Emerging abstractions: Lexical conventions are shaped by communicative context
Robert X.D. Hawkins 1 , Michael Franke2 , Kenny Smith3 , Noah D. Goodman1
1 Department of Psychology, Stanford University ({rxdh,ngoodman}@stanford.edu)
2 Department of Linguistics, University of Tübingen (mchfranke@gmail.com)
3 Centre for Language Evolution, University of Edinburgh (Kenny.Smith@ed.ac.uk)

Abstract
Words exist for referring at many levels of specificity: fromthe broadest (thing) to the most specific (Fido). What drives
the emergence of these taxonomies of reference? Recent computational theories of language evolution suggest that communicative demands of the environment may play a deciding role.
Here, we investigate local pragmatic mechanisms of lexical
adaptation that may undergird global emergence by manipulating context in a repeated reference game where pairs of participants interactively coordinate on an artificial communication
system. We hypothesize that pairs should converge on specificnames (e.g. Fido) when the context requires frequently making fine distinctions between entities; conversely, they should
converge on a more compressed system of conventions for abstract categories (e.g. dog) in coarser contexts, even if a finer
mapping would be sufficient. We show differences in the levels of abstraction that emerged in different environments andintroduce a statistical approach to probe the dynamics of emergence.
Keywords: conventions; pragmatics; communication; interaction

Introduction
Natural languages provide speakers with remarkable flexibility in the labels they may use to refer to things (Brown, 1958).
On top of an abundance of expressions made available bysyntactic combination and semantic compositionality (Partee,
1995), we have a number of overlapping and nested terms in
our lexicon. Fido, Dalmatian, dog, and animal can all reasonably be used to talk about the same entity at different levelsof abstraction. How these overlapping meanings are learned,
and why speakers choose different levels of specificity indifferent contexts, is increasingly well-understood (e.g. Xu& Tenenbaum, 2007; Graf, Degen, Hawkins, & Goodman,2016) but there remains a more fundamental question about
the structure of our lexicon: why and how do different levelsof abstraction become lexicalized in the first place?
One functional answer is suggested by recent computational approaches to language evolution, which have argued
that the lexical conventions of languages balance simplicity,or learnability, with the communicative needs of their users.
This optimal expressivity hypothesis accounts well for thelexical distributions found in natural languages across semantic domains like color words and kinship categories (Regier,Kemp, & Kay, 2015; Gibson et al., 2017), as well as the compositional systems that emerge under iterated learning with
communication in the lab (Winters, Kirby, & Smith, 2014;
Kirby, Tamariz, Cornish, & Smith, 2015). A key predictionis that the lexicon of a group should be sensitive to the pragmatic demands of their environment. For example, languages
in warm regions ought to be more likely to collapse the distinction between ice and snow into a single word, simply be-cause there are fewer occasions that require distinguishing between the two (Regier, Carstensen, & Kemp, 2016).
Still, there are several limitations to the current evidence
for this hypothesis. First, much of the relevant evidence is observational, aggregated at the level of overall language statistics, not by directly manipulating the contextual conditionsof individual language users. Second, previous experimental studies have largely focused on the functional outcomesof an iterated learning process, but have not grounded the results of this process in a cognitive, mechanistic account oflexical adaptation and convention-formation among individual agents. Finally, the phenomenon of reference taxonomies
poses a further theoretical challenge: why do languages havehierarchies of terms instead of flatly partitioning the space
into category labels as previous work has assumed?
While globally shared conventions of a language areshaped over the multi-generational timescales of cultural evolution, contextual pressures operate on the shorter timescalesof dyadic interaction. In a matter of minutes, communication partners coordinate on efficient but informative local conventions, or conceptual pacts, for the task at hand (Clark &Wilkes-Gibbs, 1986; Hawkins, Frank, & Goodman, 2017).
To understand how languages are globally shaped by communicative constraints, it may therefore be valuable to understand the local conventions rapidly formed by adaptive agentsover extended interactions.
Under the logic of a local efficiency/informativity tradeoff, we make two predictions about the emergence of abstractions in dyads. First, we expect that communicative pressures
for informativity should lead to the lexicalization of specificnames when fine distinctions must be drawn. Second, abstractions should become lexicalized precisely when the relevant distinctions are at coarser levels of the conceptual hierarchy. For example, we are often called upon to make finedistinctions between people in our social circles, hence lexicalizing efficient names for each individual; when referring
to green beans or paper towels, however, we can get away
without such specific terms – we are rarely called upon todisambiguate between entities.
Here, we develop an experimental paradigm and analytic
approach to examine the causal factors driving the emergenceof lexical conventions in real-time. We manipulated context
in a repeated reference game where pairs of participants interactively coordinated on an artificial language from scratch.
Even though a complete communication system containing
a distinct word for each object is feasible and sufficient for
all contexts, we find that abstractions begin to emerge whenfine-grained distinctions are not necessary.

