The additive presupposition says of at least one alternative that is not weaker than the prejacent that it is true. In the above set, therefore, either it is presupposed that James speaks at least four languages or that James speaks exactly three languages.

Turning now to the ignorance implicatures of (19) there are two candidates for the set of alternatives: (i) the set of alternatives is identical to the set we considered above for computing the presupposition of *even*, or (ii) it is the following set, where each member contains *even*. 5

5An anonymous reviewer asks if (i) is possible at all. If one assumes the Roothian Alternative Semantics (Rooth 1992), as we do here, there is a natural way of making sense of it. Under

15 Even superlative modifiers

{ James even speaks at least three languages, James even speaks at least four languages, James even speaks exactly three languages }

The only difference between the two sets is the presence/absence of the particle *even*, whose assertive meaning is vacuous. It is currently a hotly debated issue how presuppositions behave in the computation of implicatures (e.g. Gajewski & Sharvit 2012, Spector & Sudo 2017, Marty 2017, Anvari 2019), and the current literature contains no explicit discussion of the behavior of *even* in implicatures, or how presuppositions of alternatives behave in the computation of ignorance implicatures, as opposed to scalar implicatures. For this reason, this issue will remain as another open question, but for our purposes in this paper, it is enough to approach this issue bottom-up. That is, the examples in (4) and (5) contain numerals with superlative modifiers and focus particles, and crucially, they have the same ignorance implicatures as the versions of these sentences without the focus particles. Extending this to example (19), we expect it to have the same ignorance implicatures as the version of the sentence without *even*. Theoretically, we could obtain this result by forcing the option (i) above, or by adopting (ii) but somehow making sure that the computation of ignorance implicatures ignores *even*, which we will leave open here.

Now notice crucially that the ignorance implicatures of (19) contradict the additive presupposition of *even*. Specifically, the additive presupposition is either that James speaks at least four languages or that James speaks exactly three languages, but then it must be the case that the speaker believes it to be true, and so cannot be ignorant about its truth (cf. Stalnaker 1978). We claim that this conflict is what is behind the infelicity of (19).

To reiterate the crucial assumption of our analysis, the focus alternatives that *even* operates on are generated in the same way as the alternatives that give rise to ignorance implicatures, based on the alternatives of numerals with superlative modifiers in (13) (cf. Rooth 1992, Fox & Katzir 2011). If not, the additive presupposition could well be compatible with the ignorance implicatures. That is, if the additive presupposition could be about an alternative that was not in the set of alternatives for the ignorance implicatures, the truth of that alternative would not conflict with the ignorance implicatures.

this framework, the set of alternatives for *even* is structurally represented as the complement of the ∼-operator, and there is no reason why the mechanism used for generating ignorance implicatures cannot make use of the same set. It should also be noted that for (ii), it needs to be assumed that *even* does not always make the set of alternatives trivial, contrary to what Rooth (1992) stipulates. As Krifka (1991) and Panizza & Sudo (2020) discuss, there is independent evidence for abandoning Rooth's stipulation.

### Flóra Lili Donáti & Yasutada Sudo

### **4.2** *De re* **additive presuppositions**

Notice at this point that it is crucial for us that the additive presupposition is about a particular alternative, and in this sense *de re*. More specifically, the additive presupposition of *even* is satisfied in a given context if the truth of some alternative not weaker than is common ground. This contrasts with an existential presupposition, which says that it is common ground that some alternative not weaker than is true. Such an existential presupposition is too weak for our purposes, as it is compatible with the ignorance implicature that the speaker does not know which non-weaker alternative is true.

There is independent empirical reason to adopt the *de re* additive presupposition. Kripke (2009) argues that the additive presuppositions that additive particles like *too* trigger are similarly stronger than existential, based on examples like the following (see also Geurts & van der Sandt 2004; but see Ruys 2015).

(20) Sam is having dinner in New York tonight, too.

If it is merely existential, the presupposition that there is someone else having dinner in New York tonight will be very easy to satisfy. Rather, the intuition tells us that this sentence requires a prior mention of some particular individual, who is at least known to be in New York tonight, and perhaps also known to be going to have dinner there.

We observe that *even* behaves similarly in this regard. To see this, consider the following example.

(21) Even Daniele has a bike.

Intuitively, this example similarly requires it to be clear in the context which alternative or alternatives are relevant, at least.

Kripke (2009) analyzes the additive presupposition of *too* to be anaphoric. That is, it is not merely propositional but contains an anaphoric component that needs to be resolved to an antecedent accessible in the discourse that satisfies the relevant property. For example, the additive presupposition of (20) above has an anaphoric component that needs to be resolved, and then it furthermore presupposes that that individual is going to have dinner in New York (see also Geurts & van der Sandt 2004). We essentially adopt this analysis for *even*, but the way we state it is slightly weaker, as it does not have an anaphoric component, but an existential quantifier over alternatives that is *de re* with respect to the presuppositional attitude. At this point, we cannot tease apart these two analytical possibilities on empirical grounds, and we could as well adopt Kripke's idea, but crucially, both types of analyses, when applied to (19), will result in a conflict with the ignorance inference.

15 Even superlative modifiers

### **4.3 Predictions**

One prediction that our analysis makes is that the additive particle should also give rise to infelicity, when used in a sentence like (19) in place of *even*, because the conflict should arise as long as an additive presupposition is triggered. This prediction is borne out. Note, however, the infelicity of an example like the following is not telling, because the truth-conditional meanings of the two sentences are simply incompatible with each other anyway.

(22) Daniele speaks exactly two languages. #He speaks [at least three] , too.

Rather, we need to look at examples like (23).

(23) Daniele is allowed to smoke exactly two cigarettes today. #He is allowed to smoke [at least three] , too.

Here, the truth-conditional meanings of the two sentences should be compatible with each other. In fact, the comparative version of this example is perfectly felicitous.

(24) Daniele is allowed to smoke exactly two cigarettes today. He is allowed to smoke [more than two] , too.

According to our account, (23) is rendered infelicitous because of the clash between the additive presupposition and the ignorance implicature.6

Another prediction we make is that a similar conflict should arise with a scalar implicature of a superlative modifier as well. In order to see this, consider the following example, which is infelicitous.

(25) Andy is giving two lectures at the summer school. #Patrick is even required to give [at least four] .

Recall that a superlative modifier gives rise to a scalar implicature under a universal quantifier like a necessity modal. The second sentence of this example, therefore, has a scalar implicature that Patrick is not required to give exactly four lectures and he is not required to give more than four lectures. On the other hand, the additive presupposition requires that it be presupposed that Patrick is required to give exactly four lectures, or that he is required to give more than four. This clash explains the infelicity.

6 It is actually an open issue why the second sentence of (23) has to have an ignorance implicature, rather than a scalar implicature. See Buccola & Haida (2018) for discussion.

Flóra Lili Donáti & Yasutada Sudo

# **5 Conclusion and open issues**

To summarize, we have developed an account of the observation that numerals with superlative modifiers are not compatible with focus particles like *even*, which as far as we know has not been previously discussed. We proposed that what causes the infelicity is the additive presupposition triggered by *even*, which conflicts with the ignorance implicature of the numeral with the superlative modifier. We remarked that in order to obtain these results, two theoretical assumptions are necessary: (i) the set of alternatives for implicatures and the set of alternatives for focus operators are generated in the same way based on the alternatives for numerals with superlative modifiers (Fox & Katzir 2011, Rooth 1992, Mendia 2018), and (ii) the additive presupposition is stronger than a merely existential presupposition, and is *de re* (Kripke 2009, Geurts & van der Sandt 2004). Both of these points have been proposed in the literature on independent grounds, and we hope to have provided further support for them in this paper. Before closing, we will discuss some open issues that arise from our analysis.

### **5.1 Alternatives of superlative modifiers**

In Section 2, we simply followed previous analyses and postulated particular sets of alternatives for numerals with superlative modifiers, but we did not provide a principled account as to why these alternatives must be used. In fact, this is one of the open issues discussed in Schwarz (2016) and Mayr (2013), and unfortunately we do not have anything additional to offer. Having said that, however, we would like to discuss how to extend our analysis to other uses of superlative modifiers, which might shed some light on this question.

So far, we have only looked at cases where superlative modifiers combine directly with a numeral, but superlative modifiers can modify other types of expressions as well. In fact, it is reasonable to assume that superlative modifiers are focus sensitive operators themselves. Concretely, in examples like the following, one observes the usual focus association effects.

	- b. Andy at least introduced Patrick to Tom .

In order to capture this, we can analyze *at least* and *at most* as focus sensitive operators. As in the case of *even*, let us assume that the superlative modifiers take sentential scope, although this assumption is strictly speaking not necessary (cf. fn. 4).

15 Even superlative modifiers

	- b. ⌜at most ⌝requires contextually determined partial ordering ≤ among ALT(), and asserts the grand disjunction of {′ |′ ≤ } and the negation of the grand disjunction of {″| < ″}.

Furthermore, ALT(at most ) = {⌜at most ⌝, ⌜Exh{′∈ALT()|′<} ⌝, ⌜at most ⌝} where is the grand disjunction of {′ |′ < }.

Exh here is the exhaustivity operator à la Fox (2007). The notion of innocent exclusion used in its definition is crucial to state the above semantics in a general way.

	- b. A set is a set of excludable alternatives with respect to and iff ⊆ and and the negation of the grand disjunction of are consistent.

To see how this works, let us apply it to the following example.

(30) Pietro invited at least Daniele .

The alternatives to *Daniele* need to be ordered here in some way. One of the most natural options here is the following kind of set of alternatives, partially ordered by generalized entailment.

> ⎧⎪ ⎨ ⎪ ⎩ ⋮ Pietro invited Danile and Taka and Ruoying, … Pietro invited Daniele and Taka, … Pietro invited Daniele, …

The assertive meaning only concerns those alternatives that are commensurable with the prejacent, and so will be that Pietro invited Daniele (and possibly someone else), and the ignorance implicature will amount to the speaker's lack of certainty whether Pietro only invited Daniele or if he invited someone else.

The analysis also works for cases like the following where the scale is dense.

⎫ ⎪ ⎬ ⎪ ⎭ Flóra Lili Donáti & Yasutada Sudo

	- b. It at most takes [15 min] .

It also works when the scale is not ordered by (generalized) entailment, as in (32).

	- b. Andy won at most the silver medal.

Crucially, this analysis predicts the following examples to be infelicitous for the same reason as numerals with superlative modifiers are incompatible with focus operators with additive presuppositions, which is a good prediction.

(33) a. # Pietro even invited [at least Daniele ] . b. # Pietro also invited [at least Daniele ] .

### **5.2 Comparative modifiers**

As we saw in several places in this paper, numerals with comparative modifiers behave differently from numerals with superlative modifiers. Part of this comes from the fact that numerals with comparative modifiers do not give rise to robust ignorance implicatures. For example, the following sentences sound more felicitous than their superlative counterparts.

	- b. I have fewer than three kids.
	- b. A triangle has fewer than four sides.

If numerals with comparative modifiers do not necessarily give rise to ignorance implicatures, then it is predicted that they should be compatible with focus particles with additive presuppositions.

However, this matter is not as clearcut as one might hope. That is, the sentences like those above actually do often have have inferences that amount to something similar to an ignorance implicature or an indifference/irrelevance implicature (Meyer 2013, Lauer 2014). How this arises and what alternatives are used are interesting questions, but we cannot offer a concrete account here, and as far as we know, they are currently debated in the literature (see Fox & Hackl 2006, Mayr 2013, Schwarz 2016).

In addition to this question about alternatives, the morphosyntactic difference between comparative and superlative modifiers is also puzzling. As we discussed 15 Even superlative modifiers

in the previous subsection, superlative modifiers are focus sensitive operators and can appear in all sorts of adverbial positions. By contrast, there is no indication that comparative modifiers are focus sensitive, and in fact their distribution seems to be more constrained. We are presently not aware of a satisfactory account of why this is so.

### **5.3 When** *even* **is not additive**

The last open issue we would like to mention has to do with the additive presupposition of *even*. As mentioned in passing, the additive presupposition of *even* sometimes seems to be absent (Rullmann 1997, Crnič 2011). The following is a well-discussed example of this.

(36) Andy even won the silver medal.

This sentence has a reading that does not imply that Andy also won another medal, although according to Francis (2018), these judgments are not stable across speakers of English.

As Crnič (2011) discusses in great detail, there is currently no satisfactory account of exactly when the additive presupposition of *even* arises, and we have nothing insightful to add here. Yet, it is our prediction that in the absence of an additive presupposition, *even* should be compatible with superlative modifiers. One might then think that the fact that an example like (37) is infelicitous might be problematic for our account.

(37) Patrick won the bronze medal. ??Andy even won [at least the silver ] medal.

However, in the absence of a good understanding of the distribution of the additive presupposition, we cannot be sure if this example actually lacks an additive presupposition. In particular, entailment among the focus alternatives might be one relevant factor that correlates with the presence of additivity, as Crnič (2011) claims, and if so, the presence of *at least* in (37) should matter crucially, as with it, the focus alternatives presumably stand in an entailment relation (cf. the semantics of *at least* above).7

7An anonymous reviewer asks about examples like *I even doubt that one person came* (cf. Crnič 2011). If the numeral and its alternatives receive lower-bounded readings, then indeed the additive presupposition would be problematic because the alternatives would be entailed. However, it is well known that numerals generally can easily receive bilateral readings even in negative contexts (Geurts 2006, Breheny 2008, among others). With this as an option, such examples do not pose an issue. For the above example, the additive presupposition would be satisfied if the speaker doubts that exactly people came for at least one > 1, which seems to be a reasonable analysis.

