Introduction
Understanding language involves making inferences. People frequently convey more meaning with what they say than is conveyed by the meaning of the words:
In (1), B is not as informative as required by the communicative purpose. According to Grice (Reference Grice, Cole and Morgan1975) and other reformulations of his theory (Geurts, Reference Geurts2010; Levinson, Reference Levinson2000), this leads A to derive the implicature (1c) in a process which is two-staged: first, A determines whether B could have been more informative (I ate all of the cupcakes). Given the question (1a), the additional information would be relevant. This sensitivity to informativeness is the necessary first step of implicature derivation (Grice, Reference Grice, Cole and Morgan1975; Geurts, Reference Geurts2010; Katsos, Reference Katsos, Sauerland and Yatsushiro2009). Second, A assumes B to be cooperative and maximally informative; hence, B would have used the more informative term all if B believed it were true. A also assumes B to know about the truth of the stronger alternative (Epistemic Step, cf. Sauerland, Reference Sauerland2004). Since B used the less informative term (some), A infers the negation of the stronger term (all) on the informativeness scale <some-many-all> (Horn, Reference Horn1972), deriving a scalar implicature.
While various theories of the processing mechanisms underlying scalar implicatures are available (Sauerland, Reference Sauerland2012), L2 research in this area has exclusively employed the neo-Gricean default (or lexical) and post-Gricean non-default models to account for the cognitive aspects of this inferencing. The default model (Horn, Reference Horn, Horn and Ward2004; Horn et al., Reference Horn, Allan, Jaszczolt, Allan and Jaszczolt2012; Levinson, Reference Levinson2000) proposes an automatic and immediate pragmatic enrichment (some but not all) of scalar terms irrespective of communicative context. Thus, pragmatic interpretations require less processing effort than logical interpretations because the former need to be canceled first before the latter can be derived (Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019). In contrast, the non-default model (Sperber & Wilson, Reference Sperber and Wilson1986; Carston, Reference Carston, Carston and Uchida1998; Noveck & Sperber, Reference Noveck, Sperber and Burton-Roberts2007) proposes no default pragmatic enrichment of scalar terms. Instead, recipients process the entire sentence and derive the logical meaning. Subsequently, context conditions warranting an implicature must be satisfied to derive the pragmatically enriched meaning. Therefore, pragmatic interpretations add processing effort (Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019).
In experiments investigating the link between existentially quantified sentences like (1b) and the corresponding scalar implicature in (1c), conversations like (1a–b) would usually be situated in scenarios where it is obvious that “Sophie has eaten all of the cupcakes,” rendering her response in (1b) under-informative yet logically true. In much L2 research (cf. L1 research: Noveck, Reference Noveck2001; Noveck & Posada, Reference Noveck and Posada2003; Bott & Noveck, Reference Bott and Noveck2004; Feeney et al., Reference Feeney, Scrafton, Duckworth and Handley2004; de Neys & Schaeken, Reference De Neys and Schaeken2007; Pijnacker et al., Reference Pijnacker, Hagoort, Buitelaar, Teunisse and Geurts2009), under-informative utterances have been used as targets in binary Truth-Value-Judgment (TVJ) tasks to elicit responses from participants (yes/no; agree/disagree). Usually, rejections of under-informative target sentences such as (1b) or (2) “Some elephants have trunks” (i.e., drawing on encyclopedic knowledge) have been assumed to indicate that participants derived the scalar implicature (i.e., some, but not all interpretation), warranting a rejection. Conversely, failure to derive the scalar implicature would be interpreted to indicate that the participant employed the logical alternative (i.e., some and possibly all), warranting acceptance. Experimental predictions assumed that L2 learners, even at advanced proficiency, have less language processing resources available than native speakers (Clahsen & Felser, Reference Clahsen and Felser2006). Thus, under the default view, L2 speakers should make more pragmatic interpretations than L1 controls, and vice versa under the non-default view. Results have been inconsistent, with some reporting more implicature derivation among L2 learners compared to L1 speakers (Lin, Reference Lin2016; Slabakova, Reference Slabakova2010; Snape & Hosoi, Reference Snape and Hosoi2018), supporting the default model. Others report no differences between L2 learners and native speakers (Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019), and some report that L2 learners derive fewer implicatures than native speakers (Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021; Khorsheed et al., Reference Khorsheed, Rashid, Nimehchisalem, Geok Imm, Price and Ronderos2022), supporting the non-default model.
Truth-Value-Judgment (TVJ) tasks and implicature derivation
Katsos and Bishop (Reference Katsos and Bishop2011) were among the first to scrutinize the use of binary TVJs when measuring implicature derivation in L1 research (L1 research discussed in 1.2). They suggest that rejecting some X are Y in a context where clearly all X are Y only requires participants to realize “that a more informative statement could have been made” (ib.: 69). This sensitivity to under-informativeness is considered sufficient to warrant rejection, without the need to derive the negation of the stronger alternative, constituting the implicature. Corresponding to Katsos and Bishop’s suggestion (for which their 2011 report supplies no empirical evidence), Kissine and de Brabanter (Reference Kissine and De Brabanter2023: 3) formulate the No-Implicature Hypothesis stating that “judging a sentence of the form Some X are Y to be false in a situation where it is obvious that all X are Y does not entail the explicit representation of the reinforced reading Some, but not all X are Y.” Importantly, they argue that scalar implicature research employing the default and non-default theories (among other theories) of implicature derivation all endorse the contrasting Explicit Implicature Hypothesis, stating that “judging a sentence of the form Some X are Y to be false in a situation where it is obvious that all X are Y entails explicitly representing the reinforced reading Some, but not all X are Y” (ib.: 2). Kissine and de Brabanter (Reference Kissine and De Brabanter2023) tested these hypothesesFootnote 1 in a series of experiments where participants judged under-informative sentences in binary TVJ tasks like those employed in previous L1 and L2 research. Each TVJ was immediately followed by a forced-choice between two paraphrases (logical and pragmatic reading) of the previously encountered under-informative sentences. In line with the No-Implicature Hypothesis, the authors found that participants overwhelmingly chose the logical paraphrase of under-informative sentences irrespective of their initial judgment of them. Their evidence strongly suggests that rejections in binary TVJ tasks “occur without the corresponding implicature being derived” (p.11) and that rejections are triggered by participants’ sensitivity of the target sentence’s oddness in their respective contexts.Footnote 2 The authors further highlight that those judgments of pragmatic violations are impacted by metalinguistic attitudes, which will become important later.
In contrast to Kissine and de Brabanter’s (Reference Kissine and De Brabanter2023) findings, the L2 scalar implicature studies to date employing TVJ tasks have assumed that rejections indicate implicature derivation. This raises the question whether the adjudications between different implicature theories in previous L2 research and any claims made about participants’ pragmatic competence regarding their implicature derivation ability may be unfounded since the studies might not have measured implicature derivation after all. Regarding pragmatic competence, all that binary TVJ tasks may have measured is participants’ sensitivity to under-informativeness (i.e., the pragmatic violation). A similar argument has been put forward in L1 child research, discussed below.