Figure 1: (A) Example of fine context where one of the distractors belongs to the same fine-grained branch of the hierarchy as
the target (i.e. another striped circle), so any abstract label would be insufficient to disambiguate them. The target is highlighted
for the speaker with a black square. (B) Drag-and-drop chat box interface. (C) Hierarchical organization of stimuli.

Experiment: Repeated reference gameParticipants We recruited 278 participants from Amazon
Mechanical Turk to play an interactive, multi-player game using the framework described in Hawkins (2015). Pairs wererandomly assigned to one of three different conditions, yielding between n = 36 and n = 53 dyads per condition, after
excluding participants who disconnected before completion.1
Procedure & Stimuli Participants were paired over the
web and placed in a shared environment containing an array of objects (Fig. 1A) and a ‘chatbox’ to send messages
from a randomly generated vocabulary (Fig. 1B). On each of
96 trials, one player (the ‘speaker’) was privately shown ahighlighted target object and allowed to send a single word
to communicate the identity of this object to their partner (the‘listener’), who subsequently made a selection from the array.
Players were given full feedback, swapped roles each trial,and both received bonus payment for each correct response.
The objects that served as referents were designed to cluster in a fixed three-level hierarchy with shape at the topmost level, color/texture at the intermediate levels, and frequency/intensity at the finest levels (see Fig. 1C). Each communicative context contained four objects. Distractors could
differ from the target at various level of the hierarchy, creating different types of contexts defined by the finest distinctionthat had to be drawn. We focus on two: fine trials, where theclosest distractor belongs to the same fine-grained subordinate category (e.g. another striped circle; see Fig. 1A), andcoarse trials, where the closest distractor belongs to a coarser
level of the conceptual hierarchy (e.g. dotted circle instead ofstriped circle).2 Fixed arrays of 16 utterances (enough to al1 All materials and data are available at https://github.com/
hawkrobe/conventionalizing hierarchies; planned sample
sizes, exclusion criteria, and behavioral analysis plan were preregistered at https://osf.io/2hkjc/.
2 Even coarser trials with super-ordinate distractors (e.g. a circle
target among three square distractors) were logically possible but
would have introduced several experimental confounds; we opted toleave these trial types out of our design and conduct the minimal

low the potential for full expressibility) were randomly generated for each pair (and held constant across trials) by stringing
together consonant-vowel pairs into pronounceable 2-syllable
words (see Fig. 1B).
Critically, we manipulated the statistics of the context ina between-subjects design to test the effect of communicative relevance on lexicalization. In the pure fine and coarse
conditions, all targets appeared in fine or coarse contexts,
respectively; in the mixed condition, the two context types
were equally likelySequences of trials were constructed byrandomly shuffling targets and trial types within blocks andensuring no target appeared more than once in a row.
In addition to behavioral responses collected over thecourse of the game, we designed a post-test to explicitly probe
players’ final lexica. For all sixteen words, we asked players
to select all objects that a word can refer to (if any), and foreach object, we asked players to select all words that can refer to it (if any). Using a bidirectional measure allows us tocheck the internal validity of the lexica reported.

Results
Partners successfully learn to communicate Althoughparticipants in all conditions began with no common basis forlabel meanings, performing near chance on the first trial (proportion correct = 0.19, 95% CI = [0.13, 0.27]), most pairs
were nonetheless able to coordinate on a successful communication system over repeated interaction (see Fig. 2). A
mixed-effects logistic regression on listener responses withtrial number as a fixed effect, and including by-pair random
slopes and intercepts, showed a significant improvement inaccuracy overall, z = 14.4, p < 0.001. Accuracy also differedsignificantly across conditions (Fig. 2): adding an additional
main effect of condition to our logistic model provided a significantly better fit, χ2 (2) = 10.8, p = 0.004. Qualitatively,
the coarse condition was easiest for participants, the fine condition was hardest, and the mixed condition was roughly inmanipulation.

