The role of relevance, competence, and priors for scalar inferences

Polina Tsvilodub, Bob van Tiel, & Michael Franke*

Abstract. Although it is often assumed that the natural language expressions ‘some’
and ‘or’ are interpreted according to their first-order logic counterparts, in certain con-
texts, they receive a narrower interpretation: ‘some’ is strengthened to ‘some, but not
all’, and ‘or’ to ‘or, but not both’. This process is typically explained as an instance
of scalar inference. To test this scalar inference hypothesis, we collect experimental
evidence for the effects and interactions of three factors that have been argued to affect
the robustness of the scalar inferences of ‘some’ and ‘or’: the relevance of the stronger
alternative, the speaker’s competence about the alternative, and the prior probability
that the alternative is true. We find that the interpretation of both triggers was af-
fected by speaker competence, but only the interpretation of ‘some’ was also affected
by prior probability, while relevance did not affect the interpretation of either trigger.
Ultimately, our results suggest that the interdependence of the three factors is more
complex than just the sum of their effects.
Keywords. scalar inference; relevance; competence; prior probability; disjunction

1. Introduction. It is often assumed that the natural language expressions ‘some’ and ‘or’ are
equivalent to the existential quantifier ∃ and inclusive disjunction ∨ from first-order logic (e.g.,
Grice 1975). However, in certain contexts, ‘some’ and ‘or’ appear to receive a narrower interpre-
tation than their alleged logical equivalents. For example, neither of the intuitive inferences in (1)
and (2) follow from the proposed logical equivalence.

(1) Peter ate some of the doughnuts.
⇝ Peter did not eat all of the doughnuts.

(2) Peter ate a doughnut or a beignet.
⇝ Peter did not eat both a doughnut and a beignet.

The narrowing inferences of ‘some’ and ‘or’ are usually assumed to be instances of a more general
type of inference called scalar inference (e.g., Horn 1972, Gazdar 1979, Geurts 2010). Scalar
inferences are so-called because they are associated with lexical scales consisting of expressions
that are, inter alia, lexicalised to the same degree and ordered in terms of logical strength. In
the case at hand, ‘some’ is said to be associated with the scale ⟨some, all⟩ and ‘or’ with ⟨or, and⟩.
(Positive) utterances containing a lower-ranked scalar expression may imply that the corresponding
sentence with the higher-ranked expression is false.

Scalar inferences are often explained as a variety of conversational implicature. Conversa-
tional implicatures are inferences that can be explained on the basis of an argument that revolves
around the assumption that the speaker is cooperative. Grice (1975) develops the notion of coop-
erativity by arguing that cooperative speakers tend to follow certain conversational maxims. For

*Many thanks to Natalie Clarius, Neele Witte and Elisa Kreiss for help in creating materials and to Stela Ilieva and
Malin Spaniol for help with implementation during early stages of this project. Authors: Polina Tsvilodub, Osnabrück
University (polina.tsvilodub@gmail.com), Bob van Tiel, Radboud University Nijmegen (bobvantiel@gmail.com) &
Michael Franke, University of Tübingen (mchfranke@gmail.com).

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example, cooperative speakers tend to be truthful (Quality) and informative (Quantity). Based on
these two maxims, the scalar inference in (1) can be explained as follows:

1. The speaker said ‘Peter ate some of the doughnuts’.
2. She could have said ‘Peter ate all of the doughnuts’ (Maxim of Quantity).
3. This would have been more informative, and hence cooperative.
4. So why didn’t the speaker utter the more informative alternative?
5. Presumably, the speaker does not believe that the alternative is true (Maxim of Quality).
6. It is likely that the speaker knows whether the alternative is true or false.
7. Hence, the speaker believes that the alternative is false.

The same argument, mutatis mutandis, can be given to explain the scalar inference of ‘or’, as
exemplified in (2). In that case, the relevant alternative is ‘Peter ate a doughnut and a beignet’,
and the resulting inference says that the speaker believes Peter did not eat both a doughnut and a
beignet. We will call this implicature-based account of the scalar inferences of ‘some’ and ‘or’ the
standard account.

While the standard account is almost universally adopted for ‘some’, its application to ‘or’
has been more controversial (e.g., Simons 2001, Geurts 2006, Zondervan 2010). In particular, the
use of ‘or’ tends to trigger the inference that the speaker is unsure which of the two disjuncts is
true (e.g., whether Peter ate a doughnut or a beignet). This ignorance inference makes it a priori
unlikely—though not impossible—that the speaker knows whether or not the disjuncts may be
jointly true. Zondervan (2010) calls this the speaker expertise paradox.

