Animal, dog, or dalmatian? Level of abstraction in nominal referring expressions
Caroline Graf, Judith Degen, Robert X.D. Hawkins, Noah D. Goodman
cgraf@uos.de, {jdegen,rxdh,ngoodman}@stanford.edu
Department of Psychology, 450 Serra Mall
Stanford, CA 94305 USA
Abstract
Nominal reference is very flexible—the same object may becalled a dalmatian, a dog, or an animal when all are literally
true. What accounts for the choices that speakers make in how
they refer to objects? The addition of modifiers (e.g. big dog)
has been extensively explored in the literature, but fewer studies have explored the choice of noun, including its level of abstraction. We collected freely produced referring expressions
in a multi-player reference game experiment, where we manipulated the object’s context. We find that utterance choice
is affected by the contextual informativeness of a description,
its length and frequency, and the typicality of the object forthat description. Finally, we show how these factors naturally
enter into a formal model of production within the Rational
Speech-Acts framework, and that the resulting model predicts
our quantitative production data. Keywords: referential expressions, levels of reference, basic level, experimental pragmatics, computational pragmatics

Figure 1: Screenshots from speakers’ and listeners’ pointsof view, showing role names and short task descriptions, thechatbox used for communication and a display of three pictures of objects. The referent was identified to the speaker by
a green box.

Referring to objects is a core function of human language,
and a wealth of research has explored how speakers choose
referring expressions (Herrmann & Deutsch, 1976; Pechmann, 1989; van Deemter, Gatt, van Gompel, & Krahmer,2012). However, most of this literature has focused on the addition of modifiers (as in the choice between “the dog”, “thebrown dog”, and “the big brown dog”, e.g., Sedivy, 2003;
Koolen, Gatt, Goudbeek, & Krahmer, 2011). Here we investigate how speakers choose a simple nominal referring
expression—what governs the choice of calling a particular
object “the dalmatian”, “the dog”, or “the animal” when all
are literally true? That is, what governs the choice of the taxonomic level at which an object is referred to? Noun choice
can be seen as the most basic decision in forming a referring
expression. Like modification, these choices differ in their
specificity; unlike modification, the number of words used
does not differ—in English, some noun must be chosen. In
this paper we provide experimental evidence from a coordination game regarding the flexible choice of nominal referring
expressions and explain this data with a probabilistic modelof pragmatic production.
Previous evidence about the generation of referring expressions suggests that choice of reference level will depend on
the interplay of several factors. Grice’s Maxim of Quantity (Grice, 1975) implies a pressure for speakers to be sufficiently informative. For instance, a speaker who is trying
to distinguish a dalmatian from a German Shepherd would
be expected to avoid the insufficiently specific term “dog”(Brennan & Clark, 1996). On the other hand, recent workin experimental pragmatics has shown that the choice of referring expression depends on the cost of utterance alternatives (Rohde, Seyfarth, Clark, Jäger, & Kaufmann, 2012; Degen, Franke, & Jäger, 2013); sometimes, speakers are willingto produce a cheap ambiguous utterance rather than a costly(e.g. long or difficult-to-retrieve) unambiguous one. Finally,classic work on concepts suggests that typicality of a referent within its category affects the choice of reference (Rosch,Mervis, Gray, Johnson, & Boyes-Braem, 1976). In particular, speakers will generally choose to refer at the basic level(e.g. “dog”), but may become more specific for objects thatare atypical for the basic level term.
To evaluate the impact of these factors on nominal reference we constructed a two-player online game (Fig. 1). Participants saw a shared context of objects, one of which wasindicated as the referent only to the speaker. The speaker was
asked to communicate this object to the listener, who thenchose among the objects. Critically, the speaker and listenercommunicated by free use of a chat window, allowing us togather relatively natural referring expressions. We manipulated the category of distractor objects and used items thatvaried in utterance complexity and object typicality. This allowed us to evaluate whether each factor influences the referring expressions generated by participants. We expect thatspeakers will (1) tend to avoid longer or less frequent terms,and (2) will pragmatically prefer more specific referring expressions when the target and distractor(s) belong to the same
higher-level taxonomic category or when distractors are more
typical members of that category level.
A promising modeling approach for capturing the quantitative details of human language use is the Rational SpeechActs (RSA) framework (Frank & Goodman, 2012; Goodman & Stuhlmüller, 2013). The RSA framework has been
applied to many language interpretation tasks (e.g. Goodman & Stuhlmüller, 2013; Kao, Wu, Bergen, & Goodman,2014), but relatively rarely to production data (but see Franke,2014; Orita, Vornov, Feldman, & Daumé III, 2015). We describe an RSA model of nominal reference that includes informativeness, cost, and typicality effects. A speaker in RSA
is treated as an approximately optimal decision maker whochooses which utterance to use to communicate to a listener.
The speaker has a utility which includes terms for the cost of
producing an utterance (in terms of length or frequency) andthe informativeness of the utterance for a listener. The listener is treated as a literal Bayesian interpreter who updates
her beliefs given the truth of the utterance. These truth values are usually treated as deterministic (an object either is a“dog” or it is not); here we relax this formulation in order toincorporate typicality effects. That is, we elicit typicality ratings in a separate experiment, and model the listener as updating her beliefs by weighting the possible referents according
to how typical each is for the description used. We evaluate the quantitative model predictions against our production
data. The model also allows us to evaluate the need for eachextra component—typicality, length, frequency—and determine whether the empirical bias toward reference at the basic
level (Rosch et al., 1976) can be accounted for without building it in as a separate factor.

