establishing causal coherence across sentences: an erp study...ply that these inferences are...

Establishing Causal Coherence across Sentences:An ERP Study

Gina R. Kuperberg1,2, Martin Paczynski1, and Tali Ditman1,2

Abstract

! This study examined neural activity associated with establish-ing causal relationships across sentences during on-line compre-hension. ERPs were measured while participants read and judgedthe relatedness of three-sentence scenarios in which the final sen-tence was highly causally related, intermediately related, and caus-ally unrelated to its context. Lexico-semantic co-occurrence wasmatched across the three conditions using a Latent Semantic Anal-ysis. Critical words in causally unrelated scenarios evoked a largerN400 than words in both highly causally related and intermediatelyrelated scenarios, regardless of whether they appeared before orat the sentence-final position. At midline sites, the N400 to inter-

mediately related sentence-final words was attenuated to the samedegree as to highly causally related words, but otherwise the N400to intermediately related words fell in between that evoked byhighly causally related and intermediately related words. Nomodu-lation of the late positivity/P600 component was observed acrossconditions. These results indicate that both simple and complexcausal inferences can influence the earliest stages of semanticallyprocessing an incoming word. Further, they suggest that causalcoherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relation-ships between individual words are matched. !

INTRODUCTION

Causal relationships play a vital role in structuring themeaning of text and discourse by establishing physical,motivational, and psychological links between expressedevents, actions, and states (van den Broek, 1990; Trabasso,van den Broek, & Suh, 1989; Fletcher & Bloom, 1988;Trabasso & Van den Broek, 1985; Schank & Abelson, 1977).There is consistent behavioral evidence that, during read-ing, comprehenders make use of such causal relationshipsto establish coherence during comprehension: People arefaster to read sentences that are causally related (vs. caus-ally unrelated) to their preceding sentence (e.g., Keenan,Baillet, & Brown, 1984; Haviland & Clark, 1974; see alsoBloom, Fletcher, van den Broek, Reitz, & Shapiro, 1990;Myers, Shinjo, & Duffy, 1987). They are also faster to rec-ognize (McKoon & Ratcliff, 1989), name (e.g., Klin, 1995;Potts, Keenan, & Golding, 1988), and make lexical deci-sions (e.g., Potts et al., 1988; but see McKoon, Ratcliff, &Ward, 1994) on probe words that are introduced followingcausally related (vs. causally unrelated) sentences.

Causal coherence is often established through causalinferences—information that is not explicitly stated inthe text but which is required to establish causal relation-ships across clauses. These inferences vary in complexity:When two statements are highly causally related, they sim-ply entail the activation and integration of relevant real-

world knowledge. For example, in comprehending highlycausally related sentence pairs such as, “Dorothy pouredthe bucket of water on the bonfire. The fire went out,”readers activate their real-world knowledge that water ex-tinguishes fire (Singer & Halldorson, 1996; Singer, 1993;Singer, Andrusiak, Reisdorf, & Black, 1992). However,when statements are less causally related, more complexinferences are necessary to establish necessary and suf-ficient causal connections between them (Bloom et al.,1990; Fletcher, Hummel, & Marsolek, 1990; Long, Golding,Graesser, & Clark, 1990; Myers & Duffy, 1990; Myers et al.,1987; Graesser & Clark, 1985; Keenan et al., 1984; Van Dijk& Kintsch, 1983; Kintsch & van Dijk, 1978). For example,to connect the sentence, “The next day his body was cov-ered in bruises” to a preceding statement such as “Joey!sbrother became furiously angry with him,” one must inferthat Joey!s brother must have been so angry that he hitJoey. Although behavioral studies have clearly establishedthat the build-up of causal coherence across clauses is aprerequisite for successful comprehension, they are lim-ited in how much information they can yield about thenature and time course of causal inferencing. Self-pacedreading studies have generally measured reading times towhole sentences, rather than each individual word (e.g.,Myers & Duffy, 1990; Myers et al., 1987; Keenan et al.,1984; Haviland & Clark, 1974). And many behavioral stud-ies require comprehenders to recognize or respond toprobe words (McKoon & Ratcliff, 1986, 1989) or questions(Singer & Halldorson, 1996; Singer, 1993; Singer et al.,1992) that are presented only after a passage has been

1Tufts University, Medford, MA, 2Massachusetts General Hospital,Charlestown, MA

© 2011 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 23:5, pp. 1230–1246

read, requiring participants to explicitly look back at thatscenario to determine whether it is consistent with anyknowledge retrieved. Thus, these techniques may notprobe on-line word-by-word comprehension processes(see Keenan, Potts, Golding, & Jennings, 1990 for a discus-sion). In an effort to circumvent these problems, someinvestigators have asked participants to make lexical deci-sions (e.g., Potts et al., 1988) or to name (e.g., Klin, 1995;Potts et al., 1988) probe words. However, lexical decisionsmay still entail some retrospective context checking andnaming can, in theory, be carried out without lexico-semantic processing and may, therefore, be relatively insen-sitive to causal inferential processes (Keenan et al., 1990).Because of these limitations, it remains unclear whether

the build-up of causal coherence across sentences is a trulyincremental process that influences the processing of eachincoming word as discourse unfolds. This would be in linewith models of language comprehension holding that mul-tiple sources of information, beyond lexico-semantic andsyntactic information, influence word-by-word processing(Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995;MacDonald, Pearlmutter, & Seidenberg, 1994; Altmann& Steedman, 1988). Alternative possibilities are that theestablishment of causal coherence has a prolonged timecourse that is spread over many words, or that causalcoherence is only properly established at the end of a sen-tence when there are clear cues (e.g., a period) for a com-prehender to “wrap-up” overall meaning.If causal coherence is established on a word-by-word

basis, an important question is how quickly after the onsetof each incoming word real-world knowledge is available,that is, how quickly inferences are generated. One possibil-ity is that the generation of a causal bridging inference en-tails a short-lived detection of incoherence followed bythe retrieval and/or selection of relevant real-world knowl-edge to fill in the missing link. For example, Singer andHalldorson (1996), Singer (1993), and Singer et al. (1992)have suggested that, to link two related sentences, readersretrieve and integrate real-world knowledge only after theyhave initially constructed a mental syllogism with a missingpremise. This may be evenmore likely to occur when read-ers require more complex inferences to link intermediatelyrelated sentences (Myers & Duffy, 1990; Myers et al., 1987;Keenan et al., 1984). In a series of studies, Myers and Duffy(1990), Myers et al. (1987), and Keenan et al. (1984) exam-ined reading times and recall of sentences such as, “Thenext day his body was covered in bruises,” when precededby (1) highly causally related sentences (e.g., “Joey!s brotherpunched him again and again”), (2) intermediately causallyrelated sentences (e.g., “Joey!s brother became furiouslyangry with him”), and (3) unrelated sentences (e.g., “Joeywent to a neighbor!s house to play”). They demonstratedthat reading times to the intermediately causally relatedsentences were in between those to the highly causallyrelated and unrelated scenarios. Interestingly, however,participants showed maximum cued recall to these inter-mediately related sentences compared with both other

types of scenarios. Myers et al. (1987) explained this non-linear pattern of recall by suggesting that the generationof complex causal inferences to the intermediately relatedsentence pairs involved a stage of active, deep processingwhich later helped facilitate their cued retrieval. It maytherefore be that the generation of such complex causal in-ferences involved a distinct stage of retrieval and/or selec-tion of relevant information from real-world knowledge.

