Journal of Neurolinguistics 31 (2014) 42e54 Contents lists available at ScienceDirect Journal of Neurolinguistics journal homepage: www.elsevier.com/locate/ jneuroling Effects of semantic constraint and cloze probability on Chinese classiﬁer-noun agreement Chia-Ju Chou a, Hsu-Wen Huang b, Chia-Lin Lee c, Chia-Ying Lee a, d, e, * a Institute of Cognitive Neuroscience, National Yang-Ming University, Taiwan Department of Applied Chinese Language and Culture, National Taiwan Normal University, Taiwan c Institute of Linguistics, National Taiwan University, Taiwan d Institute of Linguistics, Academia Sinica, Taiwan e Institute of Cognitive Neuroscience, National Central University, Taiwan b a r t i c l e i n f o a b s t r a c t Article history: Received 11 February 2014 Received in revised form 24 June 2014 Accepted 25 June 2014 Available online This study aims to examine when and how readers make use of top-down information to predict or integrate upcoming words by utilizing the characteristics of Chinese classiﬁer-noun agreement, as measured by event-related potentials (ERPs). Constraint strength of classiﬁers (strong and weak) and cloze probability of the pairing noun (high, low, implausible) was manipulated. Weakly constrained classiﬁers elicited a less positive P200 and an enhanced frontal negativity than strongly constrained classiﬁers, suggesting that readers used the preceding classiﬁer to predict the upcoming noun, even before the pairing noun appeared. For ERPs elicited by the pairing nouns, there was a signiﬁcant interaction between semantic constraint and cloze probability for the N400. For nouns following the weakly constrained classiﬁers, there was a graded cloze probability effect on the N400 (High < Low < Imp). For nouns following the strongly constrained classiﬁers, both low cloze and implausible nouns elicited larger N400s than high cloze nouns; however, there was no difference between low cloze and implausible nouns. The critical comparison for the constraint effect of low cloze nouns was found for the N400 but not for frontal Keywords: Constraint Cloze probability Classiﬁer-noun agreement N400 Frontal negativity * Corresponding author. Institute of Linguistics, Academia Sinica, 128, Section 2, Academia Road 115, Taipei, Taiwan. Tel.: þ886 2 2652 5031; fax: þ886 2 2785 6622. E-mail address: [email protected] (C.-Y. Lee). http://dx.doi.org/10.1016/j.jneuroling.2014.06.003 0911-6044/© 2014 Elsevier Ltd. All rights reserved. C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 43 positivity, suggesting that the N400 reﬂects a joint effect of both beneﬁt and cost of prediction. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Studies have demonstrated that processing a word can be inﬂuenced by its preceding context. Readers are usually faster and more accurate in processing words that are congruent with their preceding context (Duffy, Henderson, & Morris, 1989; Stanovich & West, 1981). By recording eye movements during natural reading, ﬁxation and gaze durations are usually shorter for highly expected words than for unexpected words that are embedded in the sentences (Dambacher, Goellner, Nuthmann, Jacobs, & Kliegl, 2008; Kliegl, Grabner, Rolfs, & Engbert, 2004; Rayner, Ashby, Pollatsek, & Reichle, 2004). These ﬁndings suggest that contextual information plays an important role in language comprehension. However, it remains unclear when and how readers make use of contextual information to predict or integrate the meaning of upcoming words. The present study aims to address this issue by using the unique characteristics of Chinese classiﬁer-noun agreement with event-related potentials (ERPs), which provides great temporal resolution. Additionally, several ERP components (such as P200, N400, and frontal positivity) can be used to index various stages of cognitive processing. In the ERP literature, contextual effect is usually evaluated by manipulating the degree of ﬁt or semantic congruency between the context and upcoming words (Kutas & Hillyard, 1980a, 1980b, 1984), word predictability (Dambacher & Kliegl, 2007; Dambacher, Kliegl, Hofmann, & Jacobs, 2006; Van Petten & Kutas, 1990), or sentential constraint (Federmeier, Wlotko, De Ochoa-Dewald, & Kutas, 2007; Hoeks, Stowe, & Doedens, 2004; Wlotko & Federmeier, 2007). Despite the various ways to evaluate contextual inﬂuences, empirically they are determined by the cloze probability, which is measured by calculating the percentage of people who complete a sentence frame with a particular word (Taylor, 1953). A well-replicated ﬁnding indicates that N400 amplitudes are inversely proportional to the cloze probability. The reduction of N400 amplitude is found with words that can be easily integrated into the preceding word, sentence, or discourse context (van Berkum, Hagoort, & Brown, 1999; Kutas & Hillyard, 1980a, 1980b; Van Petten & Kutas, 1990, 1991). When a word in a context has a higher cloze probability, there is more of a reduction in N400 amplitude when compared to an unexpected word (Kutas & Hillyard, 1984). However, the reduced N400 for high cloze probability words in sentences may either reﬂect the use of contextual information to predict and pre-activate the upcoming word (predictive view), or the ease of integrating a word into its preceding context (integrative view). The major difference between these two views is that the predictive view assumes contextual information can be used in an anticipatory or predictive manner to exert its effect starting from the early processing stages of word recognition, such as early perceptual features analysis, to the later stages of lexical activation and selection (Federmeier, 2007; Lee, Liu, & Chou, 2013; Lee, Liu, & Tsai, 2012), whereas the integrative view assumes that language comprehension is mainly based on the post-lexical semantic integration of each embedded word in the sentence (Fodor, 1983; Schwanenﬂugel & Shoben, 1985; Van Petten & Luka, 2012). To further examine how contextual constraint affects the processing of unexpected words, Federmeier and Kutas (1999) manipulated the sentential constraint and degree of semantic overlapping between an unexpected ending word and its best competition. For example, the sentence, “They wanted to make the hotel look more like a tropical resort, so along the driveway, they planted rows of …” can be completed by one of the three ending types: (1) the expected ﬁnal word, established by cloze probability, e.g., palms; (2) within-category violation, an unexpected item from the expected semantic category, e.g., pines; or (3) between-category violation, an unexpected item from a different semantic category, e.g., tulips. Based on the integrative view, there would be no difference between within- and between-category violations, as both contain features that are not coherent within the context. However, the data from this study demonstrated not only that the expected item elicited a smaller N400 than either violation type, but the within-category violation also elicited a smaller N400 than the between-category violation. Moreover, this pattern was mainly found when the ﬁnal words