Experiment: nominal reference game
Methods
Participants and materials We recruited 56 self-reported
native speakers of English over Mechanical Turk. Participants completed the experiment in pairs of two, yielding 28
speaker-listener pairs.
Stimuli were selected from nine distinct domains, each corresponding to distinct basic level categories such as “dog.”
For each domain, we selected four subcategories to form ourtarget set (e.g. “dalmatian”, “pug”, “German Shepherd” and“husky”). Each domain also contained an additional item
which belonged to the same basic level category as the target (e.g. “greyhound”) and items which belonged to the same
supercategory but not the same basic level (e.g. “elephant” or“squirrel”). The latter items were used as distractors.
Each trial consisted of a display of three images, one ofwhich was designated as the target object. Every pair of participants saw every target exactly once, for a total of 36 trials
per pair. These target items were randomly assigned distractor items which were selected from four different context conditions, corresponding to different communicative pressures(see Fig. 2). We refer to these conditions with pairs of numerals specifying which levels of the taxonomy are present
in the distractors: (a) item12: one distractor of the same basic level and one distractor of the same superlevel (e.g. target:“dalmatian”, distractor 1: “greyhound”, distractor 2: “squirrel”), (b) item22: two distractors of the same superlevel, (c)item23: one distractor of the same superlevel and one unrelated item and (d) item33: two unrelated items.
Furthermore, the experiment contained 36 filler items, inwhich participants were asked to produce referential expressions for objects which differed only in size and color. Images

Figure 2: The four context conditions, exemplified by the dog
domain. The target is outlined in green; the types of distractors differ with condition (see text).
from filler trials were not reused on target trials. Trial order
was randomized.
Procedure Pairs of participants were connected through areal-time multi-player interface (Hawkins, 2015), with one
member of each pair assigned the speaker role and the otherto the listener role. Participants kept their allotted roles forthe entire experiment. The setup for both the speaker andthe listener is shown in Fig. 1. Each saw the same set ofthree images, but positions were randomized to rule out trivial
position-based references like “the middle one.” The target
object was identified by a green square surrounding it for the
speaker (but not listener). Players used a chatbox to send text
messages to each other. The task was for the speaker to get
the listener to select the target object.
Annotation To determine the level of reference for eachtrial, we followed the following procedure. First, trials on
which the listener selected the wrong referent were excluded,
leading to the elimination of 1.2% of trials. Then, speakers’ and listeners’ messages were parsed automatically; thereferential expression used by the speaker was extracted foreach trial and checked for whether it contained the current
target’s correct sub, basic or super level term using a simple grep search. In this way, 66.2% of trials were labelled
as mentioning a pre-coded level of reference. In the next
step, remaining utterances were checked manually to determine whether they contained a correct level of reference term
which was not detected by the parsing algorithm due to typos
or grammatical modification of the expression. In this way,
meaning-equivalent alternatives such as “doggie” for “dog”,
or contractions such as “gummi”,“gummies” and “bears” for“gummy bears” were counted as containing a level of reference term. This caught another 13.8% of trials. A totalof 20.0% of correct trials were excluded because the utterance consisted only of an attribute of the superclass (“theliving thing” for “animal”), of the basic level (“can fly” for“bird”), of the subcategory (“barks” for “dog”) or of the particular instance (“the thing facing left”) rather than a category noun. These kinds of attributes were also sometimes