### Flóra Lili Donáti & Yasutada Sudo

Due to these complications, we cannot offer a conclusive example involving *even*, but it should be remarked that nothing rules out the existence of a scalar particle like *even* that is never associated with an additive presupposition in a natural language. For instance, Italian *addirittura* is a good candidate (Daniele Panizza, p.c.). If this is the case, we predict it to be compatible with superlative modifiers. We, however, have left investigation of this for future research.

# **Acknowledgements**

We would like to thank Katherine Fraser and James Gray for judgments and helpful discussion. We also benefitted from feedback from the audiences of the 20th Szklarska Poręba Workshop in March 2019 and SinFonIJA 12 held at Masaryk University in Brno in September 2019. All remaining errors are ours.

# **Appendix**

We saw in the main part of this paper that a numeral with *at least* is incompatible with *even*, as in (1) while a numeral with *at most* is compatible with *only*, as in (5). Our explanation for the former is that the additive presupposition clashes with the obligatory implicature of *at least*. For the latter, we could resort to the fact that *only* does not trigger an additive presupposition and hence does not cause a conflict.

We also mentioned in fn. 1, a numeral modified by *at least* is incompatible with *only* and a numeral with *at most* is incompatible with *even*, as shown below.

	- b. I speak two languages. #James even speaks [at most five] .

For (38b), we could extend our analysis and maintain that the additive presupposition clashes with the ignorance implicature, but (38a) is not amenable to this explanation. Generally, when associating with a quantity expression, *only* gives rise to an inference that the relevant quantity is small, so one would expect the second sentence of (38a) to mean James speaks more than one language, and he speaks many languages.

We think that examples like these require an entirely different explanation anyway, because their comparative counterparts are equally unacceptable.

	- b. I speak two languages. #James even speaks [fewer then six] .

15 Even superlative modifiers

Here is a sketch of a possible analysis. As mentioned above, *only* associating with a quantity expression triggers an inference that the named amount is small. *Even*, on the other hand, triggers an inference that the named amount is large.

There are several accounts of when and how *only* can trigger such a scalar inference (Grosz 2012, Coppock & Beaver 2014, Alxatib 2020), which we will not get into here. For *even*, the semantics we discussed in §3 can derive a scalar inference with reasonable assumptions about the flavor of the scalar presupposition and about the alternatives of numerals.

What seems to us to be going on in the above cases with modified numerals is that these scalar inferences arise from all numerals in their range. That is, the scalar inference of (38a)/(39a) is that each > 1 is a small amount, and that of (38b)/(39b) is that each < 6 is a large amount. We leave open the compositional details of how these inferences arise from the semantics of the focus operators and modified numerals here, but they account for the infelicity of these examples. Further support for this analysis comes from the following contrast.

	- a. James only speaks [at most two] languages.
	- b. # James only speaks [at most ten] languages.

# **References**

### Flóra Lili Donáti & Yasutada Sudo

15 Even superlative modifiers

Flóra Lili Donáti & Yasutada Sudo

# **Chapter 16**

# **Classifiers make a difference: Kind interpretation and plurality in Hungarian**

# Brigitta R. Schvarcza,b & Borbála Nemesc

aBar-Ilan University bAfeka College of Engineering cBabeș-Bolyai University

This paper provides an analysis of Hungarian sortal classifiers, shedding light on the complex interplay between classifiers, plurality and kind interpretation in the language. We build on Schvarcz & Rothstein's (2017) approach to the mass/count distinction, providing further evidence for noun flexibility. We show that Num+N and Num+CL+N constructions have different interpretations; in particular, kind interpretation tells the two apart. We provide evidence against plural-as-a-classifier (Dékány 2011) and number-neutrality (Erbach et al. 2019) views and argue that classifier optionality can be accounted for by the predictions the Nominal Mapping Parameter (Chierchia 1998b) makes with respect to bare singular nouns. We claim that Hungarian nominals are born as kind-denoting expressions which then can undergo a kind-to-predicate shift explicitly triggered by a sortal individuating classifier. We analyze classifiers in Hungarian as functional operators on kinds of type ⟨, ⟨, ⟩⟩, which apply to kind denoting terms generating instantiations of that kind.

**Keywords:** classifier optionality, plurality, noun flexibility, bare nominal denotation, kind interpretation, Hungarian

# **1 Introduction**

Why does a numeral expression allow sortal classifiers in a mass/count language? It has been widely assumed that classifiers serve as mediating elements between numerals and nouns and perform an individuating or portioning out function,

Brigitta R. Schvarcz & Borbála Nemes. 2021. Classifiers make a difference: Kind interpretation and plurality in Hungarian. In Mojmír Dočekal & Marcin Wągiel (eds.), *Formal approaches to number in Slavic and beyond*, 369–396. Berlin: Language Science Press. DOI: 10.5281/zenodo.5082480

### Brigitta R. Schvarcz & Borbála Nemes

allowing mass-denoting nouns to be modified by numerals. Classifiers are obligatorily used in classifier languages since all nouns in these languages have mass denotations. In mass/count languages, on the other hand, count nouns can be directly modified by numericals. If a language exhibits wide mass/count phenomena, we do not expect it to have a functional category of classifiers. Hungarian, however, seems to contradict this paradigm.

Even though Hungarian has been categorized as a mass/count language (Schvarcz 2014, Schvarcz & Rothstein 2017), counting in this language allows an apparently optional classifier: numerical constructions involving a notionally count noun can be realised with a construction of direct modification by a numerical (henceforth NUM+N) (1a), as well as with a construction involving a sortal classifier (henceforth NUM+CL+N) (1b).

	- b. három three darab clgeneral újság newspaper 'three newspapers'

As examples (1a) and (1b) illustrate, numerals in Hungarian combine with singular nouns, despite the existence of a genuine plural marker (Schvarcz & Rothstein 2017). In addition, the language exhibits unique bare nominal phenomena. This combination of properties poses interesting questions about the category of number. There is a complex interplay between the various grammatical devices linked to the cognitive notion of number, including numerals, classifiers, plural-marking and bare noun denotations. Investigating the category of classifiers can help us gain a better understanding of the function performed by the above-mentioned devices as well as of the category of number in Hungarian and beyond.

The aim of this paper is to provide an explanation for the optional use of sortal classifiers in Hungarian, with special focus on the general classifier *darab*. Relying on novel linguistic data, we provide evidence that the presence of a classifier inside a numerical construction restricts the interpretation of the phrase: while NUM+N can have a plurality of kinds, of sub-kinds and of individuals reading, NUM+CL+N can only refer to a set of plural individuals. This interpretational difference raises questions about the denotation of the nominal and the semantic significance of the classifier.

### 16 Classifiers make a difference

The structure of this paper is as follows. In the remainder of this section, we will discuss various approaches to the Hungarian mass/count and classifier phenomena proposed in the literature. In §2, we present a range of tests where sortal classifiers make a difference in the interpretation of numerical constructions. In §3, we discuss evidence against treating plurality as a classifier as well as data that questions number-neutrality. In §4, we explore the problem of kind interpretation in Hungarian and try to place the language in Chierchia's (1998b) typology of nominal denotation. In §5, we provide a semantic analysis of sortal classifiers. In §6, we draw some conclusions and discuss implications.

We begin by providing some general background on classifiers in Hungarian, followed by a review of the existing analyses of Hungarian classifier phenomena in the literature. The phenomenon of classifiers has been often noted in the literature on Hungarian classifiers (Beckwith 1992, 2007, Csirmaz & Dékány 2014, Schvarcz & Rothstein 2017, Szabó & Tóth 2018, Schvarcz & Wohlmuth forthcoming). Some of the canonical examples include:

	- b. három three (szál) clthread rózsa rose 'three (threads of) roses'
	- c. három three darab clgeneral könyv book 'three books'

Crucially, the classifiers in (2) are optional and look like sortal classifiers. While some select nouns according to shape and size (e.g. *fej* 'head' takes nouns denoting large round objects and *szál* 'thread' combines with nouns denoting long thin objects), the general classifier *darab* combines with any notionally countable noun. The construction without the classifier has the same meaning as its classifier counterpart. (3a) and (3b) have the same meaning, while (3c) contrasts with (3b). This is due to the fact that *köteg* 'bunch' is a so called 'group or collective classifier' which groups roses into a higher order entity.

(3) a. három three rózsa rose 'three roses'

Brigitta R. Schvarcz & Borbála Nemes

> b. három three szál clthread rózsa rose 'three threads of roses'

c. három three köteg clbunch rózsa rose 'three bunches of roses'

These facts about Hungarian pose a problem for the traditional categorizations, which define two major systems of making nouns countable (Greenberg 1974, Chierchia 1998a). On the one hand, we find languages such as Mandarin Chinese and Japanese, which lack a genuine plural marker; have no distinction between count and mass nouns on the nominal level; and where bare nouns can occur as arguments of kind-taking predicates. On the other hand, there are languages, such as English, French or Dutch, where nouns are categorised as count or mass; count nouns are directly modified by numerals. These languages have a genuine morphological marker of plurality; and do not allow bare singular arguments. Hungarian exhibits both typical classifier language traits and mass/count language traits. While it has a rich classifier system and uses bare singular arguments (4), it also manifests a genuine mass/count distinction (Schvarcz 2014) and morphologically marks plurality (5).1

To account for the occurrence of sortal classifiers, three approaches have been proposed in the literature.

First, Csirmaz & Dékány (2014) suggest treating Hungarian as a classifier language, in which "bare nominals [...] are non-atomic, they denote an undifferentiated mass" (p. 142), and hence counting requires either an explicit lexical classifier (e.g. *darab*) or a null sortal classifier:

	- b. három three ∅cl cl∅ újság newspaper

1While examples such as (4) are limited and not highly productive in the language, they highlight the availability of bare singular arguments in Hungarian. The few context and constructions in which the use of the bare singular is possible are discussed in detail under §4.

### 16 Classifiers make a difference

Treating Hungarian as a classifier language is supported by the absence of plural marking on the nominal upon combining with numerals greater than 'one'. To address this issue, Dékány (2011) suggests treating the Hungarian morphological plural marker *-k* as a type of plural classifier which spans in two positions: Number and Classifier.

While Csirmaz & Dékány's analysis accounts for the use of sortal classifiers, it cannot account for the facts about plurality and mass/count phenomena manifested in the language. Other analyses suggest that the issue of plural is orthogonal and that the absence of plural marking is not related to countability (Borer 2005, Schvarcz & Rothstein 2017).

Second, regarding mass/count phenomena, Schvarcz & Rothstein (2017) argue that Hungarian has purely mass nouns, a few purely count nouns and a wide range of flexible nouns. Nouns like *újság* 'newspaper' can be used in counting contexts either as a count noun or in a sortal classifier construction. They observe that the patterns of classifier use is due to the ambiguity between a count and mass interpretation of a flexible noun. This is illustrated below, where in (7a) the mass counterpart of the flexible noun *újság* 'newspaper' obligatorily takes the general classifier *darab*, while the count counterpart of the same flexible noun (7b) can be counted without any classifier and even bars the use of one.

	- b. három three (\*darab) clgeneral újság newspaper.count 'three copies of newspapers / three titles of newspapers'

Third, Erbach et al. (2019) argue that notionally count nouns are semantically number neutral, in the sense of Farkas & de Swart (2010), denoting both atomic entities and sums thereof. Under their analysis, classifiers are required by the numeral semantics and not by the nominals (Krifka 1995, Sudo forthcoming). However, their analysis does not address classifier optionality per se.

In this paper, we will defend the noun-flexibility analysis. We base our analysis on observations emerging from the interpretations of the two structures, and show that neither plural-as-a-classifier nor number-neutrality fully explains the data on kind interpretation and classifier optionality. Our data show that the availability of a kind interpretation tells apart the two structures in (1): while the NUM+N construction (1a) can either refer to a set of individuals or subkinds, the classifier construction (1a) can only refer to a set of individuals. In addition to

### Brigitta R. Schvarcz & Borbála Nemes

identifying the function and interpretation of the classifier, we observe that plural marked nouns can freely get a subkind interpretation while classifier phrases can not. Based on this semantic difference, we maintain and provide further evidence for the claims of Schvarcz & Rothstein (2017) that plural marking cannot be treated as a classifier (contra Dékány 2011), showing that the two elements fulfill different functions. We will also discuss several cases which contradict the assumptions made by Erbach et al. (2019) and rule out a number-neutrality analysis for Hungarian.

# **2 The semantic effect of** *darab* **on kind and subkind reading**

While Schvarcz & Rothstein (2017) suggest that there is no significant interpretational difference between numerical constructions involving an overt classifier and a covert one, we observe that there is, in fact, an important semantic contrast between the two structures. For example, NUM+N in (8a) may refer to a plurality of newspaper copies; to a plurality of sub-kinds of newspapers (daily, monthly, weekly); or to a plurality of newspaper titles (*The Herald Tribune*, *The New York Times*, *The Economist*).2 In contrast, NUM+CL+N in (8b) can only have a plurality of individuals interpretation under which it can only refer to a plurality of newspaper objects, namely three copies.