were 44 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 presented in strongly constrained context. Thus, strongly constrained sentences led to a stronger prediction for the expected item than did more weakly constrained sentences, thereby supporting the predictive view. Other supporting evidence comes from Federmeier et al. (2007), who tried to separate two types of contextual effects by manipulating sentential constraint and the cloze probability of ending words in sentences. Sentences were subdivided into strongly constrained and weakly constrained types, and each ended with a high cloze probability (highly expected) word and a low cloze probability (unexpected but plausible) word (Note that the strength of sentential constraint was deﬁned by the cloze probability of the best completion for a sentence frame). Strongly constrained sentences were deﬁned as those with at least 70% agreement for the best completion, whereas weakly constrained sentences were deﬁned as sentences with no more than 40% agreement for the best completion. For example, in the strongly constrained sentence “He brought her a pearl necklace for her birthday,” 91% of participants would complete this sentence with the word “birthday”. However, some sentence frames may lack agreement with respect to their completion. For example, the weakly constrained sentence “He looked worried because he might have broken his arm” can be completed with a rather divergent end word, because the best completion word “arm” only has a cloze probability of 36%. The data from this study revealed that N400 amplitudes were graded by the cloze probability but were unaffected by constraint, suggesting that the N400 primarily indexes the beneﬁt of supportive contextual information for word processing. Meanwhile, unexpected words completing strongly constrained sentence frames elicited enhanced frontal positivity when compared to completing weakly constrained sentence frames. This effect was interpreted as the need to suppress wrong expectations, and reﬂected the possible cost associated with processing unexpected words in strongly constrained contexts (Federmeier et al., 2007). Wlotko and Federmeier (2007) further applied the same set of stimuli while using a split visual ﬁeld paradigm to examine whether the two hemispheres preferred different modes of language comprehension. Their results revealed P200 sensitivity to constraint; words in strongly constrained contexts elicited a larger P200 than those in less predictive contexts, but only when presented in the right visual ﬁeld. Conversely, the N400 responses for both visual ﬁelds departed from the typical pattern where ERP amplitude was graded by cloze probability. Expected endings in strongly and weakly constrained contexts were facilitated to a similar degree with right visual ﬁeld presentation, while expected endings in weakly constrained contexts were not facilitated compared to unexpected endings in the same context with left visual ﬁeld presentation. However, the larger frontal positivity for unexpected endings in strongly constrained contexts observed in Federmeier et al. (2007) with central presentation was not seen in either visual ﬁeld (Wlotko & Federmeier, 2007). These studies demonstrated that it was possible to dissociate the two types of contextual effects, as well as sentence frame constraint and cloze probability of completion, in different ERP components. A major caveat of these studies is that the strength of constraint is also deﬁned by the cloze probability of the most-favored completion. Thus, both sentential constraint and cloze probability are related but partially independent measures. In particular, since only strongly constrained sentences could have a high cloze probability of completion, the constraint effect on the best completion would naturally be confounded with the cloze probability effect. For example, Federmeier et al. (2007) and Wlotko and Federmeier (2007) reported that cloze probabilities of best completions for strongly and weakly constrained frames were 91% and 36.2%, respectively. Since constraint and cloze probability are inherently confounded for expected completions, the only way to evaluate the effects of constraint is to compare the processing of unpredictable items for both complete strongly and weakly constrained sentences (average cloze probability was 3% for unexpected items in both of strongly and weakly constraining contexts). Therefore, it was not possible to evaluate the effects of sentential constraint and cloze probability in an orthogonal manner in these studies. The present study aims to overcome this problem by using Chinese classiﬁer-noun agreement. In Mandarin Chinese, whenever a noun is preceded by a number or a demonstrative, a classiﬁer must come in between. For example, “one person” or “this person” in English shall be 一個人 (yi1 ge ren2, oneclassiﬁer person) or 這個人 (zhe4 ge ren2, this-classiﬁer person) in Chinese. The basic structure of a classiﬁer phrase consists of a number, a classiﬁer, and a noun. In the traditional view, Chinese classiﬁers are often not distinguished from measure words in the discussion of Chinese grammar. Chao (1968, C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 45 584e620) refers to classiﬁers as individual measures and subsumes them under the rubric of measure words. Duffy et al. (1989) stated that “any measure word can be a classiﬁer.” More recently, Tai (1992) pointed out that there is an important distinction between the notion that classiﬁers can only classify a limited and speciﬁc group of nouns, while measure words can be used as a measure for a wide variety of nouns. Tai (1992) also suggested that a “classiﬁer categorizes a class of nouns by picking out some salient perceptual properties, whether physically or functionally based, which are permanently associated with the entities named by the class of nouns; a measure word does not categorize but denotes the quantity of the entity named by a noun.” Therefore, Chinese classiﬁers are said to carry meaning (to a greater or lesser extent) about the semantic features of the entities being classiﬁed. Ahrens (1994) argued that the use of Chinese classiﬁers in modern Mandarin is semantically motivated, although not fully predictable. This is supported by recent research using semantic judgment and word-picture matching to demonstrate the connection between classiﬁer usage and the structure of conceptual categories (Gao & Malt, 2009; Kuo & Sera, 2009; Tien, Tzeng, & Huang, 2002). Studies also indicate that classiﬁer categories may gain the beneﬁt of predicting succeeding nouns that shared a classiﬁer (Saalbach & Imai, 2007; Srinivasan, 2010; Zhang & Schmitt, 1998). However, there is some speciﬁc correspondence between classiﬁers and nouns. For example, books generally take the classiﬁer 本 ben3, ﬂat objects such as 紙 (paper) take 張 zhang1, and animals usually take 隻 zhi1. Within these categories are further subdivisions; while most animals take 隻 zhi1, domestic animals (such as 牛, cow) take 頭 tou2, long and ﬂexible animals (such as 蛇, snake) take 條 tiao2, and horses take 匹 pi3. In other