subsets of 30 words by manipulating VF (RVFvs. LVF) and sublexical ambiguity. Low sublexical ambiguity wordswere those whose first character had a meaning rating smallerthan 1.6 (range 1.1–1.6, mean 1.5) and high sublexical ambiguitywords were those whose first character had a meaning ratinglarger than 2.8 (range 2.8–3.8, mean 3). A total of 120 pseudowordswere created for the lexical decision task by concatenating twocharacters that do not occur together in the corpus. Pronunciationsof pseudowords were controlled so as not to resemble those of realwords. We also manipulated the sublexical ambiguity of pseudo-words. Half of them had low sublexical ambiguity (range 1–1.7,mean 1.6) and the other half had high sublexical ambiguity (range2.6–3.8, mean 2.9). Characters used for pseudowords were notoverlapped with characters used for real words. The pronuncia-tions of pseudowords were controlled so as not to resemble thoseof the real words. Possible confounding factors such as NS1, NS2,log transformed character frequency, subjective familiarity ratingof the characters and the number of meanings corresponding tothe second character were controlled. Practice trials contained 24words and 24 pseudowords.

3.1.3. ProcedureParticipants were tested in a single experimental session con-

ducted in a sound-proof, electrically-shield chamber. They wereseated in a comfortable chair 70 cm in front of a monitor and wereinstructed to read the words for comprehension. They first partic-ipated in a practice run in order to familiarize themselves with theprocedure; a formal run followed. Each participant received 240randomized experimental trials in six test blocks. A two-to-threeminute break was given to prevent eyestrain and fatigue.

To make sure that subjects fixated at the center of the screen foreach trial, a white fixation ‘‘+” was first presented in the centrally,and a small circle was also presented around the fixation. Subjectsheld a mouse in their hands. The stimulus was triggered to presentonly when subjects use the mouse to center the circle on the cross.The target word was then presented with its inner edge two de-grees and subtended three degrees to either the left or right ofthe fixation for 150 ms, followed by a blank screen for a maximumof 1500 ms or until participant made a lexical decision response bypressing the button of mouse. If subjects judged the stimulus to bea word, they were instructed to press the right mouse button with

their right index finger, otherwise, they were told to press the leftbutton of mouse with their left index finger. At the end of each triala capital B was presented in the center to allow blinking for1500 ms. Participants were asked not to blink their eyes until theappearance of the B in order to minimize eye movement artifacts.The inter-trial interval was 2000 ms. Speed and accuracy werestressed equally. Response time, accuracy, and event-relatedpotentials data were both collected during the process. EEG dataacquisition and preprocessing are the same as experiment one.

3.2. Results

3.2.1. BehaviorAnalyses of variance (ANOVAs) with the within-subject factors

of VF and sublexical ambiguity were performed on reaction timesand accuracy for each of the word types (words and pseudowordsseparately). Trials with incorrect responses or reaction times thatexceeded 2 SD from the subject’s mean were removed from theanalysis. For words, neither main effects nor interactions were sig-nificant for reaction time analysis (all Fs 6 1; LVF/RH: 753 ms forhigh sublexical ambiguity and 746 for low ambiguity words;RVF/LH: 746 ms for high sublexical ambiguity and 750 ms forlow ambiguity words). Again, neither main effects nor interactionsreached significance for the reaction time of pseudowords (allFs 6 2; LVF/RH: 874 ms for high sublexical ambiguity and 892 msfor low ambiguity pseudowords; RVF/LH: 893 ms for high sublex-ical ambiguity and 900 ms for low ambiguity pseudowords).

The sublexical ambiguity was significant on the accuracy anal-ysis [F(1, 20) = 4.1, p = .05]. High sublexical ambiguity words wereresponded less accurately (86%) than low sublexical ambiguitywords (89%). This was true for presentation to both hemispheres,as there was no main effect of visual filed [F < 1], and VF did notinteract with sublexical ambiguity [F < 1]. For pseudowords, themain effect of sublexical ambiguity was significant for accuracy[(F(1, 20) = 18.1, p < .001]. High sublexical ambiguity pseudowordswere responded to more accurately (91%) than low sublexicalambiguity pseudowords (87%). The main effect of visual filed[F < 1] is not significant. And the effect of visual did not interactsublexical ambiguity [F = 1].

