1There is a restricted sense of the term ‘linguistic’ which would not include GCI inferences,

because in a sense pragmatic inference arises from non-linguistic convention (presuppositions,

however, would still be included). Nevertheless, GCIs are accounted for by a theory of human

communication and can be considered to be triggered by the rational design of human

reasoning. Thus they are in the broad use of the term of ‘linguistic’ origin.

An experimental study on pragmatic inferences: Processingimplicatures and presuppositions

Napoleon KatsosResearch Centre for English and Applied Linguistics

Introduction

World–knowledge based inference generation has been in the focus ofpsycholinguistic research for more than 20 years, yet only recently hasattention been given to inferences that are triggered by pragmatic mechanisms.The present research attempts to contribute to the study of processingpragmatic inferences by presenting and discussing a number ofpsycholinguistic experiments that investigate a) whether pragmatic inferencegeneration occurs on-line, during comprehension, or whether it is under thestrategic control of the reader1, i.e. occurring off-line after comprehension andb) whether certain classifications of inferences suggested by pragmatic theoryhave psychological reality.

This study was inspired by Levinson’s (2000) explicit call for a detailedmapping of pragmatic theory to claims about processing: ‘GCI [GeneralisedConversational Implicature] Theory clearly ought to make predictions aboutprocessing. But here the predictions have not yet been worked out in any detail.There is very little psycholinguistic work directly addressed to implicature andstill less of this concerns on-line processing, but one may hope for rapidprogress here’ (ibid:370)

In section 1, I briefly review the psycholinguistic literature on inference andI discuss Levinson’s approach to pragmatic inference, comparing it brieflywith the Relevance Theory framework. In section 2, I spell out what I believeto be Levinson’s predictions about processing and in section 3 I discuss fourpsycholinguistic experiments I conducted. Finally in section 4, I present theimplications that arise from this research for pragmatic theory and forpsycholinguistic research.

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2There is a restricted sense of the term ‘linguistic’ which would not include GCI inferences,

because in a sense pragmatic inference arise from non-linguistic convention (presuppositions,

however, would still be included). Nevertheless, GCIs are accounted for by a theory of human

communication and can be considered to be triggered by the rational design of human

reasoning. Thus they are in the broad use of the term of ‘linguistic’ origin.

Inference Generation

Psycholinguistic research has shown that humans, given a certainlinguistic input, generate a number of world-knowledge based inferences.Consequently, there has been a vigorous debate on how many and which typesof these inferences are generated on-line, during the course of comprehension,and which are generated during a later retrieval/elaboration period. On-lineinferences are assumed to be automatically generated without the reader’sactive engagement. On the contrary, off-line inferences are under the reader’sstrategic control, and consist of information constructed by slower processesrequiring rich cognitive resources. On one side stands the MinimalistHypothesis (e.g. McKoon & Ratcliff 1992, 1998), which claims that, accordingto an economy principle, only a restricted number of inferences is generatedon-line (those needed for global coherence and those primed by close semanticassociations); on the other side stands the Constructivist Hypothesis, whichclaims that -in addition to the inferences that the Minimalist Hypothesis grants-a wider array of inference types is generated on-line (inferences aboutsuperordinate goals of characters in narratives, about causal antecedents andabout global coherence considerations, e.g. Britton & Graesser 1996; Graesser,Singer & Trabasso 1994; van den Broek, Risden & Husebye-Hartmann 1995).Recent experimental data seem to favour the Constructivist Hypothesis, andvarious factors that influence inference generation have been noted, includinga) reader goals, b) attention and memory capacity, c) familiarity with the topicof the discourse and previous knowledge.

What is common in both Minimalist and Constructivist approaches is that theyare focused on world-knowledge based inferences and on extra-linguisticfactors of psychological nature. Both approaches are perhaps willing to acceptthat the vast number of linguistic inferences that are needed to ‘flesh out’ thelinguistic code actually happen on-line (e.g Graesser et al 1994). However, thisassumption has not been experimentally tested up to now. The present researchis focused on this grey area of linguistically-pragmatically driven inferences2.In particular, I will test whether two specific classes of inferences, GeneralisedConversational Implicatures (GCIs) and Presuppositions (Pres.), are indeedgenerated on-line, and whether there are any linguistic factors that may affecttheir generation, such as discourse structure. Research on this field is currently

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3This discussion intends to provide only the essential background for the experimental research.

See Grice (1989) and Levinson (2000) for a detailed exposition of these concepts.

under way and published literature is scarce.

Pragmatic Inferences

The term ‘implicature’ was first coined by Paul Grice (e.g. 1989) toaccount for those aspects of an utterance’s signification that are onlyimplicated by the speaker without being explicitly said. Besides ‘particularizedimplicatures’ that depend on the context of the situation (linguistic or not) andbackground knowledge, there is a type of implicature which is inferredregardless of the specific context. For example:

(1) On a Tuesday at the office, A says to B and C to D:A: Where could I find John?B: John doesn’t come to work on Tuesdays.

And:C: Somebody used my mug and didn’t wash it! I suspect it is John.D: John doesn’t come to work on Tuesdays.

In these conversations, B’s utterance particularly implicates that on the day ofutterance John will not be found at the office and D’s utterance that he can’thave used the mug. But they both implicate that John comes to work on otherdays of the week. This implicature which arises regardless of the context of useby virtue of a linguistic convention is called, according to Levinson (2000:11),Generalised Conversational Implicature (GCI). GCIs are generated by threedistinct mechanisms and accordingly categorised: Q-GCIs arise from thesubmaxims of Quantity3, summarised in the heuristic ‘what you do not say isnot the case’ (ibid: 35), which signals that for a set of alternates, the use of one(especially a weaker one) implicates that another is inapplicable (especially anotherwise compatible stronger alternate). For example, given the scale <all,some>, the speaker’s choice to use ‘some’ conversationally implicates(symbolised as +>) that ‘some but not all’ is also the case. I-GCIs are alsobased on the submaxims of Quantity, summarised by the heuristic ‘say as littleas necessary’, i.e. produce the minimum information sufficient to achieve yourcommunicational goals. M-GCIs are based on the submaxims of Manner andon the heuristic ‘What is said in an abnormal way is not normal’ (ibid: 135).What is said simply, briefly, in an unmarked way, picks up the stereotypicalinterpretation; if in contrast a marked expression is used, it is suggested that thestereotypical interpretation should be avoided. For example, ‘she made the carstop’ implicates that ‘she did not do it in the usual stereotypical way’, e.g. not

104 PRAGMATIC INFERENCES

by stepping on the brakes.