words, classiﬁers vary in how speciﬁc they are. Some (such as 頂 ding3 for hats) could only be used with a few nouns, and thus provide high semantic constraint to their paring nouns. Others (such as 條 tiao2 for long and ﬂexible things, one-dimensional things, or abstract items like a news report) are much less restricted and thus provide relatively low constraint to their pairing nouns. No matter for high or low constraining classiﬁers, all plausible nouns can be measured by their cloze probability. For example, a weakly constrained classiﬁer, 瓶 ping2 (bottle), constrained nouns, including low cloze words, 膠水jiao1 shuei3 (glume; cloze ¼ 3.45%), moderately cloze words, 水shui3 (water; cloze ¼ 51.70%), and the most preferred noun 飲料yin3 liao4 (drink; cloze ¼ 72.41%). Although the factors that govern which classiﬁers are paired with what nouns have been the subject of debate among linguists, the speciﬁcity of Chinese classiﬁer-noun agreement poses an interesting testing ground for dissociating the effects of classiﬁer semantic constraint and the cloze probability of pairing nouns. In this study, we presented Chinese classiﬁer-noun pairs that were manipulated to have two levels of classiﬁer constraint strength (strong and weak) and three levels of cloze probability for the pairing noun (high, low, implausible). Most importantly, the three levels of cloze probability were well matched between strongly and weakly constrained conditions in order to dissociate the constraint and cloze probability effects in a set of ERP components. 2. Materials and methods 2.1. Participants Twenty-three right-handed native Chinese speakers were paid to participate in this study. After artifact rejection, four participants were excluded from the analysis due to the extremely small number of valid trials in their data (less than 12 trials in at least one of the conditions). The ﬁnal analysis was conducted with 19 participants (9 males, mean age 22 years, age range 18e26 years). All participants reported normal vision without any history of neurological or psychiatric disorders. Written consent was obtained from all participants. 2.2. Experimental design and materials The semantic constraint (weak versus strong) of classiﬁers and the cloze probability of the pairing noun (high cloze, low cloze, or implausible [zero cloze probability]) were manipulated in a 2-by-3 factorial design. One hundred and twenty classiﬁers were chosen. Half of these classiﬁers were strongly constrained classiﬁers and the other half were weakly constrained classiﬁers, as determined by a norming study of Chinese classiﬁers regarding the number of nouns that can be followed by a speciﬁc 46 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 classiﬁer. Every classiﬁer would appear twice throughout the whole experiment; once it would be paired with a plausible noun and the other time it would be paired with an implausible noun. The plausible classiﬁer-noun pairs were further divided into two conditions; half were paired with a highly expected noun (high cloze probability) and the other half were paired with an unexpected but plausible noun (low cloze probability) (see Table 1). To prevent the participant from being aware that second appearance of a classiﬁer was either followed by a plausible or an implausible noun, one hundred and twenty ﬁller pairs were added. For those 120 ﬁllers, half of the classiﬁers was always followed by plausible nouns and the other half was always followed by implausible nouns, to break the rule. To determine the strength of constraint for Chinese classiﬁers and the cloze probability for their pairing nouns, a norming procedure for 184 Chinese classiﬁers was conducted with 116 native Chinese speakers (age range 18e25 years) from National Yang-Ming University and National Cheng-Chi University in Taipei, Taiwan. The 184 classiﬁers were selected from the Handbook of Common Quantiﬁers (Version 3, 1997) and the Academia Sinica Balanced Corpus of Modern Chinese (Huang & Chen, 1998), and were divided into four lists of 46 classiﬁers each. Each list was completed by 29 participants. For rating subjective familiarity, participants were instructed to imagine how often the classiﬁer was used based on their experience, and were also asked to perform the subjective rating based on the 7-point scale, ranging from 1 (unknown) to 7 (extremely familiar). For rating strength of constraint, participants were asked to estimate how many nouns could be followed by each classiﬁer based on a 5-point scale, ranging from 1 (could not think of any nouns) to 5 (more than four nouns). For the cloze probability norming of the following noun, participants were ﬁrst asked to complete the fragment of “numeral þ classiﬁer þ ___,” with the ﬁrst noun coming to their mind. In addition, we followed a procedure similar to that used in Federmeier et al. (2007), which instructed participants to give two or three additional plausible completions if possible. Thus, the cloze probability could be calculated not only for the best completion, but also for the “next best” completion. For example, among 29 participants, 25 of them completed the classiﬁer 頂 ding3 with 帽子 (mao4 zi, hat) as the best completion and 3 of them completed the classiﬁer 頂 ding3 with帽子 (mao4 zi, hat) as the next best completion. The cloze probabilities of 帽子mao4 zi for being the ﬁrst and the next best completions are 86.21% (25/29) and 10.34% (3/29), respectively. In addition to the cloze probability of best completion, we also summed up the best and the next best cloze probabilities to index the overall cloze probabilities for a speciﬁc noun. For example, for 帽子, its overall cloze probabilities for being a highly expected item is 96.55% (86.21% þ 10.34%) (see Table 1 and Table 2 for sample materials). This procedure Table 1 Example of the stimuli for each condition and their characteristics Conditions Classiﬁer-noun agreement pairs Classiﬁer Constraint rating Noun Familiarity (1e5 scale) (1e7 scale) 一頂帽子 one ding3 hat Low cloze nouns 一架飛機 one jia4 airplane Implausible nouns 一頂船員/一架國王 one ding3 sailor/one jia4 king WC High cloze nouns 一瓶飲料 one ping2 drink Low cloze nouns 一條馬路 one tiao2 road Implausible nouns 一瓶學分/一條首都 one ping2 credit/one tiao2 capital SC High cloze nouns “Next best” Overall Frequency First cloze (%) completion cloze (%) cloze (%) 3.23 (0.40) 3.02 (0.41) 3.13 (0.41) 4.13 (0.81) 3.67 (0.68) 3.90 (0.77) 63.79 (13.55) 2.07 (2.65) 0.00 10.34 (9.01) 2.18 (1.69) 0.00 73.79 (12.20) 4.25 (1.48) 0.00 123.23 (102.62) 111.77 (93.39) 124.30 (43.39) 4.31 (0.37) 4.51 (0.36) 4.41 (0.38) 5.78 (0.61) 5.88 (0.56) 5.83 (0.59) 57.47 (18.92) 1.03 (1.84) 0.00 18.16 (12.14) 2.64 (1.42) 0.00 75.63 (14.03) 3.68 (0.87) 0.00 128.07 (101.96) 114.07 (93.99) 123.82 (45.11) Note. SC: strongly constrained; WC: weakly constrained. All scales reported as mean with standard deviation in parentheses. First completion cloze reﬂects the percentage of participants who completed a given classiﬁer with the ﬁrst noun coming to their mind. “Nest best” cloze reﬂects the cloze probability for additional plausible completions, and overall cloze reﬂects the