Investigating the Effects of Distance and Number Interference in Processing Subject-Verb Dependencies: an ERP Study

Journal of Psycholinguistic Research, Vol. 31, No. 2, March 2002 (᭧ 2002)

Investigating the Effects of Distance and Number Interference in Processing Subject-Verb Dependencies: An ERP Study

Edith Kaan1

Event-related potentials (ERPs) were used to investigate whether the processing of subject-verb dependencies is influenced by (1) the linear distance between the subject and the verb and (2) the presence of an intervening noun phrase with interfering number features. Linear distance did not affect integration and diagnosis or revision processes at the verb, as indexed by early negative and P600 components. This is in accordance with hierarchy-based models of reanalysis, but is problematic for distance-based integration models. However, tracking of the subject features is affected by linear factors: more judgment errors were made in the long compared to the short condition. Furthermore, the presence of a plural object between the singular subject and the verb led to more judgment errors, and an enhanced positivity around 250 ms for the grammatical verbs. This suggests that linear factors affect feature tracking, but not integration processes following feature retrieval or repair processes following the detection of a mismatch. KEY WORDS: event-related potentials; subject-verb agreement; locality; P600.

INTRODUCTION

Establishing the relation between a subject and a verb is an important aspect of sentence production and comprehension. In English, Dutch, and many other languages, the dependency between the subject and finite verb is

This research was carried out while the author was a Grotius Fellow at the Utrecht Institute for Linguistics OTS, Utrecht University, The Netherlands. I would like to thank Edward de Haan and the Department of Psychonomics, Utrecht University, for providing access to their lab facilities, Phil Holcomb for the analysis software, Peter Bunck for technical assistance, Martine Schepers for her help with the materials, Frank Wijnen and the other members of the UiL OTS processing group for discussion, and two anonymous reviewers for comments. 1 Center for Cognitive Neuroscience, LSRC Building B203b, Duke University, Box 90999, Durham, NC 27708-0999. email: [email protected]

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0090-6905/02/0300-0165/0 © 2002 Plenum Publishing Corporation 166 Kaan reflected by the use of specific verb forms: The cat is sleeping is a correct sentence of English, whereas The cat *are sleeping is not. Combining the subject with the verb is therefore essential to retrieve the correct verb form in production. In comprehension, the subject and verb need to be combined to form a semantic/conceptual representation. For instance, in The horse kicked, the entity denoted by the subject noun phrase the horse is the agent of the kicking action. Furthermore, person and number features need to be kept track of during comprehension, e.g., to determine the correct attach- ment of the relative clause in constructions like the supervisor of the assis- tants who teaches math. Combining the subject and the verb is therefore a crucial aspect of sentence processing. However, this task is not trivial. The subject and the verb need not be close together, but may be separated by other material. Furthermore, this intervening material may have properties that interfere with the processing of the subject-verb relation. In comprehension, intervening material may affect the processing of the subject-verb dependency in several respects. First, it may affect the tracking of the subject features before the verb is encountered; second, intervening material may affect the integration of the verb with the subject; and third, intervening material may affect the repair processes that are triggered when the features of the subject and the verb do not to match. In the current study, event-related (brain) potentials (ERPs) are used to investigate the effects of both distance and interfering number properties on processing subject-verb dependencies in comprehension. Processing models make different predictions concerning the effects of intervening materials between the subject and the verb. I discuss models regarding feature tracking, integration, and repair below, and next, I describe how ERPs can be used to test these models.

Feature Tracking When processing a subject-verb clause, the syntactic features of the subject noun phrase need to be kept track of until the verb is encountered. Occasionally the wrong features may be stored. A well-known observation is that people more often produce an erroneous plural verb following subject phrases such as the key to the cabinets (singular-plural) than following the key to the cabinet (singular-singular), (Bock & Cutting, 1982; Bock & Eberhart, 1993; Bock & Miller, 1991; Eberhart, 1997; Vigliocco & Nicol, 1998). This interference effect is largest when the first noun is singular and the second plural, but almost absent if the first is plural and the second singular. This asymmetry can be accounted for by assuming that the plural is more “marked” than the singular (e.g., Eberhart, 1997; Hartsuiker et al., 2001, Vigliocco & Nicol, 1998). Comparable effects have been found for Distance and Number Interference 167 comprehension. Longer response or reading times at or immediately after a singular verb are seen when the first noun is singular and the second noun plural (Nicol et al., 1997; Pearlmutter et al., 1999). Various feature tracking models have been proposed to account for these interference effects. Two kinds of model can be distinguished (Pearlmutter, 2000). According to a hierarchical view of feature tracking, the features of the subject head noun percolate up to the phrasal level (subject NP). According to this view, only elements that are hierarchically close to the subject head noun can cause interference, occasionally leading to the percolation of the incorrect features. The linear distance between the verb and the subject NP should not affect the tracking of the features. A second view of feature tracking is the linear slot model: in this type of model, the features of the subject NP are stored in a linear slot in working memory. The activation of this information decays over time. Hence, the more material that intervenes between the subject and the verb, the more the subject information decays and the stronger the interference effects. Most experimental results support a hierarchical view of feature tracking (e.g., Hartsuiker et al., 2001; Nicol et al., 1997; Vigliocco & Nicol, 1998). For instance, Vigliocco & Nicol report the same number and pattern of agreement errors in production when the verb is produced immediately before the head noun of the subject (*Are the helicopter for the flights . . .), or when the verb comes after the head noun and potentially interfering noun (The helicopter for the flights *are . . .). This suggests that (linear) prece- dence relations do not play a role in feature tracking. Research in comprehension yields compatible results: using a self-paced reading paradigm, Pearlmutter (2000) reports longer reading times at the verb phrase when the potentially interfering noun is hierarchically closer to the head noun (e.g., The lamps near the painting of the houses were . . .), compared with when the potentially interfering noun is further away from the head noun, but lin- early closer to verb (The lamps near the paintings of the house were . . .). These results are at odds with linear slot models of feature tracking. However, some linear effects do occur. Bock and Cutting (1992) report more production errors when the linear distance between the head noun and the verb is longer (but see Bock and Miller, 1991; Nicol & Barker, 1997; Vigliocco & Franck, 2001). Furthermore, in the comprehension study by Pearlmutter (2000), longer reading times at the verb phrase were reported for plural head nouns with an interfering singular second noun phrase. In this study, three-phrase subject noun phrases were used. Other comprehension studies used only two-phrase subjects and did not report any interference effects with plural head nouns. This suggests that the activation of the features of the head noun decays as more material is processed, which makes them more susceptible to interference. 168 Kaan

Feature tracking models thus concern the storage of subject features and potential interference effects before the verb is encountered and, hence, the likelihood of retrieving the correct subject features at the verb and detecting a feature mismatch between the subject and the verb. According to hierarchical models, increasing the linear distance between the subject noun phrase and the verb, or inserting material with potentially interfering number properties between the completed subject noun phrase and the verb, should not affect feature tracking. Linear slot models, on the other hand, do predict an effect of such intervening materials.