In any case, according to the standard account, scalar inferences are inferences to the best
interpretation (Atlas & Levinson 1981). The hearer interprets the speaker’s utterance by trying
to explain why the speaker uttered one sentence rather than a more informative alternative. In
the case at hand, the most plausible hypothesis is assumed to be that the speaker believes that the
alternative is false. But it is part and parcel of inferences to the best interpretation that, depending
on the context, different explanations for the speaker’s behaviour may emerge as the most plausible.
For example, the robustness of scalar inferences has been argued to be influenced by competence,
relevance, and prior probabilities. In the next section, we discuss these three factors in more detail.
After that, we describe our experiment in which we tested the effects of these three factors on the
robustness of the scalar inferences of ‘some’ and ‘or’.

2. Factors influencing the robustness of scalar inferences.

2.1. COMPETENCE. The derivation of scalar inferences is often presented as a two-step process.
First, it is inferred that the speaker avoided producing the alternative because she does not believe
that the alternative is true. Second, this weak inference can be strengthened to the scalar inference
that the speaker believes the alternative to be false. Crucially, the step from the weak inference to
the scalar inference relies on the assumption that the speaker knows whether or not the alternative
is true. This assumption is called the competence assumption (step 6 above) (e.g., Sauerland 2004,
Soames 1982).

Goodman & Stuhlmüller (2013) experimentally investigated the effect of the plausibility of
the competence assumption on the robustness of the scalar inference of ‘some’. In their Exp. 1,
participants were presented with vignettes in which a speaker uttered sentences containing the trig-

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ger ‘some’. The vignettes were designed so as to vary with respect to the assumed knowledge of
the speaker. In the full knowledge condition, the speaker was shown to be competent; in the par-
tial knowledge condition, the competence assumption was not satisfied. Goodman & Stuhlmüller
found that participants in the partial knowledge condition were significantly less likely to derive
the scalar inference compared to the full knowledge condition.

The effect of the plausibility of the competence assumption on the scalar inference of ‘or’ has
not been studied experimentally before. However, it has been observed that there is an apparent
tension between the competence assumption for ‘or’ and its ignorance inferences. To illustrate,
consider again (2). An utterance of this sentence tends to give rise to the ignorance inferences
that the speaker does not know whether Peter ate a doughnut, and she does not know whether
Peter ate a beignet. Given these ignorance inferences, it is difficult to imagine that the speaker is
confident that Peter did not eat both a doughnut and a beignet, i.e., that the competence assumption
is satisfied (Geurts 2006, Zondervan 2010). In the case at hand, that would require, e.g., that the
speaker watched Peter having lunch from afar, seeing that Peter ate one and only one thing which
the speaker could make out to be either a doughnut or a beignet.

2.2. RELEVANCE. An alternative explanation for the speaker’s decision to produce an informa-
tionally weaker utterance is that the speaker assumed that the hearer would not be interested in
the added information expressed by the alternative. To illustrate, compare the following dialogues
(from van Kuppevelt 1996):

(3) A: How many of the boys were at the party?
B: Some of the boys were at the party.

(4) A: Were some of the boys at the party?
B: Some of the boys were at the party.

A’s question in (3) makes it clear that she is interested in the precise number of boys who were
at the party. By contrast, A’s question in (4) suggests that she is only interested in whether or not
some of the boys were at the party. In other words, the information that not all of the boys were at
the party is intuitively more relevant in the first dialogue than in the second. Consequently, in the
second dialogue, B might have chosen to produce the informationally weaker ‘some’, not because
she lacks evidence for the alternative containing ‘all’, but rather because she thought the hearer
would have little or no interest in the extra information conveyed by the corresponding sentence
with ‘all’ (the speaker might even consider the added information to be distracting for the hearer).
In line with this observation, the scalar inference is intuitively more robust in the first dialogue
compared to the second.

In a series of experiments, Zondervan (2010) investigated the effects of relevance on the ro-
bustness of the scalar inferences of ‘most’, which we may assume to pattern similarly to ‘some’
and ‘or’. Zondervan constructed vignettes that made the corresponding scalar inferences either
relevant or irrelevant, where relevance was manipulated in various ways (e.g., by means of explicit
questions, prosodic emphasis, or contextual cues). Participants were then presented with a state-
ment containing the weaker term, even though the vignette made it clear that the statement with
the stronger term was true. They had to indicate whether the statement was true or false, given the
background story. Zondervan consistently found that scalar inference rates (i.e., ‘false’ responses)

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were higher when the scalar inference was relevant than when it was not. However, the difference
was typically rather small. For instance, in Exp. 1, the scalar inference of ‘or’ was drawn less than
20% more often when it was relevant (73% of the time) than when it was not relevant (55%).