Results
Proportions of sub, basic, and super level utterance choices inthe different context conditions are shown in the top row ofFig. 3. The sub level term was preferred where it was necessary for unambiguous referent identification, i.e., when adistractor of the same basic level category as the target waspresent in the scene (item12, e.g. target: dalmatian, distractor: greyhound). Where it was not necessary (i.e., when there
was no other object of the same basic level category present,
as in conditions item22, item23 and item33), there was a clear
preference for the basic level term. The super level term wasstrongly dispreferred overall, though it was used on some trials, especially where informativeness constraints on utterance
choice were weakest (item33).
To test for the independent effects of informativeness,length, frequency, and typicality on sub-level mention, we
conducted a mixed effects logistic regression. Frequency wascoded as the difference between the sub and the basic level’s
log frequency, as extracted from the Google Books Ngram

basic

super

Utterance probability

0.6
0.4
0.2
0.0
0.8
0.6

model

0.4
0.2

m
1
ite 2
m
2
ite 2
m
2
ite 3
m
33

ite

m
1
ite 2
m
2
ite 2
m
2
ite 3
m
33
ite
m
1
ite 2
m
2
ite 2
m
2
ite 3
m
33

0.0
ite

Typicality norms To examine the influence of typicality on
speaker behavior, we obtained typicality estimates in a separate norming study. 240 participants were recruited throughMechanical Turk. On each trial, we presented participants
with an image from the main experiment and asked them“How typical is this for X?”, where X was a category label
at the sub-, basic-, or super- level. They then adjusted a slider
bar ranging from not at all typical to very typical.
Due to the large number of possible combinations of objects, we only collected norms for certain combinations ofobjects and descriptions: for each target (e.g., dalmatian),
we collected typicality at all three levels (“dalmatian,” “dog,”and “animal”). For each distractor of the same superclass as
the target (distsamesuper, e.g., a kitten), we collected typicality at all three levels of the target. For each distractor
of a different superclass (distdiffsuper, e.g., a basketball) weonly collected typicality at the super- level of the target (“animal”) and assumed lowest typicality at the other levels. This
resulted in the following distribution of 745 norms: targetsub (36), target-basic (36), target-super (36), distdiffsupersuper (168), distsamesuper-sub (331), distsamesuper-basic(93), and distsamesuper-super (45).
Each participant provided typicality ratings for 7 target, 10
distdiffsuper, and 28 distsamesuper cases (randomly sampledfrom the total set of items). Each case received between 6
and 27 ratings. Raw slider values ranged from 0 (not typical)
to 1 (very typical); average slider values were used as the
typicality values throughout our results.

sub
0.8

empirical

mentioned in addition to the noun in the trials which were
included in the analysis—4.0% of sub level terms, 12.6% ofbasic level terms, and 46.2% of super level terms contained an
additional modifier. On 0.5% of trials two different levels ofreference were mentioned; in this case the more specific levelof reference was counted as being mentioned in this trial.

Condition

Figure 3: Empirical utterance probabilities (top row) andmodel posterior predictive MAP estimates (bottom row) bycondition, collapsed across targets and domains. Error bars
indicate bootstrapped 95% confidence intervals.