	- b. Plurality of individuals interpretation Három three darab clgeneral újság-ot newspaper-acc árul sell.pres.3sg ez this az the újságárus. news.vendor 'This newspaper vendor sells three newspapers.'

2An anonymous reviewer points out that while the plurality of sub-kinds reading exists, it is rather unnatural in the case of the noun *újság*. More natural examples include:

(i) ? Három three állat animal rak lay.pres.3sg tojás-ok-at: egg-pl-acc a the hal, fish, a the hüllő reptile és and a the madár. bird 'Three animals lay eggs: the fish, the reptile and the bird.'

(ii) Két two madár bird nem not tud can repülni: fly.inf a the strucc ostrich és and a the pingvin. penguin 'Two birds cannot fly: the ostrich and the penguin.'

16 Classifiers make a difference

The ambiguity between an existential and a subkind interpretation of NUM+N constructions can also be observed in English (9). English, however, does not have a mechanism parallel to the Hungarian general classifier to disambiguate the two readings in favor of an individuating one.

	- b. This newsvendor sells three newspapers: he only has one copy left of *The New York Times, The Herald Tribune* and *The Economist*.

Based on the contrast in (8), we suggest that the role of sortal numeral classifiers in Hungarian is that of restricting subkind reading, thereby eliminating the ambiguity found in numerical expressions. A number of tests and contexts confirm our prediction for Hungarian. In order to test our hypothesis, we carefully selected structures and contexts that disallow the existential interpretation to occur. In these cases, we expected the use of a sortal individual classifier to be infelicitous.

First, kind-reference generic sentences express properties true of kinds, species or classes of objects, but not of individual objects (Krifka et al. 1995), hence they should be incompatible with the general classifier *darab*. The use of the kind classifier, *fajta* 'kind of/type of', is felicitous in such contexts.3

(10) Három three {(\*darab) clgeneral / (fajta)} clkind újság newspaper a the megszűnés ceasing.to.exist szél-én verge-sup áll. stand.pres.3sg

'Three newspapers are on the verge of ceasing to exist.' / 'Three kinds of newspapers are on the verge of ceasing to exist.'

(i) \* Két two darab clgeneral madár bird a the kihalás extinction szélén verge.sup áll: stand.pres.3sg a the strucc ostrich és and a the pingvin. penguin

3An anonymous reviewer brings our attention to an alternative interpretation: *újság* may have a title reading. In that case *darab* can refer to newspaper titles, suggesting that the titles reading may be individual-denoting. This ambiguity in the interpretation can be attributed to a polysemy between physical object and informational object senses (Pustejovsky 1995, Asher 2011). It has been discussed by Schvarcz & Wohlmuth (forthcoming) if such polysemous nouns occur in classifier expressions, the numeral can only count physical objects. Nevertheless, with nouns that do not exhibit such polysemy, the classifier expression is ruled out:

'Two birds stand on the verge of extinction: the ostrich and the penguin.'

### Brigitta R. Schvarcz & Borbála Nemes

(11) Ez this az the újságárus newsvendor árulja sell.pres.3sg a the három three {(\*darab) clgeneral / (fajta)} clkind betiltott banned újságo-t, newspaper-acc bár although tudja, know.pres.3sg hogy that azok those feketelistá-ra blacklist-sbl kerültek. get.past.3sg

'This newsvendor sells the three banned newspapers, although he knows that those have been backlisted.' / 'This newsvendor sells the three kinds of banned newspapers, although he knows that those have been backlisted.'

(12) János John három three {(\*darab) clgeneral / (fajta)} clkind marhá-t cattle-acc tenyészt: breed.pres.3sg Holstein Holstein marhá-t, cattle-acc Angus-t, Angus-acc és and barna brown svájci Swiss marhá-t cattle-acc

'John breeds three cows: Holstein, Angus and brown Swiss cow.' / 'John breeds three kinds of cows. Holstein, Angus and brown Swiss cow.'

Second, distributive operators and reciprocals require plural atomic antecedents (Link 1983, Rothstein 2009, Schwarzschild 2011, Schvarcz 2014). The verb *kiadni* 'to publish' in (13), and the adverb *gyakran* 'often' in (15) rule out a plurality of individuals interpretation. In contexts where only a plurality of subkinds interpretation is possible, the interplay between these verbs and distributive or reciprocal phrases results in the impossibility of the use of the classifier. In (13), for example, the context refers to a multiplicity of copies of different newspapers, as we expect news agencies to publish a large number of various newspaper editions. Therefore, a plurality of individuals reading induced by the classifier is ruled out.

16 Classifiers make a difference

(15) János John három three (\*darab) clgeneral újság-ot newspaper-acc vesz buy.pres.3sg keddenként, on.Tuesdays ez-ek-ből dem-pl-ela folyamatosan successively tud can.pres.3sg tájékozódni about arról, that hogy what mi happen történik pres.3sg a the nagyvilág-ban. big.world-iness 'John buys three newspapers on Tuesdays. He learns from these what

goes on in the world all the time.'

The third test involves expressions which refer to multiple instantiations of a noun. Contexts which indicate multiple instantiations of kinds are not compatible with the structure involving a sortal individual classifier. The classifier can be used in the second sentence to mark the contrast between the two interpretations of the noun: (17b) is a constellation in which *újság* in the first sentence must have a sub-kind reading, while in the second sentence, it can only be interpreted as a plurality of newspapers.

	- a. János John három three (\*darab) clgeneral újság-ot newspaper-acc vett. buy.past.3sg Összesen in.total harminc-at. thirty-acc 'John bought three newspapers. In total 30.'
	- b. János John három three újság-ot newspaper-acc vett. buy.past.3sg Összesen in.total harminc thirty darab clgeneral újság-ot. newspaper-acc

'John bought three newspapers. In total thirty newspapers.'

Fourth, kind-referring anaphoric expressions, such as *ezek a fajta* 'these kinds', are not compatible with the NUM+CL+N construction. Expressions of this kind include the kind classifier *fajta* 'kind of/type of', and thus can only refer back to a kind-denoting expression.

### Brigitta R. Schvarcz & Borbála Nemes

	- 'Mary filled the garden with three flowers: daffodils, tulips and roses. These kinds of flowers can only be planted in the spring.'

The above tests indicate that the insertion of a sortal individuating classifier in NUM+N constructions has an impact on the interpretation, namely: NUM+N can have subkind and existential readings, while NUM+CL+N can only have an existential reading.

This interpretational difference is not expected under a null classifier analysis, such as the one put forward by Csirmaz & Dékány (2014), which assigns the same semantics for the null sortal classifier as the one assumed for *darab*. If we assume a one-to-one mapping between the syntactic structure and semantic interpretation, the differences between subkind and plurality of individuals readings observed above remain unexplained. However, theoretically we could assume the existence of a semantically underspecified null classifier which could potentially derive the readings observed in this paper: under the subkind reading, the null classifier could have a semantics similar to the kind-classifier, *fajta*, while under the plurality of individuals reading, the semantics of the null classifier would be equivalent to *darab*. To the best of our knowledge, such null classifiers have not been observed in other languages.

As we will discuss in the next section, number neutrality does not fully explain the data on kind interpretation nor does it provide a solution for classifier optionality. Erbach et al. (2019) does not address the interpretational ambiguity discussed above. In addition, the kind interpretation of nominals in Hungarian is more complex than assumed in Erbach et al. (2019). Moreover, the role of the optionally used classifier in a framework in which the classifier is required by the numeral remains unsolved. In the next section, we argue that the noun flexibility approach is able to better capture the facts discussed above.

16 Classifiers make a difference

# **3 In defense of noun flexibility**

First, we provide further support to Schvarcz & Rothstein's (2017) claims. Based on syntactic and semantic evidence, we argue against Csirmaz & Dékány's (2014) 'plurality-as-a-classifier' claim, showing that classifiers and the plural neither compete for the same syntactic position nor do they have the same interpretation. In addition, by taking a closer look at kind-readings of number-neutral nominals, we give counterarguments to the number-neutrality analysis.

### **3.1 Plural is not a classifier**

As mentioned above, Csirmaz & Dékány (2014) argue in favour of treating Hungarian as a classifier language. In line with this view, Dékány (2011) suggests treating the plural in Hungarian as a classifier, whilst maintaining a strict complementarity hypothesis. However, due to the fact that Hungarian has a productive plural marker, unlike typical classifier languages, which lack such a marker (Chierchia 1998a, 2010, Cheng & Sybesma 1999), Hungarian cannot be considered a classifier language. Moreover, as Schvarcz (2014) and Schvarcz & Rothstein (2017) show, not only does Hungarian have a plural marker, but plurality is also sensitive to the mass/count distinction. We present novel data that support the claim that plurality should not be analysed as a plural sortal classifier. In this section we raise five issues: frequency of co-occurrence; the impossibility of classifier doubling; differences in the distributions of plurals and classifiers and in agreement phenomena; and interpretational contrasts.

We look first at the frequency of classifiers and plurals co-occurring in the same phrase. A corpus study reveals that plural marking and classifiers co-occur much more frequently than previously thought in contexts that were not discussed before. These include: bare adjectival phrases (20a), (21a) and (22a), definite constructions (20b), (21b) and (22b) and demonstratives (20c), (21c) and (22c). The only constructions in which the two cannot co-occur are the ones that contain either a numeral or a quantifier, which cannot combine with plural-marked nouns at any rate.4

4Addressing the observations made by an anonymous reviewer regarding the unexpected cooccurrence of plural marking and classifiers, we assume that the classifier first combines with the mass counterpart of a flexible noun deriving a count expression. This expression then can be marked plural. Since numerals combine with singular expressions, it follows that the that CL+N.pl expressions do not take a numeral and these expressions appear only with adjectives, demonstratives and definite constructions. Deriving the syntax behind constructions involving plurals and classifiers lies beyond the scope of this paper.

 If plural were a classifier, then in the above examples, we would assume a double classifier. Yet classifier doubling – either the reduplication of the same classifier (23a) or the combination of two different classifiers (23b) – is ruled out in Hungarian. In contrast, in Mandarin Chinese, a true classifier language, reduplicated classifiers serve as unit-plurality markers (24) (Zhang 2013). A similar phenomena can be found in Cantonese (25) (Wong 1998).

16 Classifiers make a difference

(23) a. \* egy one darab clpiece szál clthread virág flower b. \* egy one szál clthread szál clthread virág flower (24) a. ge-ge cl-red xuesgeng student dou all you have ziji own de de wangye website 'All of the students have their own webpages.' (Mandarin Chinese; Zhang 2013: p. 118, ex. (234a)) b. he-li river-in piao-zhe float-dur (yi) one duo-duo cl-red lianhua lotus 'There are many lotuses floating on the river.' (Mandarin Chinese; Zhang 2013: p. 118, ex. (230a)) (25) go cl go cl hoksaang student 'every student' (Cantonese; Wong 1998: p. 16)

Third, the distribution of bare classifier and bare plural expressions differ: while bare plurals are allowed in argument positions, bare classifier phrases are not. While speakers of some dialects may find (26b) acceptable, all of our informants rule out (27b).5 This difference is due to the position of the bare classifier phrase: as arguments they are unequivocally ungrammatical.

	- b. \* Tő clroot rózsá-t rose-acc ültettem plant.pres.3sg a the kert-be. garden-ill
	- b. \* Darab clgeneral újság newspaper érkezett arrive.past.3sg az the Amazon Amazon csomag-ban. package-iness

5While informants point out that (26b) may be acceptable in a context where more information is provided prior to the utterance, all of them agree that without any context it is ungrammatical.

### Brigitta R. Schvarcz & Borbála Nemes

Fourth, we look at agreement phenomena. Following the Hungarian patterns of agreement, verbs agree with their subjects in person and number, and external demonstratives agree in number and case (Kenesei et al. 1998). The plural marker induces agreement on the verb and on the demonstrative. Classifiers do not.

	- b. Ez dem a the szál clthread virág flower szép. beautiful 'This thread of flower is beautiful.'

Lastly, interpretational differences can be observed between the two expressions discussed: constructions containing a classifier cannot receive a subkind interpretation, while plural-marked nouns can have either a kind, subkind or a plurality of individuals reading. (31a) is modeled on an example from Landman & Rothstein (2010) and can refer to the guest-kind, and to a plural set of guests. We may also imagine dividing a set of guests into sub-kinds: invited guests and uninvited guests. (31a) may also be true in this scenario: *Vendégek érkeztek két órán át, meghívottak és hívatlanok* ('Guests arrived for two hours, invited and uninvited ones'). In (31b) we can only get the plurality of guests reading.

	- b. Plurality of individuals interpretation Három three darab clgeneral újság-ot newspaper-acc árul sell.pres.3sg ez dem az the újságárus. newsvendor 'This newsvendor sells three newspapers.'

16 Classifiers make a difference

	- Három three darab clgeneral vendég guest érkezett arrive.past.3sg két two órá-n hours-sup át. for 'Three guests arrived for two hours.'

These data suggest that Hungarian sortal classifiers cannot be syntactically and semantically equated to plural markers. Their distribution and interpretation differ; they exhibit different agreement patterns; and they can in fact co-occur more frequently than previously assumed. We now turn to the number-neutrality analysis proposed by Erbach et al. (2019).