sum of ﬁrst completion cloze and “next best” cloze. C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 47 allowed us to avoid the possibility of unexpected items within the set of the next best completion that participants could use to complete the fragment. The total amount of possible completions produced across all the participants for a speciﬁc classiﬁer also served as an index for the strength of constraint. From the resulting database, strongly or weakly constrained classiﬁers were deﬁned by whether they had a constraining strength less or more than 3.8. In addition, the high cloze probability nouns were chosen from those nouns that had a cloze value of 50%, which was based on the overall cloze probability. The low cloze probability nouns were chosen from those nouns that had a cloze value of 6.9% or less. The implausible nouns were chosen outside the set of possible completions and therefore had a cloze value of zero. The classiﬁers across four conditions were matched for subjective familiarity, and the completing nouns were matched for word frequency. The examples and characteristics of the six types of classiﬁer-noun pairing conditions are listed in Table 1. 2.3. Procedure Participants were seated in front of a computer monitor at a distance of approximately 75 cm in an acoustically shielded room. Each trial began with a ﬁxation cross appearing at the center of the screen for 500 ms, followed by a variable inter-stimulus interval (ISI) between 200 and 350 ms. The classiﬁer was presented in the center of the screen for 400 ms with a 600 ms ISI. The noun was then presented for 400 ms with a 600 ms ISI. Participants were encouraged to minimize eye movements, blinks, and muscle movements during this period. After the 600 ms ISI, the cue “?” was presented on the screen to instruct participants to perform an acceptability judgment for the classiﬁer-noun pair based on a 5-point scale, with 1 equaling “totally unacceptable,” and 5 equaling “highly expected and totally acceptable.” If a response was made, the next trial began after an ISI of 1500 ms; if no response was made, the next trial began 4 s after the onset of the cue. The recording session began with a short set of practice trials in order to acclimate the participants to the task. The main experimental session was divided into ﬁve blocks that lasted 5e8 min each (2 practice trials with 72 trials/session), with participants taking a short rest between each block. Different sequences were randomly assigned to each participant. Participants would view each classiﬁer twice and there were at least 50 different trials in between each classiﬁer in order to reduce repetition effects. The whole experiment lasted approximately 40 min. Table 2 Example classiﬁers and cloze probabilities of the best completion and other completions Classiﬁer 頂 ding3 帖 tie3 Subjective rating of constraint Number of completion Best completion (cloze %) Completions (meaning, cloze %) 假髮(wig, 24.14), 斗笠(bamboo hat, 6.90), 王 冠(crown, 10.34), 浴帽(shower cap, 3.45) 喜帖(wedding invitation, 24.14), 秘方(secret recipe, 10.34), 字(word, 3.45), 書信(letter, 3.45), 請 柬(invitation, 3.45), 戰帖(challenge, 3.45), 邀請 函(invitation, 3.45), 書法(calligraphy, 3.45) 雜誌(magazine, 24.14), 課本(textbook, 17.24), 小 說(ﬁction, 13.79), 漫畫(comics, 13.79), 冊 子(pamphlet,10.34), 畫冊(picture album, 10.34), 相 簿(album, 6.90), 簿子(notebook, 6.90), 日記(diary, 3.45), 目錄(catalogue, 3.45), 字帖(copybook for calligraphy, 3.45), 作業簿(homework, 3.45), 參考 書(reference book, 3.45), 報刊(newspaper, 3.45), 遊 記(travel notes, 3.45), 講義(handout, 3.45) 湯(soup, 37.93), 麵(noodle, 34.48), 粥(congee, 10.34), 湯麵(noodle soup, 10.34), 豆花(tofu pudding, 6.90), 茶水(water, 6.90), 米粉(rice noodles, 6.90), 麵 線(thin noodles, 6.90), 菜(dish, 6.90), 牛肉麵(beef noodles soup, 3.45), 紅豆(red bean, 3.45), 濃湯(thick soup, 3.45), 湯藥(Chinese medicine, 3.45), 冰(ice, 6.90), 湯圓(rice ball, 3.45), 稀飯(rice porridge, 3.45), 開水(boiled water, 3.45), 碗粿(salty rice pudding, 6.90), 陽春麵(plain noodles soup, 3.45), 乾 麵(noodles, 3.45) 3.41 5 帽子(hat, 96.55) 3.00 9 藥(medicine, 55.17) 本 ben3 4.52 17 書(book, 93.10), 碗 wan3 4.59 22 飯(rice, 75.86) 48 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 2.4. EEG recording and processing Electroencephalograms (EEG) were recorded from 64 sintered Ag/AgCl electrodes (QuickCap, Neuromedical Supplies, Sterling, Texas, USA) with a common vertex reference located between Cz and CPz. The data were re-referenced off-line to the average of the right and left mastoids for further analysis. EEG was continuously recorded and digitized at a rate of 500 Hz. The signal was ampliﬁed by SYNAMPS2 (Neuroscan Inc., El Paso, Texas, USA) with the band-pass set at 0.05e100 Hz. Vertical eye movements were recorded by a pair of electrodes placed on the supraorbital and infraorbital ridges of the left eye. Horizontal eye movements were recorded by electrodes placed lateral to the outer canthus of the right and left eyes. A ground electrode was placed on the forehead, anterior to the FZ electrode. Electrode impedance remained below 5 kU. For the off-line analysis, the EEG was epoched from 100 ms before the onset of the classiﬁer and the target noun to 900 ms after. Mean amplitude in the selected latency window (see Results) were measured with respect to the 100 ms pre-stimulus baseline. Trials contaminated by eye movement or with voltage variations larger than 60 mV were rejected prior to averaging the trials into ERPs for each condition. Band-pass ﬁlter of 0.1 and 30 Hz (zero phase shift mode, 12 dB) were employed. ERPs were calculated for each subject and each condition. 3. Results 3.1. Behavioral results Plausibility judgment data were subjected to a repeated-measures analysis of variance (ANOVA) with two levels of Constraint (strongly and weakly constrained) and three levels of Cloze probability (high, low, and implausible). There were main effects of Constraint (F(1, 18) ¼ 47.71, p < .01) and Cloze probability (F(2, 36) ¼ 1741.90, p < .01), as well as a Constraint by Cloze probability interaction (F(2, 36) ¼ 101.61, p < .01). Further analysis revealed a signiﬁcant main effect of Cloze probability in both strongly (F(2, 36) ¼ 4865.78, p < .01) and weakly constrained (F(2, 36) ¼ 5983.46, p < .01) conditions. For the strongly constrained condition, high cloze probability nouns were rated as being more plausible on average (mean ¼ 4.50, SD ¼ 0.43) than low cloze probability nouns (mean ¼ 3.58, SD ¼ 0.43, F(1, 18) ¼ 727.79, p < .01) and implausible nouns (mean ¼ 1.23, SD ¼ 0.15, F(1, 18) ¼ 9151.70, p < .01). For the weakly constrained condition, the mean score for the high cloze probability nouns (mean ¼ 4.67, SD ¼ 0.26) was slightly higher than that for low cloze probability nouns (mean ¼ 4.25, SD ¼ 0.38, F(1, 18) ¼ 151.09, p < .01) and also higher than that for implausible nouns (mean ¼ 1.23, SD ¼ 0.12, F(1, 18) ¼ 10056.80, p < .01). 