Tattoos as a window onto cross-linguistic differences in scalar implicature

Danielle Dionne & Elizabeth Coppock*

Abstract. This paper addresses the question of how to predict which alternatives are
active in scalar implicature calculation, and the nature of this activation. It has been
observed that finger implicates ‘not thumb’, and a Manner-based explanation for this
has been proposed, predicting that if English had the simplex Latin word pollex mean-
ing ‘thumb or big toe’, then finger would cease to have the implicature ‘not thumb’
that it has. It has also been suggested that this hypothetical pollex would have to be
sufficiently colloquial in order to figure in scalar implicature calculation. This paper
makes this thought experiment into a real one by using a language that behaves in
exactly this way: Spanish has pulgar ‘thumb’ (< pollex), a non-colloquial form. We
first use a fill-in-the-blank production task with both English and Spanish speakers to
gauge the likelihood with which a speaker will produce a given form as a way of de-
scribing a given digit. Production frequency does not perfectly track complexity, so we
can then ask whether comprehension follows production frequency or complexity. We
do so using a forced choice comprehension task, which reveals cross-linguistic differ-
ences in comprehension tracking production probabilities. A comparison between two
RSA models – one in which the speaker perfectly replicates our production data and
a standard one in which the speaker chooses based on a standard cost/accuracy trade-
off – illustrates the fact that comprehension is much more closely tied to production
probability than to the mere existence of sufficiently simple alternatives.
Keywords. scalar implicature; manner implicature; hyponymy, cross-linguistic differ-
ences; RSA; computational modelling

1. Introduction. Suppose you heard the following sentence:

(1) She has a tattoo on her finger.

Would you think the tattoo was on the thumb or the ring finger? If you are like most of the
participants in our English comprehension study, you will think the ring finger is more likely. The
thumb is generally considered a type of finger; people generally agree that we have 10 fingers.
So it is arguably not the semantics of finger that determines this preference; rather, there is a
scalar implicature from finger to ‘not thumb’. In Gricean terms, the pragmatic reasoning might
run as follows, ‘Why didn’t she choose thumb? It would have been equally short (Manner), more
informative (Quantity), and just as relevant (Relevance). Maybe she didn’t believe it (Quality).’

Horn (2000) observes that the relationship between thumb & finger is not parallel to the rela-
tionship between big toe & toe:

(2) a. I hurt my finger. ↝ I did not hurt my thumb.
b. I hurt my toe.   I did not hurt my big toe.

*We are grateful to the audiences at LSA 2020 and ELM 2021 for feedback on this work. Authors: Danielle
Dionne, Boston University (ddionne@bu.edu) & Elizabeth Coppock, Boston University (ecoppock@bu.edu).

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Horn (2000) concludes that although thumb acts as an alternative to finger for the purposes of scalar
implicature, big toe does not act as an alternative for toe (p. 308). He explains this in terms of
Manner: big toe is longer than toe, and therefore not a good alternative. Horn (2000, p. 308) writes:
“We would predict that if the colloquial language replaced its thumb with the polymorphous pollex
(the Latin and scientific English term for both ‘thumb’ and ‘big toe’), the asymmetry [between
finger and toe] would instantly vanish”.

Geurts (2011) zeroes in on Horn’s strategic use of the term “colloquial”, writing: “It is impor-
tant to note, however, that the adjective ‘colloquial’ is doing real work in this statement. It is not
enough for an alternative word to be in the language; it has to be sufficiently salient, as well: if
the word ‘thumb’ was rarely used, then presumably the asymmetry between [finger and toe] would
vanish too” (p. 122). That is, the prediction is really that if a stronger utterance is present in the
language and it is sufficiently salient, a scalar implicature will arise when the weaker form is used.

As it turns out, there is a language in which exactly that situation arises: Spanish. Spanish
contains a word for ‘thumb’, namely pulgar—the Spanish descendant of Latin pollex—but it is
less frequently used, and less colloquial. As we will confirm in production studies, there is a great
deal of variation in how the thumb is referred to in Spanish. Pulgar does not differ from thumb
in complexity, but it does differ in how prevalent it is in the language, and how salient it is to the
speaker as an alternative. Furthermore, pulgar ‘thumb’ is not specific to the hand, just like Latin
pollex. If Geurts (2011) is right, the asymmetry between finger and toe that exists in English is
predicted to be absent in Spanish, and there should be no implicature from dedo ‘finger’ to ‘not
thumb’ or from dedo del pie ‘toe’ to ‘not big toe’.