### **3.2 Ruling out number neutrality**

Based on a cumulativity approach to measurement, i.e. that measure DPs call upon cumulative predicates (Krifka 1989, Filip 1992, 2005, Nakanishi 2003; Schwarzschild 2006), and on an analysis under which it is the semantics of numerals requires the use of the classifier rather than that of a noun, (Krifka 1995, Erbach et al. 2019, Sudo forthcoming) argue that Hungarian notionally count singular nouns are number-neutral.

At a closer investigation, however, we find that number neutrality cannot accurately account for the data. Some of the phenomena we point out include: the inaccessibility of atoms in pseudo-partitive measure DPs and the availability of mass readings of singular nouns. For further evidence see Schvarcz & Nemes (2019).

First, contra Erbach et al. (2019), our data indicate that in Hungarian measure DPs, atoms are not accessible in the denotation of nouns – may they be notionally count, dual-life or mass. One of the major arguments of Erbach et al. (2019) relies on atomicity: while in the case of plural count nouns used in measure DPs atoms are accessible to semantic operations making them felicitous in reciprocal contexts, this does not hold of mass nouns. Their examples include *books* – a plural count noun –, *chocolate(s)* – a dual-life noun –, and *livestock* – a naturally atomic mass noun:6

6 Judgements of English native speakers are divided on the acceptability of (32a). For discussion, see Erbach et al. (2019).

### Brigitta R. Schvarcz & Borbála Nemes

	- b. I bought 200gs of chocolates, each of which was filled with a different kind of ganache.
	- c. \* I made 1.5 kgs of hummus, each of which was eaten at the party.
	- d. ? Quite a few livestock/cattle have disappeared today.
		- (Erbach et al. 2019: p. 93 (13–18))

The equivalents of all of the above sentences are ruled out or have a low degree of acceptability in Hungarian. We can see that (33a) is not unanimously accepted by informants: while it may be interpreted as a plurality of books piled on top of each other having a cumulative weight of 20 kilos, some informants can only interpret it as 20 books each of which weighs one kilo. Moreover, both native speaker authors of this paper consider this sentence slightly infelicitous, yet for different reasons. The first author points out a preference for expressing the situation described in (33a) with a different structure, roughly equivalent to 'There are books on top of each other which in total weigh 20 kilos.' The second author finds the combination of the measure phrase *kilo* and the reciprocal phrase *egymás tetején* 'on top of each other' unacceptable. As for dual life nouns, chocolate in Hungarian patterns with the mass *hummus* (33b–33c); and nouns such as *állatállomány* 'livestock' are ruled out with count quantifiers such as 'a few' (33d).

	- b. \* 200g 200g csokoládé-t chocolate-acc vettem, bought mindegyik each más different töltelék-kel filling-inst volt be.past.3sg megtöltve. filled

'I bought 200 grams of chocolate, each of which was filled with a different filling.'

c. \* Másfél one.and.a.half kiló kilo humuszt hummus-acc készítettem, prepare.past.1sg mindegyik-et each-acc megették vm-eat.past.3pl a the parti-n. party-sup 'I prepared one and a half kilo of hummus, each of which was eaten at the party.'

16 Classifiers make a difference

d. \* Elég quite kevés few állatállomány livestock tűnt disappear.be.past.3sg el vm ma. today 'Quite a few livestock have disappeared today.'

Second, Erbach et al.'s (2019) assumption that bare singular nouns lack a mass reading in argument position does not hold. Our data indicate that mass readings of singular Ns are available in fact in full argumental positions. In (34) *könyv* is preceded by the definite determiner , while in (35) it appears bare. We assume that bare singular nominals that have a kind interpretation are mass nouns (Chierchia 1998b). The bare singular nouns *könyv* 'book' and *ima* 'prayer' in (35) pattern with the bare mass nouns *homok* 'sand' and *vér* 'blood' (36), evincing a mass interpretation to such nouns.7 The interpretation of nominals in Hungarian will be further discussed in the next section.

(34) A the könyv book ritka rare jószág stuff manapság nowadays amikor when mindenki everyone már already Kindle-t Kindle-acc használ. use.pres.3sg

'Books are rare nowadays when everybody uses Kindle already.'

In sum, the number-neutral analysis may not accurately reflect the empirical

facts of Hungarian, as the discussion of the accessibility of atoms in measure DPs is English-based. In addition, as we have shown, the linguistic facts are different

7The kind interpretation in (35) is not due to the conjunction, the same holds for a bare singular:

(i) Eminens eminent tanuló-nak student-dat könyv book fölött above a the hely-e. place-poss.3sg 'The place of eminent students is above books.'

### Brigitta R. Schvarcz & Borbála Nemes

in Hungarian. Moreover, the number-neutral analysis does not account for mass readings of bare singular nouns. Further evidence ruling out the number-neutral approach to the Hungarian nominal system can be found in Schvarcz & Nemes (2019). We now turn to the interpretation of nominals in Hungarian.

# **4 Are Hungarian nouns kind-denoting?**

Although the data indicate that Hungarian cannot be considered to be a classifier language, the question remains: Is the function of the sortal classifier in Hungarian the same as in typical classifier languages? In order to provide an answer to this question we will look at the interpretation of bare nominals in Hungarian, as the use of classifiers is closely related to nominal denotation.

### **4.1 Exploring kinds in Hungarian**

Regarding the basic denotation of nominals, Chierchia (1998b) distinguishes two types of languages. On the one hand, in languages like Mandarin Chinese all nouns have a default kind interpretation and can be used as arguments without determiners. On the other hand, in languages like English, count nouns denote properties and since these nouns are of the predicative-type, in order to be used as arguments the use of determiners is required. In contrast, mass nouns in this second type of language are assumed to denote kinds and can be used determinerless in argument positions.

Focusing on classifier optionality, we contrast Hungarian with the Chinesetype of languages. In these languages, classifiers are obligatorily used in order to retrieve instantiations of a kind, thereby allowing numerical modification. Unlike in typical classifier languages, however, classifiers in Hungarian are optionally used. These facts raise the question about the interpretation of Hungarian nominals: are they kind-denoting as are their the Chinese counterparts, or property denoting as in English?

In Mandarin Chinese, bare nouns can be used as subjects of kind-level predicates (37) (Li 2013), while the kind interpretation seems to be much more limited in Hungarian. Kind-level predicates in Hungarian require the definite construction (38):

(37) jing whale kuai soon juezhong be.extinct le. prf 'Whales will soon be extinct.' (Mandarin Chinese; Li 2013: p. 90, ex. (4)) 16 Classifiers make a difference

(38) \*(A) the bálna whale a the kihalás extinction szélé-n verge-sup áll. stand.pres.3sg 'Whales are on the verge of extinction.'

In certain constructions bare nominals can have a kind interpretation. Farkas & de Swart (2003) suggest that generic interpretations of bare plurals are not usually available in Hungarian, unless they are incorporated. Schvarcz & Rothstein (2017) show that with kind-level predicates, incorporated bare plurals can be interpreted as kinds. Constructions such as (39) are limited, and their interpretation varies among informants between kind and subkind readings. The incorporated bare plural *bálnák* 'whales' in (40) can have both a plurality of individuals as well as a kind interpretation.

Carlson (1977) takes narrow-scope reading of bare plurals as an indication of a kind interpretation. This phenomenon can also be observed in Hungarian:

(41) János John és and Béla Bill rózsá-k-at rose-pl-acc keresnek look.for.pres.3pl a the piac-on. market-sup 'John and Bill are looking for roses on the market.' (Schvarcz & Rothstein 2017: p. 203, (13))

Bare plural subjects of achievement verbs have a kind interpretation, as argued by Landman & Rothstein (2010). This has been shown to hold for Hungarian as well (Schvarcz & Rothstein 2017 – see example (31) above). (42), modelled on their example, shows that this is generally available in Hungarian:

### Brigitta R. Schvarcz & Borbála Nemes

(42) Nagy-ot big-acc csalódtunk be.disappointed.past.3pl a the delfinfigyelő dolphin.watching túrá-n tour-sup mert because két two órá-n hour-sup át for báln-ák whale-pl érkeztek arrive.past.3pl (és and nem not delfin-ek). dolphin-pl 'We were very disappointed by the dolphin-watching tour since whales arrived for two hours and (not dolphins).'

Schvarcz (2018) shows that in contrastive contexts, a kind interpretation of bare plurals is widely available:

(44) Ember-ek people-pl vagyunk, be.pres.1pl nem not állat-ok. animal-pl 'We are people, not animals.' (Schvarcz 2018: p. 116, (50c))

The fact that bare plurals can be interpreted as kinds is not surprising, given the fact that this is also the case in mass/count languages, such as English (Carlson 1977). Yet the case of bare singulars in Hungarian remains unexplored. The availability of a kind interpretation with these nouns is of capital importance for determining whether nouns can indeed be seen as kind-denoting. Hungarian bare singulars can get a kind interpretation in negative sentences: when the verb is under negation (45–46) as well as in contrastive contexts (47–49):

16 Classifiers make a difference

(49) János John könyv-et book-acc szeret like.pres.3sg olvasni, read.inf nem not újság-ot. newspaper-acc 'John likes reading books, not newspapers.'

As the examples above show, in a limited number of contexts both bare singular and bare plural nouns can get a kind interpretation. Nevertheless, the kind interpretation seems to be significantly more limited in Hungarian than it is in the case of typical classifier languages, like Mandarin Chinese or Japanese. The default choice for expressing a generic is the use of the definite construction (38). Unlike typical classifier languages which generally lack a definite article, Hungarian has one.

### **4.2 A hypothesis for the denotation of Hungarian bare nominals**

Our hypothesis is that in terms of kind reference, Hungarian count nouns are property-denoting, while mass nouns are kind-denoting. The mass counterpart of a flexible pair has a default kind interpretation, and hence can appear bare in characterizing sentences (45) and generic statements (48). This is also the reason why it requires a classifier upon combination with numerals – see (7b) above. We assume that the classifier takes the mass counterpart of a flexible noun, a kind-denoting term, and turns it into a property-denoting one.

Assuming that in Hungarian the majority of nouns, if not all, are flexible between count and mass versions, which correspond to a property-denoting and to a kind-denoting term respectively, both a definite and a bare construction is available for achieving genericity. Nevertheless, the definite construction is favored, while the bare construction is more marked and is available in contextually and syntactically restricted cases only. While the default argument of kind-taking predicates in generic sentences is a definite phrase, incorporation, negative and contrasting structures seem to override this requirement. Syntactically, we assume that these constructions have a more complex structure which allows bare nominals to receive a kind reading. A more comprehensive account and a formal analysis of this issue is a subject for further study.

# **5 The semantics of Hungarian classifiers**

We define the meaning of classifiers in a framework in which kinds are perceived to be individual concepts, functions from worlds to pluralities. The newspaperkind can be thought of as the set of newspapers, the totality of newspapers, the sum of all instances of the newspaper kind (Chierchia 1998b).

### Brigitta R. Schvarcz & Borbála Nemes

Treating mass-counterparts of Hungarian flexible nouns as kind-denoting terms lends itself to an analysis of sortal individuating classifiers under which they are functional operators on kinds, expressions of type ⟨, ⟨, ⟩⟩. Classifiers serve as functions to access the instantiations of a kind modeled by the INST operation. In other words, classifiers apply to kind denoting terms generating the set of individuals such that they are instantiation of that kind. From this perspective, the semantics of the general classifier *darab* could be formalised as follows:8

	- b. JdarabK = ..INST(, )
	- c. Jdarab újságK = .INST(, newspaperkind)
	- d. JháromK = .|| = 3
	- e. Jhárom darab újságK = .inst(, newspaperkind) ∧ || = 3

Our semantics of the general classifier can be further extended to those sortal individuating classifiers in Hungarian that select nouns based on size, shape and form:

	- b. JszálK = ..INST(, ) ∧ long() ∧ thin()
	- c. Jszál rózsaK = .INST(, rosekind) ∧ long() ∧ thin()
	- d. JháromK = .|| = 3
	- e. Jhárom szál rózsaK = .INST(, rosekind) ∧ long() ∧ thin() ∧ || = 3

8 In line with Rothstein (2017) we assume that numerals in prenominal positions are functions that map entities onto the value true if they have atomic parts.

### 16 Classifiers make a difference

If we assume that classifiers take kind-denoting expressions, then examples where the plural and the classifier can co-occur (20–22) require further explanation. One option to explain such examples is to treat the bare plural noun as denoting a kind. This is further supported by (39–44) illustrating the kind interpretation of bare plurals in various contexts. Another option is to compose the structure of classifier-plural nominal co-occurrences in the following way: the classifier combines with the kind-denoting singular noun deriving instantiations of the noun, which is then pluralized. This would assume a syntax in which the plural marker *-k* is higher than the CL+N. Both of these options allow us to maintain the semantics of classifiers proposed in this paper.

# **6 Summary and implications**

This paper explored classifier optionality in Hungarian and argued that the phenomena can best be captured in a noun-flexibility approach, while the role of sortal individuating classifiers is to trigger a kind-to-predicate shift in nouns which are born as kind-denoting expressions.