2.3. PRIOR PROBABILITY. The nature of the effect of prior probability on the robustness of scalar
inferences is more contentious than the effects of competence and relevance. To illustrate, consider
the following sentence (from Geurts 2010):

(5) Cleo threw all her marbles in the swimming pool. Some of them sank to the bottom.

A priori, it is highly likely that all of the marbles sank to the bottom. How does this observation
influence the robustness of the scalar inference? Geurts (2010) intuits that the strength of the
scalar inference is unaffected by the fact that one would naturally expect all of the marbles to sink.
Geurts’ intuition contrast with the predictions made by the Rational Speech Act (RSA) model,
a recent formalisation of the pragmatic reasoning process that underlies the derivation of scalar
inferences (e.g., Frank & Goodman 2012). According to the RSA model, the prior probability of
the stronger alternative should negatively correlate with the strength of the scalar inference, so that,
in the example above, the scalar inference should be weak or even altogether absent.

Degen et al. (2015) experimentally investigated the effect of prior probability on the robust-
ness of the scalar inference of ‘some’. In Exp. 1, they presented participants with event descriptions
such as ‘John threw 15 marbles into a pool’. These event descriptions were followed by a question
such as ‘How many of the marbles sank?’. Participants had to indicate the probability of each
possible event (e.g., one marble sinking, two marbles sinking, and so on). In Exp. 2, a different
group of participants read the same event descriptions, but this time the descriptions were followed
by a well-informed character producing an utterance like ‘Some of the marbles sank’. Participants
again had to indicate the probability of each possible event, but this time based on the utterance
rather than their prior expectation. Degen et al. (2015) observed a correlation between prior prob-
ability and the strength of the scalar inference so that, when the ‘all’ situation was judged likely in
Exp. 1, it was also judged likely in Exp. 2.

2.4. PREDICTIONS. To sum up, based on literature, we hypothesize that scalar implicatures are
sensitive to these three contextual cues in the following way:

1. Competence: scalar inferences are more robust if the speaker is competent, i.e., knows
whether or not the stronger alternative is true.

2. Relevance: scalar inferences are more robust if the information expressed by the stronger
alternative is relevant to the hearer with respect to the purpose of the conversation.

3. Prior probability: scalar inferences are more robust if the information expressed by the
stronger alternative is a priori likely to be false.

In this paper, we systematically investigate these effects and their interactions on the robustness
of the scalar inferences associated with ‘some’ and ‘or’. Our goals are twofold. First, we aim
to obtain evidence as to which contextual factors influence the robustness of a scalar inference.
Recent research has focused on variability in scalar inference rates across different scalar words
(e.g., why the inference from ‘some’ to ‘not all’ is much more robust than the inference from
‘pretty’ to ‘not beautiful’, e.g., van Tiel et al. 2016). Here, we study variability in the robustness

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of scalar inferences that make use of the same lexical scales. By doing so, we obtain a more direct
insight into the mechanism that underlies pragmatic inferencing, which in turn may also provide
important new knowledge about the factors that cause cross-scalar variability.

Second, we seek to compare the scalar inferences of ‘some’ and ‘or’ with respect to their
sensitivity to the three contextual factors. If these two types of scalar inference are indeed caused
by the same underlying mechanism—as the standard account argues—it is natural to expect that
they are similarly sensitive to the pragmatic factors under investigation. Hence, if we observe
marked differences in how competence, relevance, and prior probability affect the robustness of
the scalar inferences of ‘some’ and ‘or’, that would provide at least circumstantial evidence in
favour of the idea that they are aetiologically distinct, too.

To address these goals, we conduct an experiment comparing the effects of these factors for
both triggers. We describe this experiment in the next section.

3. Experiment. To operationalize these factors experimentally, we designed context stories (vi-
gnettes) which we intuitively judged to score either high or low with respect to each factor of
interest. That is, we manipulated the contextual relevance of the stronger alternative to the lis-
tener, the speaker’s competence about the truth of the statement with ‘all’ or ‘and’, and the prior
probability of the statement with ‘all’ or ‘and’ to be true. We studied how these manipulations
influenced the the robustness of the scalar inferences of ‘some’ (‘some but not all’) and ‘or’ (‘or,
but not both’). The context stories and critical trials were designed in an analogous fashion for
both triggers and varied within-subjects, so the following descriptions apply for both triggers.

