The plural inferences of count and mass nouns are implicatures: Evidence from GreekAgata Renans1, Jacopo Romoli1, Maria-Margarita Makri2, Lyn Tieu3, Hanna de Vries2,

Raffaella Folli1, George Tsoulas21Ulster University, 2University of York, 3Macquarie University

Summary: Across languages, plural marking on count nouns typically gives rise to multiplicity inferences(MIs), indicating that there is more than one entity in the denotation of the noun. Plural marking hasalso been observed to occur on mass nouns in Greek, giving rise to a parallel abundance inference (AI),indicating that there is a large quantity of what is denoted by the noun. Kane et al. (2016) propose a unifiedimplicature account of AIs and MIs, which prima facie predicts a uniform pattern across AIs, MIs, andstandard implicatures (SIs). We tested this prediction by comparing MIs, AIs, and SIs in Greek-speakingchildren and adults. The results reflect an overall pattern of implicature calculation, supporting a unifiedimplicature analysis across the inferences.Background: In many languages, including English, plural marking can only appear on count nouns,modulo coercion (e.g., Chierchia 2011, Deal 2017). In Greek, however, the plural can appear on bothcount and mass nouns, e.g. (1) and (2). Importantly, (2) is not interpreted with coercion i.e. it is notreferring to types of water or contextually salient units of water (Tsoulas 2008). Most relevantly for us,in Greek, pluralised count nouns trigger MIs, e.g., (2a), while pluralised mass ones trigger parallel AIs,e.g., (2b); neither of these inferences arises with the corresponding singular form of these nouns (Tsoulas2008, Alexiadou 2011, Kane et al. 2016).

(1) OThe

YanisJohn

eidesaw-PL

kamilopardaleisgiraffe-pl

‘John saw giraffes’

(2) Trehundrip.3pl

ner-awater-pl

apofrom

todet

tavani.ceiling

‘Water is dripping from the ceiling’(3) a. John saw more than one giraffe b. Much water is dripping from the ceilingMIs have been analysed as implicatures in the literature (e.g., Sauerland et al. 2005, Spector 2007). Kaneet al. (2016) propose a unified implicature account of MIs and AIs. Everything else being equal, animplicature analysis of MIs predicts uniformity between MIs and SIs. Similarly, a unified implicatureanalysis of MIs and AIs extends this uniformity prediction to AIs. Note that while these unified analysesallow for heterogeneity in implicature rates across different scalar terms within a population (van Tielet al. 2016), they predict uniform patterns across populations (i.e. any differences in the rates of theinferences should be observed across populations).Previous studies: Tieu et al. (2014, 2016) test the prediction of uniformity between MIs and the SI of‘some’ in English, with 4–5-year-old children and adults. The results reflect an overall pattern of impli-cature calculation: children and adults compute both inferences more often in upward- than downward-entailing contexts, and children compute fewer of both kinds of inferences than adults (cf. Chierchia etal., 2001, Papafragou & Musolino 2003, a.o.).Experiment: In order to test the uniformity prediction for both AIs and MIs, as compared to standard SIs,we focused on Greek and adapted Tieu et al.’s (2014, 2016) paradigm, using a modified version of Katsos& Bishop’s (2011) ternary judgment task (originally used to test children on implicatures). Participantswere presented with short animations on a laptop. An experimenter read a short experimental contextand asked questions to a puppet, who responded with sentences describing the context. Participantswere instructed to judge the puppet’s sentences by rewarding the puppet with 1, 2, or 3 strawberries.On the critical targets, sentences containing a pluralised count or mass noun were uttered in contextsin which their corresponding MIs or AIs were clearly false. Plural noun type (count vs. mass) was abetween-subject factor. In both conditions, participants also received scalar implicature targets, in whichthe puppet uttered sentences containing merika (‘some’) in contexts in which the not-all implicature wasclearly false.(5) Count noun target (1st image below): Context makes clear that the tiger only fed one pig.

Puppet: I Tigri taise goyroynia. ‘The tiger fed pigs.’(6) Mass noun target (2nd image below): Context makes clear that the tiger only took a small amount

of water.

Puppet: I tigri pire nera. ‘The tiger took waters.’(7) SI target (3rd image below): Context makes clear that the lion took all of the apples.

Puppet: To Liontaraki koybalise merika apo ta mila. ‘The lion took some of the apples.’In total, participants received 3 positive plural targets, 3 negative plural targets (e.g., The tiger didn’t takewaters), 4 ‘merika’ targets, and 8 controls. 41 children (M = 4;05) and 35 adults completed the countnoun condition, and 28 children (M = 4;06) and 27 adults participated in the mass noun condition. 9participants in the count nouns condition and 12 participants in the mass noun condition were excludedfor not passing the controls, leaving a total of 33 adults and 34 children in the count nouns condition and21 adults and 22 children in the mass nouns condition.Results: The first plot below shows the proportions of reward selections acrossinferences and groups. The three graphs beneath indicate the percentage ofinference-consistent responses (‘1’ or ‘2’ strawberries for the positive targets, ‘3’strawberries for the negative targets). Logit models fitted to the MI responses(second graph from top) revealed significant effects of Polarity and Group, and asignificant interaction (all p < .001): adults computed more MIs than children,and more MIs in positive than in negative targets. Turning to AIs, both groupsgave no AI-consistent responses in the negative condition, clearly reflecting theeffect of Polarity (third graph from top). Logit models fitted to the positive targetsrevealed a significant effect of Group (p < .001), with more AIs from adults thanchildren. Finally, comparing across all three inferences (bottom graph): there weresignificant effects of Inference Type and Group (all p < .001), with a significantinteraction between Inference Type (MI vs. SI) and Group in the count noun con-dition (p < .001), but no such interaction in the mass noun condition. Models onthe positive MI and AI targets also revealed significant effects of Inference Typeand Group (both p < .001) but no interaction.

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Discussion: The experimental results reflect the signaturepattern of SIs: adults computed more MIs and AIs in pos-itive than in negative contexts, and computed more of boththan children, in parallel with the ‘merika’ targets. The pat-tern of analogous differences between children and adults inall three inferences, in combination with the effect of polarity,support an implicature account of MIs and AIs, and a uni-fication of the effects of plural morphology across the massand count divide.

In addition, our results indicate that the amount of infer-ence computation in adults varied across the three inferences,with the implicature of ‘merika’ being the strongest and thatof the mass nouns being the lowest (bottom graph). Thesedifferences, however, do not challenge a unified implicatureaccount of MIs and AIs; rather they are in line with the re-sults from van Tiel et al. (2016), who observe a wide rangeof variation in the rates of implicature computation acrossscales. The differences between AIs, MIs, and SIs (the lat-ter of which was among those computed the most in van Tielet al. (2016)) suggest that the former are weaker inferencesthan the latter. Van Tiel et al. (2016) report that semantic dis-tinctness of alternatives is relevant for explaining differencesamong scalar terms; we discuss the role of this factor in ourresults vis-a-vis different implicature accounts of MIs andAIs.