3.2.2. ERP asymmetries contingent on presentation sideFig. 3 shows ERP responses at posterior channels to words pre-

sented in the LVF and RVF, collapsed across lexicality and sublexi-cal ambiguity. To confirm that lateralized presentation of thestimuli resulted in the expected lateralization of visual processing,mean amplitude N1 responses (150–200 ms) were measured at 10posterior channels (P3, P4, P5, P6, P7, P8, PO5, PO6, PO7, PO8)where this component tend to be largest. These measures weresubjected to an omnibus 2 (lexicality: word vs. pseudoword) � 2(VF: LVF vs. RVF) x2 (sublexical ambiguity: high vs. low) � 2

400 800

-4

4

uV

Time(ms)

PO8

400 800

-4

4

PO7

uV

Fig. 3. Effects of stimulus lateralization on ERP. Grand average ERPs to all stimuli presented in the RVF/LH (at left) and LVF/RH (at right) are shown at two representativeelectrode sites (positions indicated by darkened shapes on the head diagram). N1 responses are larger contralateral to VF of presentation and are followed by an extendednegative-going effect that is also larger contralaterally.

H.-W. Huang et al. / Brain & Language 117 (2011) 77–87 83

(laterality: left vs. right recording sites) � 5 (electrode) ANOVA.The amplitude of the N1 was larger over the right than the lefthemisphere (�1.1 vs. 1.9 lV) when stimuli appeared in the LVF/RH, and it showed reverse asymmetry (2.4 vs. �1.6 lV) when stim-uli appeared in the RVF/LH [VF � laterality, F(1, 20) = 53.5,p < .001]. The reversed asymmetries of the visual N1 contingenton hemifield were as expected; it confirmed that the proceduresfor maintaining central gaze fixation and rejection of trials withlateral eye movements were adequate to ensure lateralized pro-cessing, at least at the level of visual processing indexed by theN1 (Hillyard & Anllo-Vento, 1998; Neville, Kutas, & Schmidt, 1982).

Previous studies measuring ERPs to lateralized visual stimulihave reported a sustained late negative-going effect (has been re-ferred to as the selection negativity) over lateral and posteriorscalp sites contralateral to the visual filed of stimulus presentation(Federmeier, Mai, & Kutas, 2005; Neville et al., 1982), and we alsoobserved a visually similar effect in this study (Fig. 3). The termselection negativity was taken from the literature on spatialattention in which it refers to an ERP component elicited in targetdetection paradigms that involve central fixation with selectiveattention directed to peripheral targets (see, e.g., Hillyard &Anllo-Vento, 1998).

3.2.3. ERP effects of sublexical ambiguityThe analysis of ERPs in the N400 time window (350–550 ms)

was conducted separately for words and pseudowords with tworepeated-measures ANOVAs for each word type according tostandard procedures. The first midline analysis was carried outover the five electrodes (FZ, FCZ, CZ, CPZ and PZ) and used threewithin-subject factors: 2 (VF: LVF vs RVF) � 2 (sublexical ambigu-ity: high vs. low) � 5 electrode sites. The second lateral analysis

was carried out with four within-subject factors: 2 (VF: LVF vs.RVF) � 2 (sublexical ambiguity: high vs. low) � 2 (lateral: left vs.right recording sites) � 4 electrode sites (F3/4, FC3/4, C3/4, andCP3/4). The interactions between VF and sublexical ambiguity onthe midline and the lateral analyses were significant for both wordsand pseudowords [words-midline: F(1, 20) = 5.1, p < .05; words-lateral: F(1, 20) = 3.9, p = .05; pseudowords-midline: F(1, 20) = 11.7,p < .01; pseudowords-lateral F(1, 20) = 11.3, p < .01]. Follow-up anal-yses were thus conducted separately in each VF.