English corpus ranging from 1960 to 2008. Length was coded
as the ratio of the sub to the basic level’s length.1 That is, ahigher frequency difference indicates a lower cost for the sublevel term compared to the basic level, while a higher length
ratio reflects a higher cost for the sub level term compared tothe basic level.2 Typicality was coded as the ratio of the target’s sub to basic level label typicality. That is, the higher theratio, the more typical the object was for the sub level label
compared to the basic level. For instance, the panda was relatively atypical for its basic level “bear” (mean rating 0.75)
compared to the sub level term “panda bear” (mean rating
0.98), which resulted in a relatively high typicality ratio.
Condition was coded as a three-level factor: sub necessary, basic sufficient, and super sufficient, where item22 anditem23 were collapsed into basic sufficient. Condition was
Helmert-coded: two contrasts over the three condition levels
were included in the model, comparing each level against themean of the remaining levels (in order: sub necessary, basic sufficient, super sufficient). This allowed us to determine
whether the probability of type mention for neighboring conditions were significantly different from each other, as suggested by Fig. 3.3 The model included random by-speaker
and by-domain intercepts.
A summary of results is shown in Table 1. The log odds
1 We used the mean empirical lengths in characters of the utterances participants produced. For example, the minivan, when referred to at the subcategory level, was sometimes called “minivan”
and sometimes “van” leading to a mean empirical length of 5.64.
This is the value that was used, rather than 7, the length of “minivan”.
2 We replicate the well-documented negative correlation between
length and log frequency (r = −.53 in our dataset).
3 Adding terms that code the ratio of the sub vs super level frequency and length did not lead to an improvement of model fit.

subnecessary

basic
sufficientsuper
sufficient

Proportion of sub level mention

Table 1: Mixed effects model summary.
0.8Intercept
Condition sub.vs.rest
Condition basic.vs.superLengthFrequency
Typicality
Length:Frequency

Coef β

SE(β)

p

−0.30
2.46
0.52
−0.52
−0.02
4.17
−0.30

0.35
0.24
0.23
0.14
0.08
0.84
0.11

>0.4
<.0001
<.05
<.001
>0.78
<.0001<.01of mentioning the sub level term was greater in the sub necessary condition than in either of the other two conditions,and greater in the basic sufficient condition than in the super sufficient condition, suggesting that the contextual informativeness of the sub level mention has a gradient effect onutterance choice.4 There was also a main effect of typicality, such that the sub level term was preferred for objects thatwere more typical for the sub level compared to the basic
level description (Fig. 4). In addition, there was a main effectof length, such that as the length of the sub level term increased compared to the basic level term (“chihuahua”/“dog”
vs. “pug”/“dog”), the sub level term was dispreferred (“chihuahua” is dispreferred compared to “pug”, Fig. 4). Finally,
while there was no main effect of frequency, we observed
a significant length by frequency interaction, such that there
was a frequency effect for the relatively shorter but not therelatively longer sub level cases: for shorter sub level terms,
relatively high-frequency sub level terms were more likely to
be used than relatively low-frequency sub level terms.
Unsurprisingly, there was also significant by-participant
and by-domain variation in the log odds of sub level term
mention. For instance, mentioning the subclass over the basic level term was preferred more in some domains (e.g. inthe “candy” domain) than in others. Likewise, some domains
had a greater preference for basic level terms (e.g. the “shirt”
domain). Using the superclass term also ranged from hardly
being observable (e.g. the “flower” domain) to being used
more frequently (e.g. in the “bird” domain). Nevertheless,mentioning the sub level term was always the most frequent
choice where a distractor of the same basic level was displayed. Furthermore, it was the case in all domains that the
sub level term was mentioned most frequently and the basic
level least frequently in just this condition, compared to theother three conditions.

Modeling level of reference
We formulated a probabilistic model of reference level selection that integrates contextual informativeness, utterance cost,
4 Importantly, model comparison between the reported model andone that subsumes basic and super under the same factor level revealed that the three-level condition variable is justified (χ2 (1) =5.7, p < .05), suggesting that participants don’t simply revert to thebasic level unless contextually forced not to.

subnecessary

basic
sufficientsuper
sufficient

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0

0.0

short longshort longshort longmore lessmore lessmore lesstypical typical typical typical typical typicalSub level length