3.2. Event-related potentials To evaluate the semantic constraining effect of classiﬁers and how the semantic constraint modulated the cloze probability of the pairing noun, we analyzed the ERPs elicited by classiﬁers and by the pairing nouns, respectively. The mean amplitude of ERPs components, including P200, N400, and frontal negativity, from the selected electrodes served as the dependent measure in repeated-measures ANOVAs. For each ANOVA, the Greenhouse-Geisser adjustment for degrees of freedom was applied to correct for the violations of sphericity associated with repeated measures. Accordingly, for all the F tests with more than one degree of freedom in the numerator, the corrected p-value was reported. The post-hoc tests were conducted using Tukey's procedure. 3.2.1. Classiﬁers Fig. 1 shows the ERP grand average to both strongly and weakly constrained classiﬁers. All conditions elicited early sensory components, such as the N1 and P200. These components were followed by a sustained frontal negativity, which appeared to be more negative for low than for constrained classiﬁers. Based on previous studies (Federmeier et al., 2007), we measured the mean amplitudes for the P200 between 170 and 250 ms (peaking at 210 ms) and for the frontal negativity between 300 and 700 ms on nine frontal electrodes (FZ, FCZ, CZ, F3/4, FC3/4, and C3/4). For the ERPs elicited by classiﬁers, C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 49 Fig. 1. ERP grand average for Classiﬁers at frontal and central sites. Negative values are plotted on the positive y-axis here and in all subsequent ﬁgures. a repeated-measure ANOVA was performed on the mean amplitudes of the P200 and frontal negativity with Constraint (strongly versus weakly) and electrode site (nine frontal electrode sites) as withinsubject factors. 184.108.40.206. P200 (170e250 ms). The data revealed a signiﬁcant main effect of Constraint (F(1, 18) ¼ 7.93, p < .05) and a Constraint-by-electrode interaction (F(8, 144) ¼ 3.68, p < .05). Strongly constrained classiﬁers elicited more positive P200s than weakly constrained classiﬁers. Follow-up comparisons revealed that the constraining effect was signiﬁcant at almost every electrode site (p < .001), except for CZ and C4. 220.127.116.11. Frontal negativity (300e700 ms). The data revealed a signiﬁcant main effect of Constraint (F(1, 18) ¼ 10.1, p < .01). Weakly constrained classiﬁers elicited enhanced negativity. 3.2.2. Nouns Fig. 2 displays the ERP grand average to high cloze probability, low cloze probability, and implausible nouns that completed the strongly and weakly constrained classiﬁers. Based on visual inspection, all conditions elicited the early N1 and P200, which were then followed by a broadly distributed N400 that was largest at the centro-posterior sites. The N400 amplitude appeared to be more negative for implausible nouns no matter the constraint condition. There was also a broadly distributed late positive component between 600 and 900 ms. Three components were identiﬁed for further analysis. The ﬁrst one is the P200 (170e250 ms) over nine frontal electrodes (FZ, FCZ, CZ, F3/4, FC3/4, and C3/4). The second and third components are the N400 (300e500 ms) and frontal positivity (600e900 ms), which were measured on 15 electrodes (FZ, FCZ, CZ, CPZ, PZ, F3/4, FC3/4, C3/4, CP3/4, and P3/4) over the entire head and these electrodes were further divided into three levels of Laterality (left lateral, medial, and right lateral electrode sites) and ﬁve levels of Anteriority (frontal, frontal-central, central, central-parietal, and parietal electrode sites). For the ERPs elicited by the pairing nouns, a repeated-measure ANOVA was performed on the mean amplitudes of the P200, N400, and frontal positivity with Constraint (strongly versus weakly), Cloze probability (high, low, and implausible), and electrode site (P200:9 electrodes, N400 and frontal positivity, 3 levels of Laterality by 5 levels of Anteriority) as within-subject factors. 18.104.22.168. P200 (170e250 ms). The analysis revealed neither a signiﬁcant main effect (Constraint: F(1, 18) ¼ 0.02, p ¼ .88; Cloze probability: F(2, 36) ¼ 2.32, p ¼ .12) nor any signiﬁcant interactions (all F < 1). 22.214.171.124. N400 (300e500 ms). There was a signiﬁcant main effect of Cloze probability (F(2, 36) ¼ 14.18, p < .01) and a Constraint by Cloze probability interaction (F(2, 36) ¼ 5.15, p < .05), but there was no 50 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 Fig. 2. ERP grand average for nouns from midline scalp sites, arranged from frontal at the top to parietal at the bottom. High cloze probability words: solid black lines; low cloze probability words: dashed lines; implausible words: solid gray line. reliable main effect of Constraint (F(1, 18) < 0.01, p ¼ .96). Post-hoc tests revealed that cloze probability effects were signiﬁcant for both strongly constrained (F(2, 36) ¼ 23.78, p < .01) and weakly constrained conditions (F(2, 36) ¼ 10.11, p < .01). For the strongly constrained (SC) condition, both unexpected nouns (low cloze probability and implausible nouns) elicited a greater N400 than expected nouns did (high cloze probability; (SC-High versus SC-Low, F(1,18) ¼ 40.99, p < .01; SC-High versus SC-Imp, F(1, 18) ¼ 29.4, p < .01). The difference between low cloze probability and implausible nouns was not reliable (SC-Low versus SC-Imp: F(1, 18) ¼ 0.96, p ¼ .33). For the weakly constrained (WC) condition, implausible nouns elicited a more negative N400 than both high and low cloze probability nouns (High < Low < Imp; WC-High versus WC-Low: F(1, 18) ¼ 4.36, p < .05; WC-High versus WC-Imp: F(1, 18) ¼ 20.19, p < .01; WC-Low versus WC-Imp: F(1, 18) ¼ 5.78, p < .05)). In Federmeier et al. (2007), the most critical comparison was the constraint effect for low cloze words. Here, we performed this same comparison and demonstrated a signiﬁcant Constraint effect for low cloze probability nouns (F(1, 18) ¼ 6.42, p < .05). However, this effect was not found for high cloze probability (F(1, 18) ¼ 3.17, p ¼ .08) or implausible nouns (F(1, 18) ¼ 0.72, p ¼ .40). There was also a signiﬁcant Cloze probability-byLaterality interaction (F(4, 72) ¼ 7.07, p < .01), and a Cloze probability-by-Anteriority interaction (F(8, 144) ¼ 7.89, p < .01). Follow-up comparisons revealed that the cloze probability effect was signiﬁcant at all electrode sites (all p < .01), and was most prominent at central-parietal sites located off to the right from the center of the scalp. 