A second possibility is that the activation of real-worldknowledge and the generation of causal inferences influ-ence the earliest stages of semantically processing an up-coming word. There is certainly evidence that real-worldknowledge immediately impacts lexico-semantic process-ing both within sentences (Hagoort, Hald, Bastiaansen, &Petersson, 2004; Kuperberg, Sitnikova, Caplan, & Holcomb,2003) and, at least in highly constrained contexts, across sen-tences (Otten & van Berkum, 2007; van Berkum, Hagoort,& Brown, 1999). It remains unclear, however, whether suchfast activation of real-world knowledge mediates the con-struction of causal bridging inferences, particularly thosethat are complex in nature. If this were the case, it would im-ply that these inferences are generated before the onset ofthe critical word (a true predictive inference; Cook, Limber,& O!Brien, 2001; Klin, Guzman, & Levine, 1999), or as soonas the perceptual features of the critical word appear.

It is important to note that both the possibilities outlinedabove would imply that causal bridging inferencing occursincrementally during word-by-word on-line processing. Thedifference between the two accounts is how quickly real-world knowledge is available to construct causal coherence:whether relevant knowledge is active and selected veryquickly after any incoherence is detected, or whether therelevant knowledge is already available to facilitate lexico-semantic processing of incoming words.

Finally, the level of processing and representation atwhich causal coherence is established during word-by-wordprocessing remains unclear. It has been acknowledged forsome time that the representations built during discoursecomprehension are multilayered (Kintsch, 1988, 1992; VanDijk & Kintsch, 1983), constituting a surface code (the pre-cise wording and syntax of the text), a textbase (the proposi-tional structure), and amental model ( Johnson-Laird, 1983)or situation model (Van Dijk & Kintsch, 1983), which in-cludes dimensions of causal, temporal, spatial, and other in-formation (Zwaan & Radvansky, 1998; Zwaan, Magliano, &Graesser, 1995). There is evidence that causal inferencingoccurs at each of these levels of representation: An over-lap between arguments in the propositional text-base andprior stored information (Cook, Halleran, & O!Brien, 1998;Kintsch, 1988, 1998; Myers & O!Brien, 1998; Sanford &Garrod, 1981, 1998; Myers, O!Brien, Albrecht, & Mason,1994; McKoon & Ratcliff, 1989) may lead to the activationof new information through semantic priming mechanisms(O!Seaghdha, 1997; Duffy, Henderson, & Morris, 1989;Sharkey & Sharkey, 1987). In addition, situation-level mean-ing can lead to the generation of causal inferences, evenwhen lexico-semantic relationships across conditions are

Kuperberg, Paczynski, and Ditman 1231

matched (McKoon& Ratcliff, 1989). Although lexico-semanticand situation-level information usually act synergisticallywith one another to build discourse coherence, there aresituations in which semantic associations between individ-ual words can override an overall discourse message, eitherinfluencing the final interpretation, such as in the classicsemantic illusion phenomenon (“How many animals didMoses take into the ark”; Kamas, Reder, & Ayers, 1996;Erickson & Mattson, 1981), or during the initial stages ofprocessing an incoming word (Garrod & Terras, 2000).What remains unclear is how this dynamic interaction playsout during the establishment of causal coherence as dis-course unfolds, word by word.

Event-related Potentials and the Present Study

We aimed to address these outstanding questions usingERPs—an on-line neural measure that yields informationabout the neurocognitive processes engaged as meaningis constructed during word-by-word comprehension, in ad-vance of any explicit behavioral response. Our focus was onthe modulation of two ERP components—the N400 and thelate positivity/P600.

The N400 is a negative-going waveform that peaks at ap-proximately 400 msec and whose amplitude reflects theease of semantically processing incoming words with re-spect to their preceding context and information stored insemantic memory (Kutas, Van Petten, & Kluender, 2006).The amplitude of the N400 is attenuated when there is agood (vs. poor) semantic fit between the incoming wordand its context—the N400 effect. This context may consti-tute other words in semantic priming paradigms (Bentin,McCarthy, & Wood, 1985; Rugg, 1985), sentence stems(Kutas & Hillyard, 1980, 1984), or whole discourse (vanBerkum et al., 1999).

During sentence and discourse processing, the N400 canprovide a sensitive index of how a situation model andpure lexico-semantic relationships interact to modulate se-mantic processing of each incoming word. This is because,although the amplitude of the N400 evoked by a givenword can be sensitive to the situation model built up byits context (Ditman, Holcomb, & Kuperberg, 2007, 2008;Nieuwland & Kuperberg, 2008; Chwilla, Kolk, & Vissers,2007; Otten & van Berkum, 2007; van Berkum et al., 1999),it is also modulated by pure lexico-semantic relationshipsbetween that word and its preceding content words (Ditmanet al., 2007; Otten & van Berkum, 2007; Ledoux, Camblin,Swaab, & Gordon, 2006; Van Petten, 1993). Indeed, undersome circumstances, close lexico-semantic relationshipscan temporarily dominate processing, overriding situation-level representations and leading to a so-called temporaryneural semantic illusion (Nieuwland & van Berkum, 2005).

Late positivities or P600s are a group of positive-goingcomponents which peak at a later time point than the N400and which can extend until approximately 900 msec afterstimulus onset. They are evoked in many different situationsthat require or entail a second stage of processing—by

words that violate the syntactic structure of their precedingcontext (Hagoort, 1993; Osterhout & Holcomb, 1992), bywords that are highly semantically implausible/impossiblewith respect to their context (van de Meerendonk, Kolk,Vissers, & Chwilla, 2010; Kuperberg, 2007), during thecomprehension of metaphor (De Grauwe, Swain, Holcomb,Ditman, & Kuperberg, 2010; Coulson & Van Petten, 2002),jokes (Coulson & Kutas, 2001), and emotional language(Holt, Lynn, & Kuperberg, 2009; van Berkum, Holleman,Nieuwland,Otten,&Murrel, 2009). Late positivities are likelyto subsume multiple neurocognitive processes and theirfunctional significance is debated (Kolk & Chwilla, 2007;Kuperberg, 2007; Osterhout & Hagoort, 1999; Coulson,King, &Kutas, 1998). However, there is a general consensusthat such processes often involve a continued analysis ofincoming word with respect to its context and informationstored within long-term memory. On some accounts, latepositivities may reflect an active attempt to integrate incom-ing text with information stored within long-term memory(Van Petten, Kutas, Kluender, Mitchiner, & Melsaac, 1991),possibly involving a process of selection (Federmeier,Wlotko,DeOchoa-Dewald,&Kutas, 2007), and/or the additionof newinformation into the discourse model (Burkhardt, 2006,2007).Thus far, there have been three studies that have used

ERPs to examine how causal relationships are built upacross sentences. In an early study, St. George, Mannes,and Hoffman (1997) presented participants with scenariosthat encouraged the generation of a causal inference (e.g.,“…She forgot about the turkey in theoven. Theguestsweredisappointedwith the ruinedmeal.”) or that did not encour-age such an inference (e.g., “… She put the turkey in theoven. She was disappointed when the argumentative guestsruined themeal.”). Participants then viewed a final sentencethat confirmed any inference generated, for example, “Itwas too bad the turkey was burned.” The N400, averagedacross all words in this final sentence, was attenuated inthe inference-encouraging, relative to the control scenarios.The authors concluded that the earlier activation of the causalinference facilitated the semantic processing of words in thefinal sentence. However, because ERPswere averaged acrossall words in the final sentence, and because these wordsthemselves (e.g., “burned”) may have triggered inferencesin both conditions, it remains unclear whether participantsspontaneously generated or integrated inferences duringword-by-word processing of the first two sentences.In a more recent study, Yang, Perfetti, and Schmalhofer

(2007) measured ERPs to the first word of a sentence thatwas either the same (referentially explicit) or semanticallyrelated (referentially paraphrased) to the final word of apreceding causally related sentences (e.g., “…the bombhit the ground and exploded/blew up. The explosion…”).“Explosion” evoked a smaller N400 when either “exploded”or “blew up” appeared in the previous sentence, than whena bridging inference had to be generated from informationstored within semantic memory and integrated into thediscourse model (e.g., “…the bomb hit the ground. The