For words (Fig. 4) with RVF/LH presentation, no sublexicalambiguity effects were observed in either the midline or lateralanalyses (Fs < 1). With LVF/RH presentation, a sublexical ambiguityeffect was significant in the N400 window in both analyses [mid-line: F(1, 20) = 10.7, p < .01; lateral: F(1, 20) = 7.9, p < .01], withmore negative N400 responses to low sublexical ambiguity wordsthan high sublexical ambiguity words. For pseudowords (Fig. 5)with RVF/LH presentation, sublexical ambiguity was marginallysignificant in the midline analysis [F(1, 20) = 3.8, p = .07] and sig-nificant in the lateral analysis [F(1, 20) = 4.3, p = .05]. Low sublexi-cal ambiguity pseudowords elicited more negative N400 than highsublexical ambiguity pseudowords. In contrast, with LVF/RH pre-sentation, high sublexical ambiguity pseudowords elicited morenegative N400 than low sublexical ambiguity pseudowords [mid-line: F(1, 20) = 10.7, p < .01; lateral: F(1, 20) = 7.9, p < .01].

3.3. Discussion

The goal of this experiment was to examine the sublexicalambiguity effect in two hemispheres. With RVF/LH presentation,we did not find any effect. Instead, with LVF/RH presentation, wefound different sublexical ambiguity effects in reading words and

Fig. 4. Grand average ERPs to high sublexical ambiguity and low sublexical ambiguity words presented in the RVF/LH (at left) and LVF/RH (at right) are shown at sixrepresentative electrode sites. With RVF/LH presentation, no sublexical ambiguity effect was observed. In contrast, with LVF/RH presentation, low sublexical ambiguity wordselicited more negative N400 responses than high sublexical ambiguity words.

84 H.-W. Huang et al. / Brain & Language 117 (2011) 77–87

pseudowords. The finding that low sublexical ambiguity wordselicited more negative N400 than high sublexical ambiguity wordswas consistent with Experiment 1. In contrast, high sublexicalambiguity pseudowords elicited more negative N400 than lowsublexical ambiguity pseudowords. Similar findings in two exper-iments for words supported our hypothesis that the presentationof the whole word provided a context for the first character to pickup the most relevant meaning very quickly. However, this mecha-nism is not applicable for pseudowords, since its constituent char-acters had never been concatenated together before. For lowsublexical ambiguity pseudowords, the meaning of the first char-acter is relatively more explicit, whereas for high sublexical ambi-guity pseudowords, the meaning of the first character is lessprecise. Therefore, it is difficult to make a no response to low sub-lexical ambiguity pseudowords. This may be the reason why lowsublexical ambiguity pseudowords were responded to less accu-rately than high sublexical ambiguity pseudowords.

Our data showed that the sublexical ambiguity effect appearedin the RH. No effect was found in the LH, which may not contrary toa number of alternative accounts of hemispheric asymmetries insemantic processing. For example, the coarse coding hypothesis,states that the LH and the RH differ in the granularity of theirsemantic activation with smaller, more focal activation in the LHand larger, more diffuse activation in the RH (Jung-Beeman,2005). Other accounts have stated that controlled semantic mech-anisms are recruited differently in two hemispheres. Specifically,multiple incompatible meanings are maintained in the RH becauseof the absence of attention-driven, controlled inhibition processesthat act to narrow the range of relevant meanings (this process

being selectively available to the LH; Burgess & Simpson, 1988).Some researchers have focused on differences in the hemispheres’use of context information rather than semantic activation pro-cesses as the source of asymmetries observed in semantic primingparadigms. It has been suggested that the LH is more likely to usecontext to engage in active meaning selection (e.g., Faust & Gernsb-acher, 1996), such that remotely related information is discardedor actively suppressed in the LH, but remains available in the lessselective RH. A similar proposal suggests that asymmetries in lan-guage processing arise because the LH is more top-down or expec-tancy driven; actively predicting upcoming information, whereasthe RH seems to integrates new information in a more bottom-up manner (Federmeier, 2007). Finally, some accounts have arguedthat two hemispheres use different strategies in word processing.Studies have found that word length has a greater impact on visualword recognition when letter strings are presented to the LVF thanto the RVF (Ellis, Young, & Anderson, 1988; Young & Ellis, 1985). Inlexical decision experiments, RVF reaction times are not affectedby the number of letters, while each additional letter increasesLVF reaction times (Ellis et al., 1988). The asymmetry of the lengtheffect has often been taken to imply differential modes of lexicalaccess stemming from the efficient, parallel processing of lettersin the LH and the non-parallel processing in the RH (Ellis et al.,1988; Young & Ellis, 1985).