Child–adult differences in binary TVJ tasks
Many L1 studies investigating children’s implicature derivation development have employed binary tasks. Noveck (Reference Noveck2001) initiated this line of inquiry when he had 8-to-10-year-olds make binary judgments of under-informative sentences (e.g., some giraffes have long necks) in a sentence verification task. Unlike adults, children did not reject under-informative input, albeit showing adult-like performance on logically false sentences. Subsequent studies introduced various paradigm manipulations to influence children’s rejection and acceptance rates of under-informative input, including implicature relevance (Feeney et al., Reference Feeney, Scrafton, Duckworth and Handley2004), other under-informative sentence types (Papafragou & Tantalou, Reference Papafragou and Tantalou2004; Barner et al., Reference Barner, Brooks and Bale2011), or explicit instruction and training (Papafragou & Musolino, Reference Papafragou and Musolino2003; Guasti et al., Reference Guasti, Chierchia, Crain, Foppolo, Gualmini and Meroni2005). Although we acknowledge that other studies employed fundamentally different experimental paradigms (e.g., Pouscoulous et al., Reference Pouscoulous, Noveck, Politzer and Bastide2007), the critical point about the aforementioned studies using binary TVJ tasks is that “these studies base their conclusions on the assumption that the children who accept under-informative utterances altogether lack (some or all of the) competence that is needed to generate implicatures” (Veenstra et al., Reference Veenstra, Hollebrandse and Katsos2018: 299). This resembles the assumptions made in L2 research regarding the pragmatic abilities of L2 users.
Pragmatic tolerance
Scrutinizing the cause of children’s acceptance of pragmatic violations, Veenstra and Katsos (Reference Veenstra, Katsos, Jucker, Schneider and Bublitz2018; cf. Davies & Katsos, Reference Davies and Katsos2010; Katsos & Smith, Reference Katsos, Smith, Franich, Iserman and Keil2010; Veenstra et al., Reference Veenstra, Hollebrandse and Katsos2018; Katsos & Bishop, Reference Katsos and Bishop2008; Reference Katsos and Bishop2011) suggest that children’s apparent inability to interpret under-informative sentences pragmatically could stem from researchers’ overinterpretation of binary data. They argue that agreement with under-informative statements (“Some elephants have trunks”) does not necessitate implicature derivation failure. Instead, children may be sensitive to the under-informativeness, while still being willing to tolerate—and thus accept—the less felicitous interpretation since they might not consider the violation grave enough to warrant rejection (Schmitt & Miller, Reference Schmitt, Miller, Blom and Unsworth2010).
These claims regarding pragmatic tolerance stem from Katsos and Bishop (Reference Katsos and Bishop2011) who tested adults and children using binary and ternary judgments. Ternary judgments offered an additional “intermediate” decision possibility. Participants observed scenes such as a giraffe eating all of the pears from a tree but not the apples (the tree bore both fruits). After exposure, experimenters asked an animated figure What did the giraffe eat? In under-informative trials, it would answer The giraffe ate some of the pears. In experiment 1, participants rewarded the figure’s answers by evaluating them with that’s right or that’s wrong (binary response). In experiment 2 (same input), participants could choose between three different sized strawberries (small, medium, large; ternary judgment). Middle-sized strawberries allowed for an intermediate endorsement level. Experiment 1 confirmed previous findings (e.g., Noveck, Reference Noveck2001): children provided significantly more logical responses than adults in under-informative scenarios, demonstrating “lower” pragmatic competence. However, in experiment 2 with ternary response options, no evidence was found for adult–child differences and both groups demonstrated sensitivity to under-informativeness, favoring intermediate judgments in under-informative contexts. The authors argue that intermediate responses enabled participants to demonstrate they noticed the pragmatic violations without enforcing categorical judgments. This appeared to remove adult–child differences, suggesting that previous differences were due to differences in pragmatic tolerance rather than the ability to detect under-informativeness.Footnote 3 In a similar study using quinary judgments (five-point Likert scales), Katsos and Smith (Reference Katsos, Smith, Franich, Iserman and Keil2010) confirmed that children demonstrate sensitivity to under-informativeness by choosing intermediate options in under-informative trials. Those findings brought about the Pragmatic Tolerance Hypothesis suggesting that children are sensitive to under-informativeness, but they differ with adults in their pragmatic tolerance, decreasing their tendency to reject such input.
Before reviewing previous L2 research, let us recap three issues highlighted in L1 research that are important to L2 research: first, TVJ tasks might not capture implicature derivation but merely sensitivity to under-informativeness. Mapped onto L2 literature, this means that data collected with TVJ tasks might not be able to adjudicate between competing implicature derivation theories. Moreover, although sensitivity to under-informativeness is potentially costly since it involves a consideration of the potential for a more informative statement (Katsos, Reference Katsos, Sauerland and Yatsushiro2009), it is presumably less costly than “fully fledged” implicatures and unlikely to exceed the cognitive capacity of L2 users, despite the L2 burden.Footnote 4 Thus, it would be reasonable to expect no differences to emerge between L1 and L2 users at the level of sensitivity to under-informativeness. Second, binary scales might rather shed light on pragmatic tolerance while potentially hiding participants’ true ability to be sensitive to under-informativeness (“implicature derivation” is not captured in any case; Kissine & de Brabanter, Reference Kissine and De Brabanter2023; Katsos & Bishop, Reference Katsos and Bishop2011). The proposed developmental differences regarding pragmatic tolerance between adults and children seem plausible, yet, turning to L2 research, they do not map onto L1 and L2 users. If binary TVJ tasks outcomes are heavily impacted by aspects of pragmatic tolerance, we would not expect L1–L2 differences (with adult speakers) caused by cognitive development in this respect. This raises the question which other factors unrelated to cognitive and linguistic ability caused L1–L2 differences in response behavior in binary tasks in the past. Third, L1 research suggests that graded tasks show a more nuanced picture of the balance between pragmatic and logical responding. Thus, they might present some interesting insights when investigating L1 and L2 users’ response patterns.
Previous L2 studies
We will now present an outline of previous L2 research. Recall that earlier in this paper, we introduced two theoretical accounts of implicature derivation (default and non-default) because those are important to trace previous L2 studies’ theoretical motivations and their respective conclusions given their data. Based on our discussion of the L1 literature, we acknowledge that previous L2 studies employing TVJ tasks might not have shed light on implicature derivation.
Slabakova (Reference Slabakova2010) compared judgments of under-informative sentences by English L1 speakers, Korean L2 English learners (intermediate and advanced), and Korean L1 speakers, employing four different sentence types. Experiment 1 included sentences without visual context drawing on encyclopedic knowledge (“Some elephants have trunks”). Experiment 2 included visual context (picture-stories featuring a woman and a girl). In both experiments, all language groups consistently provided “agree” responses to optimally true all and felicitous some sentences and “disagree” responses to optimally false all sentences. Under-informative sentences were critical trials. Participants were more likely to accept these in the encyclopedic than the visual context condition. Critically, in both experiments, L2 learners gave significantly more “disagree” responses to under-informative sentences (i.e., pragmatic responses) than L1. Lin (Reference Lin2016) used similar methods to test Mandarin L2 English learners’ (CEFRFootnote 5 : B1-B2) judgments of under-informative some sentences in binary judgments and also reports that L2 learners gave significantly more pragmatic responses than L1 controls.
Snape and Hosoi (Reference Snape and Hosoi2018) compared Japanese L2 English learners (B2) with L1 controls and found that L2 learners tended to respond more pragmatically than controls, which they argue supports the default model. Since the results were not statistically significant, this should be treated cautiously.
Investigating this L2 “pragmatic bias,” Dupuy et al. (Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019) asked French monolinguals, French L2 English learners (B2), and French L2 Spanish learners (B2) to give Yes/No responses to under-informative statements (The boy has hidden some cars) provided visual context. L2 learners were tested in both their L1 and L2. Both L2 groups gave more pragmatic answers than monolinguals, but only when tested in both languages within the same session. Critically, when this occurred, they demonstrated biases in both their L1 (French) and L2 (English/Spanish). The authors argue that this is not consistent with an account attributing pragmatic biases to L2 speakers’ processing disadvantages and that the findings could be attributed to an intra-experimental language switch which temporarily increased participants’ metalinguistic awareness, including their susceptibility to pragmatic interpretations in their L1. Furthermore, they argue that L2 learning might generally increase speakers’ metalinguistic awareness and compensate for the lack of proficiency compared to native speakers, resulting in equally “pragmatic” responses in native and non-native languages.