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explosion…”). The authors concluded that the activationand integration of causal bridging inferences was facilitatedby lexical priming across the two sentences. This studyillustrates the contribution of lexico-semantic priming tothe on-line establishment of causal relationships, as dis-cussed above, but it does not speak to whether the situa-tion model itself plays a role in establishing coherenceon-line. In addition, as there was no inclusion of a condi-tion that discouraged any inference activation entirely, it re-mains unclear how and when such new information wasadded to the discourse model.Finally, a study by Burkhardt (2007) measured ERPs to

words such as “pistol” in scenarios such as, “Yesterday aPh.D. student was shot/killed/found dead/ downtown. Thepress reported that the pistol was probably…”. At the pointof encountering “pistol,” following “killed” and “founddead,” a reader must integrate a causal inference (thatsomeone must have been shot) into the discourse modelin order to establish full discourse coherence. Interestingly,rather than evoking an effect within the N400 time window,“pistol” evoked a late positivity or P600 effect that showedan incremental increase across the three conditions withthe largest positivity following “found dead.” These find-ings were interpreted as suggesting that the addition ofnew information engaged later neurocognitive processes,distinct from the N400 (see also Burkhardt, 2006).The current study used ERPs to examine the neural corre-

lates of establishing causal coherence across three-sentencescenarios. Importantly, our design was parametric and basedon the studies byMyers andDuffy (1990),Myers et al. (1987),and Keenan et al. (1984) that manipulated the degree ofcausal relationship across sentences (see Table 1). In highlycausally related scenarios, the world knowledge required toestablish causal relationships is easily accessible. For exam-ple, in the highly causally related scenarios in Table 1, toestablish a causal relationship between the final sentence,“She had sunburn on Monday” and its preceding context,one would need to access common real-world knowledgethat the absence of sunscreen can lead to a sunburn if onehas fair skin. In sentences that are causally completely unre-lated to their context, such real-world knowledge is not read-ily available and comprehenders generally do not succeed inestablishing causal coherence. In intermediately causallyrelated scenarios, comprehenders will usually succeed in es-tablishing causal coherence, but this requires the generationof a more complex causal inference that adds new informa-tion to the discourse model. For example, in the intermedi-ately causally related scenarios in Table 1, to establish acausal relationship between the final sentence and its pre-ceding context, one must not only gain access to real-worldknowledge that the absence of sunscreen can lead to sun-burn but one must infer that, on this occasion, Jill forgot toapply the sunscreen to her skin.We measured ERPs as readers processed these three

types of three-sentence scenarios in which the final sentenceunfolded word by word. Importantly, unlike the originalstimuli used by Myers and Duffy (1990), Myers et al. (1987),

and Keenan et al. (1984), we matched the lexico-semanticrelationships between the individual content words acrossall three conditions using a Latent Semantic Analysis (LSA;Landauer&Dumais, 1997). This enabled us to examine thetiming of establishing causal coherence at the level of thesituation model, with any effects of lexico-semantic rela-tionships being equated across the three conditions. Weaimed to distinguish between several possibilities.

The first possibility was that close lexico-semantic re-lationships across individual words within the discoursecontext would dominate the semantic processing of an up-coming word between 300 and 500 msec, overriding thesituation model built up by the context. In other words,the processing of each incoming critical word would beshallow. This would predict no modulation of the N400across the three conditions. A distinction between the threetypes of scenariosmight, however, occur during a later stageof processing, as in a previous ERP study by Nieuwland andvan Berkum (2005), who reported no N400 effect, but a ro-bust P600 effect to words that were highly incongruous withtheir discourse context but that were lexico-semanticallyassociated with individual words in this context.

The second overall possibility was that the situationmodel constructed from the context, rather than simplylexico-semantic relationships within that context, wouldinfluence semantic processing of the upcoming word. Onthis account, critical words that were highly causally relatedto their context would be expected to evoke a smaller N400than the causally unrelated critical words. This attenuationof the N400 would reflect the implicit and incremental useof real-world knowledge to facilitate processing of an in-coming critical word (Hagoort et al., 2004) and would beconsistent with some previous ERP studies indicating thatwords that are incongruous with their global discourse con-text evoke an N400 effect (van Berkum et al., 1999). Im-portantly, processing would not simply rely on matchesbetween lexico-semantic relationships in the context andlexico-semantic associations stored within memory, butrather would be driven by an interaction between the situa-tion model built up by the context and information storedwithin semantic memory (Sanford & Garrod, 2005; McKoon& Ratcliff, 1989).

Given the availability of real-world knowledge to facili-tate the processing of highly causally related critical words,a key question was what would happen at the point ofencountering critical words in the intermediately relatedscenarios that required the activation and integration ofmore complex inferences for the text to make sense. Ifthe activation of such complex inferences followed an ini-tial detection of a coherence break, then intermediately re-lated critical words should generate just as large an N400 asthe causally unrelated critical words; any inferencing andaddition of new information to the discourse model thatallowed the intermediately related sentences to eventuallybe perceived as coherent might be expected to engage adistinct, later neurocognitive process and to produce aP600 effect (Burkhardt, 2007). This would suggest that the


Table 1. Stimuli Characteristics

Causal Relatedness Construction and Explanation Causal Relatedness Rating LSA Semantic Similarity Values Example

Highly Related The first sentence sets up a fairlynonconstraining context. Thesecond and third sentencesare highly causally related inmeaning, requiring a simpleinference to link them.

All CWs: 6.37 (0.61) All CWs: 0.16 (0.07) Jill had very fair skin.

Mid-sentence CWs: 6.36 (0.62) Mid-sentence CWs: 0.17 (0.07) She forgot to put sunscreen on.

Sentence-final CWs: 6.39 (0.61) Sentence-final CWs: 0.15 (0.06) She had sunburn on Monday.

[Here the reader must infer thatsunburn results from forgettingto put on sunscreen, particularlyif one has fair skin.]

Intermediately Related The same first and third sentenceas in the highly related scenarios.The second sentence isconstructed such that thereader is required to make acomplex inference to connectthe second and third sentences.

All CWs: 4.79 (1.04) All CWs: 0.15 (0.07) Jill had very fair skin.

Mid-sentence CWs: 4.86 (1.00) Mid-sentence CWs: 0.17 (0.08) She usually remembered towear sunscreen.


[Here the reader must make theabove inference but must alsoinfer that although Jill usuallyputs on sunscreen, this timeshe forgot.]

Causally Unrelated The third sentence is the same asin both of the other conditions.In many cases the first sentencewas also kept the same. Thesecond sentence, however, isconstructed in such a way asto make the third sentencenot follow logically from thefirst two.

All CWs: 2.14 (0.99) All CWs: 0.16 (0.07) Jill!s skin always tanned well.

Mid-sentence CWs: 2.11 (0.93) Mid-sentence CWs: 0.17 (0.07) She always put on sunscreen.


[Here there is no clear inferencethat can be made since thesecond sentence establishesthat it is highly unlikely thatJill would not put on sunscreenand the first sentence states thateven had she forgotten, shewould not be likely to sunburn.]

The example given is for a scenario where the critical word appears mid-sentence. For causal relatedness ratings and LSA values, means are shown with standard deviations in parentheses. CW = criticalword.