However, there are contexts in which GCIs are cancelled. Consider:

(2) If some of your students fail, you’ll be fired.

Whereas ‘some’ usually implicates ‘not all’, in this case ‘some’ means ‘at leastone, including all’. The point that Levinson makes is that GCIs are the defaultinterpretations, i.e. that the GCI will be inferred by default, because it is thepreferred interpretation an item or an expression will have, so long as specificcontextual assumptions do not cancel the binary possibility of interpretation.However, other recent pragmatic approaches, like Relevance Theory (RT)differ in their predictions. RT claims that all pragmatic processes are guidedby the overarching Principle of Relevance and the principle of optimalrelevance, according to which an inference will be drawn if it manages to haveenough contextual effects to be worthy the addressee’s attention while puttingthe addressee into no gratuitous cognitive effort in achieving these effects (e.g.Sperber & Wilson 1986, 1995). Therefore, pragmatic inferences are generatedonly if they are relevant enough, i.e. rich enough in cognitive effects, to beworth generating. In section 4.1. I will discuss in more detail the two rivalpragmatic theories and how the present research is related to them.

Another type of linguistic inference, presupposition, may be considered ofeither pragmatic or semantic origin (e.g. Levinson 1983 chapter 4; Saeed1997:90ff ). Presuppositions are part of the conventional meaning ofexpressions; under certain conditions they behave like entailments, inferencesof semantic nature, yet at least one property that distinguishes presuppositionsfrom entailments is that the former project even in cases where the latter areblocked by syntactic negation. For example, (3) entails ‘Mary did not reportto her boss’ and presupposes (signified as >>)‘Mary ought to report to herboss’, whereas (3’) does not entail ‘Mary did not report to her boss’ but it stillpresupposes >> ‘Mary ought to report to her boss’.

(3) Mary avoided reporting to her boss.(3’) Mary did not avoid reporting to her boss.

Presuppositions are thus considered by most pragmaticists and a number ofsemanticists (e.g. Allan 2001) as a special type of pragmatic inference.Investigating presuppositions along with GCIs is rather interesting sincepresuppositions (by virtue of sharing semantic and pragmatic properties) area type of pragmatic inference that may be reliably expected to be inferred on-line.

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Levinson’s Predictions about Processing

According to Levinson (2000), GCI theory (or any other pragmatictheory for that matter) ought to make predictions about processing. Levinsonstates that such predictions haven’t been spelled out yet, nor are they clear.Indeed, a review of the relevant literature shows that the matter has not beensubjected to research up to now. However, Levinson makes claims that arepertinent to the question that has inspired most research on inferences, whetherthe inferences occur on-line or not:

‘…take online utterance comprehension: the prediction clearlyis that GCIs should be available early, before full sentencemeaning is computed, because of their association withlinguistic expressions and their alternates’(ibid: 370).

I understand this passage to suggest that GCIs are generated on-line.Levinson’s use of the term ‘available’ (instead of a term like ‘generated’) isinteresting; I believe it has been chosen carefully, because Levinson’s theorymust make provisions for the case where the GCI is not drawn because ofcontextual assumptions: in any case the inference is ‘available’ on-line. If thereare specific contextual assumptions that block its generation, the inference isnot actually generated (but retains some activation). If there are no suchcontextual assumptions that cancel the inference, then its activation leads to itsactual generation. The point is that the screening process occurs ‘early’,‘before full sentence meaning is computed’, and therefore is an on-linephenomenon.

The Focus of the Present Study

The present study is focused on the question above: whether GCIs andPresuppositions are generated on-line. More than one type of GCIs has beentested; since GCIs are generated by the same pragmatic component, butthrough different mechanisms, it is interesting to see whether all GCI typesbehave in a consistent manner on-line. Intuitively, even if no on-line effect isobtained for GCIs, my expectations are that some effect should be detected forPresuppositions. It is especially interesting to compare how Presuppositionsbehave against GCIs, since significant difference or similarity may as well beanother argument in favour or against the distinct status of presuppositions inthe pragmatic/semantic literature.

For the purposes of this research I made a choice of GCIs to study: from thethree types of implicature, Q- and M-implicature are potentially good

106 PRAGMATIC INFERENCES

candidates for experimentation since they share a similar generationmechanism. Both are of negative character, i.e. they are triggered when theaddressee realises that an alternative expression could have been chosen butwasn’t. Thus they are both metalinguistic, in that they require the addressee toinfer that the speaker could have used an alternative expression but did not doso.

Even so, the present research will be directed towards a selection of Q- and M-subcategories. Again, it is practically impossible to examine all the subtypesargued for in the literature. Also, a few subcategories have already beensubjected to profound psycholinguistic research. For example, M-GCI isinvolved in reference assignment, in bridging inferences and in figurativelanguage. So a choice of Q- and M- subcategories has been made, picking themost representative –a subjective criterion, obviously- and the ones where lessor no research has been done (for the experimental items see Appendix 1).

Description of the Experiments and Discussion

I conducted four experiments, one off-line and three on-line, followinga method similar to the ‘three-pronged method’ developed by Graesser and hiscolleagues (e.g. Magliano & Graesser 1991; Trabasso & Suh 1993; Graesseret al 1994) for detecting the on-line generation of world-knowledge basedinferences. The three steps of this procedure are: a) careful preparation of theexperimental materials b) an off-line experiment to establish that thephenomenon under study occurs at least off-line c) a number of on-lineexperiments to test whether the phenomenon occurs on-line. The experimentalitems were in Greek and tested on adult native speakers of Greek. However,the theoretical claims and the results of this study are not supposed to belanguage specific and should obtain for any language. Linguistic items andexpressions may of course be different from language to language, but thenature of pragmatic and processing mechanisms that generate them is assumedto be universal.