3.1. MATERIALS AND PROCEDURE. This study was a 2 × 2 × 2 × 2 within-subjects rating task
(relevance × competence × prior × trigger), conducted as a web-based experiment.1

On critical trials, participants were asked to rate four sentences, one per factor, and one con-
taining an upper-bounded (‘some’) or exclusive (‘or’) paraphrase of the trigger. On each trial,
participants read a context story, followed by a sentence presented in a blue box which was meant
to elicit the likelihood rating for a given factor. The sentence had either of the following forms:

(6) Relevance rating: It is important to X to know whether Y.
Competence rating: Z knows whether Y.
‘Or’ prior rating: If A, then B. / If B , then A.
‘Some’ prior rating: If some Y, then all Y.
Inference strength rating: From what Z said we may conclude that W.

where X was the listener, Z was the speaker, Y was the target event in the background story and
W was the event under the upper-bounded or exclusive reading. A and B were the disjuncts of
Y for ‘or’ vignettes. For the inference strength elicitation, participants additionally read a critical
utterance of the form:

(7) Z says to X: Y.

which contained the trigger (‘some’ or ‘or’), presented below the background story in a red box,
before rating the inference strength sentence.

1The experiment can be viewed at https://magpie-xor-some.netlify.app/. The preregistration for
the experiment can be found at https://osf.io/v7zjp.

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Below each sentence, participants were asked to indicate how likely it is that the statement in
the blue box is true given the story, followed by a slider rating bar labeled ‘certainly false’ (left)
and ‘certainly true’ (right). The slider positions were converted to 0–100 ratings.

We designed 32 different stories per trigger type (‘some’ vs. ‘or’), resulting in a total of 64
stories. Each participant saw eight randomly sampled stories (one per prior × competence ×
relevance condition out of four possible stories) such that they saw four ‘or’ and four ‘some’
stories in randomized order. The assignment of conditions to the triggers was randomized between-
participants.

The experiment proceeded as follows (see Fig. 1). First, participants were welcomed to the
experiment and read instructions, which contained an annotated example to explain the meaning
of the slider.

Figure 1: Experiment procedure.

The main part of the experiment consisted of eight critical vignettes, randomly shuffled with
eight attention check vignettes. For each critical vignette, participants completed a block of 10
or 11 trials, consisting of a trial with a comprehension question, trials with statements eliciting
relevance, competence and prior ratings (the last factor was elicited with two statements for the
trigger ‘or’, see (6)), followed by three more trials with comprehension questions, and the critical
scalar inference strength elicitation trial. Comprehension trials were visually identical to the criti-
cal trials, but the statement to be rated only referred to the content of the background stories. They
were designed so as to be either clearly true, clearly false or uncertain given the background story.
The four comprehension statements for one vignette were sampled at random from six possible
statements (two true statements, two false statements, and two uncertain statements).

The attention checks consisted of one trial which visually matched the critical trials. On
these trials, the vignettes contained a statement which wrote out what participants were supposed
to answer (e.g., ‘Please move the slider maximally left’) in an area of text that participants had
to read to complete the trial. Participants who failed more than two of these attention checks
were excluded from the analysis. After the study, participants could voluntarily fill out a socio-
demographic questionnaire.

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3.2. PARTICIPANTS. We recruited 277 participants through the crowd-sourcing platform Prolific.
Participants were restricted to those whose first language included English, who previously took
part in at least five other Prolific studies, and whose approval rate was at least 0.9, according to
prescreening criteria of the platform. Participants took 20 minutes on average to complete the study
and were compensated £2.48 for their participation. Following our preregistered exclusion criteria,
we excluded 15 participants for not indicating their native language, 3 participants for completing
the study in under 8 minutes, 26 participants for failing more than two attention checks, and 27 for
failing more than 20% of the comprehension questions. Due to a coding error in the comprehension
questions, 206 participants were left after applying exclusion criteria, although the preregistered
target sample size after exclusions was 200 subjects. In the following analyses, data from the 206
participants is analysed.2

4. Results. Prior to conducting statistical analyses, we preprocessed the data by standardizing
(z-scoring) the responses within each factor (relevance, competence, prior) for each participant.
Based on the aforementioned predictions, we expect that participants rate the scalar inference as
more likely if (i) the alternative is rated as more relevant, (ii) the speaker is rated as being more
competent, and (iii) the alternative is rated as a priori less likely. The remainder of this section is
structured as follows: Section 4.1 provides descriptive and confirmatory analyses following pre-
registration and Section 4.2 provides additional exploratory analyses.