Taken together, the disappearance of the sublexical ambiguityeffect for words in the LH does not mean that there was no seman-tic activation or selection there. Instead, the LH may use theexistence of top-down semantic information provided by thewhole word to pick up one relevant morpheme and suppress other

F3 F4

CP4

rvf/LH display

F3

C3

CP3

lvf/RH display

Pseudowords

-5 uV

800 ms200 400 6000

F4

C4

CP4

C4C3

CP3

Fig. 5. Grand average ERPs to high sublexical ambiguity and low sublexical ambiguity pseudowords presented in the RVF/LH (at left) and LVF/RH (at right) are shown at sixrepresentative electrode sites. With RVF/LH presentation, low sublexical ambiguity pseudowords elicited more negative N400 responses than high sublexical ambiguitypseudowords. In contrast, with LVF/RH presentation, high sublexical ambiguity pseudowords were associated with more negative N400 responses than low sublexicalambiguity pseudowords.

H.-W. Huang et al. / Brain & Language 117 (2011) 77–87 85

alternative morphemes quickly. In contrast, the more bottom-up,sequential processing mode initiated by the RH allows us to seesublexical ambiguity effects. These findings are also in line withevidence showing RH is involved in meaning selection (Meyer &Federmeier, 2007; Tompkins et al., 2000). However, when process-ing pseudowords—which cannot provide top-down information—the RH does maintain all morphemes. Therefore, high sublexicalambiguity elicited more negative N400s than low sublexical ambi-guity pseudowords. Under this circumstance, the LH still picks upone morpheme among alternatives. The meanings of the first char-acter for low sublexical ambiguity pseudowords are more precisethan high sublexical ambiguity pseudowords. Therefore, low sub-lexical ambiguity pseudowords showed more negative N400 thanhigh sublexical ambiguity pseudowords.

4. General discussion

Words vary largely in their morphological structure, rangingfrom mono-morphemic words to poly-morphemic words. Despitewide acceptance that words are ‘‘decomposed” into their constitu-ent morphemes during visual word recognition, there are fewerconsensuses on how or when this decomposition occurs. In orderto solve this problem, one must first specify how morphologicalinformation is stored in long-term memory. This issue is particu-larly important for Chinese because most Chinese words arepoly-morphemic compounds, and the constituent characters canmap onto one or more than one morpheme. Although Taft(2006), using a character decision task, suggested a lemma levelof morphological representation in linking word forms and mean-ings, the exact nature of this morphological representation in lex-ical memory is still vague.

In this study, we used subjective ratings of the number of mean-ings corresponding to a character as an index of sublexical ambigu-ity, and aimed to investigate the morphological representation oftwo-character words. The sublexical ambiguity disadvantage inbehavioral results supported separate entries of morphemes.Clearly, a level of morphological representation of Chinese isneeded (Fig. 2). In the ERPs, however, words with high sublexicalsemantic ambiguity elicited less negativity than those with lowsublexical semantic ambiguity words. We suggested that moremorphemes corresponding to the initial character implies smallernumbers of words within a meaning when the neighborhood sizewas controlled. In addition, the whole word presentation of a Chi-nese word provides top-down information, and we showed differ-ent sublexical ambiguity patterns of words and pseudowords intwo hemispheres. Further studies in exploring sublexical ambigu-ity of Chinese words should take hemispheric asymmetries intoaccount.

Ultimately, it is still hard to explain ‘‘when” readers pickup onlyone relevant meaning among multiple alternatives. On the onehand, there is a potential problem with the subjective rating onnumber of meanings associated with a character; more meaningsmight carry two ideas: A character stands for many distinct mean-ings, and a character that stands for many related meanings. Stud-ies of lexical ambiguity have proposed that senses of polysemouswords are represented as a single, core meaning rather than differ-ent words, whereas meanings of homonymous words are repre-sented as different lexical entries. However, this distinction maynot able to be determined according to the subjective rating itself.On the other hand, it is worth knowing whether two types ofsublexical ambiguity representations are needed for Chinesewords. Further studies are needed to refine the morphologicalrepresentation and to evaluate whether there are sublexicalambiguity disadvantages and sense advantage effects in Chinese.

Acknowledgment

We thank Dr. Jeremy Boyd for his constructive comments.

References

Andrews, S. (1989). Frequency and neighborhood effects on lexical access:Activation or search? Journal of Experimental Psychology: Learning, Memory,and Cognition, 15(5), 802–814.