Experiment 1

Aim: Experiment 1 was conducted off-line, aiming to test whether theinferences under study are indeed generated, at least off-line, after consciousreflection. Materials: Six tokens of Q- and M- GCI and six tokens of Pres. wereembedded in short texts. Thus 18 short texts of 2 to 6 sentences were created.Each text contained at the beginning (sentences 1 to 3) a sentence which shouldgenerate an inference. Each text, however, concluded with a sentence whose

NAPOLEON KATSO S 107

4Unless one is to suppose that other people joined the workers on the stairs. Such an additional

thought, however, is not licensed by the discourse; and anyhow, its generation proves exactly

the point that the discourse is inconsistent unless some unexpected, additional inference is

conjured to rectify it.

interpretation was inconsistent with the predicted inference, thus rendering thediscourse incoherent. For example, the text which contained the Q-implicature‘some’+> ‘not all’ was the following:

(4) The strike was well organised. There were a hundred workers gatheredoutside the factory. Some of the workers went in. They headed for thefirst floor. The stairs trembled under the footsteps of a hundred angrymen.

The Q-GCI of ‘some’ suggests that not all one hundred men went in the factoryand thus it is inconsistent with the statement that ‘the stairs trembled under thefootsteps of a hundred men’ (for the texts used, see Appendix 2)4. Anotherthree texts which gave rise to no inconsistency were added in the materials asfillers.

Good care has been taken that the inferences under study were not necessaryfor establishing local coherence. As mentioned previously, any theory oninference generation agrees that backward inferences which are needed forestablishing local coherence are reliably generated on-line. The purpose of thisresearch is to see whether pragmatic inferences are generated on-line under anycondition. Therefore it is necessary to test for the inferences in cases wherethey are forward-looking, elaborative and cognitively costly. Thus all the textswere so that the inferences would not be necessary for local coherence. Forexample, they were like:

(5) What John didn’t lose during his last trip was his wallet. He thankedGod for that (5) >> but he lost something else.

Here the presupposition is not necessary for any coherence relation. Had thetext been like (5’):

(5’) John was very upset after he returned from Algeria. What he didn’t losethere was his wallet.

then the ‘but he lost something else’ inference would have been necessary forcoherence and thus generated anyhow. Moreover, as the status ofpresuppositions has given rise to a still on-going debate in semantic/pragmatic

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theory, I made sure that the presuppositions I tested did not coincide withentailments.

Subjects: Eighteen adult native speakers of Greek. Method: Twenty one short texts were arranged in random order and printed onpaper. They were then presented to subjects with the instructions to read eachtext closely but only once (with emphasis) in order to detect any inconsistencyor logical fallacy. Should an inconsistency be detected, that reliably signalsthat the subject has generated the inference that is clashing with the finalsentence of the discourse. Obviously, the attention of the subjects was criticallyfocused on finding inconsistencies and on judging the coherence of each text.

Results: Inconsistencies were detected in average 76.3% of all the cases (seetable 1). As far as type is concerned, Pres. inconsistencies were most detected(92,2%, standard deviation 1.75), with Q-GCI second (77,7%, s.dev. 2.99) andM-GCI third (52,7%, sdev 5.75) (see table 1). In a t-test, the difference of therate of detection of Pr- and M- inconsistencies was significant (t(11)= 2.8031sig.)

Table 1: Off-line detection of inconsistencies

Q-implicatures

1 2 3 4 5 6

14/18 14/18 18/18 12/18 10/18 17/18

Average: 14/18 (77.7%) (Standard deviation: 2.99)

M-implicatures

1 2 3 4 5 6

11/18 6/18 4/18 4/18 15/18 17/18

Average: 9.5/18 (52.7%) (Standard deviation: 5.75)

Presuppositions

1 2 3 4 5 6

15/18 18/18 14/18 18/18 17/18 18/18

Average: 16.6/18 (92.2%) (Standard deviation: 1.75)

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Discussion: Overall, inconsistencies were reliably detected with a high averageand no single inconsistency dropping less than 25%. The fact that Pres.inconsistencies were highly detected in a consistent pattern (lowest standarddeviation) may suggest that presuppositional meaning is almost core(semantic) meaning in its nature (but as far as the low standard deviation isconcerned, one could be wary of a ceiling effect here). Q- and M-inconsistencies are detected at a lower rate than Pres. and have a higherstandard deviation; this may imply that they both evoke an inference of purelypragmatic origin which is less strong than presuppositions. Detection of Q- andM- inconsistencies does not vary significantly, which may suggest that indeedthey share the same pragmatic status. Note that Pres. are detected significantlymore than M-GCIs only; between Pr. and Qs. there was not any significantdifference.

Why should M- inconsistencies be less detectable than Pres. inconsistencies?First, we should note that M-GCIs show high Standard Deviation. Some M-GCIs (M5, M6) were highly detected but others were only poorly so (M2, M3,M4), giving rise to the highest Standard Deviation (5.75). M5-6 and M2-3-4belong to different sub-types: M5 and M6 are reduplications, where repetitionsignals unusual duration or quantity (as appropriate: ‘he ran and ran’, ‘it rainedand rained’). On the other hand, M2 (‘cause the death of’) and M3 (‘make thecar stop’) give rise to an implicature by the use of the periphrastic, moreelaborate wording, instead of the more usual concise expression, signalling thatthe event did not occur in the stereotypical way; and M4 implicature arises bythe use of a bare (no determiner) noun phrase (‘she went to school’), whichsuggests that only the stereotypical activity took place.