Integration When the verb is encountered, the verb is combined with the subject, and is integrated into the syntactic and conceptual representation of the sentence. Processing models make different predictions concerning the effect of distance on integration difficulty. One model that assigns a major role to distance is the model proposed by Gibson (1998). In this model, each incoming word activates information that is associated with it in the mental lexicon. The level of activation may decrease as subsequent material is processed. As proposed in Gibson (1998), the information is harder to main- tain when subsequent material introduces new discourse referents (Gibson & Warren, 1997). Therefore, when more new discourse referents are introduced between a (non-matrix) subject and its verb, it is harder to integrate the verb with the subject, because more effort is needed to reactivate the information associated with the subject. This model therefore predicts that processing difficulty at the verb increases with the distance (in number of new discourse referents) between the subject and the verb, even when the subject features have been correctly tracked. Alternatively, there are models that predict that accessing the subject features and integrating the subject and the verb are not sensitive to the presence of intervening materials. For instance, McElree (2000) proposes that the parser uses a content-addressable memory. Intervening material may affect the availability of the correct features (as in the linear slot models of feature tracking), but not the accessibility, that is, the way the features are retrieved.

Revision Processes Finally, distance may affect the revision processes initiated when a feature mismatch is detected. Again, different models make different predictions. According to the Diagnosis model proposed by Fodor & Inoue (1994, 1998), the distance between the subject and the verb should not affect the processes triggered by a feature mismatch. When the parser encounters a Distance and Number Interference 169 verb that is ungrammatical given the preceding sentence context, a diagnosis process is initiated to detect the source of the anomaly: the parser traces the elements with which the verb is in a syntactic dependency (e.g., the subject) and tries to modify these elements to render a syntactically correct analysis of the sentence. For instance, if the subject is taken to be singular and the subsequent verb is plural (e.g., My old goldfish . . . have), the parser first returns to the subject and checks the mental lexicon to see whether the word form is also stored as a plural. In some cases, such as goldfish, this is indeed the case, and the plural verb is correct after all; in other cases, such as The cat *are sleeping, however, it is not. A crucial aspect of Fodor & Inoue’s Diagnosis model is that the linear distance between the subject and the verb should not affect these repair processes: when diagnosing the source of the violation, the parser first checks the syntactic dependent of the verb (the subject), regardless of the amount and nature of the material separating the subject and the verb. On the other hand, there are models that do predict that increasing the distance between the subject and the verb affects revision (e.g., Deutsch, 1998; Ferreira & Henderson, 1998; Frazier & Clifton, 1998; Gibson, 1998). According to Frazier & Clifton (1998), increasing the distance allows more time for the subject noun phrase to be semantically interpreted. This semantic interpretation makes it harder to subsequently revise the structure in garden paths. Alternatively, semantic interpretation of the subject can make it easier to deal with a feature mismatch, because it is easier to integrate the verb into the conceptual representation (Deutsch, 1998). The various types of model can be distinguished by investigating the effect of increasing distance on processing a feature mismatch between the subject and the verb. One study addressing this issue is Deutsch (1998), which tests subject-verb agreement in Hebrew. In this study, the head noun of the subject and the verb was either adjacent or separated by an attribu- tive phrase. The verb either agreed or did not agree with the subject. Reading times as measured by eye tracking showed less disruption to a mismatching verb in the long compared with the short condition. Deutsch accounts for this by assuming that the longer distance allows more time to semantically interpret the subject. Comprehension is therefore less disrupted by syntactic incongruency. An alternative explanation for these data, however, is that the features of the subject have decayed over time and the agreement errors are less noticed.