The foundation of our analysis is a flexibility-based approach to Hungarian mass/count phenomena, according to which most nouns in the language are ambiguous between a mass and count denotation (Schvarcz & Rothstein 2017). The count and mass versions are derived from the same neutral lexical root of a noun, via the COUNT and MASS operations, resulting in two identical lexical forms. Under this analysis, flexibility is a purely grammatical phenomenon and does not postulate any semantic ambiguity. This approach has numerous advantages over alternative theories of Hungarian nominal semantics. It helps explain novel data that neither a non-ambiguity (Dékány 2011, Csirmaz & Dékány 2014) nor an underspecification (Erbach et al. 2019) approach has discussed. In addition, it accounts for the optionality of sortal individuating classifiers and captures the interpretational differences of Hungarian numerical expressions.

We first explored the differences in interpretation between NUM+N and NUM+CL+N constructions and showed that there is a significant interpretational difference between them: while the former can refer to a plurality of individuals or to a plurality of subkinds, the insertion of the classifier in the latter construction restricts the reading to a plurality of individuals.

We then provided evidence in defense of the noun-flexibility approach showing that neither a plural-as-a-classifier nor a number-neutrality approach captures the semantic effect induced by the optional classifier. The distribution, interpretation, and co-occurrence of plurals and classifiers as well as the different

### Brigitta R. Schvarcz & Borbála Nemes

agreement patterns induced by the two strongly suggest that the plural cannot be treated as a classifier. Moreover, number-neutral analysis does not account for mass readings of bare singular nouns nor for the semantic input of the classifier observed in our study.

We claimed that Hungarian nominals are kind-denoting by default and can undergo a kind-to-predicate shift (Chierchia 1998a) explicitly triggered by a sortal individuating classifier. Hungarian has a unique set of properties, allowing both for bare and for definite constructions to express kind. We have shown that bare singulars with a kind-reading are available both for mass Ns and for the mass counterparts of flexible Ns, indicating that nouns are kind-denoting expressions.

Hungarian appears to be a "mixed system" in terms of the use of a mass/count system and classifiers and has unique properties with regards to the distribution and interpretation of bare nominals, which points to more typological variation between languages than has been suggested before.

# **Abbreviations**

# **Acknowledgements**

We would like to thank Gabi Danon, Yasutada Sudo, Éva Dékány for their helpful remarks and two anonymous reviewers for comments on an earlier version of the paper. We are grateful to a number of informants for judgments. We are indebted to Susan Rothstein (ז״ל – may her memory be blessed) for her suggestions on an 16 Classifiers make a difference

initial version of the proposal. We owe many thanks to Kata Wohlmuth for her help with LATEX. We also thank the audiences of the Semantics Research Group of Bar-Ilan University, the SinFonIJA 12, 15.ik Felúton, Constructions of Identity 10 and NaP2019 conferences for useful discussions and feedback on the topic. This work was partially supported by the Azrieli Fellowship to Brigitta R. Schvarcz.

# **References**

### Brigitta R. Schvarcz & Borbála Nemes

16 Classifiers make a difference

### Brigitta R. Schvarcz & Borbála Nemes

# **Part IV Other quantifiers**

# **Chapter 17**

# *Some***,** *most***,** *all* **in a visual world study**

# Barbara Tomaszewicz-Özakın

University of Cologne

In a visual world eye-tracking study I find that Polish quantifiers *niektóre* 'some', *większość* 'most of', *najwięcej* 'the most' and*wszystkie* 'all' elicit distinctive patterns of looks, consistent with their semantics. *Niektóre* 'some' has a strong scalar implicature: the meaning 'some-not-all' is processed immediately as the quantifier is heard. The superlative *najwięcej* 'the most' quickly triggers comparisons between the target and the other sets. The proportional *większość* 'most of' elicits a pattern suggesting that its verification involves the estimation of the total set. With *wszystkie* 'all' the identification of the target set is the fastest.

**Keywords:** quantifiers, semantics–cognition interface, eye-tracking, Polish

# **1 Introduction: Interpreting quantifiers**

We talk about quantities all the time while describing the world. Quantifiers are natural language expressions used to describe quantities with or without using number terms.  We can easily assess the conditions that make this sentence true/false, but how do we verify such sentences in real-life situations? The generalized quantifier theory (Mostowski 1957, Lindström 1966, Montague 1973) is silent about this issue, mainly because for philosophers (Montague 1973, a.o.) semantics was a branch of mathematics and not of psychology (Partee 2011). Psychologists, however, have long been studying the number sense in humans, a dedicated brain system for abstract representation of number and the source of our mathematical intuitions, which is employed in the judgments involving quantifiers (Feigenson et al. 2004,

Barbara Tomaszewicz-Özakın. 2021. *Some*, *most*, *all* in a visual world study. In Mojmír Dočekal & Marcin Wągiel (eds.), *Formal approaches to number in Slavic and beyond*, 399–426. Berlin: Language Science Press. DOI: 10.5281 / zenodo.5082482

McMillan et al. 2005, 2006, Clark & Grossman 2007, Dehaene 2009, 2011, Troiani et al. 2009). Speakers have different ways of referring to quantities: number terms when the specific size of the set is at issue, and vague quantifiers like *some* and *most* or context-dependent quantifiers like *few* and *many*, when they refer to approximate quantities. The present study addresses two critical questions about quantifiers: (i) what is included in the lexical representation of quantifier meanings and (ii) what the psychological mechanisms involved in the interpretation of those meanings are.

Investigating those two questions, Pietroski et al. (2009) and Lidz et al. (2011) put forth a novel hypothesis that what participants are doing to verify sentences containing quantifiers, i.e., (ii), can be directly determined by quantifier semantics, (i). To illustrate, in order to verify that most balls are blue we need to compare the numbers of blue and and non-blue balls, but how do we obtain the number of non-blue balls? If there are balls in one other color, we can simply count them. If there are more colors, we can count the numbers of balls in each other color and add them up; or we can obtain the number of all balls and subtract the number of the blue balls from it; or we can instead verify if blue balls are more than the half of all balls. But do we even need to count? Children who are not yet able to count are perfectly able to understand sentences containing *most* (Halberda et al. 2008, Odic et al. 2018), and in real-life situations we do not need to know precise quantities to use *most*.

Pietroski et al. (2009), Bates, Kliegl, et al. (2015), Tomaszewicz (2011, 2012, 2013, 2018), Hunter et al. (2017), Knowlton et al. (2021) obtained experimental evidence that *most of* induces a subconscious choice of a procedure based on subtraction for verification against visual displays, even in situations where comparing the numerosity of the target set and one other set directly would be more efficient. Why would the mind not subconsciously choose the most efficient procedure in a given situation? According to the hypothesis it is because the mind follows the "instructions" encoded in the logical function representing the meaning of a quantifier.  The participants were prompted to switch between verification procedures by a change in the linguistic input, but not by a change in the visual input. Thus, the motivation for the subconscious switch in procedures is not to maximize efficiency. Participants used the procedure associated with each quantifier, and in effect, the same display was verified differently depending on which information the visual system was instructed to use by the lexical representation of quantifier meanings.

### 17 *Some*, *most*, *all* in a visual world study

The present experiment was designed to uncover the details of the lexical semantic specification of the Polish quantifiers *niektóre* 'some' and *większość* 'most of' in comparison to *wszystkie* 'all' and *najwięcej* 'the most'. It utilizes eyemovement as a representational measure in the visual world paradigm. Visual world eye-tracking has been used to demonstrate how comprehenders rapidly integrate different sources of information in order to identify the referents in the visual display as the sentence unfolds over time (Tanenhaus et al. 1995, Allopenna et al. 1998). The visual world paradigm allows for the closest approximation of real-life visual contexts in an experimental setting. Our conscious experience is that our eyes glide from one thing to another thing, but, in fact, unless we are tracking a moving object, our eyes perceive images in a series of rapid jerky movements (saccades). We can track the series of fixations at a particular point and the saccades away from that point in order to analyze which parts of the image attract attention and how. In a visual world task, participants hear the sentence as they inspect the visual scene and their eye movements are recorded; in particular, the proportion of looks to the target in the picture is measured. This makes it an excellent tool for the investigation of incremental processing. At the point in the sentence when the quantifier is heard, participants' subconscious attention should be directed to different aspects of the scene, towards or away from the target, depending on the semantics of the quantifier.

In the current experiment, the four Polish quantifiers, *wszystkie* 'all', *niektóre* 'some', *większość* 'most of' (proportional *most*), and *najwięcej* 'the most' (superlative *most*, henceforth *most-sup*), appeared in the same carrier sentence, describing the same identical display for *some*, *most of* and *most-sup* (screens for *all* needed to differ as will be explained shortly). I employed the gumball paradigm of Degen & Tanenhaus (2011, 2016). Participants evaluated sentences of the form 'You got all/some/most of/most-sup blue balls' against displays of a ball machine dispensing balls of three colors from upper to lower chambers; see Figure 1. The correlate of the processing of the information about the quantifier semantics was the proportion of looks to the target (the set of blue balls) vs. the so-called distractors (the two other color sets).

I thus build on the results of Huang & Snedeker (2009, 2011), Grodner et al. (2010), Degen & Tanenhaus (2011, 2016), who showed that contextual effects on the interpretation of quantifiers are reflected in eye-movement patterns. Those studies investigated the time course of scalar implicatures, i.e., the pragmatic aspects of the meaning of the quantifier *some*, while I concentrate on the precise semantic distinctions between the four Polish quantifiers to test both the scalar implicature of Polish *niektóre* 'some' (Spychalska 2009) and the verification procedure associated with Polish *większość* 'most of' (Szymanik & Zajenkowski

The ball machine at the beginning of the trial

After a button press, the machine dispenses balls to the lower chambers and after 500 ms the stimulus sentence is played

Figure 1: The blue set is the target.

2010). The results of the visual verification experiments (visual search paradigm) in Pietroski et al. (2009), Lidz et al. (2011), Tomaszewicz (2011, 2012, 2013), Hunter et al. (2017), and Knowlton et al. (2021) indicate that quantifier semantics guides the subconscious adoption of a verification strategy. The visual search paradigm involves comparisons of the accuracy of judgments with reference to rapidly presented displays (200–300 ms), i.e., accuracy is taken as a proxy for the processing cost. In these prior experiments the number of different color sets affected accuracy in different ways depending on the quantifier in the stimulus sentence. So while in the visual search paradigm it is assumed that the sentence stimulus somehow provides an instruction for verification, the visual world paradigm in the current study enables us to tap into the real-time construction of this instruction as the auditory stimulus unfolds.1

# **2 The current study**

### **2.1 Methods**

In each trial (72 trials in 3 blocks of 24), participants ( = 35), saw a fixation cross and the display of a ball dispensing machine with its upper chambers filled with 3 colors of balls (bottom chambers empty), as in the left panel of Figure 1.

1 See Huettig et al. (2011) for an argument how the two paradigms, visual world and visual search, provide converging evidence for the role of working memory in the interactions between linguistic input and visual attention.

17 *Some*, *most*, *all* in a visual world study

After 2 seconds, the button in the center of the machine turned yellow, and the participants clicked on the button. Upon clicking, a grey mask was displayed for 200 ms. (Clicking the central button ensured that the participants were looking at the central fixation point at the time of the auditory stimulus onset.) Now the second display was shown: the ball machine was redisplayed with a certain number of balls of each color having dropped to the lower chamber, e.g., right panel in Figure 1.

After 500 ms, the participants heard one of the stimulus sentences in (1). Their task was to click on that lower chamber which contained the balls mentioned in the statement if they thought the statement was true, and click on the central button otherwise.

(1) Dostałeś…

got.past.2sg

'You got…'

All the sound files were cross-spliced and normalized using Praat Vocal Toolkit (Corretge 2020) so that all the quantifiers and color expressions had the same duration. Once the participants clicked indicating their response, a grey screen was displayed for 1s and the experiment advanced to the next trial. Participants' eye movements were recorded with an Eyelink 1000 eye-tracker at a sampling rate of 1000 Hz.

There were 8 conditions: 4 quantifiers (some/most-of/mostsup/all) \* 2 display types (early/late). In early trials there was only one partitioned set, e.g. the blue set in Figure 1. In late trials all sets were partitioned. The difference between the early and late displays is discussed and illustrated with pictures

in the next section (Figure 4). The displays for the test sentences for the analysis of eye-movements required a Yes response. Filler trials, half of all trials, required a No response. For Yes responses participants clicked the chamber that matched the sentence, for No responses they clicked the button. Following Degen & Tanenhaus (2011, 2016) I included what they call garden-path trials among the fillers in order to force the participants to pay attention and notice that the sentences throughout the experiment might not always be true. On these trials, one set was partitioned allowing an anticipation of a quantifier, but as the sentence unfolded it turned out this set did not match the sentence (leading to a garden-path-like effect where you had to revise your search for the target); see Figure 2.

Figure 2: garden-path condition

The displays were identical for 3 quantifiers: *some*, *most of, most*-sup. The quantifier *all* had different displays, because the top chamber for that color had to be empty, Figure 3.