Both sub-types share the common negative character of M-implicatures: useof a marked, not usual wording, signifies that a non-usual situation is involved.But it seems to be the case that the strong contrast of a reduplicated item withits normal counterpart may be giving rise to a strong (in terms of psychologicalconspicuousness) implicature, whereas the more loose connections between aperiphrastic expression and its single counterpart may give rise to a lessnoticeable implicature. So, it may be that ‘ran and ran’ evokes more stronglythe avoided counterpart ‘ran’ whereas ‘cause the death of’ or ‘make the carstop’ evoke less strongly ‘kill’ and ‘stop’. What I am suggesting is that somecontrasts between related expressions may be more marked than others. It isthese less marked contrasts that cause the significant difference between theoverall M-GCI group and Pres. inconsistencies. Let us note that the ‘weak’ M-GCIs implicatures may be giving rise to a more pardonable inconsistency,since they may be regarded as stylistically imperfect but still rathercomprehensible sentences. ‘Cause the death of’ may be pardoned as a verbose

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version of plainly meaning ‘kill’. But Pres. violations, on the other hand, canlead to almost logical contradictions. Stating ‘Mary is a lawyer’ after havingread ‘John is a better doctor than Mary’ can be considered a false statement.

Overall, Experiment 1 is fundamental since it establishes that the inferencesunder study are generated at least off-line. As Graesser et al (1994: 371) note,many a study has failed to obtain results or been misdirected by avoiding theoff-line version of the experiments. It is crucial to make sure that what issought for on-line has likelihood to appear, by establishing that it appears off-line. Thus, any negative results that are obtained can indeed be interpreted asnegative values and not as the illusionary effect caused by the absence of thephenomenon. Of course, from Experiment 1, we can not know whether theinferences were also generated on-line. The method and the instructions givento subjects critically directed their attention to the inconsistencies, biasing theirprocessing of texts. But having established that the texts do trigger theinferences, Experiment 1 gives the license to proceed to on-line experimentsusing the same materials.

Experiment 2

Aim: Experiment 2 aims to test whether the inferences that were generated off-line in Experiment 1 are also generated on-line. Experiment 2 measuredreading time and predicted that if subjects do generate the inferences on-line,then significant slowdowns in reading time should manifest when subjects readsubsequent information which is inconsistent with the inference (the slowdownis caused by the difficulty of integrating the new information with the existingmental representation of the discourse).Materials: The materials for this test were the same 18 texts used in the off-lineexperiment which gave rise to an inconsistency (these were called now INCONversions) and 18 almost identical texts, which however were minimally alteredso that they did not generate any inference and were in the whole consistent(these were called CON versions). For example INCON (1) was:

(4) The strike was well organised. There were a hundred workers gatheredoutside the factory. Some of the workers went in. They headed for thefirst floor. The stairs trembled under the footsteps of a hundred angrymen.

NAPOLEON KATSO S 111

And it was altered to CON (1) as (4’):

(4’) The strike was well organised. There were a hundred workers gatheredoutside the factory. All the workers went in. They headed for the firstfloor. The stairs trembled under the footsteps of a hundred angry men.

Subjects: Thirty adults, native speakers of Greek Method: Thirty six texts were divided in two groups, A and B. Each groupcontained eighteen texts, half in the INCON versions and half in the CONversions. So Group A contained texts Q 1,3,5, M 1,3,5, and Pr 1,3,5 in theINCON version and texts Q 2,4,6, M 2,4,6, Pr 2,4,6 in the CON version; GroupB contained Q 1,3,5, M 1,3,5, and Pr 1,3,5 in the CON version and texts Q2,4,6, M 2,4,6, Pr 2,4,6 in the INCON version. Also one other text was addedas a trial item and another one as filler, so in total each group contained twentytexts (9 CON, 9 INCON and 1 trial and 1 filler).

Each text was analysed into the separate sentences that comprised it. Thesentences were typed in Microsoft Paint format to be compatible with SuperLab Pro software. The software was programmed to show on the computerscreen only one sentence at a time. For the next sentence to appear, the subjectshad to press the mouse button. Between two consequent sentences of the sametext there was a 600milisecs interval. After the sentences of each text wereread, an instruction asked the subjects to paraphrase what they had read. Afinal instruction asked them to press the mouse button if they were ready forthe next group of sentences. Between the final instruction and the first sentenceof the next there was a 1000milisec interval. The texts were arranged to appearin random order. Fourteen subjects read the texts in Group A and sixteensubjects read the texts in Group B. All subjects were explicitly told to ‘readeach sentence only once, so that every one who has done the experiment willhave a similar pace’. Moreover, they were told to press the mouse buttonimmediately after they read each sentence.

Super Lab Pro is software developed for measuring response time (RT). Whensubjects pressed the mouse button to read the next sentence, the time theyneeded to read the previous sentence was recorded. When analysing the data,RTs were collected for each sentence. RTs were then sorted out depending onwhether they were in the CON or the INCON version. As explained above, inboth versions the final sentence was the same, but in the INCON version oneof the previous sentences gives rise to an inference which, if detected, isinconsistent with the final sentence. If the inferences were generated, thenreading the final sentence of each text in the INCON version should be slowerthan reading the same sentence in the CON version.

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Results: CON and INCON RTs were computed for all three types (Pr-, Q-,M-GCI) (see table 2). Overall, consistency was a significant factor F(1,28)=20.572 p< .001. All types of inferences were read in the CON version in (meantime) 3492 milisecs and in the INCON in (mean time) 4235 milisecs.Therefore, the INCON version was read with a mean slowdown of 743milisecs, which suggests that they were generated on-line. Type of inferencewas also a significant factor F(2,56)= 12.283. p< .001, as was the overallinteraction between type and consistency was significant, F(2,56)= 4.171p< .001.

Analysis by type of inference: The Q-GCIs were read in the INCON with aslowdown of 588 milisecs (with respect to the CON version), which issignificant F(1,28)= 4.972 sig. p< .035. Similarly the M-GCIs were read in theINCON version with a significant slowdown of 517 milisecs F(1,28)= 8.804sig. p< .01. Finally, the Pres. were read in the INCON version with asignificant slowdown of 1124 milisecs F(1,28)= 20.176 sig. p< .001. Thedifference between the slowdown caused by Q- and the one caused by M-inconsistencies was not significant F(1,28)= .42 n.s. But the difference of theslowdown caused by Pres. and by M-GCIs was significant F(1,28)= 5.636, sig.p< .03, as it also was for Pres. and Q-GCIs F(1,28)=5.376 sig. p< .03.