Andrews, S. (1992). Frequency and neighborhood effects on lexical access: Lexicalsimilarity or orthographic redundancy? Journal of Experimental Psychology:Learning, Memory and Cognition, 18(2), 234–254.

Atchley, R. A., & Kwasny, K. M. (2003). Using event-related potentials to examinehemispheric differences in semantic processing. Brain and Cognition, 53(2),133–138.

Beeman, M., Friedman, R. B., Grafman, J., Perez, E., Diamond, S., & Lindsay, M. B.(1994). Summation priming and coarse semantic coding in the righthemisphere. Journal of Cognitive Neuroscience, 6(1), 26–45.

Beretta, A., Fiorentino, R., & Poeppel, D. (2005). The effects of homonymy andpolysemy on lexical access: An MEG study. Cognitive Brain Research, 24(1),57–65.

Burgess, C., & Simpson, G. B. (1988). Cerebral hemisphere mechanisms in theretrieval of ambiguous word meanings. Brain and Language, 33(1), 86–103.

Carreiras, M., Perea, M., & Grainger, J. (1997). Effects of orthographic neighborhoodin visual word recognition: Cross-task comparisons. Journal of ExperimentalPsychology: Learning Memory and Cognition, 23(4), 857–871.

Chapman, L. J., & Chapman, J. P. (1987). The measurement of handedness. Brain andCognition, 6(2), 175–183.

Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internallexicon. In S. Dornic (Ed.), Attention and performance VI (pp. 535–555). Hillsdale,NJ: Erlbaum.

Ellis, A. W., Young, A. W., & Anderson, C. (1988). Modes of word recognition in theleft and right cerebral hemispheres. Brain and Language, 35(2), 254–273.

Faust, M., & Chiarello, C. (1998). Sentence context and lexical ambiguity resolutionby the two hemispheres. Neuropsychologia, 36(9), 827–835.

Faust, M. E., & Gernsbacher, M. A. (1996). Cerebral mechanisms for suppression ofinappropriate information during sentence comprehension. Brain and Language,53(2), 234–259.

Federmeier, K. D. (2007). Thinking ahead: The role and roots of prediction inlanguage comprehension. Psychophysiology, 44(4), 491–505.

Federmeier, K. D., Mai, H., & Kutas, M. (2005). Both sides get the point: Hemisphericsensitivities to sentential constraint. Memory and Cognition, 33(5), 871–886.

Forster, K. I. (1976). Accessing the mental lexicon. In R. Wales & E. Walker (Eds.),New approaches to language mechanisms (pp. 257–287). Amsterdam: North-Holland.

Forster, K. I., & Shen, D. (1996). No enemies in the neighborhood: Absence ofinhibitory neighborhood effects in lexical decision and semantic categorization.Journal of Experimental Psychology: Learning Memory and Cognition, 22(3),696–713.

Grainger, J., & Jacobs, A. M. (1996). Orthographic processing in visual wordrecognition: a multiple read-out model. Psychological Review, 103(3), 518–565.

Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the studyof visual selective attention. Proceedings of the National Academy of Sciences ofthe United States of America, 95(3), 781–787.

Holcomb, P. J., Grainger, J., & O’Rourke, T. (2002). An electrophysiological study ofthe effects of orthographic neighborhood size on printed word perception.Journal of Cognitive Neuroscience, 14(6), 938–950.

Hoosain, R. (1991). Psycholinguistic Implications for linguistic relativity: A case study ofChinese. Hillsdale: Lawrence Erlbaum.

Huang, C. M. (2005). Neighborhood size effect in reading chinese two-characterwords: an ERP study. Unpublished master’s thesis. National Yang-MingUniversity, Taipei, Taiwan.

Huang, H. W. (2009). The sublexical semantic ambiguity effect for reading Chinesedisyllabic compounds. Unpublished doctoral dissertation. National Yang-MingUniversity, Taipei, Taiwan.

Huang, C. R., & Chen, K. J. (1998). Academia Sinica balanced corpus (version 3). Taipei,Taiwan: Academia Sinica.

Huang, H. W., Lee, C. Y., Tsai, J. L., Lee, C. L., Hung, D. L., & Tzeng, O. J. L. (2006).Orthographic neighborhood effects in reading Chinese two-character words.Neuroreport, 17(10), 1061–1065.