Contrary to earlier research, a recent study reports less pragmatic responding among L2 users. Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021) tested Italian L1 speakers and Italian L2 English and Spanish learners employing binary judgments. Contrary to previous studies, input was aural only, and time limits were introduced. The increased processing difficulty should potentially amplify the effect that processing difficulties lead to more pragmatic responses in L2 learners. However, they provided fewer pragmatic answers compared to L1 controls. The authors argue that their findings differ from Slabakova’s (Reference Slabakova2010) because Slabakova’s L2 participants were immersion students which might have improved their pragmatic abilities (Bouton, Reference Bouton1992). Yet, it remains unclear why immersion students “outscored” L1 controls and derived more pragmatic interpretations compared to Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021), rather than finding no differences.
Previous L2 findings are inconclusive. Yet, researchers made assertive claims about L1 and L2 speakers’ implicature derivation processes and pragmatic abilities. While the individual reports provide reasons for the differences between them, those reasons assume that the binary tasks provided information about some aspect of the participants’ cognitive abilities. Considering that TVJ tasks may not capture implicature derivation but rather only sensitivity to under-informativeness, we argue that there is no reason to expect that L2 users are insensitive to pragmatic violations (as was indicated by “agree” responses in Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021), and therefore there is no reason to expect L1–L2 differences. Instead, we suggest that one possibility to account for differences in response behavior might be differing metalinguistic attitudes. Veenstra and Katsos (Reference Veenstra, Katsos, Jucker, Schneider and Bublitz2018: 271) highlight the “fundamental challenge of any [TVJ], namely that it involves a meta-linguistic judgment.” This notion that metalinguistic attitudes guide decision-making processes is reminiscent of related L2 experimental pragmatics research where it has been repeatedly argued that the attitudes impacting such decisions can be shaped by cross-linguistic and contextual factors. Those may include the cross-linguistic differences in the quantification of “some” (Stateva et al., Reference Stateva, Stepanov, Déprez, Dupuy and Reboul2019), the employed quantifier structures (Degen & Tanenhaus, Reference Degen, Tanenhaus, Carlson, Hölscher and Shipley2013; Grodner et al., Reference Grodner, Klein, Carbary and Tanenhaus2010), the perceived speaker likeability (Sikos et al., Reference Sikos, Kim and Grodner2019), the discourse context (Yang et al., Reference Yang, Minai and Fiorentino2018; Dupuy et al., Reference Dupuy, Van der Henst, Cheylus and Reboul2016), the type of judgment question asked (i.e., truth- vs. felicity-judgments; Kissine & de Brabanter, Reference Kissine and De Brabanter2023), the prosody in aural input (Chen et al., Reference Chen, Huang and Politzer-Ahles2018; Bill et al., Reference Bill, Romoli and Schwarz2018), or the perceived honesty traits of speakers (Feeney & Bonnefon, Reference Feeney and Bonnefon2012). For example, Feeney and Bonnefon (Reference Feeney and Bonnefon2012) found effects of politeness contexts and individuals’ honesty traits on the interpretation of scalar expressions. They demonstrated that in face-threatening contexts their participants gave fewer pragmatic answers compared to non-face-threatening contexts. They also provided evidence that participants gave more pragmatic interpretations the higher they rated their self-perceived honesty (regardless of context). Studies like this demonstrate that there are impactful factors other than cognitive that might “push” participants to accept or reject pragmatic violations.
Considering that binary responses might be heavily influenced by metalinguistic attitudes, we propose the possibility that L1–L2 differences observed in previous L2 studies might not have to do with pragmatic abilities but rather reflect a mix of participants’ metalinguistic attitudes shaped by their linguistic backgrounds and contextual factors.
The current study Footnote 6
We replicate previous L2 studies using a binary TVJ task and supplement the research with a graded task. Unlike previous L2 research, we reframe the inquiry to investigating sensitivity to pragmatic violations (i.e., under-informativeness) and acceptance thereof instead of implicature derivation abilities. Since previous L2 studies made assertive claims about L2 users’ pragmatic abilities, we first and foremostly aim to provide evidence for the (perhaps unsurprising) fact that both L1 and L2 users are sensitive to under-informativeness and that there are no between-group differences at this fundamental level of pragmatic ability. In this regard, the graded task does not necessarily yield additional information regarding sensitivity to under-informativeness since every rejection in binary tasks, no matter how strong the impact of metalinguistic attitudes, already requires sensitivity to the pragmatic violation in the first place.Footnote 7 Nonetheless, the graded task provides additional, more nuanced insights into the balancing act between sensitivity to pragmatic violations on the one hand and acceptance of such violations on the other hand. It sheds light on the trade-offs that participants from the different language groups are willing to make between the two poles. Such details are masked in the binary data; thus, the graded data ideally fathom and expand the insights gained from the binary data.Footnote 8 Taken together, both the binary and the graded task contribute to our investigation of participants’ judgments of pragmatic violations and might inform future studies.
In both tasks, we expect both groups to demonstrate sensitivity to under-informativeness and we expect no between-group differences in this respect. In addition, since we keep contextual factors unmanipulated, we also have no reason to expect that proficient L2 users and L1 users differ in their judgment of such pragmatic violations. As a result, response behaviors should not differ between groups in both the binary and the graded task. To ensure consistency with previous studies, we test competent L2 learners (>B2), using newer, more appropriate statistical tools—Bayes factor (BF) statistics—to test evidence for H0. Finding such evidence would impact our perspective on L1–L2 differences with regard to their sensitivity to under-informativeness and their tolerance thereof.
Regarding our experimental hypotheses, recall that previous L2 studies employing binary tasks, although having framed their work against the background of implicature derivation, report findings in both directions, that is, L2 users provide more rejections (Slabakova, Reference Slabakova2010) or L2 users provide fewer rejections (Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021). Therefore, we test the binary data in both directions separately (i.e., using two one-tailed tests; Ziori & Dienes, Reference Ziori and Dienes2015). Note that although we believe it likely that the null hypothesis will be supported, testing a BF requires us to test a model of H1. The H1s that we will test are that L2 learners give fewer pragmatic responses than L1 learners and that L2 learners give more pragmatic responses.
There is an absence of prior L2 research using graded scales, yet as rightly argued by reviewers, the graded task—albeit more nuancedly so—taps into the same decision-making process as the binary task. Therefore, we test the graded data in both directions separately as well (i.e., using two one-tailed tests). Again, we will test whether L2 learners give fewer pragmatic responses than L1 learners and whether L2 learners give more pragmatic responses.Footnote 9
All analyses were performed using R Statistical Software (v4.1.2; R Core Team 2021).