4.1. PREDICTOR RATINGS AND CONFIRMATORY ANALYSES. Descriptively, participants’ factor
ratings by-story agreed well with the designed classification of the stories (Fig. 2, red vs. blue
color on x-axis). That is, the ratings for the relevance, competence and prior statements were not
distributed uniformly across the stories, but aligned with our prior categorisations (Fig. 2, x-axis),
validating our experimental manipulation of the explanatory factors.

We analysed the ratings using a Bayesian linear mixed effects model, regressing the target
scalar inference ratings against the fixed effects of predictor ratings (i.e., the relevance, compe-
tence, and prior ratings elicited by the same participant for that vignette), the effect of trigger, and
their two-, three-, and four-way interactions.3 We included random intercepts and random slope
effects for the main effects of trigger, relevance, competence and prior by-subject, as well as ran-
dom intercepts by-vignette.4 The categorical effect of trigger was dummy coded, using ‘some’ as
the reference level. For all regression coefficients we used a wide and uninformative prior given
by a t-distribution with mean 0, standard deviation of 2 and 1 degree of freedom. The model was
fitted using the R brms package (Bürkner 2017).

We focus on the slope coefficients for the effects of relevance, competence and prior, once for
‘some’ and once for ‘or’. We check whether the posterior estimate of each effect was in either
one of three intervals: (1) negative effect (≤ -0.05), (2) no effect (between -0.05 and 0.05), or

2All analyses were also conducted on data from 200 subject, excluding the last six submissions. No noteworthy
quantitative or qualitative differences of the results were observed.

3The data and the analyses can be found under https://github.com/magpie-ea/
magpie-xor-experiment

4Model in R syntax style: inference-rating ∼ trigger * relevance * competence *
prior + (1 + trigger + relevance + competence + prior || subjectID) + (1 |
vignette). For computational tractability reasons, the correlation of random effects by-subject was set to 0.

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Relevance Competence Prior

S
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O

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−2 −1 0 1 2 −2 −1 0 1 2 −2 −1 0 1 2

−2

−1

0

1

2

−2

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2

Participants' factor ratings

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Prior categorization of vignettes low high

Figure 2: Ratings for relevance, competence and prior statements (x-axis) plotted against ratings
for the strength of scalar inferences (y-axis). The top row shows ratings for ‘some’ (enriched
to ‘some, but not all’). The bottom row shows ratings for ‘or’ (enriched to ‘or, but not both’).
Ratings for stories initially categorised (by the experimenters) as low (red) w.r.t. a given factor are
on average lower (x-axis) than for those categorised as high (blue).

(3) positive effect (≥ 0.05). We set the threshold for considering an effect as positive or negative
to ±0.05 because we consider 0.05 to be the Region of Practical Equivalence (ROPE) for the
effect sizes we expect (Kruschke 2014). We interpret the data as providing evidence in favor of
an effect (positive, negative, no effect) if the posterior probability of the effect being true is ≥
0.95 (i.e., 95% of posterior samples are in the corresponding interval). The probabilities of the
respective coefficients lying in a particular interval are reported below (i.e., for instance, if P =
0.95 is reported, it means that 95% of the posterior samples of the given coefficient are in the
respective interval). In particular, we speak of evidence in favour of the scalar inference account if
the prior effect is negative, and the relevance and competence effects are positive. Fig. 3A shows
examples of simulated posterior distributions over effect size samples which would confirm all our
hypotheses (for better visual comparison to the observed results shown in Fig. 3B).

Consistent with predictions of the standard account, for the trigger ‘some’, we found a clear
negative effect of prior probability of the stronger alternative ‘all’ being true, as indicated by the
probability of the negative effect of prior being P = 0.999 (Fig. 3B, Prior (some), orange
color). Similarly, we found a clear positive effect for speaker competence (P = 1, Fig. 3B,
Competence (some), green color). However, we did not find a clear effect of relevance
(Fig. 3B, Relevance (some), split colors). If anything, the data supported the result that
relevance may only marginally influence the robustness of the enriched interpretation. In con-