Table 2: Experiment 2 reading time results (in msec)

GROUP Mean

QCON

1 4880

2 3244

Total 4007

QINCON

1 3866

2 5232

Total 4595

MCON

1 3193

2 3120

Total 3154

MINCON

1 3984

2 3396

Total 3671

PRCON

1 3208

2 3413

Total 3317

PRINCON

1 5104

2 3862

Total 4441

NAPOLEON KATSO S 113

Analysis item by item: An item by item analysis showed that the overallsignificant slowdown in cases of inconsistency was not due to a distributedslowdown in individual items. Certain items caused a high slowdown and someitems no slowdown (overall Standard Deviation =922.5 milisecs, highest value3406, lowest -217. Standard Deviation for the slowdown caused by Q-GCIs658, by M-GCIs 726 and by Pres. 1268 milisecs). Negative response timeswere recorded as well, which however were not significantly low. (A negativeresponse time means that the INCON version of a text was read faster than theCON version. Had negative response times been significantly low, that couldundermine the whole methodology of the experiment since an INCON versionshould either be read more slowly or equally fast as a CON version, but neversignificantly faster than a CON version).

However, it should be noted that one may seriously question the feasibility ofan item by item analysis at the present point. It should be taken underconsideration that different groups of subjects read different items and thatreading pace may vary in such a way that only overall results may beappropriately compared, not individual items. As the number of subjectsreading each group will increase, this objection will weaken, assuming thatreading pace differences will be gradually levelled. In an alternative approach,one could calculate the reading pace of each group by comparing reading timevalues for similar sentences (in both CON and INCON versions of each textthere is a number of sentences which were identical: besides the minimallyaltered sentence and the final sentence which is the one under study, the restof the sentences are the same).

Discussion: Experiment 2 provides sound evidence that all Pres., Q- and M-GCI inferences are generated on-line. Moreover, it shows that the slowdowncaused by Pres. inconsistencies is significantly bigger than the slowdown causeby either Q- or M-GCI inconsistencies, suggesting that Pres. may be a type ofinference that attaches to the item/expression that triggers it more strongly thaneither Q- or M-GCIs. M- inconsistencies caused a bigger slowdown than Q-,but not significantly so, thus suggesting that Q- and M- inferences may beconsidered the same type of inference.

Correlation of experiments 1 and 2: It is interesting to see whether there wasany correlation between how highly subjects detected an anomaly in the off-line test and how high a slowdown it caused in the on-line experiment.

Overall correlation: Comparison of the general results obtained by the off- andthe on-line experiment shows that they are compatible. The Pres., Q- and

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M-GCI inferences that were detected off-line also caused a slowdown on-line,creating an effect which was significant in both cases.

Correlation by type: In the off-line Experiment 1, Pres. was the type ofinference whose inconsistencies were most detected. Q-GCI was second andM-GCI was third. The difference between the behaviour of Pres. and Q-GCIs,and Q- and M-GCIs was not significant, but the difference between Pres. andM-GCIs was. In the on-line Experiment 2, a similar picture is depicted. Pres.was the type of inference whose inconsistency caused the biggest slowdown.In this experiment second were M- and third Q- GCIs. But again the differencebetween M- and Q-GCI slowdowns was not significant. Yet the slowdowncaused by Pres. inconsistencies was significantly bigger than the ones causedby either M- or Q- inconsistencies. Thus it seems that both experimentsconverge to the same picture: Pres. behave as the significantly strongestinference, whereas Q- and M-GCIs behave with no significant differencebetween them.

Correlation by item: The correlation of all eighteen inferences’ slowdownagainst how many times they were detected off-line was not significant (0.225)(even when extreme values were excluded or capped (see figures 4 and 5). Asfor items within one type, the correlation was high for the Q-items (0.850) butnot for the M-items (-0.249) or the Presuppositions (0.255) (see figures 1,2 and3). However, I cannot think of any reason intrinsic to Q-GCIs or to the textswhich contained them that may account for the significance of the correlationspecifically for this type of items.

The failure to obtain a general significant correlation between off-line andonline effects in the item by item analysis may be interpreted in various ways.One may again question the feasibility of such an analysis at the present pointas mentioned above. If one puts such objections aside, then, in order topreserve the assumption that items that belong in the same type of inferenceshould behave similarly, individual variation must be ascribed to differentproperties of each text. Discourse relations may be important, certain itemsmay have been in focus/topic whereas others not, some items could beemphasized whereas others not. For example, in text (4) the number of peopleinvolved is topicalised:

(4) There were a hundred workers gathered outside the factory. Some ofthem…

NAPOLEON KATSO S 115

Figure 4

Figure 3

Figure 1 Figure 2

Figure 5

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But the item that triggers the inference is not topic in all texts. Moreover, lexicalor syntactic negation may be significant, generally causing slowdowns, as in(6-6’):

(6) Mary accused John of not painting the portrait with traditionaltechniques. She liked innovation

vs.

(6’) Mary accused John of painting the portrait with traditional techniques.She liked innovation.

Another way of dealing with this non-correlation is to accept that off-line andon-line tasks are inherently different. The discrepancy of off- and on-linemethodologies only justifies the approach of the present study, which took bothinto account. In these experiments, the major variable between the off-line andon-line tasks is the depth of processing in which subjects indulge. Coherencejudgement tasks of Experiment 1 require an in-depth processing which ispsychologically implausible in the process of ordinary language comprehension.Experiment 2 resembles more closely natural comprehension, since a distractiontask was given and sentences disappeared after they were read, to channelattention to the ‘core content’ of each discourse and to burden memory in a waysimilar to everyday comprehension.

Finally, granting again that the item by item is feasible under the presentcircumstances, it is unsettling that Experiment 1 and 2 correlate highly in thelevel of the overall result and the type of inference, but when it comes down tothe actual items no correlation is achieved. This may well suggest that the goodcorrelation at the level of type of inference is a chance effect. And sincecorrelation at the level of type cannot guarantee a good correlation at the actualitem by item level, perhaps the different inference types -constructed bypragmatic theory- have no psychological reality. This suspicion is strengthenedby the observation that standard deviation of how big slowdowns were causedwas high (for Q-GCIs 658, for M-GCIs 726 and for Pres. 1268 milisecs,average: 992 milisecs). Maintaining that items whose behaviour in thisexperiment differed by 500, 1000 or even more milisecs (biggest differencerecorded: 3623) should belong to the same group seems to call urgently forfurther justification through future experimentation.