126.96.36.199. Frontal positivity (600e900 ms). The results indicated a main effect of Cloze probability (F(2, 36) ¼ 3.45, p < .05) and a marginally signiﬁcant interaction between Constraint and Cloze probability (F(2, 36) ¼ 3.15, p ¼ .06). There was no main effect of Constraint (F(1, 18) ¼ 1.08, p ¼ 1.31). Follow-up comparisons revealed that high cloze probability nouns elicited a larger positivity than implausible nouns (F(1, 18) ¼ 7.09, p < .01) in the strongly constrained condition, and this effect was most prominent at central-parietal electrode sites. There was no difference between high cloze probability and C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 51 low cloze probability nouns (F(1, 18) ¼ 3.81, p ¼ .06), and no difference between low cloze probability and implausible nouns (F(1, 18) ¼ 0.51, p ¼ .48). For the weakly constrained condition, low cloze probability nouns elicited a larger positivity than implausible nouns (F(1, 18) ¼ 7.03, p < .01). However, there was no difference between high cloze probability and low cloze probability nouns (F(1, 18) ¼ 2.53, p ¼ .12) and no difference between high cloze noun and implausible nouns (F(1, 18) ¼ 1.13, p ¼ .30). To examine the critical contrast reported by Federmeier et al. (2007), which showed that the less expected continuations within strongly constrained sentence frames elicited the largest positivity, our comparisons revealed that the effect of constraint was observed for high cloze probability nouns. High cloze probability nouns that completed the strongly constrained classiﬁers elicited a larger positivity than those with weakly constrained classiﬁers (SC-High versus WC-High: F(1, 18) ¼ 4.87, p < .05). The constraint effect for low cloze probability nouns was not signiﬁcant (SC-Low versus WC-Low; F(1, 18) ¼ 1.78, p ¼ .19). 4. Discussion This study aimed to examine when and how the semantic constraint of Chinese classiﬁers may affect the processing of their following nouns by using ERP components to index the multiple stages of cognitive processing. In each experimental trial, participants would ﬁrst read a classiﬁer with either a strong or weak constraint for its following noun. Later, a high cloze probability noun, a low cloze probability noun, or an implausible noun would then appear to complete the classiﬁer-noun phrase. The current design allowed us to examine the brain responses elicited by classiﬁers and their pairing nouns separately. In particular, comparing the brain responses elicited by strongly and weakly constrained classiﬁers provided a great opportunity to examine whether the brain makes use of the preceding classiﬁer to predict the upcoming noun, even before the pairing noun is shown. The ERPs evoked by classiﬁers revealed a signiﬁcant effect of constraint on the P200 and frontal negativity. The weakly constrained classiﬁers that could be completed with a larger set of nouns produced a less positive P200 and a more negative frontal negativity than the strongly constrained classiﬁers that could only be completed with a limited set of nouns. Studies have suggested the sustained frontal negativity may reﬂect working memory demands and the need to maintain and select among candidate items during recollection (King & Kutas, 1995; Lee & Federmeier, 2006, 2009; Rugg, Allan, & Birch, 2000). For example, King and Kutas (1995) manipulated whether the main subject was the subject (subjectesubject relative [SS] sentences), or the object (subject-object relative [SO] sentences) in a relative clause. They found an enhanced sustained negativity for the main verb in SO sentences relative to SS sentences. Furthermore, such a frontal effect was greater in participants with good comprehension than in participants with poor comprehension. In contrast to the SS sentences wherein the head noun was almost immediately assigned its appropriate thematic role, the head noun in SO sentences must be stored in working memory. Therefore, the frontal negativity may in part be related to differences in memory storage requirements. Lee and Federmeier's series of studies embedded noun/verb (NV) homographs in syntactic-constrained but semantically neutral contexts and found that NV homographs elicited a sustained frontal negativity when compared to matched unambiguous words (Lee & Federmeier, 2006, 2009). The sustained frontal negativity has also been observed in other cases where there are multiple referents for a class unambiguous word (Nieuwland & Van Berkum, 2006). In our study, the strongly constrained classiﬁer may lead to a strong prediction for its following nouns. However, for the weakly constrained classiﬁers, participants might need to maintain all possible candidates for further selection before the noun appears, eliciting a larger frontal negativity compared to strongly constrained classiﬁers. We further measured the ERPs elicited by pairing nouns to examine whether the semantic constraint of the classiﬁer would affect the processing of pairing nouns at multiple stages, as previous studies have suggested that contextual information may facilitate the perceptual processing of the upcoming word, as measured by the P200 (Lee et al., 2012; Wlotko & Federmeier, 2007), and reveal beneﬁt and cost of semantic predictions in the N400 and frontal positivity responses (DeLong, Urbach, Groppe, & Kutas, 2011; Federmeier, 2007). In the early time window of the P200, our data revealed that the ERPs elicited by the nouns revealed neither semantic constraint nor cloze probability effects. Studies using the split visual ﬁeld paradigm have demonstrated that the P200 is larger (i.e., more 52 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 positive) for strongly constrained sentence endings for right but not left visual ﬁeld presentations (Federmeier, Mai, & Kutas, 2005; Wlotko & Federmeier, 2007). However, using the same set of stimuli as Wlotko and Federmeier (2007), Federmeier et al. (2007) found no constraint effect on the P200 with central presentation. It was suggested that only the left hemisphere could use contextual information to predict the perceptual features of the upcoming words and, with a feature match, could engender an enhanced P200. Therefore, it might be difﬁcult to demonstrate the contextual effect on the P200 with the central presentation. Nonetheless, previous studies have observed the P200 effect with central presentation (Barber, Vergara, & Carreiras, 2004; Hsu, Tsai, Lee, & Tzeng, 2009; Lee et al., 2012). For example, by manipulating the cloze probability of the ending word in a sentence, Lee et al. (2012) demonstrated a predictability effect on the P200, in which low-predictability words elicited a less positive P200 than high-predictability words. In the current study, ERPs to preceding classiﬁers also showed a semantic constraint effect on the P200, even though the pairing nouns had not appeared yet. Therefore, the constraint effect of classiﬁers on the P200 should have nothing to do with feature matching. This is congruent with Wlotko and Federmeier (2007), who found that the P200 was identical for both expected and unexpected items in a strongly constrained context, suggesting that the P200 was not affected by whether or not the predicted word was actually presented. Thus, the P200 may be sensitive to the state change, rather than the feature matching, between expectation and the item actually presented (Wlotko & Federmeier, 2007). When using context to predict upcoming words, there are potentially two types of processing consequences: the beneﬁts of prediction and the costs of incorrect predictions. Although it has been well recognized that the N400 is sensitive to cloze probability and semantic congruency (DeLong, Urbach, & Kutas, 2005; Kutas & Hillyard, 1984; Wlotko & Federmeier, 2007), it is difﬁcult to determine whether the reduced N400 for high cloze and semantically congruent words reﬂects the beneﬁt of prediction or whether the enhanced N400 for low cloze words or semantically incongruent words reﬂects the cost of incorrect