Event-Related Potentials In the present study ERPs are used to get a clearer view of whether the intervening material between the subject noun phrase and the verb affects feature tracking, integration, or the revision processes subsequent to detect- 170 Kaan ing a mismatch. ERPs are obtained by recording a person’s EEG at the scalp, while the person is presented with stimuli (e.g., written words). Next, the EEG, time-locked to the presentation of the stimuli of interest, is aver- aged across trials, yielding the ERP response. ERPs have been found to be sensitive for various linguistic manipulations (e.g., Hagoort et al., 1999, for a recent overview). Syntactic violations, among which are agreement violations, have been found to elicit two kinds of ERP component. The first is a negativity with a left anterior distribution (left anterior negativity, or LAN), occurring between 300 and 500 ms after onset of the anomalous word (e.g., Coulson et al., 1998; Friederici et al., 1993, 1996; Gunter et al., 1997; Münte et al., 1993, 1997a; Neville et al., 1991; Osterhout & Mobley, 1995; Rösler, et al., 1993; Vos et al., 2001) or earlier, between 100 and 200 ms (early LAN, or ELAN) (Friederici et al., 1993, 1996; Neville et al., 1991). The 300 to 500 ms LAN component has been claimed to reflect a processing stage in which lexical-syntactic information is processed and word form violations, such as number violations, are detected (Friederici, 1995; Friederici et al., 1996). The second component is a late positivity (P600, or “syntactic positive shift”), which either follows the ELAN/LAN (Coulson et al., 1998; Friederici et al., 1993, 1996; Gunter et al., 1997; Neville et al., 1991; Osterhout, 1997; Osterhout & Holcomb, 1992; Osterhout & Mobley, 1995; Rösler et al., 1993; Vos et al., 2001) or occurs without it (Hagoort et al., 1993; Kaan et al., 2000; Münte et al., 1997a,b; Osterhout et al., 1996; Osterhout & Nicol, 1999). In contrast to the LAN and ELAN effects, which occur only for words that are ungrammatical given the preceding sentence context, the P600 has also been reported for words that are only apparently ungrammatical, that is, words that are correct given a less-preferred analysis of the preceding sentence context. The P600 component has therefore been claimed to reflect repair or reanalysis processes after real or apparent syntactic violations (Friederici et al., 1996; Münte et al., 1997a; Osterhout et al., 1994) or to reflect difficulty with syntactic integration in general (Featherston et al., 2000; Kaan et al., 2000). Furthermore, different aspects of the P600 have been associated with different aspects of sentence processing: the latency of the positivity has been claimed to reflect the ease of diagnosing the error and onset of revision processes (Friederici, 1998), whereas the duration and amplitude have been suggested to reflect the cost of reprocessing (Friederici, 1998; Osterhout et al., 1994) or syntactic integration difficulty (Kaan et al., 2000). ERPs are therefore a suitable tool to test the effect of intervening material on detecting a feature mismatch (LAN component), integration of the verb and the subject (P600 amplitude), and revision when a mismatch is detected (P600 onset/amplitude). Several studies have investigated the effect of intervening material on the LAN and P600 components for verb form violations. However results Distance and Number Interference 171 are rather mixed, and are hard to interpret. First, Gunter et al. (1997) investigated verb form violations in Dutch passive constructions. In this study, the critical dependency was that between auxiliary and a following lexical verb, which was either a (correct) passive participle (e.g., was . . . saved) or an (incorrect) infinitival verb (e.g., was . . . save). The participle/infinitive was separated from the auxiliary either by the subject and a by-phrase (“simple” condition) or by a by-phrase and an adjunct clause (“complex” condition). The LAN component was not affected by this complexity manipulation. The P600 amplitude, on the other hand, was smaller in the complex than in the simple condition. Vos et al., (2001) investigated the processing of subject-verb agreement violations in Dutch. The subject noun was separated from the (matching or mismatching) verb either by a simple verb phrase and the conjunction word and (“simple” condition) or by a subject relative clause (“complex” condition). In contrast to the Gunter et al. (1997) study, the P600 was not affected by the complexity manipulation. The LAN, however, was present in the simple condition, and absent in the complex condition, but only when participants retained a list of three words in memory while reading the sentences. A final study is Münte et al. (1997b), testing subject-verb agreement violations in German. Münte et al. compared main clauses in which the subject was immediately followed by the correct or incorrect verb with embedded clauses in which the subject and the verb were separated by several words. Münte et al. report a smaller P600 amplitude and shorter peak latency for the main clause compared to the embedded conditions. Furthermore, a frontal negativity was found between 1000 and 1400 ms in the main clauses. In addition to yielding mixed results, these ERP studies suffer from confounding factors, such as the presence of a clause boundary preceding the target word in the simple, but not in the complex, condition. Also, these studies manipulate complexity (Vos et al., 2001) or both complexity and distance (Gunter et al., 1997; Münte et al., 1997b) between the dependent elements. It is therefore unclear what the effect of a distance manipulation is per se. Finally, none of the studies tested whether the verb form violation is actually noticed by the participants. The interpretation of the observed differences in the LAN or P600 component therefore remains unclear; e.g., it may be that the mismatch is less often detected in the long/complex cases and that the (repair) processes that are reflected by the ERP components are less often initiated in these conditions. Alternatively, the mismatch may have been detected in all cases, but the (repair) processes themselves may be sensitive to complexity or distance between the dependent elements. The aim of the present study was to investigate the effects of distance, and the presence of intervening number properties on the processing of sub- 172 Kaan ject-verb dependencies, keeping other factors constant across conditions, such as the linear position of the target word in the sentence and the position of clause boundaries. In addition, participants were asked to judge the sentence for grammaticality. This made it possible to see whether a mismatch was actually noticed by the participants and to restrict the ERP analysis to those trials that were correctly judged as grammatical or ungrammatical. In partic- ular, the following issues were tested:

Distance Manipulation ● If feature tracking is sensitive to linear distance, more judgment errors should occur when the distance between the subject and the verb increases. Furthermore, the verbs in the long conditions may elicit a LAN/P600 in the grammatical cases (because, incidentally, some verbs will initially be treated as ungrammatical), but a smaller LAN/P600 in the ungrammatical cases (because some verbs will initially be treated as grammatical). However, such differences in the ERPs need not occur in an analysis based on trials to which there are accurate responses. ● If integration of the verb and the subject becomes more difficult when the distance between the subject and the verb increases (Gibson, 1998), the long conditions will elicit a larger P600 at the verb compared with the short conditions, both in the grammatical and ungrammatical conditions. ● If diagnosis and revision processes are sensitive to the presence of intervening material between two dependents, then the P600 for ungrammatical versus grammatical verbs will be either larger or later (Frazier & Clifton, 1998) or smaller (Deutsch, 1998) in the long conditions compared to the short.

Number Manipulation ● If feature tracking is affected by linear factors, more judgment errors are expected when a singular subject noun phrase is separated from the verb by a plural object, compared with other number combinations. Furthermore, the verbs in the singular-plural conditions may elicit a LAN/P600 in the grammatical cases (because incidentally, some verbs will initially be treated as ungrammatical), but a smaller LAN/P600 in the ungrammatical cases (because some verbs will initially be treated as grammatical). Such effects are not expected if feature tracking is merely hierarchical, and insensitive to the properties of noun phrases outside of the subject noun phrase. Distance and Number Interference 173

EXPERIMENT: MATERIALS AND METHOD

Subjects Sixteen monolingual native speakers of Dutch participated (four male; age 18 to 25, mean age 21.5). All participants were right-handed and had normal or corrected-to-normal vision. Most participants were undergraduate students at the University of Utrecht. Participants were paid for participation.

Materials One hundred and sixty quadruplets were constructed of the structure exemplified in (1). For the purpose of illustration, the critical verb is shown in bold type and the subject is underscored. A paraphrase is given only for the a-condition since the meaning of the sentence does not differ across conditions. (1) a. [Short distance subject-verb, grammatical] Hoewel volgens het gerucht de keizer de dissident zal gaan verbannen is er veel tegenstand. Litt: Although according to the rumor the emperor the dissident will-SG go ban is there a lot of opposition “Although the emperor will ban the dissident according to the rumor, there is a lot of opposition” b. [Long distance subject-verb, grammatical] Hoewel de keizer volgens het gerucht de dissident zal gaan verbannen is er veel tegenstand Litt: Although the emperor according to the rumor the dissident will-SG go ban is there a lot of opposition c. [Short distance subject-verb, ungrammatical] Hoewel volgens het gerucht de keizer de dissident *zullen gaan verbannen is er veel tegestand. Litt: Although according to the rumor the emperor the dissident will-PL go ban is there a lot of opposition d. [Long distance subject-verb, ungrammatical] Hoewel de keizer volgens het gerucht de dissident *zullen gaan verbannen is er veel tegenstand. Litt: Although the emperor according to the rumor the dissident will-PL go ban is there a lot of opposition The critical word was the first verb of an adjunct clause. To avoid potentially confounding wrap-up effects, the critical verb was separated from the clause boundary by two additional verbs. The critical verb either agreed in number with the subject [grammatical (1a,b)] or did not agree 174 Kaan