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coherence break itself triggers inferential processes that en-tail an active retrieval and/or selection of relevant informa-tion from long-term semantic memory.If, on the other hand, a more complex causal inference

was readily available before or at the time the incoming in-termediately causally related critical word is processed,then these words should be just as easy to process as thehighly related critical words, that is, they should evokean N400 with the same amplitude. On this account, evenmore complex inferencing that adds new information tothe discourse model is assumed to be a relatively implicitprocess. Importantly, the N400 modulation on the criticalword itself is assumed not to reflect the work of any infer-ential process, but rather to reflect the semantic process-ing consequences of earlier, more implicit inferencing(for discussion, see Nieuwland, Ditman, & Kuperberg,2010; van Berkum, 2009).

METHODSConstruction and Development of Stimuli

Two-hundred-thirty-one scenarios (sets of sentence triplets)were initially constructed, each with three conditions: highlycausally related, intermediately related, and causally un-related. In the highly causally related scenarios, the finalsentence was causally linked in meaning to the first twosentences through a simple inference requiring real-worldknowledge. In the intermediately related scenarios, thesame first and third sentences were used as in the highly re-lated scenarios, but the second sentence was constructed sothat the reader was required to make a more complex in-ference to connect the second and third sentences. Thehighly related and intermediately related scenarios werethe same as those described in previous studies (Ditman& Kuperberg, 2007; Kuperberg, Lakshmanan, Caplan, &Holcomb, 2006). The causally unrelated scenarios were con-structed by modifying either the highly related or the in-termediately related scenarios so that the third sentencedid not logically follow from the first two sentences. Examplescenarios are presented in Table 1.Attempts were made to match the content words across

all three levels of causal relatedness in terms of numbers ofword repetitions and their semantic similarity values (SSVs)as quantified using LSA (Landauer, Foltz, & Dumais, 1998;Landauer & Dumais, 1997).1 The scenario triplets werecounterbalanced across three lists so that, in each list, thesame final sentence was not seen more than once but,across lists, all final sentences were seen in all three condi-tions. The scenarios were then randomized within lists.The stimulus set was then further constrained based

on a norming study that aimed to select those scenariotriplets in which the three experimental conditions weremost clearly distinguished in terms of their causal related-ness. Scenarios were presented to 12 undergraduate stu-dents from Tufts University (7 women, 5 men; mean age =21.1 years, SD = 2.5), four for each of the three lists, whodid not participate in the ERP study. The participants were

asked to rate the causal connection between the thirdsentence in each scenario and the first two sentences ona scale from 1 (weak causal relationship) to 7 (strongcausal relationship). Scenario triplets were then elimi-nated if, within each triplet, any of the following criteriawere met: (1) the average rating of the highly relatedscenario was lower than that of either the intermediatelyrelated or the causally unrelated scenario; (2) the average-rating of the intermediately related scenario was the sameor lower than that of the causally unrelated scenario; (3) thehighly causally related scenariowas ratedbelow4; (4) the inter-mediately related scenario was rated below 3; and (5) thecausally unrelated scenario was rated above 3.

After excluding scenario triplets based on these criteria,159 scenario triplets remained. An additional pretest wascarried out to verify that participants directly establisheda causal connection in the highly related scenarios, gen-erated a more complex inference to the intermediatelyrelated scenarios, and failed to generate consistent infer-ences to the causally unrelated scenarios. The discoursescenarios were presented in random order to 12 Tufts un-dergraduate students (four for each of the three lists). Atthe end of each discourse scenario, a “Why” question waspresented in italics. This question was constructed from thefinal sentence in each scenario (e.g., in the example givenin Table 1, “Why did Jill have sunburn on Monday?”). Partic-ipants were asked to write a one-sentence clear answer tothe question or to indicate “don!t know” if there was no ob-vious answer. Inspection of answers indicated that, for98% of the highly related scenarios, participants wrote re-sponses that were very similar to the second sentence forthat scenario (i.e., they repeated what they had just read).For 81% of the intermediately related scenarios, participantswrote responses that were very similar to the second sen-tence of the highly related condition for that scenario, eventhough they had not seen that sentence (i.e., they made theexpected inference). For 92% of the causally unrelated sce-narios, participants either indicated “don!t know” or wroteresponses thatwere very different from the second sentenceof the highly related-condition for that scenario (i.e., theyeither failed to make an inference or any inferences gener-ated were inconsistent across participants).

In all these scenarios, the first two sentences each con-tained between 4 and 10 words and the final sentence con-tained between 2 and 7 words. Critical words in the thirdsentence of each scenario were selected on the basis ofa separate causal rating study that has previously beendescribed (Pretest 2 ratings in Kuperberg et al., 2006). In77 scenario triplets, the critical word was followed by oneto three additional words. These fell at clause boundariesbut were not accompanied by a comma. In 82 scenariotriplets, the critical word was the final word of the thirdsentence and this appeared with a period. Thus, acrossall participants, the same mid-sentence or sentence-finalcritical word appeared in all three conditions. However,across all participants, the same words did not appear inthe mid-sentence position and sentence-final position.


Semantic similarity values were calculated using LSA(Landauer et al., 1998; Landauer & Dumais, 1997; avail-able on the Internet at http://lsa.colorado.edu). Pairwisecomparisons using tasaALL space (1st year college studentreading level) were carried out comparing all contentwords in a scenario and the critical word (see Table 1).An ANOVA including all items revealed no significant dif-ferences in semantic similarity values between the threelevels of causal relatedness [F(2, 471) = 0.140, p = .87],and no interaction between causal relatedness and positionof the critical word [F(2, 471) = 0.008, p = .99]. A maineffect of position in this ANOVA reflected the increased con-text (more words) preceding sentence-final critical wordsrelative to mid-sentence critical words [F(1, 471) = 14.4,p < .001]. In addition, an ANOVA including all items con-firmed significant differences in causal ratings across thethree experimental conditions [F(2, 471) = 887.8, p <.001], with no interactions between causal relatedness andposition [F(2, 471) = 0.2, p = .81], and no main effects ofposition [F(2, 471) = 0.4, p < .52; see Table 1 for meansand standard deviations]. Follow-ups confirmed that themean rating of the highly related scenarios was significantlygreater than that of the intermediately related scenarios[t(316) = 16.5, p < .0001], which was, in turn, significantlygreater than that of the causally unrelated scenarios [t(316)=23.1, p< .0001; see Table 1].

The final set of 159 scenario triplets was then dividedinto three lists so that, in each list, the same final sentencewas not seen more than once but, across lists, all final sen-tences were seen in all three conditions. Each list thus con-tained 159 three-sentence scenarios, with 53 of each of thethree conditions. In approximately 50% of these scenariosin each list, the critical word appeared at the sentence-finalposition and, in approximately 50%, the critical word ap-peared before the sentence-final position. Within each list,scenarios were pseudorandomized within each list suchthat the same condition did not appear in more than twoconsecutive trials.

ERP Study

Participants

Twenty-one participants (13 women, 8 men; mean age =20.9 years, SD = 1.8) were recruited from the Tufts Uni-versity undergraduate population by advertising. All par-ticipants were right-handed, native English speakers (noother language learned before the age of 5) who had nor-mal or corrected-to-normal vision, were not taking any psy-choactive medication, and had no history of head trauma.Written informed consent was obtained from all partici-pants prior to participation.

Stimulus Presentation and Task

The participants! task was to rate how easy or difficult it wasto connect the third sentence to the first two sentences in

each scenario by pressing one of three buttons: easy toconnect, in-between, difficult to connect. Participants weretold that there were no “right” answers to their ratings andto go with their first instincts. They were given nine prac-tice trials after which they were given the opportunity toask for clarification prior to beginning the main portionof the experiment.Each participant was assigned to one of the three coun-

terbalanced lists and was seated in a comfortable chair in adimly lit, sound-attenuated room, separate from the experi-menter. Scenarios were presented on a computer monitorusing white text on a black background. In each trial, theword READY was displayed until the participant pressedany key on a response box to begin the experiment. Aftera fixation cross was displayed in the center of the screenfor 500 msec, the first two sentences were presented intheir entirety for 3.4 sec each. The third sentence wasthen presented word by word with each word displayedfor 500 msec with a 100-msec interstimulus interval. Themid-sentence critical words never appeared with a comma.The sentence-final critical words always appeared with aperiod. After each sentence, a question mark cue was dis-played until the participant made his or her judgment, afterwhich the next trial began. Participants were instructed towait until the question mark cue before responding. Inaddition, at five random points in the middle of the experi-ment as well as after the last trial, participants were asked acomprehension question about the content of the scenariothey had just read.