Design
The between-participant factor was “language group”: native English monolinguals and German L2 English learners. Participants rated sentence-picture correspondence on either a binary (Disagree and Agree) or a five-point scale (Disagree, Somewhat Disagree, Neither, Somewhat Agree, and Agree). There were four sentence types: optimally true all, optimally false all, felicitous some, and under-informative some. Under-informative trials are the targets. In binary trials, high agreement suggests higher acceptance of pragmatic violations, while disagreement implies lower acceptance. In quinary tasks, strong agreement tendencies indicate higher acceptance, while strong disagreement tendencies suggest lower acceptance. Any response other than full agreement indicates sensitivity to under-informativeness in both tasks. While we could test our hypotheses with analyses conducted exclusively on responses to under-informative items, high/low scores on this measure may also reflect broader preferences to agree/disagree more generally in the experimental context at the upper/lower ends of the Likert scale, rather than pragmatic sensitivity.Footnote 10 To control for this, we conducted analyses which compared responses to under-informative items to those to felicitous items (where high agreement levels are expected). Data from optimally true/false all trials were not analyzed. Instead, high performance on those trials served as baseline inclusion criterion in the final dataset (as in Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019).
Participants
Participants were recruited via Prolific and randomly allocated to either the experiment featuring the binary or the graded task. For the binary condition, the final sample comprised 55 English monolinguals and 54 German L2 English learners. One additional L2 participant was excluded for scoring below 75% correct in optimally true/false all trials. The remaining L2 participants demonstrated a minimum proficiency equivalent to B2 (i.e., LexTALE score above 60). For the graded condition,Footnote 11 the final sample included 74 English L1 monolinguals and 80 German L2 English learners. L2 participants demonstrated a minimum proficiency equivalent to B2 (i.e., LexTALE score above 60; section 2.2.3). Additional participants (16 L1 and 13 L2 participants) were tested but excluded for scoring below 80% correct in optimally true/false all trials (pre-registered criterion).
Participants reported no cognitive impairments or learning disabilities and confirmed monolingual upbringing—the latter since our study focuses on L2 development rather than bilingualism and early bilinguals might have implicature derivation advantages over L2 learners (Siegal et al., Reference Siegal, Matsuo, Pond and Otsu2007; Antoniou & Katsos, Reference Antoniou and Katsos2017).
Materials
Twelve scenarios were created, each featuring a set of nine kitchen items, such as apples or mugs (there were always exactly nine apples/mugs/etc.).Footnote 12 Each scenario appeared once per sentence type. Throughout the experiment, some consistently denoted four out of nine items.
Procedure
Due to Covid-19 restrictions, the experiment was conducted online on Gorilla (www.gorilla.sc; Anwyl-Irvine et al., Reference Anwyl-Irvine, Massonnié, Flitton, Kirkham and Evershed2020). It took 5–10 minutes.
After giving consent, and before beginning the experiment, participants completed the LexTALE’s English version which measures proficiency in a valid and fast way (Lemhöfer & Broersma, Reference Lemhöfer and Broersma2012; Poort & Rodd, Reference Poort and Rodd2019). Participants falling below 60% (B2) could not continue.
In the experiment, participants were introduced to a context where they watch a cooking show on TV together with a foreign friend. The chef moves kitchen items from one place (e.g., shelf) to another (e.g., table). The friend, whose L1 is not English, comments on the chef’s every move but occasionally makes language mistakes. During testing, participants saw cartoons of kitchen scenes featuring target items such as apples or mugs. Each cartoon comprised two pictures (Figure 1). The first picture, always on the left, showed all items in one place (e.g., all apples on the table; all mugs on the shelf). The second picture, always on the right, showed some, none, or all of the initial items in a different place (after the chef had moved them; Figure 1). In each trial, the cartoon was accompanied by a written sentence representing the friend’s comment, like The chef put some/none/all of the apples on the plate. The sentence and cartoon pictures appeared on the screen simultaneously. Participants had to evaluate whether their friend described the scene correctly using a binary or five-point Likert scale displayed below the sentence. There were 48 trials per participant (twelve scenarios × four sentence types).
Figure 1. Example of the four sentence types: optimally true all (top left), optimally false all (top right), felicitous some (bottom left), infelicitous (= under-informative) some (bottom right). Participants must judge the extent to which the sentences match the picture in each case. Note that responses for the first three types are expected to be highly consistent for proficient speakers (i.e., they will respectively show high levels of: agreeing, disagreeing, and agreeing). For under-informative some, high levels of agreement indicate logical responding, whereas low levels of agreement indicate sensitivity to under-informativeness (i.e., that the use of some here is under-informative).
To mimic real-life communication situations, participants were asked to react quickly and intuitively. Similarly to Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021), we introduced 7000 ms time limits per trial. We extended their 3000 ms limit because we provided written and visual input, instead of only aural input. A countdown appeared for the last 5000 ms. The screen proceeded automatically after 7000 ms, regardless of whether a response was provided.
Data analysis plan
Given non-normal data distribution, we conducted non-parametric tests (Wilcoxon signed-rank and Wilcoxon rank-sum tests) to check whether participants from both language groups demonstrate sensitivity to under-informativeness in both tasks. In addition, for both the binary task and the graded task, we evaluate evidence against two hypotheses: (i) the hypothesis that L2 users give fewer pragmatic responses to under-informative input and (ii) the hypothesis that L2 users give more pragmatic responses to under-informative input.Footnote 13 Note that nonsignificant p-values cannot differentiate between no between-group differences (H0) and no evidence for any conclusion. We therefore used BF as our method of inference which can make this distinction. A BF provides a measure of the extent to which our data support a hypothesized difference between the groups (H1) versus the null that there is no difference between the groups (H0). BF computation therefore requires us to have a model of H1, that is, the distribution of expected differences if H1 is true. Following Dienes (Reference Dienes2008, Reference Dienes2014), we compute BFs modeling H1 as a half-normal (thus testing a one-sided prediction, since we are testing a directional hypothesis) with a mean of 0 and the SD set to a rough estimate of the predicted difference if H1 is true. This method requires three numbers: (1) an estimated mean difference in the data; (2) the associated standard error; and (3) an estimate of the predicted mean difference under H1. For (1) and (2), similar to the non-parametric t-test, we use the mean rank difference and associated standard error.Footnote 14 For (3), ideally, we would base this on previous data, but there is no study with similar materials using this scale and our pilot did not find a difference we could use, so we lack a relevant value. We used the motivated-maximum approach (cf. Silvey et al. under review and “related room to move” hypothesis in Dienes (Reference Dienes2019)). This involves basing a predicted effect size on a maximum and then setting our predicted effect size to twice this value (since our model of H1 is a half-normal with a mean of 0, the maximum is equal to approximate twice the SD). Since we use rank scores, we can compute the logically possible maximum difference between group 1 (n=n1) and group 2 (n=n2) if no participant in group 2 scored higher than any participant in group 1 (1:n1 ranks are group 1 participants, n1+1:n1+n2 are group 2 participants). This is equal to (n1+n2)/2, that is, in our study: (80+74)/2=77 for the graded data and (54+55)/2=54.5 for the binary data. We therefore set our rough predicted effect sizes to be equal to half of this value, that is, 38.5 for the graded data and 27.25 for the binary data. It is important to emphasize that while this sets limits on the possible scale of effect, H1 is modeled as a distribution and our use of a half-normal distribution with a mean of 0 corresponds to our expectation that smaller values are more likely.
Although BFs are defined on a continuous scale, we also interpret them according to discrete evidential categories introduced by Jeffreys (Reference Jeffreys1961) and expanded by Dienes (Reference Dienes2014) whereby BF>10 indicates strong evidence for H1, BF>3 indicates substantial/moderate evidence for H1, BF<1/3 indicates moderate/substantial evidence for H0, BF<1/10 indicates strong evidence for H1, and otherwise the evidence is ambiguous (i.e., the data are unable to adjudicate between the hypotheses). Since we acknowledge the choice of predicted value to inform H1 is subjective, we also calculated Robustness Regions (RR) for each BF, which show the range of estimates of H1 for which our data would support the same conclusion (i.e., to accept H1/H0 or inconclusive) based on the cutoffs of BF>3 or BF<1/3. This RR is noted as [x1, x2] with x1 being the smallest standard deviation and x2 the largest standard deviation (as recommended by Dienes, Reference Dienes2021). To compute the range, the full range of possible values was tested (i.e., 0 to the maximum of 77 for the graded data and 54.5 for the binary data) in increments of 0.1. Note that larger predicted values bias finding evidence for H0 and smaller values bias finding evidence for H1.