[ungrammatical (1c,d)]. The distance between the subject and the critical verb was manipulated by having a three-word phrase (e.g., volgens het gerucht “according to the rumor”) either preceding the subject [short distance (1a,c)] or following it [long distance, (1b,d)]. The position of this phrase does not have any consequences for the meaning or the structural complexity of the clause, although the order in the long conditions is more common.2 In the short condition, the verb and the subject were separated by two words or one syntactic constituent (the object); in the long condition, the subject and verb were separated by five words or two constituents (prepositional phrase and the object). Furthermore, since the sentences were presented in isolation, both the three-word phrase and the object noun phrase referred to entities that were newly introduced to the reader. Hence, the subject and the verb were separated by two new discourse referents in the long condition, and by only one in the short. To test for the effect of number interference, the grammatical number of the subject and object was varied between items, so that there were 40 items in each of the four subgroups schematized in Table I: 1) singular subject, same (singular) object; 2) singular subject, different (plural) object; 3) plural subject, same (plural) object; 4) plural subject, different (singular) object. An example of the singular-same condition is given in (1). An example for each of the other three number combinations are given in (2) for the short grammatical condition. A complete list of the experimental items can be obtained from the author. (2) a. [Singular subject, different object] Omdat volgens het reglement de trainer de atleten had moeten inschrijven mochten ze niet starten. Litt: Because according-to the regulations the coach the athletes had-SG need sign-up allowed they not start “Because the coach ought to have signed up the athletes as was required by the regulations, they were not allowed to start.” b. [Plural subject, same object] Toen ondanks het protest de houthakkers de bomen wilden gaan kappen ging Greenpeace actie voeren. Litt: When despite the protest the woodchoppers the trees wanted-PL go cut went Greenpeace protest

2 This difference between the long and the short condition may obscure some of the distance effects. However, note that more judgment errors were made in the long compared to the short, which is the reverse of what is expected on the basis of frequency or potential differences in felicity among the conditions. Also, participants did not consider the short condition strange or more noticeable than the long, when asked after the experiment. Distance and Number Interference 175

Table I. Overview of the Number Manipulation Verb Condition Subject Object Grammatical Ungrammatical* Singular-Same Singular Singular Singular Plural Singular-Different Singular Plural Singular Plural Plural-Same Plural Plural Plural Singular Plural-Different Plural Singular Plural Singular * Verb number in the two versions.

“When the woodchoppers wanted to cut the trees despite the protest, Greenpeace held a demonstration.” c. [Plural subject, different object] Toen na de excursie de toeristen de reisleidster wilden gaan trakteren protesteerde de chauffeur. Litt: When after the tour the tourists the guide(fem) wanted-PL go treat protested the driver “When the tourists wanted to treat the female guide [to, e.g., a drink] after the tour, the driver protested.” In addition, 120 filler items were constructed, all consisting of two clauses to resemble the experimental items. Sixty filler items were grammatically correct and sixty contained gender agreement violations, such as *de park (“the-FEM/MASC park-NEUTER”), where a neuter noun is preceded by a non-neuter determiner; or other word form violations, such as werd . . . *redden (“was . . . to-save”) where an auxiliary of the passive form is incor- rectly followed by an infinitival verb, instead of a passive participle. Care was taken that the position of the errors in the sentence was varied. Thus, each participant saw a total of 280 items, half of which were ungrammatical. Four experimental lists were created according to a Latin square design, so that each list contained 40 items in each of the four experimental main conditions (1a-d) and 10 items for each different number combination within each main condition. Each item only occurred once in each list and appeared in a different condition in other lists. The order of fillers and experimental items was pseudorandomized within each lists. Furthermore, each list of 280 sentences was divided into eight blocks of equal length. The order of the eight blocks was randomized across participants. Each participant read only one list, and each of the four lists was read by a total of four participants.

Procedure Participants were individually tested in a dimly lit room. The participant was seated 1.10 m in front of a computer screen. Sentences were presented 176 Kaan word-by-word in the center of the screen. A trial began with a fixation point, which lasted 1200 ms, followed by a blank screen of 450 ms, followed by the first word of the sentence. Sentences were presented at a rate of 530 ms per word (315 ms of word, 215 ms of blank screen). The last word of the sentence was followed by blank screen of 680 ms. Next, a question mark was presented, with the words goed “ok” and fout “wrong”. These words remained on the screen until the participant indicated a response by pressing a button on a button box. Next, the message druk voor volgende zin “press for next sentence” was presented until the subject indicated readiness for the next sentence by pressing a button. Participants were requested to read the sentences carefully and to indi- cate at the question mark whether the sentence was grammatically correct or not. Some examples of ungrammatical sentences were given in the instructions. However, no examples of number violations were given. Accuracy was encouraged by giving participants a small financial bonus if they made less than 24 errors. Participants were asked not to blink from the first word up to but not including the question mark prompt. Participants were familiarized with the task by a practice block of eight sentences, half of which were ungrammatical. The experiment took about 2.5 hours per subject (1 hour for preparation, 1.5 hours for recording).

EEG Recording EEG was recorded from 30 Sn scalp electrodes (Electrocap), referenced to the right mastoid. The following electrode positions were used: 1) midline: Afz, Fz, Cz, Pz, POz, Oz; 2) left/right hemisphere: Fp1/2, Fc1/2, CP1/2, O1/2, F3/4, C3/4, P3/4, F7/8, C5/6, P5/6, T3/4, T5/6 (Sharbrough, 1991). Horizontal EOG was recorded from Sn electrodes attached to the left and right outer canthus; vertical EOG was recorded from electrodes above and below the right eye. Impedance was kept below 5 k⍀ for EEG electrodes, and below 7 k⍀ for EOG. EEG was acquired with a sampling rate of 200 Hz, using an Ampligraph amplifier (0.045 Hz lower cutoff; 100 Hz upper cutoff) combined with Neuroscan acquisition software. Data were bandpass filtered off-line between 0.3 and 40 Hz, with a 24 dB per octave roll off.