predictions. Recent studies suggest that two ERPs components, the N400 and frontal positivity, might reﬂect these two processes. In the work of Federmeier et al. (2007), although semantic constraint and cloze probability were confounded for high cloze completions, low cloze completions under strongly and weakly constrained frames were carefully matched for cloze probability. This critical contrast revealed no N400 difference between these two conditions, and suggested no additional effect of sentential constraint on the N400. However, a selective enhancement of the frontal positivity for low cloze completions that were embedded in strongly constrained sentences was found. Thus, the authors suggested that the frontal positivity appeared to reﬂect the disconﬁrmation of semantically based predictions. The difference in the response to less expected words might be caused by the presence of a preferred competitor in the strong constraint condition. In this circumstance, additional resources might be needed to override or suppress a strong prediction for a different word or concept. Similar conclusions were reached by demonstrating the contextual constraining effect on sentence completion, suggesting that in strongly constrained sentence contexts, the less expected the word, the larger the frontal positivity (DeLong et al., 2011). DeLong et al. (2011) also reported a similar observation and proposed that the N400 and frontal positivity might reﬂect the beneﬁts of conﬁrmed predictions and the costs of disconﬁrmed predictions about upcoming words in sentences or discourse. Our study, on the other hand, demonstrated an interaction between semantic constraint and cloze probability for the N400, indicating that the N400 might reﬂect a joint effect for both the beneﬁt and cost of the prediction. ERPs elicited by the pairing nouns revealed different patterns of cloze probability on the N400 when completing the strongly and weakly constrained classiﬁers. For the weakly constrained condition, there was a graded effect of cloze probability (High < Low < Imp) on the N400. For the strongly constrained condition, highly expected nouns also elicited a smaller N400 than did both low cloze and implausible nouns. However, there was no signiﬁcant difference between low cloze and implausible words (High < Low ¼ Implausible). The reduced N400 for high cloze words, irrespective of highly constrained or weakly constrained conditions, can be interpreted as a beneﬁt of contextual information that facilitates the semantic processing of expected words. However, the difference between low cloze and implausible words was only signiﬁcant in weakly constraint condition. This might be due to the strongly constrained classiﬁer forming a strong prediction for the best completion. Therefore, the low cloze noun showed a greater cost in the strongly constrained than in the weakly constrained condition, and elicited an enhanced N400 that made it act like an implausible noun. This is C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 53 further supported by the post-hoc comparison, which revealed a constraint effect for low cloze nouns, but not for high cloze probability or implausible nouns. The plausible but unexpected nouns following the strongly constrained classiﬁers elicited larger N400 amplitudes than those following the weakly constrained classiﬁers did. Therefore, the constraint effect for low cloze words suggests such a “cost” only emerges in the processing of low cloze nouns in strongly constrained condition. In addition, if frontal positivity reﬂects the cost associated with the violation of expectance in strongly constrained contexts or recovery from the violation, implausible or low cloze nouns should elicit a greater positivity than high cloze nouns, especially in the strongly constrained condition. This is not the case in our ﬁndings. Our data revealed that the high cloze nouns elicited a greater positivity than low cloze nouns, especially in the strongly constrained condition. In fact, the nature of the frontal positivity is not yet precisely understood. Some studies have failed to ﬁnd a constraint effect on frontal positivity. For example, Wlotko and Federmeier (2007) used the same stimuli as in their prior study (Federmeier et al., 2007), but failed to observe a constraint effect on the frontal positivity when stimuli were lateralized to either the right or left visual ﬁelds. Thornhill and Van Petten (2012) asked participants to read a set of strongly and weakly constrained sentence frames that were completed by either the best completion, the low cloze ending that was semantically related to the best completion, or the low cloze ending that was semantically unrelated to the best completion. They also found no difference for the low cloze endings of both strongly and weakly constrained sentence frames. In summary, by utilizing the characteristics of Chinese classiﬁer-noun agreement, the current experimental design allowed us to evaluate how readers make use of the semantic constraint of classiﬁers to predict the following noun as well as how such a constraint modulates the processing of the following nouns. The data indicates that, when reading a strongly constraining classiﬁer, readers tend to form a strong prediction for its following noun. Therefore, the brain response elicited by the classiﬁer showed a reduced frontal negativity. When encountering the plausible but unexpected noun (low cloze noun), there was an extra cost for such a wrong prediction, demonstrated by a similar increase in N400 amplitude. As for reading weakly constrained classiﬁers, readers tended to activate a set of possible candidates, reﬂected in the enhanced frontal negativity elicited by the classiﬁer and the graded cloze probability effect on the N400 elicited by the pairing nouns. However, this study failed to observe the constraint effect on frontal positivity. Further studies are needed to clarify the nature of frontal positivity. Acknowledgments This study was funded by National Science Council (NSC 101-2628-H-001-006-MY3). We thank Kara Federmeier for her helpful suggestions, and Chun-Hsien Hsu, Ying-Ying Zheng, Chih-Ying Huang, and Yo-Ning Liu for technical support during the collection of the experiment data. We thank Ya-Ning Chang, Chih-Ting Chang, Pei-Chun Chao, and two anonymous reviewers for their valuable comments and suggestions on the earlier version of this paper. References Ahrens, K. (1994). Classiﬁer production in normals and aphasics. Journal of Chinese Linguistics, 22, 202e247. Barber, H., Vergara, M., & Carreiras, M. (2004). Syllable-frequency effects in visual word recognition: evidence from ERPs. Neuroreport, 15, 545e548. van Berkum, J. J., Hagoort, P., & Brown, C. M. (1999). Semantic integration in sentences and discourse: evidence from the N400. Journal of Cognitive Neuroscience, 11, 657e671. Chao, Y. R. (1968). A grammar of spoken Chinese. University of California Press. Committe, N. L. (1997). Handbook of common quantiﬁers (Vol. 17). Taipei, Taiwan: National Languages Committe. Dambacher, M., Goellner, K., Nuthmann, A., Jacobs, A., & Kliegl, R. (2008). Frequency and predictability effects on event-related potentials and eye-movements. International Journal of Psychology, 43, 46e47. Dambacher, M., & Kliegl, R. (2007). Synchronizing timelines: relations between ﬁxation durations and N400 amplitudes during sentence reading. Brain Research, 1155, 147e162. Dambacher, M., Kliegl, R., Hofmann, M., & Jacobs, A. M. (2006). Frequency and predictability effects on event-related potentials during reading. Brain Research, 1084, 89e103. DeLong, K. A., Urbach, T. P., Groppe, D. M., & Kutas, M. (2011). Overlapping dual ERP responses to low cloze probability sentence continuations. Psychophysiology, 48, 1203e1207. DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117e1121. 