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5Levinson defines the uses ‘discourse goals’ referring to a computational approach to discourse

sketched by Allen (1983). Surely, further research can shed more light to the concept.

Experiment 3

Aim: Levinson himself weakens the claim that GCIs are default inferences,acknowledging cases where the inference is ‘cancelled’, ‘evaporated’ or‘doesn’t go through’ (ibid:51-52). At least for the cases where the discourse setsa restricted goal5 and the reader understands an obstacle to that goal, if theobstacle can be overcome without generating a GCI, then there is no need togenerate it. Readers are assumed to know how many inferences to generate bymonitoring the discourse through a maxim or relation, which determines thedepth of inferencing which is appropriate. The plurality in the terms Levinsonevokes to describe the phenomenon, reflects, I think, the vagueness whichsurrounds this particular point. Translating Levinson’s remarks into processingpredictions, the claim is that there are certain discourse conditions under whichGCIs are not generated on-line. The aim of Experiment 3 is to test such caseswith the methodology used in Experiment 2.

Materials: One particular Q-GCI was tested: ‘some’ Q+> ‘not all’; this is oneof the two GCIs used by Levinson to clarify his claims (the other being the caseof the numerals). Seventeen of the subjects which participated in Experiment 2were tested on three additional texts. In the beginning, these texts set out adiscourse goal, a question, which in the rest of the discourse is satisfied by thesemantic meaning of the item that could generate a Q-GCI. For example, onediscourse begins with:

(7) When the journalists asked why Mr and Mrs X were arrested, the ChiefInspector said that some of their documents were forgeries.

In this case, for Mr and Mrs X to be arrested it is sufficient that they be foundwith at least one forged document, so that the implicature ‘not all theirdocuments were forgeries’ is not necessary and according to Levinson need notbe activated. If this is true, then no slowdown should arise if in the rest of thediscourse it is mentioned that ‘all the documents were forgeries’. So the textwith the (predicted but not generated) implicature is dubbed INCON and NO-CLASH as in:

(8) When the journalists asked why Mr and Mrs X were arrested, the ChiefInspector said that some of their documents were forgeries. The policehave detected counterfeit seals and signatures in every official documentthey had on them.

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6Strictly speaking all one can claim is that the consistency effect is a considerably bigger for

CLASH than NOCLASH. I am not entitled to claim that there is no consistency effect for

NOCLASH at all, because the mean of 4671 milisecs for the NOCLASH control is dependent

on the reading speed of this subject group. See the following discussion.

(8) is tested against a similar CON version as:

(8’) When the journalists asked why Mr and Mrs X were arrested the ChiefInspector said that their documents were forgeries. The police havedetected counterfeit seals and signatures in every official document theyhad on them.

If indeed the inference is not generated, then one expects no slowdown effect,whereas a slowdown would suggest that the inference is generated. Two moretexts were prepared in similar manner (again, see Appendix 2).

Subjects: Seventeen adults, native speakers of GreekMethod: Three texts were added complementary to Experiment 2, so that thesesubjects were tested on all 20 texts of Experiment 2 plus the additional three ofExperiment 3. Group A consisted of eight subjects, which read all three texts inthe CON version and group B consisted of nine subjects, which read all texts inthe INCON version. Experiment 3 was not well designed, since each group readtexts in only one version, group A read all CON and B all INCON. Thereforein the analysis a mixed ANOVA test was used, with clashing as within subjectvariable and consistency as between subject variable.

Results: Group A read the CON NOCLASH in 4671 milisecs and the CONCLASH in 4971 milisecs. Group B read the version of INCON NOCLASH in3892 and the INCON CLASH in 6015 milisecs. Overall, INCON CLASH wasread with a 1044 milisecs slowdown compared to the CON CLASH, and theINCON NOCLASH was read 779 milisecs faster than the CON NOCLASH (seetable 3). The outcome for consistency was not significant F(1,13)= .024 n.s. butit was significant for clashing F(1,13)= 6.045 p< .036. The interaction of thesetwo main effects was F(1,13)=3.426 p< .09 which for such a small number ofsubjects in each group can be considered marginally significant.

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Table 3: Experiment 3 reading time results (in msec)

GPCLASH MeanStandardDeviation

NOCLASH3 4671 20074 3892 1251Total 4256 1634

CLASH3 4971 17794 6015 2407Total 5528 2132

As noted, Group A (number 3 in table 3) read the CON version of NOCLASHin 4671 whereas Group B (number 4 in table 3) read the INCON version ofNOCLASH in 3892 milisecs. Obviously, there was no slowdown (no clash)when reading the INCON version. The fact that Group B read the INCONversion actually faster than Group A read the CON version may be attributed tofaster reading pace. However, the -assumed- faster reading Group B read theINCON version of CLASH slower than Group A read the CON version.

Discussion: Experiment 3 indicates that the Q-GCIs under these particulardiscourse circumstances were not generated on-line, suggesting that discoursefactors may be important in inference generation. Approaches on inferencegeneration up to now have only considered the effect of non-linguisticparameters. As it has been shown that readers’ goals (such as reading forexamination vs. reading for leisure) affect the number and type of knowledge-based inferences that are generated, (e.g. van den Broek et al 2001; Narvaez etal 1999) so may discourse goals be affecting what pragmatic inferences aregenerated. But the phenomenon noted here poses a challenge for a generaltheory of default inferences. How ‘default’ are inferences that are not generatedunder certain circumstances? And what is it that makes them ‘evaporate’?Levinson (ibid:52) suggests that readers are monitoring the discourse andgenerating or suppressing inferences under the guidance of the maxim ofRelevance. From the perspective of theoretical pragmatics, Carston (e.g. 1995)has addressed several weaknesses of a default account that has to explain howthe default can be overridden.