Analysis Epochs were comprised of the 100 ms preceding and the 2400 ms following the presentation of the critical verb. Less than 9% of the trials were rejected because of excessive eye movements or amplifier blocking. Statistical analyses were performed on the mean amplitude at the six midline and ten lateral electrodes (Fp1/2, F3/4, C3/4, P3/4, O1/2) during the Distance and Number Interference 177 following time intervals: 150 to 300 ms, 300 to 500 ms, 500 to 700 ms, and 700 to 900 ms from the onset of the critical verb, corresponding to the time range of the P2, LAN, early P600, and late P600 reported in the literature, respectively, and a window of 1200 to 1700 ms, covering the late negativity (discussed later). As there were some slight differences among the conditions just before the onset of the critical word, the interval from the 100-ms prestimulus to the 100-ms poststimulus was used as baseline. However, results did not differ qualitatively from analyses using a more conventional 100-ms prestimulus baseline. For each of the intervals, an SPSS GLM repeated measurement analysis was conducted, with the following within-subject factors: grammaticality (2 levels); length (2 levels); number of the subject noun phrase (2 levels); same/different subject and object number (2 levels); anterior-posterior (6 levels for midline, 5 for lateral); and, if applicable, hemisphere (2 levels). For effects involving more than one degree of freedom, the Greenhouse-Geisser correction was applied to protect against Type I error due to a violation of sphericity (Greenhouse & Geisser, 1959). Only corrected p-values are reported. Because a significant interaction of a condition with a location factor does not necessarily reflect a real difference in the location of the sources underlying the condition effects but can be an artifact of the additivity assumptions that under- lie the ANOVA approach (McCarthy & Wood, 1985), a second analysis was conducted on the z-scores of the mean amplitudes (e.g., Kounios & Holcomb, 1994). Only those interactions with a location factor which remained significant after this correction are reported. Furthermore, only effects are reported that involve at least one of the factors among grammaticality, length, number of the subject noun phrase, or same/different. For the behavioral data, the percentage of correct responses in the end-of- sentence judgment task and response times for the correct responses only (absolute cutoff of 3000 ms) were analyzed using an SPSS GLM repeated measurements analysis, separately for subject means (F1) and item means (F2).

RESULTS

Behavioral Data Table II gives the mean percentage of correct responses for the experimental items, as a function of grammaticality, length, number of the subject NP, and whether the subject and object had the same or different grammatical number. More errors were made in the ungrammatical compared to the grammatical conditions [F1(1,15) ϭ 10.57, p Ͻ .01; F2(1,156) ϭ 18.23, p Ͻ .001]. Furthermore, performance was worse in the long versus the short 178 Kaan

Table II. Percentage of Correct Responses on the End-of-Sentence Grammaticality Judgment Task, as a Function of Grammaticality, Distance Between the Subject and the Verb, Number Properties of the Subject, and Whether the Subject and Object were Same or Different in Number Grammatical (%) Ungrammatical (%) Subject Object Short Long Short Long Singular Same 97.5 96.8 96.8 95.0 Different 95.6 95.0 87.5 88.1 Plural Same 100.0 94.3 93.1 93.1 Different 96.8 97.5 96.8 92.5 Mean 97.4 95.9 93.6 92.2 conditions, with a significant difference by subjects [F1(1,15) ϭ 4.94, p Ͻ .05; F2(1,156) ϭ 3.09, p ϭ .080]. This suggests that the length manipulation affected the tracking of the subject features. More errors were made when the subject noun phrase had a different number than the object, but this was significant only by items: [F1(1,15) ϭ 2.83, p ϭ .113; F2(1,156) ϭ 4.58, p Ͻ .05]. In accordance with results from earlier production and comprehension studies, accuracy was worse in conditions in which the subject was singular and the object plural [Number of the subject noun phrase ϫ same/different: F1(1,15) ϭ 5.49, p Ͻ .05; F2(1,156) ϭ 8.60, p Ͻ .01]. This was especially so in the ungrammatical conditions [grammaticality ϫ number of the subject noun phrase ϫ same/different: F1(1,15) ϭ 6.37, p Ͻ .05; F2(1,156) ϭ 4.74, p Ͻ .05]. In contrast, performance on the plural subject items was worse when the object was plural (same number) than when it was singular (different number), especially in the short and long grammatical cases, leading to a four-way interaction of grammaticality ϫ length ϫ number of the subject noun phrase ϫ same/different [F1(1,15) ϭ 5.48, p Ͻ .05; F2(1,156) ϭ 4.25, p Ͻ .05]. Responses times were shorter to ungrammatical than to grammatical sentences (539 ms and 581 ms, respectively) [F1(1,15) ϭ 4.57, p Ͻ .05; F2(1,156) ϭ 10.83, p Ͻ .001]. This pattern is expected, because most errors occurred before the last word in a sentence, and participants therefore had more time to prepare the response in the ungrammatical case. No other effects were significant in the response time analysis.

Electrophysiological Data Effects of Grammaticality and Length Only those trials that were correctly judged grammatical or ungrammatical were entered into the reported analyses. Due to the relatively small number of errors, the effects in the ERPs did not differ qualitatively from Distance and Number Interference 179 those obtained for all trials taken together. ERPs at the critical verb for the four main conditions, collapsed over number, are displayed in Fig. 1. Positive is plotted down in this and other plots. A summary of the significant effects is given in Table III. The grammaticality manipulation had three different effects. First, between 300 and 500 ms, in the time region of the LAN, the ungrammatical conditions were more negative than the grammatical conditions. The distribution was, however, different from a standard LAN effect, because the negativity was bilateral and had a central rather than frontal maximum, leading to a grammaticality ϫ anterior-posterior interaction, which was significant at lateral sites only. Second, at around 500 ms after onset of the critical verb, ERPs to the ungrammatical conditions became more positive than the grammatical (P600 component). This positive difference was larger in the left compared to the right hemisphere in the 500 to 700 ms interval; and larger at central- posterior than frontal sites in the 700 to 900 ms interval.