The four studies reported here test these predictions. We first conducted production studies
each in English and Spanish to gauge the salience of available alternatives. We use a fill-in-the-
blank production task with both English and Spanish speakers to gauge the likelihood with which
a speaker will produce a given form as a way of describing a given digit. We find that produc-
tion frequency does not perfectly track complexity, so we can then ask whether comprehension
follows production frequency or complexity. We do so using a forced choice comprehension task,
which reveals cross-linguistic differences in comprehension that tracks production probabilities.
Finally, we carry out a comparison between two RSA models, one in which the speaker perfectly
replicates our production data and a standard one in which the speaker chooses based on a stan-
dard cost/accuracy trade-off. Comparing both of these models to our comprehension data leads us
to conclude that the activation of alternatives for the purpose of scalar implicature calculation is
much more closely tied to production probability than to the mere existence of sufficiently simple
alternatives.

2. Production studies. We conducted two production studies: one in English and one in Spanish.
Both tasks contained images of body parts with tattoos (see Figure 1).

2.1. PARTICIPANTS. All participants were recruited on Prolific. All studies involved different
groups of participants. In the English production study, all participants were self-reported mono-
lingual native English speakers who were born and currently live in the United States. In the
Spanish production study, all participants were self-reported monolingual native Spanish speakers
who were born and currently live in Mexico. There were 24 American English speakers and 23
Mexican Spanish speakers in the production studies.

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Figure 1: Stimulus Items for Production and Comprehension tasks

2.2. MATERIALS. Participants completed a task in which they were asked to look at a series
of pictures. All of the pictures were body parts with a tattoo on them. The tattoos served as an
indicator of which digit or body part the speaker was talking about. The target items showed photos
of a tattoo on the thumb, ring finger, pinky, big toe, fourth toe and pinky toe. There were six filler
items (all different from each other): two photos of tattoos on a leg, two photos of tattoos on an
arm, and two photos of tattoos on the back.

2.3. PROCEDURE. Participants were shown a series of images, one by one. With each image,
they were asked to fill in the blank of the sentence: She has a tattoo on , or its translational
equivalent, in the case of Spanish. The order of images was randomized. All participants were
presented with all six target items and all six filler items.

2.4. NORMALIZING PRODUCTION RESULTS. After the data collection process was completed,
all responses for the production study were normalized by hand. This included removing additional
words such as “left” or “right” (e.g. “right pinky” became “pinky”). Directional terms (“left”),
initial articles, and other non-essential words were stripped away so that all that was remaining
was the word or phrase that was used to refer to the digit itself. This removed excess noise from
the data and allowed us to group responses together that were essentially identical in form – dedo
de la mano (‘digit of the hand’ - finger) vs. dedo (‘digit’), for example. Additionally, responses
were coded for specificity — 1 for specific words/phrases that could refer to only one digit (e.g.
“thumb” or “pulgar”) and 0 for non-specific words/phrases that could refer to more than one digit
(e.g. “finger” or “dedo de la mano”).

2.5. RESULTS. For the thumb image, 100% of English speakers responded with thumb — a spe-
cific term. In contrast, Spanish speakers were not unanimous in their responses. Figure 2 presents
the production results for digits on the hand. While the single-word translational equivalent to
‘thumb’, pulgar, was preferred, only approximately 42% of participants used it. Mano – Span-
ish for ‘hand’ – was the second most frequent (17.4%). Spanish speakers preferred using specific
terms 63.2% of the time.

For the ring finger images (presented in Figure 2), English speakers preferred the specific
term ring finger 83% of the time, but some participants (17%) did produce the general term fin-
ger. Again, Spanish speakers presented more variation in their responses than English participants,
with seven unique utterances produced. 35% of Spanish participants produced dedo anular (‘ring
finger’); 26% produced dedo (‘finger’). Overall, Spanish participants trended like English partic-
ipants with preference for specific (52.2%) over general terms (47.8%), although the preference
was not as strong (see Figure 2).

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Figure 2: English and Spanish production of finger terms;“specific” terms refer to a single digit

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English production for the pinky was similar to English production for the ring finger (see
Figure 2): there is variation between specific (e.g. pinky – 45.8%, or pinky finger – 41.7%) and
general term usage (12.5% finger). The single word pinky is used (and most frequently), but almost
as many participants chose to use the two-word alternative pinky finger. Spanish speakers also
gave varying responses. The most common was the single-word equivalent for ‘pinky’, meñique
(39.1%), vs. 21.7% for dedo ‘finger’.

For the big toe images, English speakers favored the specific term big toe (83.3%) over the
general term toe (16.7%). In contrast, Spanish participants produced six different descriptions
(shown in Figure 3). The most common response (69.6%) was the specific term dedo gordo del
pie (lit. ‘fat digit of the foot’), or ‘big toe’. The second most common response (34.8%) was the
general term dedo del pie (lit. ‘digit of the foot’), which translates to toe.