54 C.-J. Chou et al. / Journal of Neurolinguistics 31 (2014) 42e54 Duffy, S. A., Henderson, J. M., & Morris, R. K. (1989). Semantic facilitation of lexical access during sentence processing. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15, 791e801. Federmeier, K. D. (2007). Thinking ahead: the role and roots of prediction in language comprehension. Psychophysiology, 44, 491e505. Federmeier, K. D., & Kutas, M. (1999). A rose by any other name: long-term memory structure and sentence processing. Journal of Memory and Language, 41, 469e495. Federmeier, K. D., Mai, H., & Kutas, M. (2005). Both sides get the point: hemispheric sensitivities to sentential constraint. Memory and Cognition, 33, 871e886. Federmeier, K. D., Wlotko, E. W., De Ochoa-Dewald, E., & Kutas, M. (2007). Multiple effects of sentential constraint on word processing. Brain Research, 1146, 75e84. Fodor, J. A. (1983). The modularity of mind: An essay on faculty psycholgy. The MIT Press. Gao, M. Y., & Malt, B. C. (2009). Mental representation and cognitive consequences of Chinese individual classiﬁers. Language and Cognitive Processes, 24, 1124e1179. Hoeks, J. C., Stowe, L. A., & Doedens, G. (2004). Seeing words in context: the interaction of lexical and sentence level information during reading. Cognitive Brain Research, 19, 59e73. Hsu, C. H., Tsai, J. L., Lee, C. Y., & Tzeng, O. J. (2009). Orthographic combinability and phonological consistency effects in reading Chinese phonograms: an event-related potential study. Brain and Language, 108, 56e66. Huang, C. R., & Chen, K. J. (1998). Academia Sinica balanced corpus (version 3). Taipei,Taiwan: Academia Sinica. King, J. W., & Kutas, M. (1995). Who did what and when? using word- and clause-level ERPs to monitor working memory usage in reading. Journal of Cognitive Neuroscience, 7, 376e395. Kliegl, R., Grabner, E., Rolfs, M., & Engbert, R. (2004). Length, frequency, and predictability effects of words on eye movements in reading. European Journal of Cognitive Psychology, 16, 262e284. Kuo, J. Y., & Sera, M. D. (2009). Classiﬁer effects on human categorization: the role of shape classiﬁers in Mandarin Chinese. Journal of East Asian Linguistics, 18, 1e19. Kutas, M., & Hillyard, S. A. (1980a). Event-related brain potentials to semantically inappropriate and surprisingly large words. Biological Psychology, 11, 99e116. Kutas, M., & Hillyard, S. A. (1980b). Reading senseless sentences: brain potentials reﬂect semantic incongruity. Science, 207, 203e205. Kutas, M., & Hillyard, S. A. (1984). Brain potentials during reading reﬂect word expectancy and semantic association. Nature, 307, 161e163. Lee, C. L., & Federmeier, K. D. (2006). To mind the mind: an event-related potential study of word class and semantic ambiguity. Brain Research, 1081, 191e202. Lee, C. L., & Federmeier, K. D. (2009). Wave-ering: an ERP study of syntactic and semantic context effects on ambiguity resolution for noun/verb homographs. Journal of Memory and Language, 61, 538e555. Lee, C.-Y., Liu, Y.-N., & Chou, C. J. (2013). Contextual predictability facilitates early orthographic processing and semantic integration in visual word recognition: an Event-related potential study. In F. Shi, & G. Peng (Eds.), East Flows the Great River: Festschrift in Honor of Prof. William S-Y. Wang’s 80th birthday (pp. 169e186). Hong Kong: City University of Hong Kong Press. Lee, C. Y., Liu, Y. N., & Tsai, J. L. (2012). The time course of contextual effects on visual word recognition. Frontiers in Psychology, 3, 285. Nieuwland, M. S., & Van Berkum, J. J. (2006). Individual differences and contextual bias in pronoun resolution: evidence from ERPs. Brain Research, 1118, 155e167. Rayner, K., Ashby, J., Pollatsek, A., & Reichle, E. D. (2004). The effects of frequency and predictability on eye ﬁxations in reading: implications for the E-Z reader model. Journal of Experimental Psychology: Human Perception and Performance, 30, 720e732. Rugg, M. D., Allan, K., & Birch, C. S. (2000). Electrophysiological evidence for the modulation of retrieval orientation by depth of study processing. Journal of Cognitive Neuroscience, 12, 664e678. Saalbach, H., & Imai, M. (2007). Scope of linguistic inﬂuence: does a classiﬁer system alter object concepts? Journal of Experimental Psychology: General, 136, 485. Schwanenﬂugel, P. J., & Shoben, E. J. (1985). The inﬂuence of sentence constraint on the Scope of facilitation for upcoming words. Journal of Memory and Language, 24, 232e252. Srinivasan, M. (2010). Do classiﬁers predict differences in cognitive processing? A study of nominal classiﬁcation in Mandarin Chinese. Language and Cognition, 2, 177e190. Stanovich, K. E., & West, R. F. (1981). The effect of sentence context on ongoing word recognition: tests of a two-process theory. Journal of Experimental Psychology: Human Perception and Performance, 7, 658e672. Tai, J. H. (1992). Variation in classiﬁer systems across Chinese dialects: towards a cognition-based semantic approach. Chinese Language and Linguistics, 1, 587e608. Taylor, W. L. (1953). “Cloze Procedure”: a new tool for measuring readability. Journalism Quarterly, 30, 415e433. Thornhill, D. E., & Van Petten, C. (2012). Lexical versus conceptual anticipation during sentence processing: frontal positivity and N400 ERP components. International Journal of Psychophysiology, 83, 382e392. Tien, Y. M., Tzeng, O. J. L., & Hung, D. L. (2002). Semantic and cognitive basis of Chinese classiﬁers: a functional approach. Language and Linguistics, 3, 101e132. Van Petten, C., & Kutas, M. (1990). Interactions between sentence context and word frequency in event-related brain potentials. Memory and Cognition, 18, 380e393. Van Petten, C., & Kutas, M. (1991). Inﬂuences of semantic and syntactic context on open- and closed-class words. Memory and Cognition, 19, 95e112. Van Petten, C., & Luka, B. J. (2012). Prediction during language comprehension: beneﬁts, costs, and ERP components. International Journal of Psychophysiology, 83, 176e190. Wlotko, E. W., & Federmeier, K. D. (2007). Finding the right word: hemispheric asymmetries in the use of sentence context information. Neuropsychologia, 45, 3001e3014. Zhang, S., & Schmitt, B. (1998). Language-dependent classiﬁcation: the mental representation of classiﬁers in cognition, memory, and ad evaluations. Journal of Experimental Psychology: Applied, 4, 375.
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