Table III. F and p Values for the Significant Effects (p Ͻ .05) in Each Time Window for Analyses Involving Midline (Afz, Fz, Cz, Pz, Opz, Oz) and Lateral Sites (Fp1/2, F3/4, C3/4, P3/4, O1/2). Degrees of Freedom in Parentheses Time window 150–300ms 300–500ms 500–700ms 700–900ms 1200–1700ms Midline G (1,15) 82.16** 37.82** 43.26** GAp (5,75) 9.73* 11.28** GN1S (1,15) 8.37ϩ S (1,15) 13.55** 4.94ϩ Lateral G (1,15) 88.49** 33.98** 39.76** GAp (4,60) 4.72† 8.85* 14.21** GH (1,15) 30.12** GHAp (4,60) 4.07† GN1S (1,15) 8.87* GN1HAp (4,60) 4.92* LN1H (1,15) 6.09† 4.91† LN1SH (1,15) 6.37† LN1SAp (4,60) 4.53† N1 (1,15) 4.97† S (1,15) 11.20* 4.95† Abbreviations: G, grammaticality; L, length; number of the subject noun phrase; S, same/different number for subject and object; Ap, Anterior-posterior; H, hemisphere. Two letters denote interaction, e.g., GH stands for the interaction between grammaticality and hemisphere. ** p Ͻ .001. * p Ͻ .01. † p Ͻ .05. 180 Kaan

Third, at about 1000 ms after onset of the verb, the ungrammatical condition became more negative than the grammatical, leading to a main effect of grammaticality between 1200 to 1700 ms. This negativity was most prominent at central sites, especially in the right hemisphere. Note that the negativity started during presentation of the final word of the adjunct clause and ended during the presentation of the first word of the main clause. Crucial to the purpose of the experiment, ERPs to the short conditions patterned closely with those to the long (see Fig. 1). No main effect of

Fig. 1. ERPs to the critical verb for the midline and five lateral electrodes for the grammatical short, grammatical long, ungrammatical short, and ungrammatical long conditions, relative to a baseline of Ϫ100 to 100 ms. Distance and Number Interference 181 length or significant interactions between length and grammaticality were seen in any of the intervals [typical Fs Ͻ 1; smallest p ϭ .259]. This is in contrast to the behavioral data, which did show an effect of distance.

Effects of Number In addition to length, the number properties of the subject and the object noun phrases were manipulated. The crucial condition was the condition with a singular subject and a plural object, since interference effects in production and comprehension have been shown to be largest when a singular is followed by a plural compared with other number combinations. ERPs for the grammatical and ungrammatical auxiliaries, collapsed over length, are displayed in Fig. 2 for the midline parietal (Pz) electrode, in the singular-same (2A), singular-different (2B), plural-same (2C), and plural-different (2D) conditions. The difference waves (ungrammatical minus grammatical conditions) for each of the four number manipulations are given in Fig. 3 for the midline and five lateral electrodes. As can be seen in Fig. 2B and 3, the singular-different condition (singular subject-plural object) displayed a larger early positivity around 250 ms in the grammatical compared with the ungrammatical condition (arrow labeled “1”). No such difference was seen for the other number conditions. However, the three-way interaction of grammaticality ϫ number of subject noun phrase ϫ same/different was not significant for the 150 to 300 ms interval [midline: F Ͻ 1, N.S.; lateral F(1,15) ϭ 3.074; p ϭ 1].3 Turning to the later intervals, the singular-same (singular subject- singular object) condition showed a larger P600-effect between 500 and 700 ms compared with the other number conditions (arrow labeled “2” in Figs. 2A and 3). No difference among the number conditions was seen in the 700 to 900 ms interval. Table IV gives the mean amplitude in the 500 to 700 ms interval for the grammatical and ungrammatical conditions at the Pz electrode for each of the four number conditions. A comparison among the number conditions using Helmert contrasts showed that the effect of grammaticality was significantly different for the singular-same condition versus the mean of the three other conditions [midline: F(1,15) ϭ 11.98,

3 The grammaticality ϫ number of subject noun phrase ϫ hemisphere ϫ anterior/posterior interaction in the 150 to 300 ms window (Table III) reflects the fact that the difference between the ungrammatical and grammatical in the singular subject conditions became larger from frontal to posterior sites, with the largest anterior-posterior difference seen in the left hemisphere. The early frontal negativity seen for the plural subjects is reminiscent of the early LAN effect (e.g., Friederici et al., 1996; Neville et al., 1991). However, this effect was sta- tistically unreliable: analyses restricted to the frontal electrodes showed no main effect or interaction involving the factor grammaticality. 182 Kaan

A B

C D

Fig. 2. ERPS to the critical verb at the Pz electrode for the grammatical and ungrammatical conditions, collapsed over length. A, singular same (singular subject, singular object); B, singular different (singular subject, plural object); C, plural same (plural subject, plural object); D, plural different (plural subject, singular object). 1, early positivity for the grammatical verbs in the singular-different condition; 2, earlier and larger late positivity for the ungrammatical verbs in the singular-same condition. Distance and Number Interference 183

Fig. 3. Difference waves (ungrammatical minus grammatical) at the critical verb, for midline and 5 lateral electrodes for the singular subject-same; singular subject-different; plural subject-same; and plural subject-different conditions, relative to a Ϫ100 to 100 ms baseline. 1, early positivity for the grammatical relative to ungrammatical verbs in the singular-different condition; 2, earlier and larger late positivity for the ungrammatical relative to the grammatical verbs in the singular-same condition. p Ͻ .01; lateral: F(1,15) ϭ 6.287, p Ͻ .025], whereas the remaining conditions did not differ among each other (p Ͼ .2). Separate analyses for grammatical and ungrammatical conditions suggest that the P600 difference among the number conditions is mainly due to a difference in the way the ungrammatical sentences are processed: grammatical sentences showed no 184 Kaan

Table IV. Mean Amplitude in Micro Volts (␮V) Between 500 and 700 ms at the Pz Electrode for the Grammatical and Ungrammatical Conditions and the Difference Between Them, as a Function of Number of the Subject Noun and Whether Subject and Object are Same or Different in Number (Standard Deviation in Parentheses) Mean amplitude Grammatical Ungrammatical Difference Subject Object (␮V) (␮V) (␮V) Subject-singular Object Same 1.21 (8.49) 24.34 (12.45) 23.13 Object Different 3.58 (8.74) 17.93 (15.47) 14.35 Subject-plural Object-Same 4.48 (9.93) 17.99 (14.14) 13.51 Object Different 1.92 (9.84) 18.49 (11.94) 16.57 effect of the number manipulation [p Ͼ .1], whereas the ungrammatical did [midline: F(1,15) ϭ 5.92, p Ͻ .05; lateral: F(1,15) ϭ 4.85, p Ͻ .05]. The difference waves in Fig. 3 suggest that the late positivity not only had a larger amplitude between 500 and 700 ms in the singular subject-singular object condition, but also a somewhat earlier onset. To test this, and to further test the early positive difference seen around 250 ms for the grammatical versus ungrammatical singular-different conditions, mean amplitudes were com- puted for consecutive windows of 50-ms intervals, from 150 to 650 ms after onset of the critical verb, and statistical analyses were conducted on these intervals for each of the four number combinations. Corresponding to the visual observation with respect to the early positivity, the singular-different, but not the other conditions, showed a main effect of grammaticality in the 250 to 300 ms interval [midline: F(1,15) ϭ 6.332, p Ͻ .025; lateral: F(1,15) ϭ 4.849, p Ͻ .05]. Furthermore, the onset of the late positivity for the ungrammatical conditions was indeed earliest for the singular subject-singular object condition: the main effect of grammaticality was significant (p Ͻ .01) in the 450 to 500 ms and following intervals; in the singular subject-plural object and plural subject-singular object conditions the effect of grammaticality started in the 500 to 550 ms interval; in the plural subject-plural object condition, the effect started at 550 to 600 ms.