For the ring toe image, English participants showed increased dispersion in their responses.
The majority of participants (58.3%) used the general term “toe”. The remaining participants
(41.7%) produced various specific terms for the digit (“fourth toe” and “ring toe” to name a few).
Spanish speakers had a much higher rate of general term usage, with 82.6% of participants prefer-
ring terms like dedo del pie ‘toe’, dedo ‘digit’, or pie ‘foot’.

Finally, the production results for the pinky toe were similar between English and Spanish.
English speakers preferred using the general term toe far less than a specific term (20.8% and
79.2%, respectively). There was less dispersion in English production results for the pinky toe
than for the ring toe. In Spanish, participants generally preferred a specific term (52.2%) over a
general term (47.8%), but the trend was not as strong as in English. In contrast to English, Spanish
production data exhibited a much larger amount of dispersion for the pinky toe than the ring toe,
as shown in Figure 3.

These production results support Spanish speakers’ intuitions that the Spanish single-word
alternative pulgar ‘thumb’ is less prevalent than thumb is in English. If Geurts (2011) is right, then
Spanish speakers and English speakers should differ in scalar implicature calculation due to the
differences in prevalence of the alternative forms for ‘thumb’.

3. Comprehension Studies. We are now in a position to address our main research question:
Upon hearing a general term for a digit (e.g. finger or toe), what alternatives do English and Spanish
speakers use to compute alternatives? Are alternatives activated in accordance with their salience
(as measured by production frequency in our production experiments) or in accordance with their
complexity (as measured by number of words)? Although these two things are correlated, they are
not identical. For example, if Geurts (2011) is right, and the salience of alternatives matters, then
English and Spanish will differ with respect to the scalar implicature associated with finger due to
the difference in production probability for the more specific forms (thumb vs. pulgar). If com-
plexity is all that matters, then there should be an implicature in both languages, because there is an
equally simpler, yet more informative alternative in both languages. Through our comprehension
studies, we are able to distinguish among these hypotheses.

3.1. PARTICIPANTS. 45 American English participants and 48 Mexican Spanish participants, re-
cruited via Prolific, completed the comprehension task. English participants were self-reporting
American monolinguals that were born and currently reside in the United States. Spanish partici-
pants were self-reporting Mexican monolinguals that were born and currently reside in Mexico.

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Figure 3: English and Spanish production of toe terms; “specific” terms refer to a single digit

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3.2. MATERIALS. The target items for the comprehension studies consisted of 6 image pairs.
Three of the pairs were images of hands and three of the pairs were images of feet such that all
possible hand combinations and all possible foot combinations were presented. No target pairs
consisted of an image of a digit on the hand and an image of a digit on the foot. The images were
the same six images from the production study (see Figure 1).

In addition to the 6 target image pairs, participants were also presented with 6 filler image
pairs. Three of the filler pairs were “easy”, where the utterance clearly matched only one of
the images (e.g. She has a tattoo on her back, with a pair of images that contained only one
back tattoo). The other three filler pairs were considered “hard”; these image pairs contained,
for example, two different back tattoos. Filler pairs that were “easy” acted as attention checks,
since there was a clear correct response. Participants who failed one or more “easy” fillers were
eliminated from the results.

3.3. PROCEDURE. On each trial, a pair of images was presented, both showing a tattoo on a body
part. On critical trials, the images showed tattoos on two different fingers, or two different toes:
thumb on the left, ring finger on the right, for example. Along with the images, participants read
an utterance of the form She has a tattoo on her X, where X was a general term: finger or toe or the
Spanish translational equivalent (dedo or dedo del pie). Participants were asked “Which picture
are they talking about?” and clicked on an image. Item order and left-right presentation of the
images were randomized.

3.4. RESULTS. Responses were simply coded as the image the participant clicked on (e.g. “thumb”
for the image with the tattoo on the thumb). The p-values we report are the result of conducting
a 1-sample proportion test, where the null hypothesis, or the probability of choosing the correct
image is 0.5. The assumption is that the data follow a Bernoulli distribution. We ran a Benjamini-
Hochberg adjustment on the p-values.

In the comprehension study, when participants were asked to choose between the thumb image
and the ring finger image given the statement “She has a tattoo on her finger”, 75% of English
participants chose the image of the ring finger, p = 0.004 (see Figure 4). In contrast, just over half
of the Spanish speakers chose the ring finger image over the thumb image, but the error bar, which
depicts a 95% Confidence Interval, distinctly crosses the 50% mark, showing that the Spanish
participants’ responses are not statistically significantly different from chance (p = 0.627).