Figure 3: The displays for the all condition

17 *Some*, *most*, *all* in a visual world study

### **2.2 Predictions**

The visual world studies in Huang & Snedeker (2009, 2011), Grodner et al. (2010), and Degen & Tanenhaus (2011, 2016) compared the quantifiers *some* and *all* in order to establish whether the processing of the scalar implicature of *some* is delayed. The literal interpretation of *some*, as in *You got some of the blue balls*, is that you got at least one blue ball, so if you got all of them, the sentence is true.2 But in most contexts, we understand this sentence as saying that we got some blue balls but not all of them. This interpretation is a pragmatic inference: the speaker would have said 'You got all of the blue balls' if we got all of them because that would be a more informative statement. Quantifiers *all* and *some* form a scale, so when *some* is used instead of the stronger *all*, the meaning 'some-notall' is inferred – this inference is called a scalar implicature (Horn 1972, Levinson 1983, a.o.). Huang & Snedeker (2009) found that the 'some-not-all' reading was delayed in comparison with *all*, but Grodner et al. (2010) found no delay and Degen & Tanenhaus (2011, 2016) hypothesized that the reason for the delay in Huang & Snedeker (2009) was the availability of other descriptions of the scene: sentences with number terms in addition to *some* and *all*. Degen & Tanenhaus (2016) indeed found that when no such alternatives were present, the processing of the *some-not-all*-implicature was not delayed relative to the processing of the meaning of *all*, but it was somewhat delayed when those alternatives were available. Thus, the processing of pragmatic meaning may be no more costly than the processing of the literal meaning of a quantifier, depending on the context. In the present experiment, I used the gumball paradigm of Degen & Tanenhaus (2011, 2016) to investigate the time course of processing of both pragmatic and semantic information.

Given the findings of Degen & Tanenhaus (2011, 2016), the implicature of the Polish 'some', *niektóre*, could be delayed due to the presence of alternative utterances that could describe the same situation. However, Spychalska (2009) argues that the implicature of *niektóre* is stronger than that of English *some*, so if we find no delay, the current methodology is a useful tool for the investigation of cross-linguistic semantic differences. To get more specific information about the time course of the processing of *niektóre*, it is compared with the two majority quantifiers whose literal meaning allows us to make precise predictions for processing, given the findings in Tomaszewicz (2011, 2012, 2013, 2018) that each of them drives a distinct verification procedure consistent with its semantics.

2As observed in Spychalska (2009) in Polish *niektóre* must mean 'at least two', because the quantifier occurs only in the plural form.

The superlative *most* (*most*-sup) in the sentence *You got the most blue balls* (true in the right top panel of Figure 4) requires a comparison between the balls in the lower chambers of the machine. In Figure 4, the machine has dropped down 4 blue, 3 red, 2 green balls. Figure 4 illustrates how the stimulus sentence unfolds: when hearing the quantifier *najwięcej* (*most*-sup, i.e., 'the most'), you already know you need to compare the numbers of the balls and if you had already determined that the blue set is the biggest, you can anticipate the adjective 'blue'. The proportional quantifier, *most of*, on the other hand, requires you to compare the numbers of the blue balls in the lower and the upper chambers. So these two majority quantifiers require very distinct patterns of eye-movements. The proportion of the looks to the target blue set at the moment of hearing the quantifier should be lower with *most*-sup because two comparisons are needed (with the red and the green set) whereas with *most of* just one comparison is necessary (between the lower and upper blue sets). This predicted contrast between *most of* and *most*-sup allows us to test whether the looks to the target with *some* will be delayed like with the *most*-sup. In addition, obtaining this contrast would set up a baseline for further visual world studies on majority quantifiers cross-linguistically.

The experiment also includes a comparison with the universal quantifier *all*, but it was not possible to present it together with the same displays as for the three other quantifiers; see the bottom panels of Figure 3. The displays for *all* contained the same numbers of balls in the lower chambers and the colors were in the same order as in the corresponding displays for the other quantifiers (the exact location of the balls within a chamber was a little different because the displays were generated with a random scatter). The displays for *all* make it very easy to anticipate the color adjective at the point of hearing the quantifier so this condition provides us with a time course for the highest proportion of looks to the target (I already note here that this is not the baseline for statistical comparisons because I want to compare the quantifiers with the same identical displays).

The contrasts described above are predicted for the displays where the quantifier provides a point of disambiguation as to which color is the target set. This is the early condition, i.e., in these displays target identification can happen earlier than the color adjective is heard. The looks to the target set in the early condition should begin to increase in the quantifier window (as in Degen & Tanenhaus 2016). In contrast, in the LATE condition, see Figure 5 below, the point of disambiguation is the color adjective.

The theoretical predictions outlined above may be affected by possible confounds, because in experiments on visual identification participants may exhibit

### 17 *Some*, *most*, *all* in a visual world study

Dostałeś najwięcej/większość/niektóre… 'You got most-sup/most-of/some …'

Dostałeś wszystkie … 'You got all …'

Figure 4: Sample displays in early condition – target: blue.

different kinds of biases that come, for instance, from the way the visual system works. One of them is the bias to look more at larger set sizes (also found in the study of Degen & Tanenhaus (2016). This means that already during the preview of the picture, before the sentence is heard, participants will tend to look at the blue set in the top right panel of Figure 4. We also know that when precise counting is impossible or simply not needed as in the current experiment, people use the Approximate Number System (ANS) that generates a representation of magnitude rather than an exact cardinality, (Feigenson et al. 2004, Dehaene 2009, 2011). It is also known that with a 500 ms display ANS automatically enumerates the total set (the superset) and up to two color subsets in parallel, (Halberda et al. 2006). Thus, the time course of eye-movements over the three regions of interest is expected to reflect the following effects in the early condition (the summary is in Table 1).

Dostałeś większość/najwięcej/niektóre… 'You got most-of/most-sup/some…'

Dostałeś wszystkie… 'You got all…'

Figure 5: Sample displays in late condition – target: blue.

### **2.2.1 Preview and 'You got' (early)**

During the 500 ms preview of the display (the right panel of Figures 1–3) and during the beginning of the sentence (*Dostałeś…* 'You got…') I expect no differences in the looks to the target for the four quantifiers, except for the bias to look at the biggest set (the blue target pops out as different than the other two sets, hence it may attract looks early on).

### **2.2.2 Quantifier (early)**

In the early condition, where the quantifier disambiguates which set is the target, in the quantifier window, I expect fewer looks to the target with *most*-sup than with *most of* and with *all*. The theoretical prediction explained above is

### 17 *Some*, *most*, *all* in a visual world study

that *most*-sup requires two comparisons (between the blue and the red set, and between the blue and the green set in the lower chambers), while *most-of* requires one (between the blue set in the lower chamber and that in the upper chamber). During the preview and the 'You got' window the looks may be attracted to the biggest and partitioned set which is the blue target, therefore in the quantifier window the looks may already move to the distractors. The quantifier *all* requires no comparisons. This prediction is summarized as 'mostsup < most-of, all' in Table 1 ('fewer looks to the target set in the mostsup condition than in the most-of and all conditions').

The predictions for *some* in the early condition in the quantifier window depend on which interpretation could be in the minds of the participants at this point. If the scalar implicature, 'some-but-not-all', has already been processed, the identification of the target should be (almost) just as easy as with *all*: it cannot be the red nor the green set, and the blue partitioned set has already stood out during preview. Hence, 'some-not-all = all' in Table 1. This interpretation would also attract more looks to the blue target than with *most*-sup, 'some-notall > mostsup'. If, instead, participants are first considering the literal meaning of *some*, 'some-and-possibly-all', then their looks will be directed to the green and red color sets as with *most*-sup, 'some-possibly-all = mostsup'.

Finally, there should be more looks to the target in the early condition in the quantifier window with *some* on the 'some-but-not-all' interpretation than with *most of*, 'some-not-all > most-of'. With both quantifiers the looks will be attracted to the partitioned set, the blue target, but with *most of* you need to estimate the numerosities of the two blue subsets and compare them to verify that the sentence is true.

### **2.2.3 Color + 'balls' (early)**

In the early condition, all but one of the effects observed in the quantifier window are predicted to be carried over to the color window. The exception is the quantifier *most of*, which now may attract a similar proportion of looks to the target as *some-not-all*, 'some-not-all = most-of'. The alternative is that with *most of* there will still be more looks away from the target, 'some-not-all ' most-of', because '*most of the blue balls'* requires more operations for visual verification than the comparison of the top and bottom blue set as I stated above. Pietroski et al. (2009) and Lidz et al. (2011) propose that sentences with *most of* are verified against visual displays of multicolored dots not by directly comparing two sets but by a subtraction procedure: you estimate the superset, you estimate the target set, subtract and compare the result with the target. This procedure involves

more steps than direct comparison of two sets but the reason it is followed is because it is directly specified in the lexical semantics of the proportional quantifier *most*. Lidz et al. (2011) argue that sentential meanings are "individuated more finely than truth conditions" (p. 2) precisely because they interface with perception systems such as visual cognition. It has been established that numbers can be represented as "noisy magnitudes" even for the purposes of basic arithmetic operations like addition and subtraction (Feigenson et al. 2004, Degen & Tanenhaus 2011), so the subtraction procedure is possible even with a 200 ms display, but crucially it is less efficient than direct comparison. This effect was shown in the visual search studies of Pietroski et al. (2009), Lidz et al. (2011), Tomaszewicz (2011, 2012, 2013), Hunter et al. (2017), Knowlton et al. (2021), which measured accuracy of Yes-No responses. In the current study I should find evidence that participants follow the subtraction procedure, as specified in (2), in contrast to direct selection of the two sets as in (3), if we find fewer looks to the target in the color window than with some-not-all because of continuing looks to the top blue set in order to establish the total set. Perhaps, the proportion of looks to the target will even be as low as with *most*-sup ('some-not-all = most-of?/somenot-all > most-of/most-of = mostsup?' in Table 1). Such a result in the color window in the early condition would provide support for a higher number of processing steps involved in (2) as opposed to (3).

#[blue(x) & below(x)] > #[blue(x) & above(x)]

The differences expected to occur in the late condition are presented in the following subsections.

### **2.2.4 Preview and 'You got' (late)**

I expect no differences. As can be seen in Figure 5, bottom-right panel, the target set cannot be identified during the preview by the big set bias (the blue bottom set is not the only large set).

### 17 *Some*, *most*, *all* in a visual world study

(a) early

### **2.2.5 Quantifier (late)**

In the late condition, the target set can only be reliably disambiguated upon hearing the color adjective, that is, in the last time window of interest. However, I do expect differences in the quantifier window already.

Because the target set cannot be biased during the preview, upon hearing the quantifier *most*-sup, the looks could be immediately directed to the largest of the bottom sets, the blue target, while with *most of* and *some* the looks will also be directed to the upper sets and with *all* to the other bottom sets. Thus, the prediction is 'mostsup > all, most-of, some' in Table 1. Alternatively, the identification of the largest set with *most*-sup is delayed until the color window, but given the big set bias, I find this option unlikely.

I also expect more looks to the target with *some-not-all* than *most of* because *most of* requires the estimation of the numerosity of the bottom blue set relative to the top set (in one of the two ways in (2–3) discussed above). Additionally, there should be fewer looks to the target with *some-not-all* than with *all* because the set for the latter is unpartitioned. These two effects should persist in the color window.

In the late condition, the *some-possibly-all* interpretation is not tested because all sets are partitioned.

### **2.2.6 Color + 'balls' (late)**

At the point of hearing the color adjective in the late condition, the proportion of looks to the target with *most*-sup should be lower than with other quantifiers because now the looks are attracted to the other two color sets in order to make the comparisons to confirm that indeed the blue set is the largest, 'mostsup < all, most-of, some'. Could it be that once the largest set is identified already in the quantifier window, participants stop making the comparisons upon hearing 'blue' because it matches the already identified target? I do not think so, simply because the 'identification of the target' as early as the quantifier happens unconsciously, and only when the color is heard are the participants aware of the semantics of the full sentence, thus I expect the processing to keep going and to follow the semantics of the superlative sentence: 'There are more blue balls than the balls in any other color'. Accordingly, I expect that in the color window, comparisons with other colors will take place.

Of all of the above, the predictions of main theoretical interest are the following:

17 *Some*, *most*, *all* in a visual world study

### **2.3 Results: Behavioral**

The mean accuracy on the test conditions (i.e, early and late that required a Yes response) was 95%. Of the 35 participants, 30 got 97–100% correct and 3 got less than 70% correct (54%, 58%, 65%). I kept all of the responses because I did not aggregate the data for statistical analyses and I used the eye-movement data only from the correct trials. I removed the extremely long outlier reaction times (three standard deviations above the mean); those constituted 1.3% of the Yes and No data and were equally found in all conditions and regions of interest. The accuracy of the responses and reaction times (RTs) are plotted in Figure 6.

Figure 6: Accuracy and log-transformed reaction times (error bars represent standard errors)

I fitted a mixed-effects regression model of the log-transformed RTs and a mixed effects logistic regression model of the (binary) yes/no variable in R version 3.6.2 (R Core Team 2017) using the lme4 package version 1.1-21 Bates, Mächler, et al. (2015). The *p*-values were obtained using model comparison and the Satterthwaite approximation implemented in the lmerTest package (Kuznetsova et al. 2017).

For the accuracy data, there is a significant main effect of quantifier ( 2 = 13.72, df = 3, = 0.003). With ANOVA-style contrast coding, there are no differences in pairwise comparisons between the conditions. There are also no differences in pairwise comparisons when all is the baseline; with some as the baseline only most-of is significantly different, = 2.066, SE = 0.758, = 2.742, = 0.026 (including the Bonferroni correction for multiple comparisons). Summing up, the accuracy across the conditions ranged from 87% to 99%, and participants were significantly less accurate in the some condition in comparison to the mostof condition. We do not see such a difference in reaction times (right panel in Figure 6): some is not slower than the other conditions, which means that this

condition was not harder, but either that people were fast and made mistakes (which is unlikely given that most-of and mostsup had similar RTs) or rather that they believed 'No, I did not get *some* of the balls in color x, I got *most* of them.'