Results
LexTALE
The LexTALE provides a language proficiency measure on a 0% to 100% scale. One L2 participant in the graded task condition scored below 60% (B2) and could not participate. The remaining participants scored: L1 mean=93.62, SD=5.71, range 77.5:100; L2 mean=84.44, SD=9.81, range 62.5:100. All participants in the binary task condition passed the language test (L1 mean=94.66, SD=5.30, range 77.5:100; L2 mean=86.65, SD=8.52, range 66.25:100).
Optimally true and optimally false all sentences
Performance on these sentence types was used to filter out low-performing participants. In the graded condition, responses were recoded numerically as follows: optimally true all: Disagree=1, Somewhat Disagree=2, Neither=3, Somewhat Agree=4, Agree=5; optimally false all: Disagree=5, Somewhat Disagree=4, Neither=3, Somewhat Agree=2, and Agree=1. In the binary condition, responses were recoded numerically as follows: for optimally true all: Disagree=0, Agree=1; for optimally false all: Disagree=1; Agree=0. Participants scoring less than “4” on average on each of the optimally true/false all trials in the graded task and participants scoring less than 75% correct answers on optimally true/false all trials were removed. In the graded data, 16 L1 and 13 L2 participants were removed. This was unexpectedly high compared with our pilot (conducted in person). On inspection, those participants showed patterns like 100% “disagree” responses to optimally true all items, suggesting low levels of attention, a well-known side effect of online research. In the binary data, only one L2 participant was removed. Table 1 shows performance on control items for the remaining sample across both task types.
Table 1. Performance on control items
Main analyses
Analyses were conducted over the under-informative some and felicitous some data. Since the screen automatically advanced after 7000 ms, 26 responses were missing (less than 1% of the data) for the graded data and three responses (of 2616) for the binary data.
We visualize the data in two ways: first, Figures 2–3 present the proportion of different responses across participants per group for each task type.
Figure 2. Proportion of different responses across participants for the two types of sentences with some (binary tasks).
Figure 3. Proportion of different responses across participants for the two types of sentences with some (quinary tasks).
On inspection, the data of each task appear to corroborate each other. The response patterns in both tasks look similar for both groups: as expected, for felicitous some sentences, most responses are “agree” responses. Critically, however, for the under-informative sentences, most responses in both tasks are “disagree” responses, reflecting (a) a sensitivity to the pragmatic under-informativeness in both groups and (b) an intolerance of such under-informativeness in both groups. Interestingly, the other responses in the graded task are spread, including intermediary responses, suggesting some tolerance of pragmatic violations in both language groups.
Second, we recoded responses numerically and computed participant averages (Figures 4–5). In both tasks, both groups demonstrate similar response patterns. Regarding under-informative items, a large proportion of participants in both groups always answered “disagree” (equaling an average of 1 or 0), with other participants having average scores which indicate that they gave more mixed responses, though leaning toward the scale’s “disagree” end (no participant in either group always “agreed” which would be an average of 5 or 1). In fact, the binary data suggest that only two L2 participants show an overall tendency to tolerate pragmatic violations (i.e., average score > .5). However, the graded data indicate that without the pressure of having to make a dichotomous choice, a number of participants in both language groups tend to tolerate (i.e., average score > 3) the pragmatic violations.
Figure 4. Violin plots showing distribution of participant average scores for each language group on the two types of some sentences (binary tasks). Means are shown in red. High scores indicate high levels of agreement that the presented sentences match the pictures.
Figure 5. Violin plots showing distribution of participant average scores for each language group on the two types of some sentences (quinary tasks). Means are shown in red. High scores indicate high levels of agreement that the presented sentences match the pictures.
The visualizations demonstrate that both groups show strong tendencies to disagree with under-informative some sentences in contexts where all would be more appropriate, regardless of the task type. Nonetheless, the more nuanced graded data indicate that if given the option to participants in both language groups tend to be willing to show some tolerance of pragmatic violations.
Turning to our statistical analyses, these were conducted over participant average scores (Figures 4–5). Since the data are not normally distributed, we employed non-parametric tests. Our first hypothesis is both language groups are sensitive to under-informativeness. To test this, we tested with Wilcoxon signed-rank tests whether mean response scores for control items (felicitous some) were significantly lower than those for target items (under-informative some). Since this logic applies to both task types, the tests were conducted for both task types. Results demonstrate that mean response scores to under-informative some were lower than those to felicitous some in both language groups, confirming our first hypothesis (binary task: L1: p<.001, r=.89 // L2: p<.001, r=.89; graded task: L1: p<.001, r=.87; L2: p<.001, r=.87).
Our second and key question concerns the between-group differences. For both task types, we began by computing difference scores between felicitous some and under-informative some for each participant and conducted Wilcoxon rank-sum tests comparing both groups.Footnote 15 This suggested no significant between-group difference (p > .05). Given the shortcomings of the p-value statistic (see Data Analysis Plan), we calculated BFs.
For the binary data, we used the same difference scores used to compute the rank-sum test and computed BFs to test each of the hypotheses that (1) English L2 speakers give fewer pragmatic responses to under-informative sentences than English L1 speakers (which predicts lesser rejection of under-informative some and thus smaller difference scores for L2 participants) and (2) English L2 speakers give more pragmatic responses than English L1 speakers (which predicts greater rejection of under-informative some and thus larger difference scores for L2 participants). Recall that although we do not anticipate group differences, we must test the null hypothesis against the positive hypothesis that such differences exist. Starting with 1, using the method described above, we required a mean difference (in rank scores) between the groups (mean=6.99) and standard error (SE=5.56). Computing and comparing this data summary’s probability under H0 versus under H1 where H1 is modeled as a half-normal with a mean of 0 and a SD set to our rough estimate of predicted difference (i.e., 27.25) resulted in BF=0.76. As BF>1/3, we conclude that our evidence is ambiguous; hence, we cannot determine from this data whether H0 or H1 is more likely. For 2, the hypothesis in the opposite direction, our data model (mean=−6.99; SE=5.56; predicted difference=27.25) resulted in BF=0.095. We take this as evidence for H0. RRs were RR[7.0:∞] indicating that we could have used a four times smaller estimate of the predicted effect and still found evidence for H0 (BF<1/3).Footnote 16
For the graded data, we again used the same difference scores used to compute the rank-sum test and computed BFs to test each of the hypotheses that (1) English L2 speakers give fewer pragmatic responses to under-informative sentences than English L1 speakers and (2) English L2 speakers give more pragmatic responses than English L1 speakers. For (1), our data model (mean=−4.69; SE=7.19; predicted difference=38.5) resulted in BF= 0.12. We take this as evidence for H0. RRs were RR[12.5:∞] indicating that we could have used a three times smaller estimate of the predicted effect and still found evidence for H0. For (2), our data model (mean=4.69; SE=7.19; predicted difference=38.5) resulted in BF= 0.33. We take this as evidence for H0. RRs were RR[38.32:∞].