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Figure 3: Distributions of the posterior samples of each factor of interest for the trigger ‘some’
and ‘or’. The colors and the vertical dashed lines indicate the effect intervals (from left to right:
negative, no, positive effect). Points indicate posterior means, thick lines indicate 66% credible
intervals, thin lines indicate 95% credible intervals. A: Idealized hypothetical results implied by
the standard account (for comparison to the obtained empirical results). The effect size was set to
0.25 for visualization purposes. B: Main experimental analysis results.

negative no effect positive by-subject random slope
some relevance 0.006 0.519 0.474 0.07

competence 0 0 1 0.13
prior 0.999 0.001 0 0.16

or relevance 0.001 0.204 0.795 0.07
competence 0 0.007 0.993 0.13
prior 0.635 0.357 0.008 0.23

Table 1: Results of the separate by-trigger exploratory models. Columns indicate probabilities of
respective effects being in the indicated range, except for the column ‘by-subject random slope‘
which shows estimates.

trast, for the trigger ‘or’, we only found a positive effect of competence (P = 0.993, Fig. 3B,
Competence (or), green color). We found no credible effects of the prior of the stronger alter-
native being true; results for relevance patterned with results for ‘some’ (Fig. 3B, Prior (or),
Relevance (or), split colors). Therefore, the main analysis did not provide strong evidence in
favor of the scalar inference account for ‘or’. Comparing the overall results to ‘some’, they provide
evidence against the identity hypothesis positing that the two triggers are interpreted via the same
underlying mechanism.

4.2. EXPLORATORY ANALYSES. The descriptive results visually suggested a possible effect of
prior for ‘or’ (see Fig. 2, lower right), which, however, was not borne out in the main analysis. To
investigate the results for ‘or’ in more detail, we explored an analysis wherein the predictor ratings
were averaged by-vignette and then regressed against the implicature strength ratings. This analy-
sis amounts to averaging over the different participants and thereby removing possible by-subject

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variability.5 Under this model, additionally to the main results, there was a credible effect of prior
probability for ‘or’ (P = 0.999 of a negative effect), suggesting that by-subject effects might have
overridden the main effect in the main analysis. Following up, models for each trigger separately
were fit (see Tab. 1). Those models included full random effects; the one for ‘or’ patterned with the
main analysis, showing no credible prior effects (P = 0.635 of a negative effect). Yet, the largest
by-participant random slope was the by-subject estimate for the effect of prior, compared to the
other factors. Compared to the ‘some’ model, this slope estimate was also larger (Tab. 1, last col-
umn). Furthermore, an exploratory correlation analyses revealed a potential co-linearity between
factors in the case of ‘or’ (R2 = -0.106 for relevance and prior, R2 = 0.127 for competence and
relevance). No significant correlations were found for ‘some’. Taken together, it seems that par-
ticipants interpreted the prior statements for ‘or’ stories quite variably, which might be explained
by differences in the prior expectations set up in the stories and whether these were contextual or
based on world knowledge. This, in turn, might have influenced the (perceived) relevance to the
listener and led to the observed correlated effects. Future research should address these aspects
more systematically.

5. Discussion. Our experiment provides novel results on the effects of the factors relevance, com-
petence and prior on the interpretation of ‘some’ and ‘or’. However, future work may extend upon
our results in several ways. First, our experiment considered how relevant the stronger alternative
was to the hearer’s interests. Yet other work rather focuses on relevance in terms of discourse pur-
pose (e.g., van Kuppevelt 1996), which could be formalized, e.g., in the form of explicit questions
in the background stories. Second, this paper looked at the derivation of the exclusive reading of
‘or’ through the lens of a scalar inference based account. However, alternative accounts derive the
exclusive reading in terms of a distinctness condition, suggesting that disjunctions are infelicitous
whenever the two disjuncts overlap, or either does not address the QUD (e.g., Simons 2001). Es-
pecially for the latter point, the disjuncts need to be interpreted exhaustively, which results in the
exclusive reading. Our results provide no conclusive evidence with respect to this alternative ac-
count, calling for follow-up experiments manipulating exhaustivity and distinctness. Beyond that,
it will be interesting to determine which other factors—e.g., typicality (van Tiel 2014), prosodic
and linguistic prominence (Breheny et al. 2006), and politeness (Bonnefon et al. 2009)—might
have similar effects. To sum up, our study provides new data on the effects of three contextual
factors on the exclusive interpretation of ‘or’, and showed that they had a different effect than on
the upper-bounded interpretation of ‘some’, calling into question the assumption that the interpre-
tations of the two expressions are subject to the same pragmatic mechanisms.

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