The plot of the RTs in Figure 6 shows significant effects of the early/late condition ( 2 = 9.39, df = 1, = 0.002) and Quantifier ( 2 = 72.91, df = 3, < 0.0001) and their interaction ( 2 = 11.62, df = 3, = 0.009). Pairwisecomparisons with mostsup as the baseline confirm what we see in the plot: that only the all condition is significantly faster ( = −0.085, SE = 0.014, = −6.085, < 0.0001). This is expected given that as discussed in §2.1, all had the easiest screens (since the accuracy with all, mostsup and most-of was very high, we do not see differences due to the difficulty of the screens).

Note that while the semantics of mostsup requires comparisons with the two other color sets in the bottom chamber, we see that these comparison procedures have no effect on the accuracy nor on the reaction times. This is compatible with the predictions (as summarized in Table 1) where on the early condition, looks to the target with mostsup could benefit from the big set bias in the first two time windows with the rest of the time spent on looking at the other colors; on the late condition in the quantifier window there should be more looks to the target and then fewer in the color window than with the other quantifiers. In the next section we will see that the predicted differences are in fact reflected in the eye movements.

### **2.4 Results: Eye-movements**

The pre-processing of the eye-movement data and plotting was carried out using the VWPre package (version 1.2.2, Porretta et al. 2018). The first line in Figures 7–8 shows the plots of the proportion of looks to the target for the early and late conditions. The black lines mark the time windows in the audio stimulus adjusted by 200 ms (i.e., 200 ms post the actual onset).3

I fitted generalized additive mixed models (GAMMs) using the packages mgcv (version 1.8-31; Wood & Scheipl 2017, Wood 2017) and itsadug (version 2.3; van Rij et al. 2020) to the eye data because a regression line is unable to capture the

3 200 ms was chosen following Degen & Tanenhaus (2016) because the earliest language mediated fixations are at 200–250 ms after the relevant acoustic landmark that could establish a point of disambiguation (Salverda et al. 2014) The proportion of looks for each interest area has been converted to empirical logits because proportions are inherently bound between 0 and 1 but logits provide a transformation resulting in an unbounded measure suitable for use in the statistical tests (Barr 2008).

### 17 *Some*, *most*, *all* in a visual world study

nonlinear nature of the time course data as in Figures 7–8. GAMM is a nonlinear regression analysis which in addition to linear effects includes smooth terms as well as random smooths to capture the random effects. Model comparisons involve the full model, with all terms and interactions, and a nested model that excludes the main term and the smooth term corresponding to the predictor and the interactions with these terms (Winter & Wieling 2016, Sóskuthy 2017, Wood 2017). Because in this experiment the predictions are only about the parametric terms (the proportion of looks to the target within a given time window) and not about the differences between the shapes of the curves, significance testing is based on the *t*-values and I only report those.

The third line in Figures 7–8 shows the model predictions for each of the time windows without random smooths, Preview (−500–0 ms), *Dostałeś* 'You got' (0– 1030 ms), the quantifier (1030–2122 ms), *niebieskich/zielonych/czerwonych kulek* 'blue/green/red balls' (2122–3682 ms). The fourth line shows the model predictions including the random smooths that capture the random effects of Subject, Item and Trial. The fifth line summarizes the statistical findings showing which of the contrasts were significant – the unpredicted significant effects are highlighted in grey. The non-highlighted findings match the predictions summarized in Table 1.

In the early condition, Figures 7, there is a main effect of Quantifier in each time window (Preview: 2 = 10.96, df = 9, = 0.009; 'You got': 2 = 105.5, df = 9, < 0.0001; the quantifier: 2 = 55.8, df = 9, < 0.0001; the color window: 2 = 140.35, df = 9, < 0.0001). Pairwise comparisons reveal the following differences (-values include the Bonferroni correction for multiple comparisons):

In the Preview window, there are more looks to the target with mostsup than with most-of ( = −0.388, SE = 0.119, = −3.268, = 0.004) and with some than most-of ( = −0.389, SE = 0.107, = −3.617, = 0.001).

In the 'You got' window, the proportion of looks to the target is higher with mostsup than with some ( = −0.653, SE = 0.102, = −6.382, < 0.0001) and than with most-of ( = −1.259, 0.111, = −11.377, < 0.0001), as well as with some in comparison with most-of ( = −0.604, SE = 0.101, = −6.011, < 0.0001).

In the quantifier window, the trend is reversed and there are fewer looks to the target with mostsup than with some ( = 0.637, SE = 0.115, = 5.528, < 0.0001) and with most-of ( = 0.866, SE = 0.123, = 7.02, < 0.0001).

In the color adjective plus noun 'balls' window, there are still fewer looks to the target with mostsup than with some ( = 0.574, SE = 0.106, = 5.436, < 0.0001). But now there is no difference between mostsup and most-of. There are now fewer looks to the target with mostsup than with all ( = 1.761, SE =

0.166, = 10.6, < 0.0001). Also some has fewer looks to the target than all ( = 0.95, SE = 0.172, = 5.533, < 0.0001), but it has more looks to the target than most-of ( = −0.41, SE = 0.102, = −4.003, = 0.0002).

Strikingly, there are differences between the quantifiers already during Preview and in the 'You got' window. An exploratory analysis is needed to find out what drove the differences. Perhaps it was some property of the previous trial such as the quantifier, the location of the target (left, center, right) or the numerosities of the sets. Or perhaps this reflects the anticipation of which sentence would best describe the display given that in the late condition, there are no differences at Preview, but the differences start at 'You got' such that most-of and some get more looks than mostsup. 4

Crucially for the goals of the experiment, in the quantifier window the unexpected trends from the previous windows do not persist. Instead, I find that the predictions have been met: with mostsup there were fewer looks to the target than with some and most-of. This is the prediction 'mostsup < most-of, all' in Table 1. (There is no significant difference between mostsup and all, which was unpredicted, however, some is also not significantly different from all and it can be seen in the plot with random effects that there is a lot of variation with all).

I predicted that the lower proportion of looks to the target with mostsup than with most-of should be due the fact that its semantics requires two comparisons (between the target and the two color sets in the lower chamber) while with most-of one comparison is required (between the lower and upper subsets of the partitioned set). The question was whether with some the looks to the target would be the same as with mostsup suggesting that the processing of the scalar implicature does not happen in the quantifier window. I find that this is not the case: there are more looks to the target with some than with mostsup and some is no different from most-of (and all). This result is compatible with the prediction 'some-not-all > mostsup' in Table 1, meaning that the scalar implicature, 'some-but-not-all', has already been processed in the quantifier window. I find no support for the alternative, that first the literal meaning of *some*, 'someand-possibly-all', is processed, 'some-possibly-all = mostsup' in Table 1.

4A reviewer objects to this saying that it is unlikely that the participants would try to guess the upcoming quantifier or were clairvoyant. But my suggestion is that the big-set bias has consequences for the mental representation of the description of the visual scene. See Huettig et al. (2011) for the explanation of the interaction between the visual stimuli and higher order cognitive biases as induced by task goals and language. In the late condition, a salient visual cue is absent and the looks do not diverge during Preview. In the early condition, the target set pops out and may bias some mental description of the scene, which is additionally affected by the memory of any salient features of the previous trial.

Figure 7: Results: early condition. (†) marks *disambiguation*.

It was also predicted that in the early condition in the quantifier window some-not-all would get more looks to the target than most-of ('some-not-all > most-of' in Table 1), but instead we find that some is no different from mostof ('some = most-of' in Figure 7) – we do find evidence for this effect but in the next region.

The prediction for the color adjective window was 'some-not-all > most-of' if most-of requires more looks between the two partitioned sets than some in order to establish the total set of the balls in the target color for the Subtraction procedure, (2). I hypothesized that the proportion of looks to the target with most-of could be as low as with mostsup, which is what we find ('mostsup = most-of' in Figure 7). However, looking at the plots we see that the difference between most-of and some is rather small; there are more looks to the target with some at the very beginning and mostly at the end of the region. The trajectory for most-of is quite different than for mostsup, where the looks diverge between the target and the distractors. Still, the proportion of looks to the target within the whole region is as low with most-of as with mostsup, which is consistent with a higher number of processing steps involved in the Subtraction procedure in contrast with the direct Selection procedure.

The results for the late condition are presented in Figure 8. In the late condition, there are also effects of Quantifier in each time window (Preview: 2 = 18.4, df = 9, = 0.009; 'You got': 2 = 113.12, df = 9, < 0.0001; the quantifier: 2 = 112.61, df = 9, < 0.0001; the color window: 2 = 163.25, df = 9, < 0.0001). In contrast to the early condition, in the Preview window multiple comparisons show no significant differences. In other windows pairwise comparisons reveal the following differences (*p*-values include the Bonferroni correction for multiple comparisons):

In the 'You got' window, in the late condition, mostsup got fewer looks to the target than all the other conditions (some, = 0.847, SE = 0.102, = 8.321, < 0.001, all, = 2.754, SE = 0.855, = 3.223, = 0.005, most-of, = 1.274, SE = 0.108, = 11.749, < 0.0001). some received fewer looks to the target than mostof ( = 0.434, SE = 0.102, = 4.258, < 0.0001). As in the early condition, this result is unexpected and requires an exploratory analysis.

In the quantifier window, in the late condition, there were more looks to the target with mostsup than with some ( = −0.562, SE = 0.099, = −5.688, < 0.0001) and than with most-of ( = −0.838, SE = 0.11, = −7.606, < 0.0001). This is in line with the prediction 'mostsup > all, most-of, some' in Table 1 (except that mostsup is not significantly different from all). We also find that there are more looks to the target with some than with most-of ( = −0.276, SE = 0.103, = −2.685, = 0.029), which fits the prediction 'some-not-all > most-of'

Figure 8: Results: late condition. (†) marks *disambiguation*.

in Table 1 and further indicates that with some there is no delay in the processing of the scalar implicature.

I predicted that the latter effect would persist in the color window, but instead I find that some was no different from most-of. This could be related to the low accuracy with *some*, namely, participants who accepted 'some-notall' as the description of the display (recall that I analyzed the looks with correct responses only) nevertheless compared the numerosities of the different color sets. However, some had significantly fewer looks to the target than all ( = 1.676, SE = 0.276, = 6.071, < 0.0001). This effect, 'some-not-all < all' was predicted to occur already in the quantifier window, but we find it later.

In the color window, with mostsup there were fewer looks to the target than with the other quantifiers: some ( = 0.642, SE = 0.097, = 6.641, < 0.0001), all ( = 2.075, SE = 0.277, = 7.505, < 0.0001), and most-of ( = 0.46, SE = 0.101, = 4.536, < 0.0001). This results is exactly as predicted: with mostsup, as the color adjective is heard the looks must be directed to the other two color sets in order to make the comparisons to confirm that the target set is indeed the largest.

# **3 Discussion and conclusions**

The results of the current study contribute to the debate about the processing of the scalar implicature of the quantifier *some* during visual verification (Huang & Snedeker 2009, Grodner et al. 2010, Degen & Tanenhaus 2011, Huang & Snedeker 2011, Degen & Tanenhaus 2016) and provide novel predictions for experiments on the processing of *some* in comparison with other quantifiers in languages other than English. I find support for the claim in Spychalska (2009) that the Polish counterpart of *some*, *niektóre*, has a strong implicature – I find that the meaning 'some-not-all' is processed immediately as the disambiguating quantifier is heard. I compared *niektóre* 'some' to *większość* 'most of' and *najwięcej* (the superlative *most*) and *wszystkie* 'all'. In the prior visual world eye-tracking studies, *some* and *all* were compared on the basis of the theory that these two quantifiers form a scale, so when *some* is used instead of the stronger *all*, the inferred meaning is 'some-not-all' (Horn 1972, Levinson 1983, a.o.). However, the results of Degen & Tanenhaus (2011, 2016) showed that whether there is a delay in the processing of the 'some-not-all' meaning depends on whether the experiment contains alternative descriptions using number terms and not just *some* and *all* ('You got some/all/two/three/four/five of the blue gumballs'). When those alternatives are available the processing of the 'some-not-all', implicature is delayed

### 17 *Some*, *most*, *all* in a visual world study

relative to the processing of the meaning of *all*. Without such alternatives, *some* is not delayed relative to *all*.

In the current experiment, adopting the gumball paradigm of Degen & Tanenhaus (2011, 2016) alternative descriptions of the visual scene contained the quantifiers *most of* and the superlative *most* (*most*-sup) because (i) they allowed for more specific predictions about the time course of the looks to the target than just a comparison with *all*, and (ii) two alternative strategies for *most of* could be tested. Specifically, the semantics of the superlative *most* requires comparisons between the target color set and the two other colors in the lower chambers of the ball machine, which I expected to elicit a distinctive pattern of looks that would serve as the baseline for statistical comparisons (the displays were identical for *some*, *most of* and *most*-sup, but they had to be different for *all*). As in the study of Degen & Tanenhaus (2016) the quantifiers were compared with two types of displays, early and late (Figures 4–5 in §2.2). In the early condition, the quantifier in the stimulus sentence ('You got some/all/most of/the most blue/red/green balls') disambiguated which set of the three sets of balls in the bottom chambers was the target. In the late condition, the target was identifiable only when the color adjective was heard.