Electrophysiological Recording

Activity was recorded at 29 active tin electrodes placed onthe scalp and held in place on the scalp by an elastic cap(Electro-Cap International, Eaton, OH; see Figure 1 for thefull montage). In addition, electrodes were placed belowthe left eye and at the outer canthus of the right eye tomoni-tor vertical and horizontal eye movements, and also overthe left mastoid and right mastoid (recorded actively tomonitor for differential mastoid activity). All EEG electrodeimpedances were maintained below 5 k! (impedance foreye electrodes was less than 10 k!).The EEG signal was amplified by an Isolated Bioelectric

Amplifier System Model HandW-32/BA (SA Instrumenta-tion Co., San Diego, CA) with a bandpass of 0.01 to 40 Hzand was continuously sampled at 200 Hz by an analogue-to-digital converter. The stimuli and participants! behavioralresponses were simultaneously monitored by a digitizingcomputer.

ERP Analysis

Analyses were first carried out onwaveforms evoked by crit-ical words in all trials, binned by the three levels of causalrelatedness. Only trials free of ocular and muscular artifact


were averaged for analysis. Analyses were conducted onmean amplitude values 325–475msec, which encompassedthe N400 effect, and 500–800 msec following word onset,using the 100 msec of activity that preceded critical wordonset as a baseline.All sites were included in a systematic columnar “pattern

of analyses” that we have applied in prior studies (e.g.,Ditman et al., 2008; Kuperberg, Kreher, Sitnikova, Caplan,& Holcomb, 2007). This approach has the advantage ofyielding statistical information about the distribution ofERP effects across the whole scalp, allowing the detec-tion of differences in the distribution of effects along theanterior–posterior (AP) axis of the scalp. In addition, atmedial, lateral, and peripheral electrode columns, it allowsthe detection of differences across the two hemispheres.Repeated measures analyses of variance (ANOVAs) at

each time window were performed at the midline, medial,lateral, and peripheral columns (see Figure 1). As we hada priori hypotheses, we proceeded straight to ANOVAswhich contrasted each level of causal relatedness with

one another (causally unrelated vs. highly related, causallyunrelated vs. intermediate related, intermediately relatedvs. highly causally related). In addition to causal related-ness, each ANOVA included position (mid-sentence criticalword, sentence-final critical word) and AP distribution(with the number of levels depending on the number ofelectrode sites in each column, see Figure 1) as within-participant factors. The three lateral electrode columns alsoincluded hemisphere (left, right) as a within-participantfactor. Follow-ups of interactions with position were car-ried out by comparing causal relatedness for a given criticalword position, rather than comparing effects of positionfor a given level of causal relatedness. The Greenhouse–Geisser (1959) correction was applied when evaluatingeffects with more than one degree of freedom to protectagainst Type 1 errors resulting from violations of sphericity.In these cases, we report original degrees of freedom andthe corrected probability level.

Behavioral Analysis

Means for the first through third classifications (1 = dif-ficult to connect; 2 = in between; 3 = easy to connect)given to each of the three predetermined levels of causalrelatedness were contrasted using a repeated measuresANOVA in which causal relatedness and position werewithin-participant factors.

RESULTSERP Data

325–475 msec

Causally unrelated versus highly causally related. Criti-cal words that were causally unrelated to the preceding con-text evoked a larger amplitude N400 than words that werehighly causally related to their preceding context [maineffect of causal relatedness: midline, F(1, 20) = 12.05, p <.01; medial, F(1, 20) = 10.22, p < .01; lateral, F(1, 20) =12.00, p< .01; peripheral, F(1, 20) = 13.29, p< .01]. In ad-dition, a Causal relatedness! Position!Hemisphere inter-action was observed at all non-midline columns [medial:F(1, 20) = 11.15, p < .01; lateral: F(1, 20) = 12.39, p <.01; peripheral: F(1, 20) = 4.61, p< .05]. Follow-up simpleeffects ANOVAs at these columns revealed that the differ-ence between causally unrelated and highly causally relatedcritical words was generally left-lateralized for mid-sentencecritical words (main effects of causal relatedness, significantat left medial, left lateral, left peripheral, and right peripheralcolumns: all Fs > 5.10, all ps < .05), but broadly distributedacross both hemispheres for sentence-final critical words(main effect of causal relatedness significant at all columns,all Fs > 4.60, all ps < .05). There were no other interac-tions with causal relatedness and position (all ps > .10)(see Figure 2).2

Figure 1. Electrode montage. Electrodes placed in the standardInternational 10–20 System locations included five sites along themidline (FPz, Fz, Cz, Pz, and Oz) and eight lateral sites, four overeach hemisphere (F3/F4, C3/C4, T3/T4, and P3/P4). Eight additional10–20 sites were altered to form a circle around the perimeter of thescalp. These altered sites included FP1/FP2 (33% of the distance alongthe circle between T3/T4), F7/F8 (67% of the distance between FPzand T3/T4), T5/T6 (33% of the distance between T3/T4 and Oz), andO1/O2 (67% of the distance between T3/T4 and Oz). In addition,eight extended 10–20 system sites were also used (FC1/FC2, FC5/FC6,CP1/CP2, and CP5/CP6). The dotted lines represent the four columnsused in analyses (i.e., midline, medial, lateral, peripheral). In the midlineANOVA, the AP Distribution factor had five levels (FPz, Pz, Cz, Pz, Oz);in the medial ANOVA, it had three levels (FC1/FC2, C2/C4, CP1/CP2); inthe lateral ANOVA, it had four levels of electrode site (F3/F4, FC5/FC6,CP5/CP6, P3/P4); in the peripheral ANOVA, it had five levels (FP1/FP2,F7/F8, T3/T4, T5/T6, O1/O2).


Causally unrelated versus intermediately related. Criticalwords in causally unrelated scenarios evoked a larger N400than in the intermediately related scenarios, as evidencedby significant main effects of causal relatedness at midlineand medial columns and marginal effects at lateral and pe-ripheral columns [midline: F(1, 20) = 4.72, p< .05; medial:F(1, 20) = 5.92, p < .05; lateral: F(1, 20) = 3.27, p < .10;peripheral: F(1, 20) = 3.04, p < .10]. No interactions withposition reached significance (all Fs < 2.60, all ps > .06).

Intermediately related versus highly causally related.Unlike the contrast between the causally unrelated andhighly related critical words, the N400 effect to the criticalwords in the intermediately related contrast was modu-lated by sentence position at midline sites, as reflectedby a Causal relatedness ! Position! AP distribution inter-action at the midline column [F(4, 80) = 3.60, p < .05].Follow-up simple effects ANOVAs indicated that, when crit-ical words appeared mid-sentence, the N400 to the inter-

mediately related critical words was more negative thanto the highly related words. This effect was greatest at moreanterior than posterior sites, as reflected by a Causal relat-edness ! AP distribution [F(4, 80) = 3.51, p < .05]. How-ever, when critical words appeared at the sentence-finalposition, there were no differences between these twoconditions at any electrode site (main effect of causal relat-edness: F < 1.00, p > .70; Causal relatedness ! AP distri-bution: F < 1.00, p > .40).At non-midline columns, there were no differences

in N400 evoked by intermediately and highly causally re-lated critical words (no main effects of causal relatedness)except at left-lateralized sites where intermediately relatedcritical words appeared to evoke a larger negativity thanhighly related critical words regardless of sentence position[Causal relatedness ! Hemisphere interaction at the me-dial column: F(1, 20) = 5.08, p< .05; marginal main effectof causal relatedness at the lateral: F(1, 20) = 4.14, p< .06;and peripheral: F(1, 20) = 3.53, p < .08 columns].