Discussion
This study investigates L2 and L1 users’ response behavior toward under-informative input using binary and graded TVJ tasks. In the binary task, L1 and L2 participants overwhelmingly showed pragmatic responses when “forced” to make dichotomous judgments. In Katsos and Bishop’s (Reference Katsos and Bishop2011) terms, both groups showed intolerance of pragmatic violations. In the graded task, the same tendency emerged; however, the spread of responses revealed some tolerance of the pragmatic violation in both groups. (Or alternatively, some recognition that there was an alternative logical interpretation, and sensitivity to that). We did not find evidence of between-group differences in either experiment. Additional Bayesian analyses with the binary data provided evidence against the hypothesis that L2 users provide more pragmatic responses to under-informative sentences than L1 users, as reported by Slabakova (Reference Slabakova2010). Evidence against the hypothesis that L2 users provide fewer pragmatic responses than L1 users (Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021) was ambiguous. Bayesian analyses with the graded data provided evidence against both the hypotheses that L2 users provide more pragmatic responses to under-informative sentences than L1 users and that L2 users provide fewer pragmatic responses than L1 users. These findings contradict claims about between-group differences in previous L2 work and help reshape our perception of L2 learners’ pragmatic abilities compared to L1 users.
Sensitivity to under-informativeness
Regarding fundamental pragmatic abilities, our data show that both language groups are sensitive to under-informativeness, and that this sensitivity did not differ between groups. This is unsurprising. As outlined above, we would not expect developmental differences between adult L1 and L2 users that might impact such fundamental pragmatic awareness, nor would we anticipate the “cognitive burden” of considering the potential for a more informative statement to exceed the cognitive capacity of L2 users. Regarding implicature derivation, it is highly likely that participants were able to derive the implicature, thereby accessing the pragmatic interpretation of some (some but not all), as indicated by ceiling performances in the felicitous some trials. Here, agreement only makes sense when one has access to the some but not all interpretation. However, for the reasons outlined above, our TVJ data, specifically data from under-informative trials, cannot fully speak to the issue of implicature derivation. Even if we assumed based on the ceiling responses in felicitous trials that participants had access to the pragmatic interpretation, data from TVJ tasks do not reveal processing order or time course although this information is crucial to adjudicate between different theoretical accounts of implicature derivation. Such information would require other experimental methods like eye-tracking or ERP techniques that provide insights into different implicature derivation stages (Huang & Snedeker, Reference Huang and Snedeker2009; Yoon et al., Reference Yoon, Wu, Frank, Noelle, Dale, Warlaumont, Yoshimi, Matlock, Jennings and Maglio2015). For example, Noveck and Posada’s (Reference Noveck and Posada2003) ERP data indicated no immediate cognitive reaction to under-informative sentences, which allowed them to make assumptions about time courses and triggers of implicature processing. Similar L2 research might reveal similar processing details that go beyond sensitivity to under-informativeness, allowing nuanced theoretical conclusions.
L1–L2 differences
As discussed in 1.3, only Dupuy et al. (Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019) reported null results, although their statistics were inappropriate for evaluating evidence for H0. In contrast, Slabakova (Reference Slabakova2010), Lin (Reference Lin2016), and Snape and Hosoi (Reference Snape and Hosoi2018) found that L2 participants were more likely to reject under-informative input than L1 participants (though results were only significant in the first two studies), whereas Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021) report the opposite. Why do we not see the same patterns reported in previous studies?
The experimental pragmatics literature offers some explanations as to which factors might “push” participants to accept or reject pragmatic violations. For example, Feeney and Bonnefon (Reference Feeney and Bonnefon2012) found effects of politeness contexts and individuals’ honesty traits on the interpretation of scalar expressions (see Previous L2 studies). Assumptions of honesty were influential in a study by Sikos et al.’s (Reference Sikos, Kim and Grodner2019) as well. They manipulated speakers’ social attributes (i.e., likeability and L2 status) and found that those manipulations influenced acceptability ratings of under-informative items in binary tasks. If speakers were perceived as likeable, participants were more likely to accept under-informative utterances, and, vice versa, if speakers were perceived less likeable, participants were less likely to accept under-informative items. Interestingly, made-up “non-native speakers” were rated lowest by the participants (who were native speakers) in terms of likeability, and their under-informative utterances were most likely to be penalized. Importantly, replicating the same experiment using a graded task, Sikos et al. (Reference Sikos, Kim and Grodner2019: 9-10) find that the “correlation between speaker likeability and acceptance of critical items is eliminated” and conclude that “when forced to select between two inapt options in a binary choice task, social factors can tip the balance so that participants choose to reject [under-informative] statements more often for certain speakers.” This demonstrates how social factors independent of linguistic influences might impact decision-making processes in binary tasks. Turning to L2 studies, social influences like those influential in Feeney and Bonnefon’s (Reference Feeney and Bonnefon2012) and Sikos et al.’s (Reference Sikos, Kim and Grodner2019) studies might have been at play in Slabakova’s (Reference Slabakova2010) second experiment where pragmatic responses in both groups increased compared to the first experiment. Unlike the first experiment where participants judged non-contextual sentences drawing on encyclopedic knowledge (“Some elephants have trunks”), the second experiment introduced picture-stories featuring real humans where the context implied that the speaker attempted to deceive the hearer through their under-informative use of some. As noted by Dupuy et al. (Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019), the pragmatic interpretation becomes more relevant in such scenarios, because that is what the speaker is intending to convey.
While factors other than cognitive factors may impact decision-making in the context of judging pragmatic violations, the question remains why Slabakova (Reference Slabakova2010) found a pragmatic “advantage” among L2 users compared to L1 users. Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021) suggest that the immersion students recruited for Slabakova’s study might have had a cognitive advantage over the L1 users, resulting in higher implicature derivation rates. However, this argument does not apply when we assume that the study’s binary tasks might not have captured implicature derivation but rather display the trade-off between two competing interpretations of some. In addition, ceiling performances in felicitous control tasks across both language groups in Slabakova’s (Reference Slabakova2010) study indicate that both groups could access the pragmatic interpretation. Another possibility might be that the L2 pragmatic bias in L2 users was the result of their L1 background. In contrast to our own and other L2 studies (Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019; Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021) featuring linguistically and culturally relatively similar languages (German, Italian, Spanish, French, and English), Slabakova’s (Reference Slabakova2010) study featured English and Korean, two relatively “distant” linguistic and cultural backgrounds. Perhaps, there are linguistically and culturally engrained tendencies toward accepting or rejecting utterances perceived as “wrong” or “untruthful.” In addition, cross-linguistic differences like the perceptions of what fraction of a quantity felicitously constitutes “some” (Stateva et al., Reference Stateva, Stepanov, Déprez, Dupuy and Reboul2019) could influence decision-making at the intercept of logical meaning, pragmatic meaning, and social context. This interplay of quantifier meaning and social context is demonstrated by Zhang and Wu (Reference Zhang and Wu2020) who report that Chinese speakers employ different interpretations of some depending on whether the quantifier is used as informative (pragmatic interpretation) or polite item (logical interpretation). Thus, it seems plausible that pragmalinguistic failure which “occurs when the pragmatic force mapped by [L2 users] onto a given utterance is systematically different from the force most frequently assigned to it by native speakers of the target language” (Thomas, Reference Thomas1983: 99) might cause different response patterns across L1 and L2 groups. Yet, the available evidence seems to speak against this view of fundamental group differences caused by cultural and linguistic backgrounds: when tested in their L1, the Korean speakers’ response behavior did not differ to English L1 users in Slabakova’s (Reference Slabakova2010) study. The same appears to be true for Snape and Hosoi’s (Reference Snape and Hosoi2018) Japanese and English speakers, as indicated by their descriptive data.