Experiment 4

Aim: Experiment 4 is focused on examining the behaviour of Pres. and Q-GCIsthrough a different perspective: if the inference they trigger is indeed on-line,then when subjects are asked to verify whether the content of the inference istrue or not, it should not make a significant difference whether the content of theinference has been implied and strengthened by a second sentence, as opposed

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to it being only implied. For example, ‘some X are Y’ has the Q-GCI +> ‘notall X are Y’. To strengthen the Q-GCI I used sentences like ‘the rest of the X areZ’. After that subjects were asked to verify as quickly as possible whether ‘notall X are Y’ is true. The prediction is that the more automatic the inference is,the less difference it should make whether the inference is facilitated by theadditional sentence which strengthens it or not. Besides Q-GCIs and Pres. I alsotested Entailments (Ent.), which are purely semantic inferences. For semanticinferences, I expected no significant difference in response time regardless ofwhether they are facilitated or not, and so they could function as a base forcomparison.

Materials: Twelve short texts were created in two versions. 1 Sentence Version(1SENT), which contained only one sentence and 2 Sentence Version (2SENT),which contained the original sentence and an additional one which facilitatedthe inference that the original sentence triggered. E.g. a Q-GCI 1SENT versionwas:

(9) Some of John’s friends study abroad.

and the 2SENT version was:

(9’) Some of John’s friends study abroad. The rest however are studying atthe same university as he is.

The 2SENT version was aiming to facilitate the Q implicature ‘not all of John’sfriends study abroad’. And for the Pres, a 1SENT was:

(10) John avoids seeing his boss after he struck the deal with the Japanese.

And the 2SENT version was:

(10’) John avoids seeing his boss after he struck the deal with the Japanese.But he knows that sooner or later he must give a detailed report of whathappened. (aiming to facilitate that ‘John ought to see his boss’)

And for ENT:

(11) 1SENT: When the robbers pulled their guns out some of the customerspanicked.

And the 2SENT version was:

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Figure 6

(11’) When the robbers pulled their guns out, some of the customers panicked.They started screaming and ran for the door (aiming to facilitate: ‘atleast one of the customers panicked’).

In total twelve texts were tested; four for each type. The Q-GCIs and the Ents.were all for the same item, ‘some’. The Pres. however were for four differentitems. The materials also contained fifteen fillers, which required False as aresponse.

Subjects: Twelve adult native speakers of Greek Method: Experiment 4 was designed as a judgement task. Super Lab Prosoftware was used, as described in Experiment 2. Subjects were presented withshort texts in either 1SENT or 2SENT version and then they were asked to judgeas True or False, as quickly as they could, sentences that explicitly stated theimplied content of the Ent., Q-GCI or Pres. In all cases where the response timewas important for the experiment the verification task was affirmative (subjecthad to press the TRUE button). For each new sentence to appear subjects hadto press the TRUE button. There was a short interval of 600 milisecs beforeeach new sentence appeared on the screen. To make subjects answer theverification task quickly, the sentence which had to be verified appeared on thescreen only for 7 seconds. Group A read fifteen fillers and six of theexperimental items in 1SENT version and the other six in 2SENT version.Group B read the same fillers and the twelve experimental items in converseversion.

Results: In the 1SENT version Entailments were read at 3789 milisecs and in the2SENT in 4055, the difference being (1SENT-2SENT= -266 milisecs) notsignificant in a t-test: t(10)= .62 n.s. Presuppositions were read in 1SENT in3185 and in 2SENT in 3167, giving a difference of 17 milisecs which again wasnot significant t(7)= .0177 n.s. But Q-GCIs were read in 1SENT in 3961 and in2SENT in 2587, the difference being 1374 milisecs, which was significant t(9)=5.23, sign. p< .001. (See figures 6 and 7)

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Figure 7

Discussion: Experiment 4 indicates that Ents. and Pres. behave in a similar way,in that their inferred meaning is activated on-line so strongly that judgment isnot affected by additionally strengthening what is already implied. However, thesignificant increase of RT in the 1SENT condition for Q-GCIs suggests that theQ-inference was not generated in the 1ENT condition as strongly as Ent. andPres. Therefore we can claim that presuppositional meaning behaves likesemantic (entailment) meaning, whereas Q-implicatures, which are purelypragmatic inferences, behave in a significantly different way, and seem to beless strongly activated. From this experiment we can only deduce that Q-GCIsare not generated on-line in the same way that Ent. or Pres. are. We are notlicensed to deduce that they are not generated on-line at all. To test that, anothertype of inference should have been tested as well, a type known not to begenerated on-line, and then compared to Q-GCIs performance. It is interestingthat according to Experiment 4 we might have to consider a notion of degrees/ scale of strength of activation.

Correlation of Experiments 1, 2 and 4: The suggestions of Experiment 4 arecompatible with the results of Experiment 1 and 2 for each type of inference.Both in off- and on-line tasks, Pres. behave in a different way compared to Q-GCIs; a way which is similar to entailments and may indicate their uniquestatus, between semantic and pragmatic meaning. However, an accuratecorrelation of the results is not feasible, since in Experiment 4 Q-GCIs arerepresented by only one item, ‘some’, whereas in Experiment 2 I tested sixdifferent Q-GCIs. To achieve the triangulation of the three experiments I shouldhave to use in all of them the same or similar materials.

Objections to the methodology of Experiment 4: An interesting phenomenon,well attested in the literature, is accuracy variation in reasoning tasks; subjectsvary in their responses depending on whether they answer by taking intoaccount any inferred pieces of information or whether they answer based onlyon what has been explicitly said (e.g. Newstead 1995). As noted above, in allcases where RT was under study, the verification task demanded the responseTRUE. But in a number of cases, subjects apparently ignored the inferred part

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of the meaning and reasoned based only on the explicit/semantic meaning. Forexample, some subjects presented with the sentence ‘Some apples are ripe’responded FALSE at the sentence ‘Not all apples are ripe’. This attestedphenomenon cannot be used to suggest that the inference was not generated on-line; subjects may be generating GCIs but then ignoring them, judging that theymust rely only on what has been explicitly stated. But this phenomenonundermines the results of the experiment, since a number of subjects bychoosing FALSE provided less RTs for comparison than what was planned.Moreover, any ambivalence subjects had on whether to take inferredinformation into consideration while reasoning may have been reflected in theirRT. As expected, the number of ‘wrong’ answers fell dramatically in the 2SENTcondition. This can be explained since the second sentence was explicitly statingthe inferred meaning (see table 4). Moreover, as one could have predicted, therewere no errors for Ents. (which do not require the generation of any pragmaticinference).