For the big toe and ring toe image pair, English participants showed a slight preference for
the ring toe image, with roughly 63% of participants choosing that image. However, the error bar
indicates that this result is not statistically different from chance (p = 0.145). Spanish participants
actually showed a stronger trend toward the ring toe given the translational equivalent of “She has
a tattoo on her toe” (p = 0.007).

A full summary of the results is given in Table 1. The estimate is the estimated true proportion
in the greater population. We conducted a Benjamini-Hochberg adjustment to obtain the adjusted
p-values. What we can take away from these results is that the following implicatures exist in
English:

• finger ↝ not thumb
• toe ↝ not pinky toe

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Figure 4: Observed frequency with 95% CI of choosing Thumb or Ring Finger in English and
Spanish.

In Spanish, we have:

• dedo del pie ↝ not pinky toe
• dedo del pie ↝ not ring toe

Not all of these implicatures are predicted by the assumption that complexity alone is what drives
the activation of alternatives. In the next section, we develop precise computational models in
order to understand the significance of these results more deeply.

4. Bayesian modeling.

4.1. MODEL DEFINITIONS. To gain a better understanding of complexity and prevalence, and
their roles in scalar implicature, we compared two Rational Speech Act models (Frank & Goodman
2012, Goodman & Stuhlmüller 2013; i.a.) that differ in how the speaker is defined. The first
model incorporates a traditional speaker model that penalizes longer – more complex – utterances
(henceforth referred to as the Complexity model). The second model is a prevalence-based speaker
model that has perfect knowledge of speaker production (henceforth referred to as the Production
model). For both models, the space of possible states includes six underlying states, corresponding
to the six target digits (thumb, ring finger, pinky, big toe, ring toe, and pinky toe). Literal meanings
for each utterance from the production study were hand-specified as a subset of the states.

For the complexity-based speaker model, as presented earlier, the Speaker chooses an utter-
ance based on accuracy and cost. Length is equivalent to length in words, and L0(s ∣u) is the
probability that a literal listener will choose a state s given an utterance u. The model contains

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Condition Language Estimate p-value adj. p-value
1 Big toe vs. ring toe Eng 0.64 0.097 0.145
2 Big toe vs. ring toe Spa 0.72 0.002 0.007*
3 Big toe vs. pinky toe Eng 0.34 0.05 0.10
4 Big toe vs. pinky toe Spa 0.50 1.00 1.00
5 Ring toe vs. pinky toe Eng 0.24 0.0006 0.002*
6 Ring toe vs. pinky toe Spa 0.21 0.00009 0.0009*
7 Thumb vs. ring finger Eng 0.75 0.002 0.004*
8 Thumb vs. ring finger Spa 0.56 0.47 0.627
9 Thumb vs. pinky finger Eng 0.80 0.0001 0.0009*

10 Thumb vs. pinky finger Spa 0.63 0.086 0.145
11 Ring finger vs. pinky finger Eng 0.45 0.651 0.781
12 Ring finger vs. pinky finger Spa 0.52 0.885 0.965

Table 1: p-values and adjusted p-values for each language/condition pair.

two free parameters. Alpha (α) is the ‘rationality parameter’, which corresponds to how much the
speaker maximizes utility, where utility in this context corresponds to accuracy, that is, probability
that the literal listener selects the correct referent. The parameter β is a multiplier on cost, where
cost is measured as number of words in the utterance. The cost parameter reflects speakers’ degree
of preference to be as concise as possible when speaking. Model parameters for the Complexity
model were tuned to the thumb/ring finger data point from the experimental results of the English
comprehension study: α set at 1 and β set at 2.

S(u ∣ s)∝ exp(α ⋅L0(s ∣u) − β ⋅ length(u))
A pragmatic listener was then built on top of the Complexity speaker model. As usual in RSA,
a pragmatic listener chooses an interpretation using Bayes’ Rule, reasoning about the likelihood
that a speaker would choose various utterances under various hypotheses about what the speaker
intends.

L(s ∣u)∝ S(u ∣ s) ⋅ P (s)
In contrast to the Complexity model, the Production model is fed the exact production proba-

bilities for each utterance collected from the production study. The speaker in this model chooses
an utterance based on the empirically observed frequencies in my production data. We write
F (u ∣ s) to denote the frequency with which an utterance u was used in the production experiments
to describe state s (i.e. the finger or toe that had the tattoo).