While the null results of our binary and graded tasks correspond to our expectations (based on using two relatively similar language groups and the absence of manipulations of social context), it would nevertheless be interesting to see whether recruiting culturally and linguistically vastly different samples and manipulating social contexts would impact participants’ response patterns in both binary and graded tasks and potentially cause group differences. Note, such investigations might no longer be a question of whether participants are completing the experiment in their L2 but instead depend on general L1-specific factors. For example, future studies could manipulate speakers’ honesty traits which might provoke differing pragmatic tolerance across groups with vastly different L1 backgrounds. This could be achieved by creating a scenario where a hearer is cooking and cannot see the TV in the living room, yet s/he requires information about the TV chef’s actions and relies on the speaker’s report. Prior to testing, the speaker’s honesty traits could be manipulated (“S/he misguides people for a laugh”; “S/he is very honest”) and participants are assigned to different “honesty” and binary/graded conditions.
We now turn to Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021) who report results in the opposite direction. Their participants gave fewer pragmatic responses in L2 than L1. We did not find evidence for this in either experiment. When we use the same binary scale as Mazzaggio et al., evidence for H0 is inconclusive. It trended in the direction of more evidence for H0 but did not cross our pre-registered boundary of BF< 1/3 for substantial evidence. In the graded task, we found evidence for H0 that L2 users did not provide fewer pragmatic responses than L1 users. Taking both experiments’ results together, while we can conclude that L2 participants are not less sensitive to pragmatic violations than L1 speakers, we cannot rule out that they are nevertheless more willing to be tolerant of such violations when given a binary scale. On inspection of the data, this ambiguity seems driven by the fact that two L2 participants give more “agree” than “disagree” responses. To resolve the group differences question, it would be necessary to recruit further participants. Here, we stopped at our pre-registered maximum (which was set based on available resources).
Given the inconclusive BF, we cannot say that our data are incompatible with Mazzaggio et al.’s (Reference Mazzaggio, Panizza and Surian2021). Still, we tentatively consider why we don’t find evidence for the positive effect in that paper: in addition to the potentially relevant contextual factors discussed above, the literature offers several other relevant factors which influence the decision-making in the context of pragmatic violations, including the effects of the exact type of wording used to elicit responses (Agree/Disagree; Yes/No; That’s (not) right; Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021; Kissine & de Brabanter, Reference Kissine and De Brabanter2023), the partitive quantifier formulations (some X vs. some of the X; Degen & Tanenhaus, Reference Degen, Tanenhaus, Carlson, Hölscher and Shipley2013), or the discourse context (question under discussion (QUD), Yang et al., Reference Yang, Minai and Fiorentino2018; Dupuy et al., Reference Dupuy, Van der Henst, Cheylus and Reboul2016). On the one hand, those factors might have mediated the impact of language background in previous L2 studies, which could explain the inconsistent results. On the other hand, those factors have varied in an unsystematic manner across all the previous L2 studies and are not specific to Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021). What is exclusive to Mazzaggio et al.’s (Reference Mazzaggio, Panizza and Surian2021) study is the use of aural input. While they introduced aural input to increase the L2 users’ cognitive burden with the aim of adjudicating between different theoretical accounts of implicature derivation, the aural nature of the input might have had a different effect, caused by prosody. The experimental pragmatics literature suggests that prosody can impact the rate of pragmatic inferences (cf. Reboul & Stateva, Reference Reboul and Stateva2019). For example, Chen et al. (Reference Chen, Huang and Politzer-Ahles2018) demonstrated with Chinese L1 users that prosodic cues (i.e., stress on individual words) can help listeners arrive at the inference intended by the speaker (cf. Bill et al., Reference Bill, Romoli and Schwarz2018, experiment 2). It makes sense that intonation impacts comprehension, and it might potentially be the case that Mazzaggio et al.’s (Reference Mazzaggio, Panizza and Surian2021) recording (unintentionally) featured relevant prosodic cues. Yet, it remains unclear why such cues would impact one but not the other language group. Regarding the differences between Mazzaggio et al.’s (Reference Mazzaggio, Panizza and Surian2021) and our current work, what seems more plausible is that their tight time limit (3000 ms) in combination with the aural nature of their input might have impacted response patterns. Perhaps their L2 users did in fact require more time to process the sentences. For example, it might have taken them longer to decode the phonological input. Even just a few additional milliseconds of processing take time away from the taxing decision-making process over which interpretation to give preference to. While this view does not explain the direction of the L1–L2 differences reported by Mazzaggio et al. (Reference Mazzaggio, Panizza and Surian2021), it might contribute to an account of why there might have been differences in the first place. And although our study featured a time limit as well, it was considerably less pressing (7000 ms), potentially allowing both language groups enough time to arrive at their decision without the need to rush it.
Comparison of binary and quinary data
Our results contrast Katsos and Bishop’s (Reference Katsos and Bishop2011) in that they do not suggest large differences in participants’ pragmatic responding when given a binary versus graded scale. Most likely, children’s tolerance of pragmatic violations when given binary choices in previous studies is an age-based developmental factor which does not apply in our study. Nonetheless, we see qualitative differences in our two experiments’ response patterns. In the graded task, while many individual participants give 100% “disagree” responses, many others provide intermediate responses including some participants whose responses suggest that on average they are using the “agree” end of the scale (i.e., Figure 5 includes participants who are above 3, the scale’s halfway point). Similar to Sikos et al.’s (Reference Sikos, Kim and Grodner2019) study, our data suggest that when given a non-dichotomous choice participants demonstrate some tolerance of pragmatic violations. In contrast in the binary task, a larger proportion of participants give 100% “disagree” responses and, while there is still some evidence of intermediary responding across trials (i.e., participants who do not give 100% “disagree” responses), there are only two (L2) participants whose responses indicate they mostly “agreed” (i.e., above 0.5 in Figure 4).
Given this pattern, we conducted some additional (non pre-registered) analyses to explore statistically whether these qualitative differences hold if the quinary responses’ spread—for comparability—is translated into the assumptions of “traditional” binary coding. We use a coding inspired by Jasbi et al. (Reference Jasbi, Waldon and Degen2019). In their study, they suggest two linking hypotheses regarding assumptions about how responses on graded scales link to binary scales. (They assume that with binary data, “agree”/“disagree” responses indicate not-deriving/deriving implicatures, respectively). They differentiate a “strong” linking hypothesis, which assumes that only full “disagree” responses indicate implicature derivation (while any other response indicates that it was not) and a “weak” linking hypothesis which assumes that implicatures are derived when any of “disagree,” “somewhat disagree,” “neither,” or “somewhat agree” are chosen. We explored the implications of applying these two types of coding in our own data, re-coding responses using the “weak” and “strong” criteria (Figures 6−7). For the “strong” and “weak” re-codings separately, we fitted mixed logit models with a random intercept by participant. The models revealed a significant effect of task type on response behavior only under the strong interpretation (weak link: β=0.10, SE=0.26, z=0.39, p>.05; strong-link: β=4.71, SE=0.52, z=8.99, p<.05). In terms of Jasbi et al.’s criteria, this is in the direction of suggesting more implicature derivation in the binary task. This interpretation is unintuitive: it seems unlikely that the less nuanced scale would be more rather than less revealing as to pragmatic competence. Instead, we suggest the difference seen in the strong coding underlines the point we made above (cf. Sikos et al., Reference Sikos, Kim and Grodner2019)—that there is a larger proportion of full “disagree” responses in the binary data, which does not reflect differences in pragmatic “competence” (i.e., whether participants derive implicatures) but rather in participants’ willingness to accept violations. Nevertheless, this underlines the importance of being clear in our assumptions when interpreting results of pragmatic studies.