Sub level typicality

Figure 4: Probability of using sub, basic and super levelterms. Left: when the sub length is relatively short (.67,2] orlong [2,4.67) compared to the basic level term length. Right:
when the target object was relatively more [1.06,1.91) or less(.88,1.06] typical for the sub compared to the basic level term.
and typicality. As in earlier Rational Speech-Acts (RSA)
models (Frank & Goodman, 2012; Goodman & Stuhlmüller,2013), the speaker seeks to be informative with respect to aninternal model of a literal listener. This listener updates her
beliefs to rule out possible worlds that are inconsistent withthe meaning of the speaker’s utterance. Rather than assumingthat words have deterministic truth conditions, as has usually
been done in the past, we account for typicality by allowing
each label a graded meaning. For instance, the word “dog”
describes a dalmatian better than a grizzly bear, but it also
describes a grizzly bear better than a tennis ball. The speakeralso seeks to be parsimonious: the speaker utility includes
both informativeness and word cost; cost includes both lengthand frequency.
Formally, we start by specifying a literal listener L0 who
hears a word l at a particular level of reference in the context
of some set of objects O and forms a distribution over thereferenced object, o ∈ O :PL0 (o|l) ∝ [[l]](o).
Here [[l]](o) is the lexical meaning of the word l when applied to object o. We take this to be a real number indicating
the degree of acceptability of object o for category l. We relate this to our empirically elicited typicality norms via an exponential relationship: [[l]](o) = exp(typicality(o, l)).5 This
relationship is motivated by considering the effect of a small
difference in typicality on choice probability: in our elicitation experiment a small difference in rating should mean thesame thing at the top and bottom of the scale (it is visuallyequivalent on the slider that participants used). In order for asmall difference in typicality rating to have a constant effecton relative choice probability (which is a ratio), the relationship must be exponential.
Next, we specify a speaker S1 who intends to refer to aparticular object o ∈ O and chooses among possible nouns l ∈5 Cases where typicality was not elicited were assumed to havetypicality 0.

beta_f●●●

Empirical proportion

●

●

● ●

0.75

●

●

●

●

●

●

●

●

●

●

●

●

item12
item22
item23

●
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0.50

condition

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item33●●refLevel
●

0.25

●

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●
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0.00

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0.25

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basic

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sub

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super

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0.00

beta_llambda3000●●● ●●●●● ●

count

1.00

2000
1000
0
0

1

2

2

3

4 8

10

12

14

value

Figure 6: Posterior distribution over model parameters. Maximum a posteriori (MAP) λ = 10.8, 95% highest density interval (HDI) = [9.7, 12.8]; MAP βl = 2.5, HDI = [1.9, 3.1];
MAP β f = 1.3, HDI = [0.8, 1.8].

●

0.50

0.75

1.00

Model predicted probability

Figure 5: Mean empirical production data for each level ofreference against the MAP of the model posterior predictiveat the by-target level.

L (o). We take L (o) to be the three labels for o at sub, basic,
and super level. The speaker chooses among these nouns in a
way that is influenced by informativeness of the noun for theliteral listener (ln PL0 (o|l)), the frequency (ĉ f ) and the length(ĉl ), each weighted by a free parameter:
PS1 (l|o) ∝ exp(λ ln PL0 (o|l) + β f ĉ f + βl ĉl )Length cost ĉl was defined as the empirical mean number ofcharacters used to refer at that level and frequency cost ĉ f was
the log frequency in the Google Books corpus from 1960 to
the present.
We performed Bayesian data analysis to generate model
predictions, conditioning on the observed production data(coded into sub, basic, and super labels as described above)and integrating over the three free parameters. We assumed uniform priors for each parameter: λ ∼ Uni f (0, 20),
β f ∼ Uni f (0, 5), βl ∼ Uni f (0, 5). We implemented both thecognitive and data-analysis models in the probabilistic programming language WebPPL (Goodman & Stuhlmüller, electronic). Inference for the cognitive model was exact, while weused Markov Chain Monte Carlo (MCMC) to infer posteriors
for the three free parameters.
Point-wise maximum a posteriori (MAP) estimates of themodel’s posterior predictives at the target level (collapsing
across distractors for each target, within each condition) arecompared to empirical data in Fig. 5. On the by-target level
the model achieves a correlation of r = .79. Looking at results on the by-domain level (collapsing across targets) andon the by-condition level (further collapsing across domains,
as in Fig. 3) yields correlations of .88 and .96, respectively.
The model does a good job of capturing the quantitative patterns in the data, especially considering the sparsity of ourdata at the by-target level. One clear flaw is that the model
predicts greater use of the super level label than people exhibit. Further systematic deviation appears likely for specificitems. On examination, candy items like “gummy bears” or“jelly beans” were particularly problematic, being referred toprimarily by their sub level term in all contexts.
Parameter posteriors are presented in Fig. 6. Informativeness is weighted relatively strongly, while length is weighted
somewhat more strongly than frequency. Note that the 95%
highest density intervals (HDIs) for all three weight parameters exclude zero, indicating that some contribution of eachis useful in explaining the data. In order to ascertain whether
typicality was indeed contributing to the explanatory power ofthe model, we ran an additional Bayesian data analysis withan added typicality weight parameter βt ∈ [0, 1]. This parameter interpolated between empirical typicality values (whenβt =1) and deterministic (i.e. 0 or 1) a priori values based on
the true taxonomy (when βt =0). We found a MAP estimatefor βt of .94, HDI = [0.88, 1], strongly indicating that it isuseful to incorporate empirical typicality values. Finally, we
ran a model including a parameter weighting the product of
frequency and cost, corresponding to the interaction term in
our regression analysis. Its posterior distribution was strongly
peaked at 0, indicating that any contribution of the interactionis already captured by other aspects of the model.