Figure 2. Top: ERPs at midlineelectrode sites to critical wordsin highly causally related,intermediately related, andcausally unrelated scenarios.Bottom: Voltage mapsshowing mean differencesacross the N400 time window(325–475 msec) betweencritical words, comparingeach level of causal relatednesswith one another.


500–800 msec

Causally unrelated versus highly related. When theyoccurred in the mid-sentence position, critical words inthe causally unrelated scenarios continued to evoke a largernegativity than highly related critical words in this time win-dow, particularly at nonmidline sites and particularly overthe left hemisphere. This was reflected by significant inter-actions between causal relatedness, hemisphere, and posi-tion at the medial and lateral columns [medial: F(1, 20) =6.15, p< .05; lateral: F(1, 20) = 6.01, p< .05], with follow-up ANOVAs at these columns showing a significant or near-significant Causal relatedness!Hemisphere effect only forthe mid-sentence critical words [medial: F(1, 20) = 4.65,p < .05; lateral: F(1, 20) = 3.89, p < .07]. There was alsoa significant main effect of causal relatedness at the periph-eral column [F(1, 20) = 4.78, p < .05].

Causally unrelated versus intermediately related. Therewere no significant main effects or interactions involvingcausal relatedness (all Fs < 3.70, all ps > .06).

Intermediately related versus highly related. There wereno significant main effects or interactions involving causalrelatedness (Fs < 2.50, all ps > .12). There was a Causalrelatedness ! Hemisphere interaction at the medial col-umn [F(1, 20) = 5.40, p < .05], but analyses at left andright medial columns separately failed to reveal significanteffects ( ps > .5).

Behavioral Data

Themean classifications for each level of causal relatednessare given in Table 2. As expected, a repeated measures 3(causal relatedness: highly causally related, intermediatelyrelated, causally unrelated) ! 2 (position: sentence-finalcritical word,mid-sentence critical word) ANOVA examiningthe differences in these mean ratings showed a significantmain effect of causal relatedness [F(2, 40) = 943.21, p <.001]. Pairwise comparisons indicated that the highly relatedscenarios were classified as more related than the interme-diately related scenarios [F(1, 20) = 280.97, p < .001] thatwere, in turn, classified as more related than the causallyunrelated scenarios [F(1, 20) = 1005.94, p < .001]. Therewas no interaction between causal relatedness and posi-tion [F(2, 40) < 1].

DISCUSSION

The present study examined neural activity associatedwith establishing causal relationships across sentencesduring on-line comprehension. Despite being matched onlexical–semantic relationships, neural modulation to criticalwords differentiated between the highly causally related,intermediately related, and causally unrelated scenarios.Critical words in the causally unrelated scenarios evoked alarger widely distributed N400 than those in both the highlyrelated and intermediately related scenarios. This was thecase regardless of whether the critical word appeared be-fore or at the sentence-final position. At many electrodesites, the amplitude of the N400 to critical words in the in-termediately related scenarios fell in between that evokedby the highly causally related and intermediately relatedwords with one exception: The N400 evoked by intermedi-ately related sentence-final critical words atmidline sites wasattenuated to the same degree as to the highly causally re-lated words. No LPC/P600 effect was observed to either thecausally unrelated or the intermediately related, relative tothe highly causally related, critical words.

Overall, these findings demonstrate that causal coherenceacross sentences, at the situation level, can immediatelyinfluence semantic processing of incoming words, at leastwhen participants are asked to actively judge causal relation-ships between sentences. Below we discuss the specific im-plications of these patterns of neural modulation.

N400 Modulation to Critical Words in theCausally Unrelated versus the Highly CausallyRelated Scenarios

The larger N400 to critical words in the causally unrelatedrelative to highly causally related scenarios is consistentwith previous studies demonstrating that readers are imme-diately sensitive to coherence breaks during comprehen-sion, even when lexico-semantic relationships betweenindividual words are close. For example, van Berkum et al.(1999) showed that single words that were incongruouswith their entire discourse context evoked a larger N400than words that were discourse congruous, even whenthese words were consistent with their immediate sentencecontexts. In addition, Camblin, Gordon, and Swaab (2007)demonstrated a clear N400 effect to discourse-incongruousrelative to congruous words that were related to their

Table 2. Mean (Standard Deviation) Causal Relatedness Judgment Scores

Causal RelatednessMean Relatedness Judgment

Score: All Scenarios

Mean Relatedness JudgmentScore: Scenarios with

Mid-sentence Critical Words

Mean Relatedness JudgmentScore: Scenarios with

Sentence-final Critical Words

Highly related 2.88 (0.07) 2.88 (0.07) 2.87 (0.09)

Intermediately related 2.19 (0.19) 2.20 (0.20) 2.19 (0.21)

Causally unrelated 1.37 (0.17) 1.39 (0.19) 1.35 (0.21)


preceding content words (e.g., to “legs” in the scenario“Lynn!s wool sweater was uncomfortable and itchy. She fid-geted as the rough material irritated her skin. Lynn couldn!tstop scratching her arms and legs.”).3

In these previous studies, a variety of different types ofdiscourse incongruities were introduced, the critical wordalways fell at the sentence-final position, and contextualconstraint was fairly high. In the present study, congruitywas always mediated by causal relatedness and, in nearly50% scenarios, the critical word did not appear with a pe-riod, falling before the end of the sentence. Thus, thesefindings clearly indicate that, at least when explicitly askedto make coherence links, readers! build-up of causal co-herence is established at the situation level and influencesthe earliest stages of semantically processing incomingwords. Moreover, they show that this is the case even whencritical words appear before the sentence-final word(although, in all cases, these mid-sentence words ap-peared at clause boundaries). Indeed, when critical wordsoccurred before the sentence-final position, the modula-tion of the N400 extended into the later 500–800 msecwindow, suggesting that readers may have continued to es-tablish causal coherence as more information becameavailable.

We interpret the attenuation of the N400 to the highlyrelated, relative to the causally unrelated, critical words asreflecting facilitation due to a three-way match between themeaning of the incoming lexical item and an interactionbetween the context and the reader!s stored semantic andreal-world knowledge (Kutas et al., 2006; Hagoort et al.,2004; Kutas & Federmeier, 2000).