S(u ∣ s)∝ F (u ∣ s)
This ensures that the Production model has full awareness of what utterances are more or less

prevalent for speakers – these are utterances speakers actually produced. As in the Complexity
model, a pragmatic listener is coded on top of the Production model. In fact, the Pragmatic Listener
model is the same for both Speaker models. Because of the difference in the way that the speaker

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Complexity model

Production model

Figure 5: Model predictions plotted against comprehension results; Inaccurate model predictions
are circled in red.

is defined, however, the Pragmatic Listener in the Production model has full awareness of actual
production probabilities.

4.2. MODEL PERFORMANCE. The model predictions are presented alongside the empirical re-
sults in Figure 5. The Complexity model inaccurately predicts no implicature for the thumb/pinky
item in English. This is because the model is only considering the fact that these two utterances
are equally complex. Additionally, the model incorrectly predicts that there will be an implicature
for ring finger/pinky in English since the one-word term pinky is an available alternative.

For digits on the feet, the Complexity model incorrectly predicts no implicature for ring
toe/pinky toe in English and big toe/ring toe in Spanish – since they are equally complex. However,
the production results suggest that they are not equally viable as alternatives. Since ring toe and
pinky toe are equally as complex, if complexity alone determined which alternatives were available
to speakers, we would expect no implicature to arise. However, the presence of the implicature toe

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Figure 6: Comparison of Model Results

↝ ‘not pinky toe’ suggests that pinky toe is a more prevalent alternative than ring toe. These results
suggest that something else is going on in calculating implicatures that complexity alone cannot
account for. The Complexity model fails to understand that two alternatives with equal complexity
may differ in their prevalence. The Production model, on the other hand, is capable of accounting
for this.

Overall, the Production model performs much better than the Complexity model; see Figure
5. The only incorrect prediction it makes is that there would be no implicature for big toe/pinky toe
in English. The Production model predicts stronger implicatures for the thumb/ring finger items in
English and Spanish, and for the thumb/pinky finger items in English. In other words, where the
comprehension results trend rightward, at just over 75%, for the thumb/pinky item in English, the
Production model predicts 100% of participants selecting the pinky finger over the thumb. Other-
wise, the model predictions fall in line with all empirical results for the comprehension experiments
in Spanish and English.

Figure 6 plots the rate at which listeners chose the image on the right along the x-axis against
the probability assigned to the image on the right by each model on the y-axis. A perfect model
would assign probability at the exact same rate as actual production. The R2 for the Complexity
model is only 30.6%. This means that the Complexity model accounts for 30.6% of the variation
present in the comprehension studies. The R2 for the Production model, in contrast, is 76.3%,
which is to say that the Production model accounts for 76.3% of the variance in the data. There is a
stark contrast in the explanatory power of each model. This shows that listeners have a good mental
model of speakers, and that their mental model is not purely complexity-based. In fact, the com-
parison of these model results suggests that speakers are considering prevalence over complexity,
since the prevalence-based Speaker model does not include a cost parameter.

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5. Conclusions. The results outlined above suggest that Spanish and English speakers do differ
with respect to the scalar implicatures associated with finger in accordance with the prevalence
of the words for ‘thumb’, ‘ring finger’ and ‘pinky finger’. Our empirical results support the idea
that differences across languages in the implicatures associated with general terms are closely
tied to differences in production probabilities for more specific terms. Since pulgar ‘thumb’ is
not as prevalent in Spanish as thumb is in English, it is not available in the set of alternatives
to finger, which is why speakers do not calculate an implicature. Our modelling results further
support the conclusion that alternatives are constrained based on prevalence: the Production model
significantly outperforms the Complexity model. While complexity does assist in determining the
set of alternatives present for speakers, it is not as explanatory as full awareness of what speakers
actually produce. Prima facie, these findings go against structural theories, like Katzir (2007) and
Horn (2000), that constrain the set of alternatives based on complexity alone. These results also
support the idea that activation of alternatives is not an all-or-nothing matter, and this is an idea
that is naturally captured in a Bayesian pragmatic model that relies on gradient speaker production
probabilities.

References

Frank, Michael C. & Noah D. Goodman. 2012. Predicting pragmatic reasoning in language games.
Science 336(6084). 998.

Geurts, Bart. 2011. Quantity implicatures. Cambridge: Cambridge University Press.
Goodman, Noah D. & Andreas Stuhlmüller. 2013. Knowledge and implicature: Modeling lan-

guage understanding as social cognition. Topics in Cognitive Science 5(1). 173–184.
Horn, Lawrence R. 2000. From if to iff: Conditional perfection as pragmatic strengthening. Journal

of Pragmatics 32(3). 289–326.
Katzir, Roni. 2007. Structurally-defined alternatives. Linguistics and Philosophy 30(6). 669–690.

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