Discussion and conclusion
The choice speakers make of how to refer to an object is influenced by a rich variety of factors. In this paper, we specifically investigated the choice of level of reference in nominalreferring expressions. In an interactive reference game task inwhich speakers freely produced referring expressions, utterance choice was affected by utterance cost (in terms of lengthand frequency), contextual informativeness (as manipulated
via distractor objects), and object typicality. The interplay ofthese factors is naturally modeled within the RSA framework,where speakers are treated as choosing utterances by softmaximizing utterance utility, which includes terms for informativeness and cost. In previous formulations of RSA models, informativeness was determined by a deterministic semantics; here we “softened” the semantics by allowing nouns
to apply to objects to the extent that those objects were rated
as typical for the nouns. The resulting model provided a good
fit to speakers’ empirical utterance choices, both qualitatively
and quantitatively.
The model predicts a well-documented preference forspeakers to refer to objects at the basic level when not constrained by contextual considerations (Rosch et al., 1976). In
our model, this preference emerges naturally from cost considerations: basic-level labels tend to be shorter and more
frequent than sub and super level terms. However, speakers did not always use the basic level term, even when unconstrained by context. In certain cases where object typicality was relatively high for the sub level term comparedto the basic level term, that term was preferred (as was thecase for “panda bear”), suggesting an interesting interplay between typicality and level of description. While our results
show that a model can capture several basic-level phenomena
through frequency, length, and typicality features, it leaves
open the origin and causal role of these linguistic regularities.
Future research will be needed to determine how linguistic
regularities are related to conceptual regularities and why.
An interesting analogy can be drawn from choosing a noun
to choosing a set of adjectives; that is, between selection ofa level of reference in simple nominal referring expressions
and selection of a set of features to include in modified referring expressions. For the latter, a much discussed phenomenon is that of overinformative modifier use (Gatt, Krahmer, van Deemter, & van Gompel, 2014)—for example, saying “big blue” when all objects in the context are blue. The
preference for the basic level in the super sufficient condition
and the still substantial use of sub level terms in the basic
sufficient condition can also be considered overinformative.
However, we showed that a Rational Speech-Acts model using non-deterministic semantics, derived from typicality estimates, predicts that speakers should use these more specificdescriptions. The extent to which similar considerations may
apply to modified referring expressions should be explored.
Future research should also examine the interaction of thesechoices: circumstances under which speakers choose a modifier and how nominal and modifier choice interact.

Acknowledgments
This work was supported by ONR grant N00014-13-1-0788 and aJames S. McDonnell Foundation Scholar Award to NDG and an SNF
Early Postdoc. Mobility Award to JD. RXDH was supported by the
Stanford Graduate Fellowship and the National Science Foundation
Graduate Research Fellowship under Grant No. DGE-114747.

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