Modulation of the ERPs to Critical Words in theIntermediately Related Scenarios

Of most interest was how ERPs were modulated to inter-mediately related critical words, relative to highly causallyrelated and causally unrelated words. Such intermediatelyrelated critical words were congruous with their contextbut required the activation and integration of complexcausal inferences (Myers & Duffy, 1990; Myers et al., 1987;Keenan et al., 1984). Once again, we observed modulationof the N400 component, but no modulation of the laterP600. These findings are consistent with previous studiesexamining ERP correlates of establishing causal coherence(Yang et al., 2007; St. George et al., 1997). They do, how-ever, contrast with those of Burkhardt (2007), who foundthat scenarios requiring complex causal bridging infer-ences were associated with P600 modulation, interpretingthis as reflecting the cost of adding new information tothe discourse model. Of note, however, at least some ofthe scenarios employed in Burkhardt!s study may have re-quired participants not only to generate a causal inferencebut also to temporally reorder the described events, poten-tially leading to later reanalysis processes. Such temporalreordering is known to engender processing costs (Raisig,Welke, Hagendorf, & van der Meer, 2007; Claus & Kelter,

2006) and may have been a factor in driving the modula-tion of the P600 rather than causal inferencing per se. Inorder to further examine this hypothesis, we are currentlymanipulating causal relatedness and temporal order withinthe same paradigm.In the Introduction, we outlined two possibilities for

how the N400 might be modulated in intermediately re-lated (vs. highly causally related and unrelated) scenarios.If no causal inference was activated by the time the criticalword was presented, then the intermediately related criti-cal words should evoke an N400 that was equal in ampli-tude to that of the causally unrelated words. If, however,a full inference was activated, then the intermediately re-lated critical words should generate a smaller N400 thatwas equal to that of the highly causally related critical words.As noted in the Introduction, both these predictions makethe assumption that the process of activating new informa-tion from semantic memory (i.e., the initial stage of infer-ence generation) is relatively automatic and implicit (vandenBroek, 1994;McKoon&Ratcliff, 1989), and that the am-plitude of the N400 to an incoming lexical item reflectsthe consequence of such activation rather than the processof inference generation itself (see Nieuwland et al., 2010;van Berkum, 2009).What we observed was an N400 to the intermediately re-

lated critical words that was consistently smaller than thatto the causally unrelated words. This suggests that at leastsome inferencing had taken place to facilitate their pro-cessing. This finding is consistent with the early study bySt. George et al. (1997), who also contrasted incongruousscenarios and scenarios that encouraged the activation ofa bridging inference. In that study, however, the infer-ence word itself was explicitly presented in a final probesentence and the authors! measure of inferencing wasthe N400 averaged across all words in this final sentence.The current findings suggest that any inferencing servedto facilitate the build-up of causal coherence during incre-mental, word-by-word processing.At midline electrode sites, intermediately related critical

words that appeared in the sentence-final position evokedan N400 that was attenuated to the same degree as to thehighly related critical words. This suggests that the end ofthe sentence itself (indicated by a period) may have actedas an explicit cue to encourage the establishment of a fullcausal inference. When the intermediately related criticalwords appeared before the sentence-final position, eventhough they were at clause boundaries, the N400 wasgenerally not attenuated to the same degree as to thehighly related critical words.4 This partial attenuation ofthe N400 is consistent with the idea that causal inferencingis unlikely to be an all-or-none process (see Lassonde &O!Brien, 2009; Sanford & Garrod, 2005; van den Broek,1994; Just & Carpenter, 1992; McKoon & Ratcliff, 1990).In other words, rather than constituting the activation/access of a specific inference (a specific lexical item), asmay occur in highly constrained contexts, inferential activa-tionprocessesmaybemore general, facilitating the activation


and processing of multiple lexical items sharing related fea-tures (Sanford & Garrod, 2005; Cook et al., 2001; Klin et al.,1999; van den Broek, 1994). (Note that the N400 evoked by adiscourse-incongruous word is modulated by the degree towhich this word shares semantic features with an expectedword (Ditman et al., 2007; Federmeier & Kutas, 1999; Kutas& Hillyard, 1984).

Relationship between Findings and Models ofDiscourse Processing

Discourse processing models fall into two major classes.Memory-based models place most emphasis on the implicitactivation of information from long-term memory duringthe build-up of coherence (Myers & O!Brien, 1998; Sanford& Garrod, 1998; McKoon & Ratcliff, 1992; Kintsch, 1988).For example, according to the resonance model (Myers &O!Brien, 1998; Albrecht&O!Brien, 1993), incomingmaterialcontinually and autonomously interacts with stored infor-mation and “resonates” as a function of the degree ofmatch.Similar ideas that fast “pattern matching” facilitates the pro-cessing of upcoming material are presented in Kintsch!s(1988) Construction–Integration Model, in Sanford andGarrod!s scenario-mapping and focus theory (Sanford &Garrod, 1981, 1998; Sanford, 1990), and in McKoon andRatcliff!s (1992) minimalist hypothesis. These memory-based models emphasize that implicit resonance with in-coming material is often sufficient for inferencing. Theyhave been relatively silent on how such material is subse-quently selected or integrated into the discourse structure.Constructionalist models, on the other hand, have empha-sized the idea that comprehenders actively engage inferen-tial processes in a “search for meaning” (Graesser, Singer, &Trabasso, 1994). These models acknowledge a phase ofinitial pattern matching but place more emphasis on whatinformation is selected to build up coherence during theintegration phase of processing (Long & Lea, 2005). Thesetwo classes of models are distinguished mainly by their dif-ferent emphases on the two stages of coherence building(Long & Lea, 2005), and, indeed, aspects of both modelshave been incorporated into hybrid models of text com-prehension such as the landscape model (Rapp & van denBroek, 2005; van den Broek, Rapp, & Kendeou, 2005).The N400 is believed to be a memory-based ERP compo-

nent: It is highly sensitive to information stored within se-mantic memory at various grains of representation, whichimpacts directly on both sentence and discourse compre-hension (van Berkum, 2009; Kutas et al., 2006; Kutas &Federmeier, 2000). During sentence processing, theN400 is not necessarily sensitive to the demands of select-ing words from competing activated alternatives: For exam-ple, the amplitude of the N400 to unexpected words inhighly constrained contexts (where selection demandsare relatively high) is the same as to unexpected wordsin less constrained contexts (Federmeier et al., 2007). Latepositivites, on the other hand, may better reflect selectionprocesses that resolve conflict between competing lexical

items (Federmeier et al., 2007, where the positivity has amore frontal distribution), or between competing sentence-level representations (Kolk & Chwilla, 2007; Kuperberg,2007, where the positivity has a more posterior distribu-tion). In the current dataset, ERP modulation was observedin the N400 component rather than the late positivity/P600component.We interpret this as broadly supportingmemory-based models of discourse processing.

On the other hand, as they stand, memory-basedmodelscannot easily account for the pattern of N400 modulationobserved across the three levels of causal relatedness inthis study. In most such models, resonance is conceptual-ized as a function of the match between lexico-semanticrelationships within the input and lexico-semantic relation-ships stored within long-term memory. Lexico-semanticrelationships have been described in terms of argumentoverlap between propositions (Kintsch & van Dijk, 1978),matches between semantic and contextual features (Cooket al., 1998; Myers & O!Brien, 1998) and, more recently, interms of lexico-semantic co-occurrence, as indexed usingLSA (Kintsch, 2001; Landauer et al., 1998; Landauer &Dumais, 1997). In many situations, lexico-semantic rela-tionships play an important role in establishing coherence:They influence ratings of text coherence, processing timesof sentence pairs, and free recall of natural texts (Wolfe,Magliano, & Larsen, 2005; Foltz, Kintsch, & Landauer,1998). In ERP studies of coherent discourse, words thatare semantically related to the set of individual words intheir context evoke a smaller negativity than words thatare not semantically related to their context (Ditman et al.,2007; Otten & van Berkum, 2007). And, in a recent study,Yang et al. (2007) showed that lexico-semantic relationshipsdirectly influence the amplitude of the N400 during the es-tablishment of causal coherence across clause boundaries.In the present study, however, simple lexico-semantic co-occurrence, as indexed using LSA, wasmatched across thethree types of scenario. This suggests that any resonancebetween the input and stored information that influencedthe immediate build-up of causal coherence also occurredat the situation level. Exactly what features resonated at thislevel, however, remains unclear. We know from previousERP studies that various dimensions of the situation model,including temporal variables (Ditman et al., 2008) and theirlinguistic codes (Ferretti, Kutas, & McRae, 2007), can im-mediately impact the N400. Pragmatic knowledge and re-lationships also play an important role (Nieuwland et al.,2010; van Berkum, 2009; Nieuwland & Kuperberg, 2008).Regardless of the precise variables that drove N400 modula-tion in this study, the implication is that long-term memoryis multilayered, with information represented at differ-ent levels, and that resonance can occur at any or all theselevels.