DISCUSSION

The goal of the experiment was to investigate the effect of intervening material on the tracking of subject features, the integration of the subject and the verb, and the revision processes triggered when a number mismatch is detected. To this aim, sentences were tested in which the subject noun Distance and Number Interference 185 phrase was separated from the grammatical or ungrammatical verb either by two words (one constituent, one new discourse referent) or five words (two constituents, two new discourse referents). In addition, the effect of intervening number properties was investigated by manipulating the grammatical number of the subject noun phrase and that of the object noun phrase intervening between the subject and the verb. The length manipulation had the following effects. Performance in the end-of-sentence grammatically judgment task was less accurate in the long conditions compared with the short conditions. However, the ERPs showed no effects of distance at the critical verb in either grammatical or ungrammatical sentences. Regarding the number manipulation, more judgment errors were made when the subject was singular and the object was plural compared with other number combinations. The number properties of the noun phrases also affected the ERP waveforms: first, the singular subject-plural object condition showed an enhanced positive component between 250 and 300 ms for the grammatical relative to the ungrammatical verbs. Second, the number manipulation affected the P600 for ungrammatical verbs, but the pattern was somewhat unexpected. The positivity had a later onset when the clause contained more plural noun phrases. In addition, the positivity was larger between 500 and 700 ms for those items in which both the subject noun and the object were singular compared with other items. First, the ERP effects of grammaticality are discussed; next, the effects of length and the number manipulation and their implications for processing models are dealt with.

Effects of Grammaticality As expected, the ungrammatical conditions elicited a posterior positivity between 500 and 900 ms. The positivity is broadly distributed between 500 and 700 ms, but has a centroparietal focus between 700 and 900 ms (Hagoort & Brown, 2000; Kaan et al., 2000). The positive complex corre- sponds to the P600, or “syntactic positive shift,” which has often been reported in response to real or apparent syntactic incongruencies and most likely sig- nals attempted repair or difficulty with syntactic integration after an (apparent) syntactic violation has been detected (e.g. Friederici, 1995; Hagoort et al., 1993; Kaan et al., 2000; Münte et al., 1997a; Osterhout & Holcomb, 1992). The P600 was preceded and followed by negative components. First, between 300 and 500 ms, the ungrammatical conditions showed a bilateral negativity at central and posterior sites. Most studies reporting a negative component in this time interval for syntactic violations, however, report an anterior negativity that is left lateralized (LAN) (e.g., Coulson et al., 1998; 186 Kaan

Friederici et al., 1996, Rösler et al., 1993), although more centrally distributed negativities (Friederici et al., 1993, using auditory presentation; Münte et al., 1997a, for pseudowords) and left-central negativities (Friederici & Frisch, 2000) have been reported as well. The LAN effect has been claimed to reflect difficulty within a stage of parsing dealing with lexical-syntactic information and detection of agreement errors. This interpretation is per- fectly compatible with the occurrence of the early negativity in the present context. However, what exactly determines the differences in distribution of the early negativity among the various studies remains a question for further research. The P600 was followed by a late negativity with a right-central maximum. The presence of this negativity coincided with the presentation of the last word of the first clause. A sustained frontal negativity following a P600 has been reported by Friederici et al. (1996) and Münte et al. (1997b). The onset of this component has been found to be related to a phrase boundary (Friederici et al., 1996), and has been claimed to reflect working memory– related processes of phrasal closure (Kutas, 1997, for slow negativities at the end of a clause). However, the distribution of the negativity in the present study is more comparable to that of negativities found at the final or penultimate position of sentences that contain a syntactic (e.g., Hagoort et al., 1993; Osterhout & Holcomb, 1992, Friederici & Frisch, 2000) or semantic anomaly upstream (Friederici & Frisch, 2000). Since the distribution of this end-of-sentence negativity resembles that of the N400 component found for semantic anomalies (e.g., Kutas & Hillyard, 1980, 1984; Kutas & Van Petten, 1994), the end-of-sentence negativity has been claimed to reflect difficulties with semantic integration of words following a syntactic anomaly (Hagoort et al., 1993; Osterhout & Holcomb, 1992). A possible interpretation of the late negativity in the present study is therefore that it reflects semantic integration difficulty caused by the syntactic anomaly and that this semantic integration is clause-bound: the grammatical anomaly has consequences for semantic integration only in the same clause. However, more research is needed to corroborate this hypothesis.

Effects of Length The distance manipulation had no effect on the ERP waveforms, although the performance in the judgment task was worse for the long compared with the short conditions. The fact that the judgment performance was worse in the long conditions suggests that the distance between the subject and the verb affects the activation level of the number properties of the subject and the probability of detecting a feature mismatch. This supports a linear slot and decay model of feature tracking, as discussed earlier. Distance and Number Interference 187