Jung-Beeman, M. (2005). Bilateral brain processes for comprehending naturallanguage. Trends in Cognitive Sciences, 9(11), 512–518.

Kutas, M., & Hillyard, S. A. (1980). Event-related brain potentials to semanticallyinappropriate and surprisingly large words. Biological Psychology, 11(2), 99–116.

Kutas, M., & Van Petten, C. (1994). Psycholinguistics electrified: Event-related brainpotential investigations. In M. Gernsbacher (Ed.), Handbook of psycholinguistics(pp. 83–143). New York: Academic Press.

McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model ofcontext effects in letter perception: Part 1. An account of basic findings.Psychological Review, 88, 375–407.

Meyer, A. M., & Federmeier, K. D. (2007). The effects of context, meaning frequency,and associative strength on semantic selection: Distinct contributions fromeach cerebral hemisphere. Brain Research, 1183(1), 91–108.

86 H.-W. Huang et al. / Brain & Language 117 (2011) 77–87

Neville, H. J., Kutas, M., & Schmidt, A. (1982). Event-related potential studies ofcerebral specialization during reading. I. Studies of normal adults. Brain andLanguage, 16(2), 300–315.

Rodd, J., Gaskell, G., & Marslen-Wilson, W. (2002). Making sense of semanticambiguity: Semantic competition in lexical access. Journal of Memory andLanguage, 46(2), 245–266.

Rodd, J. M., Gaskell, M. G., & Marslen-Wilson, W. D. (2004). Modelling the effects ofsemantic ambiguity in word recognition. Cognitive Science, 28(1), 89–104.

Sears, C. R., Hino, Y., & Lupker, S. Y. (1995). Neighborhood size and neighborhoodfrequency effects in word recognition. Journal of Experimental Psychology:Human Perception and Performance, 21(4), 876–900.

Snodgrass, J. G., & Mintzer, M. (1993). Neighborhood effects in visual wordrecognition: Facilitatory or inhibitory? Memory and Cognition, 21(2), 247–266.

Taft, M. (2006). Processing of characters by native Chinese readers. In P. B. Li,Elizabeth, Li Hai Tan, Ovid J. L. Tzeng (Eds.), The handbook of East Asianpsycholinguistics (pp. 237–249). Cambridge; New York: Cambridge UniversityPress.

Taft, M., Liu, Y., & Zhu, X. (1999). Morphemic processing in reading Chinese. In A.Inhoff, J. Wang, & H.-C. Chen (Eds.), Reading Chinese script: A cognitive analysis(pp. 91–113). Mahwah, NJ: Lawrence Erlbaum Associates.

Taft, M., & Zhu, X. (1995). Processing bound morphemes in English and Chinese. In L.Feldman (Ed.), Morphological aspects of language processing. Hillsdale, NJ:Lawrence Erlbaum Associates.

Tompkins, C. A., Baumgaertner, A., Lehman, M. T., & Fassbinder, W. (2000).Mechanisms of discourse comprehension impairment after right hemispherebrain damage: Suppression in lexical ambiguity resolution. Journal of Speech,Language, and Hearing Research, 43(1), 62–78.

Tsai, J. L., Lee, C. Y., Lin, Y. C., Tzeng, O. J. L., & Hung, D. L. (2006). Neighborhood sizeeffects of Chinese words in lexical decision and reading. Language andLinguistics, 7(3), 659–675.

Young, A. W., & Ellis, A. W. (1985). Different methods of lexical access for wordspresented in the left and right visual hemifields. Brain and Language, 24(2),326–358.

Zhou, X., & Marslen-Wilson, W. (1994). Words, morphemes and syllables in theChinese mental lexicon. Language and Cognitive Processes, 9, 393–422.

Zhou, X., & Marslen-Wilson, W. (1995). Morphological structure in the Chinesemental lexicon. Language and Cognitive Processes, 10, 545–600.

Zhou, X., Marslen-Wilson, W., Taft, M., & Shu, H. (1999). Morphology, orthography,and phonology in reading Chinese compound words. Language and CognitiveProcesses, 14, 525–565.

H.-W. Huang et al. / Brain & Language 117 (2011) 77–87 87