Open Questions

If resonance does, indeed, occur at multiple levels of se-mantic representation, this raises the question of how


activity at each of these levels interacts during discourseprocessing. Under what situations do lexico-semantic rela-tionships in the text-base override the situation model, andvice versa?

In the current study, we showed that the situationmodelcan influence the earliest stages of semantically processingincoming words. However, it is clear from other studiesthat there are situations in which close lexico-semanticrelationships within a context override situation-levelmeaning, leading either to a shallow final interpretation(a semantic illusion; Ferreira, 2003; Sanford, 2002; Kamaset al., 1996; Erickson & Mattson, 1981) or to no earlyN400 modulation, but later processing costs within the latepositivity/P600 time window (Nieuwland & van Berkum,2005; see also Hoeks, Stowe, & Doedens, 2004; Kuperberget al., 2003). The interplay between these levels of repre-sentation may be influenced by several factors includ-ing the rate of language presentation (Camblin, Ledoux,Boudewyn, Gordon, & Swaab, 2007), linguistic focus (Sturt,Sanford, Stewart, & Dawydiak, 2004), the presence of ex-plicit coherence markers (connectives) in the text (Millis& Just, 1994), and an individual!s working memory capacity(Nakano, Saron, & Swaab, 2010).

In the present study, the participants’ task was to expli-citly judge causal coherence so they had a relatively highstandard of coherence (a factor that has been discussedmore in relation to traditional constructionalist frameworksthan memory models; van den Broek, Risden, & Husebye-Hartman, 1995). In addition, the presentation rate was rela-tively slow (the SOA was 600 msec) and participants hada long time to read the first two sentences (3.4 senten-ces each). It may well be that, if participants were askedto read these types of scenarios more passively, and/or thepresentation of individual sentences and words was faster(Camblin, Ledoux, et al., 2007), resonance would have oc-curred mainly at the level of lexico-semantic relationships,with the effects of the situation model influencing laterstages of selection or integration, in keeping with construc-tionalist models. These hypotheses will be tested in futurestudies. What the current study does show is that, at leastwhen comprehenders are focusing on causal relationships,they can activate the necessary information for complexcausal inferencing fast enough to influence the earlieststages of lexico-semantic processing, and that this cannotbe accounted for by lexico-semantic relationships alone.

A second open question is precisely when did causal in-ferencing take place in the present study. One possibility isthat the implicit retrieval of relevant stored informationoccurred before the onset of the critical word (a true pre-dictive inference; Cook et al., 2001; Klin et al., 1999), andthat this led to a “preactivation” of its semantic features, fa-cilitating semantic access. This may have been particularlylikely in the present study as participants were given afairly long time to read the first two sentences which mayhave encouraged them to predict the content of the thirdsentence. Another possibility is that the retrieval of this in-formation was initiated only once the critical word was pre-

sented (a bridging inference; Singer & Ferreira, 1983). Thisexperiment cannot distinguish these possibilities becauseit remains unclear whether the amplitude of N400 itselfalways reflects the consequences of truly predictive se-mantic processing (Federmeier, 2007) or whether it reflectsa three-way mapping between context, stored information,and lexico-semantic features of an incoming word that isinitiated only once that word is presented (Hagoort, 2005;Holcomb, 1993). In other types of paradigms using highlyconstrained sentence and discourse contexts, however,there is evidence that the N400 can be modulated priorto the onset of a critical word by semantic predictions ofthat word (Federmeier, 2007; DeLong, Urbach, & Kutas,2005; van Berkum, Brown, Zwitserlood, Kooijman, &Hagoort, 2005). Future studies will determine whether thisis also the case during the establishment of causal coher-ence when the context is less highly constrained.A third set of questions asks how the current pattern of

ERP data can be reconciled with the nonlinear pattern ofmemory retrieval originally described by Keenan et al.(1990) and Myers et al. (1987), as well as the nonlinear pat-tern of hemodynamic response reported in fMRI studies(Kuperberg et al., 2006; Mason & Just, 2004) across similartypes of three-sentence scenarios. As discussed in theIntroduction, participants! recall of intermediately relatedscenarios is superior than their recall of both highly causallyrelated and unrelated scenarios (Keenan et al., 1990; Myerset al., 1987). And, in an fMRI study using similar stimuli tothose used here, we reported that participants took longestto judge the overall coherence of intermediately relatedscenarios, and recruited a large network of regions distrib-uted across right and left temporal–prefrontal cortices tothese scenarios (Kuperberg et al., 2006).5 Although thereasons for this discrepancy between theERP and fMRI find-ings are unclear, one possibility is that it reflects differencesin the temporal resolution between techniques such thatthey are each likely to have captured different stages of neu-rocognitive processing. ERPs measure neural response toindividual events, and they therefore provided a sensitivemeasure of fast, on-line processes that were time-lockedto the onset of the critical words. In contrast, fMRI indexeshemodynamic activity spread out over many words andduring decision-making, and may have better capturedmore “off-line” processes of evaluating the full meaning ofthe intermediately related scenarios (including the specificinferences added to the discourse model). The increasedneural activity and reaction times associated with evaluatingthe meaning of the intermediately related scenarios may,in turn, have reflected their active consolidation withinepisodic memory, leading to their improved recall on latertesting.

Conclusions

In sum, these results demonstrate unambiguously thatcausal coherence, driven by relationships at the situationlevel, can influence incremental word-by-word discourse


comprehension, even when semantic relationships be-tween individual words are matched and even when com-plex inferences are required to link intermediately relatedsentences. They further demonstrate that, at least whencomprehenders are actively engaged in building causal co-herence, inferencial processes that add new informationto the discourse model can impact at the earliest stagesof lexico-semantic processing.

AcknowledgmentsThis work was funded by National Institute of Mental Health (RO1MH02034 to G. R. K.), and the National Alliance for Research inSchizophrenia and Depression (NARSAD) with the Sidney J. BaerTrust (G. R. K.). We thank Mante Nieuwland and Ellen Lau fortheir insightful comments on the manuscript. We are also gratefulto Abigail Swain for her assistance in analyzing data and to EricFields for his help with manuscript preparation.

Reprint requests should be sent to Gina R. Kuperberg, Depart-ment of Psychology, Tufts University, 490 Boston Avenue, Med-ford, MA 02155, or via e-mail: [email protected].

Notes

1. Note that the causally unrelated scenarios used in the pres-ent study differed from the causally unrelated scenarios that wehave used in previous studies (Ditman & Kuperberg, 2007;Kuperberg et al., 2006) where LSA values of content words withinthese unrelated scenarios were not matched with those of thehighly and intermediately related scenarios.2. For scenarios where the critical word appeared before thesentence-final position, we also examined ERPs at the sentence-final word and found no significant main effects of causal re-latedness or interactions between causal relatedness and APdistribution between these two conditions (or between any othertwo conditions) in this time window.3. In Camblin, Gordon, et al. (2007), associated incongruouswords evoked a larger anterior positivity and larger posteriornegativity than nonassociated incongruous words within a latertime window.4. Interestingly, the distribution of the effect to the intermedi-ately related versus highly related scenarios was more anteriorthan the classic N400 effect. It is possible that this was because itreflected, in part, a prefrontally mediated more directed searchthat activated relevant information, although this is speculative.5. Mason and Just (2004) also showed increases in the hemody-namic response to intermediately related scenarios relative tohighly causally related or causally unrelated scenarios, but onlywithin the right hemisphere. However, as discussed by Kuperberget al. (2006), there were several methodological factors that mayhave limited power to detect more widespread activation in thisstudy.

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