In order to investigate the effects of distance on integration and revision, ERP analyses only included items that were correctly judged to be grammatical or ungrammatical. No effect of distance was seen in either the grammatical or ungrammatical conditions. The absence of a main effect of length on the ERPs suggests that the distance manipulation does not affect the ease of reactivating the subject information and integrating the subject and the verb once features are retrieved. This is opposed to what is predicted by Gibson’s (1998) model (cf. also Konieczny, 2000). According to Gibson, integration is predicted to be harder, and hence, the P600 component is expected to be larger, when the dependent elements are separated by material that introduces more new discourse referents. The present results are compatible with the idea that syntactic features are stored in a content- addressable memory store (McElree, 2000). This implies that the parser retrieves stored features directly, without a linear or hierarchical search. In this model, retrieval effort is not affected by linear or hierarchical distance, although distance may affect the likelihood of retrieving the correct features, as reflected by judgment errors. The absence of a length ϫ grammaticality interaction on the P600 suggests that distance does not affect the repair processes following the detection of a syntactic violation. These data are somewhat problematic for the model proposed by Deutsch (1998), according to which increasing distance leads to an easier integration of the verb into the conceptual representation and causes comprehension to be less disrupted by a syntactic congruency. The data are compatible with the reanalysis model of Fodor & Inoue (1994, 1998); in this model, the linear distance between two syntactically dependent elements, such as a subject and a verb, does not affect diagnosis and revision processes. Of course, the present data do not provide conclusive evidence in favor of this model: to this aim it must also be shown that distance does have an effect where Fodor & Inoue predict it does, namely when an (apparently) ungrammatical input is not a syntactic dependent of the source of the error, i.e., that part of the previous sentence context that needs to be revised to obtain a correct analysis (Sturt et al., 1999, for evidence against the Fodor & Inoue proposal). One caveat regarding the present experiment, however, is that the results may have been affected by the grammaticality judgment task employed. This task may have induced memory strategies and may therefore have obscured potential effects of distance, which may have been visible with a different task. In addition, it may be that the distance manipulation used was not taxing enough to induce effects at the verb. Grodner et al. (2000) do report effects on reading times at the verb when manipulating the amount and complexity of intervening material between the subject noun phrase and the verb in grammatical sentences, which suggests that integra- 188 Kaan tion is sensitive to intervening material. Also, the ERP studies cited in the introduction did find an effect on the LAN or P600 for ungrammatical sentences when the complexity of the intervening materials, or both distance and complexity, were manipulated (Gunter et al., 1997; Münte et al., 1997b; Vos et al., 2001). Although these ERP studies suffer from confounding factors, the fact that these complexity manipulations have an effect on the LAN or P600, whereas the present distance manipulation does not, suggests that the syntactic complexity of the intervening materials or of the sentence as a whole (e.g., the presence of wh-trace dependency, or center embeddings) may have a larger effect on integration and revision than linear distance. More controlled experiments are needed to investigate these issues.

Effects of Number More end-of-sentence judgment errors were made in the singular-plural condition relative to other number combinations. This pattern is in accordance with data from production (e.g., Bock & Cutting, 1982; Bock & Eberhart, 1993; Bock & Miller, 1991; Eberhart, 1997; Vigliocco & Nicol, 1998) and comprehension studies (Hartsuiker et al., 2001; Nicol et al., 1997; Pearlmutter et al., 1999), even though in the present study the interfering plural noun phrase is not part of the subject noun phrase. In addition, ERPs for items to which there are accurate responses showed an enhanced positivity of around 250 ms for the grammatical versus ungrammatical singular subject-plural object conditions. An early positivity has been reported for syntactic anomalies in the same time region (Neville et al., 1991) or somewhat later, around 345 ms (Mecklinger et al., 1995). In the latter study, the positivity was elicited by words that were apparently ungrammatical given the preceding sentence context but were compatible under a different structural analysis. This situation resembles the singular subject- plural object cases; at the verb, the subject feature may initially be retrieved as a plural, but it is later corrected into a singular. Hence, the singular verb is only apparently ungrammatical, given the preceding context. Although the present data should be interpreted with caution since the number manipulation was between, rather than within, items, the present results are not expected under hierarchical models of feature tracking—at least, not under those models that only take feature percolation within the subject noun phrase into consideration (Hartsuiker et al., 2001). Note that the present results do not exclude hierarchical mechanisms of feature tracking, because hierarchical distance is not pitted against linear distance in the current study. Effects of hierarchical distance are very likely to be stronger than effects of linear distance (Hartsuiker et al., 2001; Pearlmutter, 2000). The present results only show that feature tracking can be affected by linear fac- Distance and Number Interference 189 tors, such as the distance between the subject noun phrase and the verb and the properties of material between the subject noun phrase and the verb. The number manipulation also affected the P600, which was more delayed when more plural noun phrases preceded the verb and was latest when both subject and object were plural. This effect was rather unexpected, since error rates in production do not differ among singular-singular, plural- plural, and plural-singular conditions. The onset of the P600 has been claimed to correspond to the ease of diagnosing the source of the error and the onset of the repair processes (Friederici et al., 1998). The later onset of the P600 for the ungrammatical conditions with plural noun phrases therefore suggests that repair attempts are initiated later. A tentative explanation for this difference in P600 onset may be that the presence of a plural noun phrase puts more burden on the sentence-processing mechanism than a singular noun phrase; a plural noun phrase may trigger more complex semantic and discourse operations than a singular noun phrase (Pearlmutter et al., 1999). Although these additional semantic and discourse processes may not have affected the processing of grammatical verbs, they may have drawn some resources away from diagnoses and repair, leading to a delayed P600 for items with more plural noun phrases. Note that the delay does not need to affect the nature of the diagnosis and repair operations. The present results, therefore, remain compatible with hierarchy-based models of repair. The absence of a similar effect of plural noun phrases in production can be accounted for by assuming that discourse operations are completed before a clause is formulated and uttered. Processes related to setting up plural discourse referents are therefore unlikely to affect the pattern of subject-verb agreement errors. The question remains: Why is the amplitude of the positivity between 500 and 700 ms larger for the singular-singular conditions? A larger P600 amplitude has been associated with repair being harder (e.g., Osterhout et al., 1994) or the violation being more salient (Coulson et al., 1998). However, it is unclear why the singular-singular condition should differ from the plural-plural in these respects. Moreover, the singular-singular condition seems to affect an early subcomponent of the P600. The positivity in the 500 to 700 ms time region has an equal anterior-posterior distribution over the scalp and can be distinguished from the later phase of the positivity (700 to 900ms), which has a posterior focus (Hagoort & Brown, 2000). The early frontal/central phase has been claimed to reflect integration difficulty and complexity, whereas the later posterior phrase has been associated with repair processes in case of an ungrammatical continuation (Friederici et al., in press; Hagoort et al., 1999). The present results are problematic for this interpretation, since two subcomponents seem to be dif- ferentially affected by factors other than sentence complexity and the gram- 190 Kaan maticality of the verb. Determining which cognitive processes these P600 subcomponents reflect is therefore an interesting topic for further research.

CONCLUDING REMARKS

To summarize, results from the present study suggest that the distance between the subject and a verb (in terms of number of words, number of constituents, or number of new discourse referents) does not affect the ease of integration of the verb or repair processes at the verb once a feature mismatch is detected, as indexed by the P600 component. This is problematic for distance-based models of integration, such as Gibson (1998), but is in accordance with models that incorporate a content-addressable memory (McElree, 2000) and with the hierarchy-based diagnosis model of reanalysis proposed by Fodor & Inoue (1994, 1998). However, feature tracking may be affected by linear factors: increasing the distance between the subject and the verb affects the likelihood of detecting a feature mismatch, as does the presence outside the subject noun phrase of a noun phrase with interfering number properties.

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