In the early condition, the results showed no delay for the Polish counterpart of *some* as compared to *all* and *most of*, as well as a higher proportion of looks to the target than with *most*-sup. I considered two alternatives in the predictions. On the one hand, if the 'some-not-all' meaning was processed early (at the point of hearing the quantifier), the identification of the target set should be just as easy as with *all*, given that the target set was partitioned and as such stood out already during the preview. If, on the other hand, the 'some-possibly-all' meaning was processed first, the looks should be first directed to the unpartitioned sets as with *most*-sup. The results show support for the first option: the 'some-not-all' meaning is processed early. In the late condition, I also find evidence for the 'some-not-all' interpretation in comparison with *all* and *most of*.

The second novel finding concerns the semantics of the majority quantifier *most of*. Pietroski et al. (2009) and Lidz et al. (2011) propose that the verification of sentences like 'You got most of the blue balls' involves a procedure of subtraction (schematized in (2) vs. (3) in §2.2). This procedure requires multiple steps: estimate the superset (the blue balls remaining in the top chamber and the blue balls in the bottom chamber), estimate the target set (the blue balls in the bottom chamber), subtract and compare the result with the target. Subtraction involves more steps than direct comparison of two sets (3 in §2.2), so my hypothesis was that I should find fewer looks to the target in the quantifier and color windows because of the continuing looks to the top blue set in order to establish the total

set. Indeed, in the color window there were significantly fewer looks to the target than with both *some* and *all*; the proportion of looks was as low as in the *most*-sup condition. The low proportion of looks with *most*-sup can be directly linked to the superlative semantics requiring comparisons with the other color sets. *most of* could be verified by merely comparing the top and bottom numerosities of the partitioned set, but this simple comparison would have elicited a similar proportion of looks to the target as with *some*. The profiles of eye-movements with *some* and *most of* looked similar but the proportion of looks to the target was lower with *most of* than with *some*. In the late condition I also predicted fewer looks to the target with *most of* than with *some* in both the quantifier and color windows, and this effect was observed in the quantifier window.

The fact that the pattern of looks with *most of* is compatible with the subtraction procedure and not with the more efficient direct comparison procedure supports the hypothesis in Pietroski et al. (2009) and Lidz et al. (2011) that the mind follows the "instructions" encoded in the lexical representation of quantifier meanings. They argue that lexical semantics interfaces with the cognitive system, which means that lexical meanings require more fine grained distinctions than just truth-conditions. The present experiment showing that with the same display there are distinctive patterns of looks for the three Polish quantifiers *some*, *most of* and *the most* supports the idea that lexical semantics provides direct instructions to visual cognition processes.

# **Abbreviations**

# **Acknowledgments**

I would like to thank Piotr Gulgowski for the help with programming in Experiment Builder and for recording participants. Special thanks are due to Yaman Özakın for designing and creating the images of the ball machine. I am very grateful for helpful comments and discussion to Judith Degen, Roumi Pancheva and Maria Spychalska. Thanks are also due to the two anonymous reviewers for very useful comments and suggestions.

This research was funded by the OPUS 5 HS2 grant (DEC-2013/09/B/HS2/ 02763) from the Polish National Science Center (NCN).

17 *Some*, *most*, *all* in a visual world study

# **References**

for immediate pragmatic enrichment. *Cognition* 116(1). 42–55. DOI: 10.1016/j. cognition.2010.03.014.

17 *Some*, *most*, *all* in a visual world study

# **Chapter 18**

# **Group-denoting vs. counting: Against the scalar explanation of children's interpretation of 'some'**

### Katalin É. Kissa , Lilla Pintérb & Tamás Zétényic

aResearch Institute for Linguistics bPázmány Péter Catholic University, Research Institute for Linguistics cBudapest University of Technology and Economics

The computation of scalar implicatures based on the scale ⟨*some, all*⟩ represents a problem for children. This paper argues that the source of children's difficulties with interpreting 'some' is that it is ambiguous; it has a non-partitive interpretation, corresponding to 'a few', which forms a scale with non-partitive 'many', and a partitive reading, corresponding to 'a subset of', which forms a scale with 'all'. The two readings have different distributions; they are selected by different predicates, and in Hungarian, they occur in different structural positions. We tested and confirmed the hypothesis that young children are not sensitive to the partitivity feature of 'some'-phrases; they first acquire the non-partitive reading, which they overgeneralize for a while. Experiment 1, a forced choice task, showed that the default reading of 'some' NPs for six-year olds is the 'a few' interpretation. Experiment 2, a truth value judgement task, demonstrated that children also accept the 'not all' interpretation of 'some', and the acceptance rates of the 'a few' and the 'not all' readings are similar irrespective of the partitivity feature of the given NP.

**Keywords:** scalar implicature, 'some', counting quantifier, partitive, Hungarian, language acquisition

# **1 Introduction**

Whereas adults interpret *some* e.g. in *Some horses jumped over the fence* as 'some but not all', children understand it as 'some and possibly all' (e.g. Noveck 2001,

Katalin É. Kiss, Lilla Pintér & Tamás Zétényi. 2021. Group-denoting vs. counting: Against the scalar explanation of children's interpretation of 'some'. In Mojmír Dočekal & Marcin Wągiel (eds.), *Formal approaches to number in Slavic and beyond*, 427–457. Berlin: Language Science Press. DOI: 10.5281 / zenodo.5082484

### Katalin É. Kiss, Lilla Pintér & Tamás Zétényi

Papafragou & Tantalou 2004). It has been claimed that the basic meaning of plural *some* is 'some and possibly all', and the 'some but not all' reading is a pragmatic inference, a scalar implicature, which children cannot access (see Noveck 2001, Chierchia et al. 2001, Papafragou & Musolino 2003, Guasti et al. 2005, Foppolo et al. 2012, Huang & Snedeker 2009, Katsos & Bishop 2011, Barner et al. 2011). The assumption that children generally have problems with computing scalar implicatures cannot explain though why pragmatic inferencing has proved to be much easier for them in the case of scales involving cardinal numbers than in the case of the scale involving *some* and *all* (Papafragou & Musolino 2003).

Recently it has been proposed that a scalar implicature is often a problem for children because they lack knowledge of the relevant scalar alternatives. That is, young children accept *some* in situations which could be more appropriately described by *all* because they are still not aware of the fact that *all* is a stronger alternative of the same scale that includes *some* (Barner et al. 2011, Foppolo et al. 2012, Pagliarini et al. 2018).

We argue that the source of children's difficulties with interpreting *some* and its Hungarian equivalent *néhány* is that *some/néhány* is ambiguous. It has a nonpartitive interpretation, corresponding to 'a few', which forms a scale with nonpartitive *many*, and a partitive reading, corresponding to 'a subset of', which forms a scale with *all*. 1 The two variants of *some/néhány* have different distributions; they are selected by different predicates, and in Hungarian, they occur in different structural positions. We have hypothesized that for young children, the primary reading of 'some' NPs is the non-partitive reading; this is what explains their behaviour in the experiments cited above. We tested this assumption with the two experiments to be presented in this paper.

The paper is organized as follows: §2 presents linguistic evidence of the ambiguity of *néhány* 'some'. §3 surveys previous experiments testing children's interpretation of 'some'. §4 presents our own experiments with *néhány*. §5 is the conclusion.

# **2 Group-denoting versus counting** *néhány/some***: Linguistic evidence**

For adults, a *some* NP in English or a *néhány* NP in Hungarian is often ambiguous, e.g.:

1*Many* also has a non-partitive reading, paraphraseable as 'a large number of', and a partitive or proportional reading, paraphraseable as 'a large subsection of'. This well-known ambiguity is discussed in connection with examples (13a–13b), (16a–16b), and (17–18).

18 Group-denoting vs. counting

(1) Találkoztam meet.past.1sg néhány some diákkal. student.with 'I met some students.'

The Hungarian sentence and its English equivalent in (1) can mean both that I met a small indefinite number of students, and that I met a (small) subset of a contextually given set of students. (To what extent the 'small' component is part of the latter, partitive meaning, as well, appears to be individual dependent – as was revealed by the reactions of the adult control group of our experiments. In the experiments of Degen & Tanenhaus (2015), the default set size associated with *some* by English adults is 6–8.)

In two structural positions in the functional left periphery of the Hungarian sentence, the *néhány* phrase ceases to be ambiguous. These are the two preverbal slots of the basic Hungarian sentence: a topic slot (the specifier of an iterable TopP), accessible to referential constituents, and an immediately preverbal slot (the specifier of PredP) harboring a non-referential, predicative complement – as shown in Figure 1 (for details, see É. Kiss 2002, 2008, 2010, Szabolcsi 1994, 1997, among others).

Figure 1: Hungarian sentence structure

The topic and the filler of SpecPredP can be separated by sentence adverbials, by distributive quantifiers, and by an exhaustive focus. The topic precedes the (first) pitch accent, whereas the constituent in SpecPredP either itself bears a pitch accent, or follows another pitch-accent-bearing element.

In the topic position, the *néhány* phrase is understood to denote a (small) subset of a contextually given set – see examples (2a) and (2b), where the topic status of the *néhány* phrase is ensured by its position preceding the universal quantifier, the locus of pitch accent (denoted by ʹ). (2a) and (2b) represent the same structure, with the grammatical functions distributed in different ways; they illustrate that

Katalin É. Kiss, Lilla Pintér & Tamás Zétényi

word order in the preverbal section of the Hungarian sentence is determined by logical role rather than grammatical functions.

(2) a. [TopP Néhány some diák student [DistP ʹminden every professzorral professor.with [PredP konzultált.]]] consult.past.3sg 'Some students consulted with every professor.' b. [TopP Néhány some diákkal student.with [DistP ʹminden every professzor professor [PredP konzultált.]]] consult.past.3sg

'With some students, every professor consulted.'

The topic of the sentence represents the logical subject of predication, therefore, it must have restricted reference, i.e., must be specific. Partitivity corresponds to a type of specificity (Enç 1991, Farkas 2002, Kamp & Bende-Farkas 2019), thus the partitive interpretation associated with *néhány* in topic position is a manifestation of its specificity feature.

In the specifier of the PredP projection, by contrast, the non-partitive interpretation of *néhány*, corresponding to 'a few', is evoked (see 3a). SpecPredP is filled by the non-referential complement of the verb, and its filler has the smallest possible scope (Szabolcsi 1983), which is also true of the *néhány*-phrase in SpecPredP (see 3b). As opposed to the topicalized, partitive *néhány* NP in (2a) and (2b), the non-partitive *néhány* NP in SpecPredP bears a pitch accent. The relative stress of *néhány* within the NP is also different in the two cases: whereas the partitive *néhány*, a strong determiner, itself bears the secondary stress of the *néhány* NP, in the non-partitive *néhány* NP, the pitch accent falls on the nominal determined by *néhány*.

	- b. [[DistP ʹMind-három all-three professzor professor [PredP néhány some ʹdiákkal student.with konzultál.]] consults 'Each of the three professors is consulting with some students.'

### 18 Group-denoting vs. counting

The left periphery of the Hungarian sentence can also include a focus slot between PredP and TopP, in the specifier of a focus phrase (FocP). The focus elicits verb movement from Pred to Foc, hence a preverbal *néhány* NP can sit either in SpecPredP or in SpecFocP. (SpecFocP position is traditionally marked by small capitals.) Whereas the *néhány* NP in SpecPredP is [−partitive], the exhaustive/ contrastive *néhány* phrase in SpecFocP is [±partitive], i.e., it can either mean 'a few, not many', or it can mean 'a (small) subset of a contextually given set, not the whole set' – see *néhány diák* 'some students' in (4). The excluded alternative shares the partitivity feature of the *néhány* phrase. When *néhány diák* 'some students' is understood as [−partitive], the excluded alternative is the [−partitive] 'many students' – see (5a). When it is understood as [+partitive], what it excludes is 'all students' – see (5b).

'I consulted not (only) with some students but with many.'

b. [NegP Nem not [FocP (csak) only ʹnéhány some diákkal student.with konzultáltami consult.past.1sg [PredP *t*i …]]] hanem but minddel. all.with 'I consulted not (only) with some students but with all.'

In our experiments, we intended to test the interpretations of *néhány* NPs in SpecTopP and in SpecPredP, where they are not ambiguous; i.e., we excluded focussed *néhány* phrases. Since the verb moves to Pred in neutral clauses, and moves on to Foc in focus constructions, an immediately preverbal *néhány* can, in principle, occupy either SpecPredP or SpecFocP; however, the filler of SpecFocP and the filler of SpecPredP behave differently under negation, which makes their identity easily testable. Namely, FocP negation elicits no further verb movement, resulting in a Neg–SpecFocP–V order – as shown in (5a) and (5b). PredP negation, on the contrary, elicits V-to-Neg movement, yielding a Neg–V–SpecPredP order (6a); (7a). A non-partitive *néhány* phrase inside a negated PredP is marginal; it tends to be replaced by the negative polarity indefinite *egy…sem* 'not even one; no' (6b); (7b):

### Katalin É. Kiss, Lilla Pintér & Tamás Zétényi

	- b. [TopP A the professzor professor [NegP ʹnem not konzultálti consult.past.3sg [PredP egy one diákkal student.with sem even *t*i …]] 'The professor did not consult with any student.'