Figure 6. Strong and weak link from response options to researcher inference about scalar implicature rate, exemplified for under-informative some when the quantifier all would be more informative (adapted from Jasbi et al., Reference Jasbi, Waldon and Degen2019).
Figure 7. Inferred implicature rates on under-informative some trials (see Figure 6) as obtained with the binary and quinary judgment task. Y-axis represents the proportion of total responses which would be considered as “implicature derived” or “implicature not derived” depending on the applied linking hypothesis (strong/weak) (cf. Jasbi et al., Reference Jasbi, Waldon and Degen2019).
The interplay of “felicitous some” and “infelicitous some” trials
Although not part of our main inquiry, we now discuss the often-overlooked interplay between felicitous and infelicitous some trials. As noted earlier, only the some but not all interpretation licenses agreement in felicitous trials. The fact that participants consistently perform at ceiling in the felicitous trials in our study and elsewhere (Dupuy et al., Reference Dupuy, Stateva, Andreetta, Cheylus, Déprez, Henst, Jayez, Stepanov and Reboul2019; Slabakova, Reference Slabakova2010; Mazzaggio et al., Reference Mazzaggio, Panizza and Surian2021) seems to suggest that they can generally access the pragmatic interpretation of some, irrespective of processing nuances within and between groups. The ceiling performances also seem to suggest that the impact of the logical reading (some and possibly all) is minimal in felicitous trials. This makes sense: based on their linguistic experience (Kissine & de Brabanter, Reference Kissine and De Brabanter2023) and an expectation of cooperation (Grice, Reference Grice, Cole and Morgan1975), participants are likely to assume that if speakers use the term “some” they aim to convey the pragmatic meaning. Reversely, regarding infelicitous trials, assuming participants can access the pragmatic interpretation of some, we would expect rejections. Yet, across studies and task types, participants do not consistently reject infelicitous input. So far, we argued that agreement in binary tasks or, similarly, the response spread in graded tasks result from the balancing act between sensitivity to violations and the tolerance thereof. However, we suggest that perhaps any response other than extreme “reject” responses indicate whether participants could access the logical interpretation some and possibly all. Pilot data of the current study yielded anecdotal participant feedback suggesting that they took multiple infelicitous trials to realize that there is a logical “reading” of some available, and some participants failed entirely to entertain the logical reading. Both behaviors are understandable because participants’ linguistic experience and their expectation of cooperativity strongly speak against expecting some being used in a logical manner. Thus, perhaps it is not the pragmatic but rather the logical interpretation which is not always readily available to participants in TVJ tasks. In fact, if or once available, it might even be considered odd as it is extremely unexpected. Therefore, we wonder whether among all the inquiries into participants’ access to the pragmatic interpretation (and its derivation), we overlooked that what might rather be under scrutiny in infelicitous trials is participants’ access to the logical interpretation.
Since this notion is speculative and our data cannot speak to it, we propose future research could investigate the following aspects: first, if and when (i.e., number of infelicitous trials) participants entertain the logical interpretation throughout an experiment like ours. Second, considering that participants overwhelmingly seem to demonstrate access to the pragmatic interpretation through their agreement in felicitous trials (although we cannot be certain), we wonder if response behaviors gradually change across infelicitous trials, specifically once participants start considering the logical interpretation. From this moment, their decision-making might not only depend on their awareness and tolerance of pragmatic violations anymore but might additionally be mediated by the availability of the logical interpretation. (Access to the logical interpretation might even impact behavior in felicitous trials because it presents a viable interpretation option). This also raises the question whether explicitly alerting participants to the logical interpretation before the experiment changes their response behaviors across trial types.
The inquiries regarding the availability of the logical interpretation might further complicate matters regarding TVJ tasks. The general question remains, what do they measure exactly? Our data only show that L1 and L2 users consistently notice that some sentences are under-informative, and that the two groups do not differ in their binary and graded response behaviors. We discussed potential reasons why our data and the data of previous L2 studies might differ from each other. Yet, TVJ tasks reveal little about underlying factors determining participants’ decision-making process, and the TVJ data yielded by our study and others may be insufficiently informative for exploring them.
Limitations and future directions
A potential limitation is that we worked with a particular L2 group—German speakers—without testing them in their L1. Such controls are reported elsewhere (Slabakova, Reference Slabakova2010) and are important in ruling out that between-group differences are not due to cross-linguistic differences, like in quantifier interpretation (Stateva et al., Reference Stateva, Stepanov, Déprez, Dupuy and Reboul2019). However, as we found no evidence of group differences, this seems less critical here.
Although we consider using BFs as an advantage of this study, we acknowledge that there is subjectivity in priors which we use as our H1 model. Dienes Reference Dienes2008; Reference Dienes2019 emphasizes the importance of using priors with parameters in the right ballpark for the theory in hand. Ideally, we would have used values from previous data, but unfortunately for our graded data analyses, we lacked appropriate data on the same scale; thus, we used a scale factor for the prior working backward from a maximum based on the scale (“motivated-maximum approach”; Silvey et al. Reference Silvey, Dienes and Wonnacott2021). While this is better than using uninformed defaults, we acknowledge that it is potentially biased in the direction of overestimating the potential effect size (thus biasing H0). To mitigate against this, we provided sensitivity analyses showing the extent to which our findings would hold given different priors. Moreover, with the binary data we had an available value from previous work to inform the H1 prior, and we compared analyses using this prior (with estimates based on logistic regression) to analyses with a prior based on the motivated-maximum approach (same approach as graded data). Reassuringly, we found qualitatively the same results when testing each of the directional hypotheses (i.e., evidence in favor of H0 for the hypothesis that L2 participants would give more pragmatic responses, ambiguous evidence for the hypothesis that they would give less pragmatic responses). This gives us some confidence in the motivated-maximum approach. In allowing us to quantify evidence for H0, BFs allow us to draw stronger conclusions about the “lack of” group differences compared with previous studies with null results in this respect. We acknowledge that with the binary data, BF analyses revealed that we did not have a sufficient sample to rule out one of our hypotheses. We considered this in the Discussion.
Conclusion
This study demonstrates that both proficient L2 English learners and English monolinguals are sensitive to under-informativeness in contexts with under-informative some. In contrast to previous research, we found no evidence of between-group differences in the extent of pragmatic responses when using either binary or quinary tasks. Moreover, for the quinary task, we found evidence that there was no between-group difference either in the direction of more pragmatic responding in L2 or less pragmatic responding in L2. Regarding individual participants’ response patterns, in both groups we found some differences with the binary and quinary scale. This was in the direction of apparently more pragmatic (less logical) responding in the binary task, which we interpret in terms of metalinguistic attitudes pushing participants to reject pragmatic violations in this particular task and context.
Replication Package
The pre-registration as well as study materials, analysis scripts, and anonymized data are available on https://osf.io/6bt53/.
Acknowledgements
We would like to thank the three anonymous reviewers for their detailed and helpful feedback on earlier versions of this manuscript. We also thank the participants in our pilot and main study.
Competing interests
The authors have no conflicts of interest to declare.
R Packages
lme4
Bates, D., Maechler, M., Bolker, B. & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. doi: 10.18637/jss.v067.i01
lmerTest
Kuznetsova, A., Brockhoff, P.B. & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed EffectsModels. Journal of Statistical Software, 82(13), 1-26. doi: 10.18637/jss.v082.i13
ordinal
Christensen, R. H. B. (2022). ordinal - Regression Models for Ordinal Data. R package version 2022.11-16. https://CRAN.R-project.org/package=ordinal.
RColorBrewer
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Open access