Table 4: Experiment 4 accuracy of responsesWrong answersper subject q-implicature presupposition1SENT 0.5 0.9 2SENT 0.08 0.25

General Discussion

The present research has shown that there is good reason to claim thatPres. and Q- and M-GCIs are indeed generated on-line, during the process ofcomprehension. There is no particular reason why the same result should notobtain for I-implicatures as well. The results are not supposed to be languagespecific and should obtain in any natural language, as the inference generationmechanisms are not language specific. Another strong suggestion has been thatPres. behave on-line significantly differently from GCIs. The experimental dataseem to support their being treated as a special type of inference, not groupedtogether with GCIs. Moreover, Q- and M- GCIs, which share a commonpragmatic origin, did not behave significantly different in any experiment. Afurther issue may be raised: If Q- and M-GCIs behave similarly on-line, can oneclaim that the theoretical distinction between the maxims that generate themdoes not have psychological validity? This of course calls for further research;there already are theoretical pragmatic accounts in which a single principleguides the generation of all inferences, like the Principle of Relevance inSperber & Wilson’s (1986, 1995) Relevance Theory.

Moreover, Relevance Theory (RT) is able to explain better the problematiccases where Levinson has to ‘bend’ the concept of a default inference to allowfor cancellation. Levinson admits that ‘this [the fact that GCIs can be cancelled]may cast some doubt on whether default logics are the right formalism for

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7A parallel explanation may possibly be found if one looks into large corpora for frequency

patterns in discourses: it may be the case that in most discourses the GCI an item triggers is

important for relevance. Thus by an economy mechanism the GCI is generated because

frequency considerations inform the parser that it is highly probable that it will prove to be

relevant (the importance of frequency information in discourse and sentence processing is

acknowledged by most psycholinguists e.g. McKoon & Ratcliff 1998, Pickering 1999.

Admittedly, the issue is complicated even when taking frequency into consideration, since this

brings back the question why the GCIs weren’t generated in the Exp 3 . I am under the

impression that Levinson has quite insightfully remarked the afore-cited passage: ‘much closer

work is required on the exact nature of conditions under which inferences are abandoned’

(italics in the original, Levinson 2000:53) and that research on the topic will have profound

implications for both theoretic pragmatics and experimental psycholinguistics.

modelling GCIs’ (Levinson 2000:52) and notes that ‘much closer work isrequired on the exact nature of conditions under which inferences areabandoned’ (italics in the original, ibid:53). These cases, however, can be easilyaccommodated by RT, which would grant that the only inferences that arecomputed are the ones whose rich contextual effects outweigh the cognitive costof generating them. In Experiment 3 computing the inference would not giverise to any contextual effects and would thus be uneconomical. Therefore thenon-generation of the inferences fits in ideally with the RT predictions.However, RT has to spell out an account for the strong evidence fromExperiment 1, 2 and 4, which indicate that GCIs are generated on-line evenwhen they are not contributing to local coherence (and indeed RT has to accountfor any of the forward-looking, elaborative inferences that are generated on-line). A suggestion would be to measure relevance as the number (or type) ofquestions an inference answers. The more important the question is, the morerelevant it becomes to generate that inference. For example, when a group of ahundred individuals is introduced and it is said that ‘some went in’ it is in asense relevant to keep track of the members of the group and to infer that someof them stayed outside; if John is ‘able to solve a problem’ it is relevant toindulge in forward-looking inferences on whether he managed to solve it or not7.Thus, in these cases the inferences can be said to be generated because theyproduce rich cognitive effects by answering standard, relevant underlyingquestions. As it seems, however, RT has not explicitly and technically spelledout its processing predictions on presuppositions and implicatures, while on thecontrary Levinson’s approach has given clearer predictions on which one canformulate psycholinguistic models. Finally, if all pragmatic inferences aretriggered by a single principle of relevance, another challenge for RT is toexplain why a specific class of pragmatic inferences, namely presuppositions,is more likely to be generated on-line than other inferences like implicatures.Conversely, the challenge for Levinson’s approach is to explain why one needsto postulate different heuristics for the generation of Q- and M-GCIs if as far asprocessing is concerned they seem to behave in an identical way.

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Implications for psycholinguistics and inference generation.

As noted in the introduction, there is scarce literature on processingpragmatic inferences and even less experimental research has been done. Thepresent study is able to contribute to the Minimalist-Constructivist debate, notin its restricted sense -since the inferences under study here are not based onworld knowledge- but in the general sense of how shallow or deep inferencingis involved in comprehension. This research adds new types of inferences to thecognitive landscape that are generated on-line even when they are forward-looking, thus enriching the depth of inferencing that comprehension involves.In this sense, the Constructivist Position is strengthened and the MinimalistPosition is weakened, since comprehension seems to involve more inferencingthan what is strictly necessary to achieve local coherence.

A phenomenon which was detected in this study, and is pertinent topsychological research, is the Discourse Restoration Effect: in Exp 2 and 3,subjects were asked -as a distraction task- to paraphrase what they read. Inseveral cases subjects reading an INCON version of a text, without noticing it,remedied the inconsistency and retold the story in a way that was coherent. Thisphenomenon brings into play interacting factors such as memory andinformation storage. Finally, theoretical and experimental attention has recentlybeen channelled on children’s acquisition of the concept of implicature (e.g.Noveck 2000; Gualmini et al 2001). Combined research on implicatureacquisition and processing may offer fruitful suggestions for a number ofdisciplines such as theoretical pragmatics, psycholinguistics, reasoning studies,discourse analysis, stylistics, rhetoric, and may also have useful applications foreducation.

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