1.00

coarseproportion correct

0.75

mixed
fine

0.50
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75

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Figure 2: Players learn to coordinate on a successful communication system. Each point is the mean proportion of correct
responses by listeners; curves are nonparametric fits.
between. Finally, the (log) response time taken by the speaker
to choose an utterance also decreased significantly over thecourse of the game, t = −19.7, p < 0.001, indicating that lexical mappings became increasingly established or accessible.
Partners converge on similar lexica Another indicator of
successful learning is convergence or alignment of lexica
across partners in a dyad. Before using post-test responses
to compute similarity across partners, however, we examine the internal consistency within an individual’s post-test
responses. For each participant, we counted the number ofmismatches between the two directions of the lexicon question (e.g. if they clicked the word ‘mawa’ when we showed
them one of the blue squares, but failed to click that same
blue square when we showed ‘mawa’). In general, participants were quite consistent: out of 128 cells in the lexicon
matrix (16 words × 8 objects), the median number of mismatches was 2 (98% agreement), though the distribution has
a long tail (mean = 7.3). We therefore conservatively take aparticipant’s final lexicon to be the intersection of their wordto-object and object-to-word responses.
Using these estimates of each participant’s lexicon, we
compute the overlap between partners. For most pairs, partners aligned strongly by the end, with a median post-test overlap of 97.6% (125 out of 128 entries). Because these matrices were extremely sparse, however, just a a few mismatches
could have a large impact on performance. Overall accuracy
in the game is strongly correlated with alignment: partners
who reported more similar lexica at the end tended to perform better at the task (r = 0.77).
Despite these markers of success at the group level, individual performance was bimodal: a subpopulation of 29
games (11% of coarse games, 18% of mixed, and 39% offine) still showed relatively poor performance, sometimes atchance, by the end of the game. For the subsequent analyses
focusing on the content of the lexicon, we exclude games with
fourth-quartile accuracy below the pre-registered criterion of
75% to ensure we are examining only successful lexica.

Contextual pressures shape the lexicon We predicted thatin contexts regularly requiring speakers to make fine distinctions among objects at subordinate levels of the hierarchy,
we would find lexicalization of specific terms for each object(indeed, a one-to-one mapping may be the most obvious solution in a task with only 8 objects). Conversely, when no such
distinctions were required, we expected participants to adaptively lexicalize more abstract terms. One coarse signature ofthis prediction lies in the efficiency of the resulting lexicon:
lexicalizing abstract terms should require participants to use
fewer terms overall.
To test this prediction, we counted the number of words ineach participant’s reported lexicon (i.e. the words for which
they marked at least one object in the post-test). We foundthat participants in the coarse condition reported significantly
smaller, more efficient lexica (m = 4.9 words) than participants in the mixed and fine conditions (m = 7.4,t = 10.3, p <0.001 and m = 7.6,t = 9.5, p < 0.001, respectively; see Fig.
3A). At the same time, the smaller lexicon provided equivalent coverage of objects: the median number of objects whereparticipants agreed on the same word or words was 7, 6.5, and7, respectively.
If participants in the coarse condition can get away withfewer words in their lexicon, what are the meanings of thewords they do have? We counted the numbers of ‘specific’terms (e.g. words that refer to only one object) and ‘abstract’terms (e.g. words that refer to two objects) in the post-test. We
found that the likelihood of lexicalizing abstractions differedsystematically across conditions (see Fig. 3A). Participants inthe fine condition reported lexica containing exclusively specific terms, while participants in the coarse condition reported
significantly more abstract terms (m = 2.5, p < 0.001).
These data also reveal an interesting asymmetry in lexicon content across conditions: while abstractions are entirely
absent from the fine condition, participants in the other conditions often reported a mixture of terms (see Fig. 3B). In thecoarse condition, for instance, participants could in principle
perform optimally with only four abstract terms and no specific terms. While this was the modal system that emerged(reported in the post-test by nearly 1/3 of participants), theaverage proportion of abstract (vs. specific) terms within eachparticipant’s lexicon in the coarse condition (m = 0.56) was
significantly higher than in the other conditions (p < 0.001,
exploratory).

Model-based Analysis
Our post-test provides some insight into the end-result of lexicalization under different communicative contexts, but understanding the dynamics of lexicalization requires a moredetailed analysis of behavioral trajectories. How do lexica
shift and develop over the course of interaction?
In this section, we present a statistical model of this progression. We assume that on any given trial, speakers andlisteners are rationally producing and interpreting utterances
given some internal lexicon, and we use a Bayesian statis-

word meanings
not used12

one object

8two objects

40B# words referring
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A 16coarsecondition

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10 20 30 40

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Figure 3: Pragmatic demands of context shape the formation of abstractions. (A) Mean number of words participants reportedwith specific meanings (applying to 1 object) or abstract meanings (applying to 2 objects). (B) Diversity of terms within
reported lexica: many participants in the coarse condition reported a mixture of abstract and specific terms.
tical model to infer their lexicon from their behavior. First,
this analysis validates our post-test measures of lexical meaning against actual behavioral usage — if participant reports
are internally consistent, the model’s posterior near the endof the game should predict their post-test responses. Second,
we can examine the time-course of lexical emergence by inspecting lexica inferred from early behavior in the game.

Generative model
We begin with a generative model of how agents use theirunderlying lexicon to produce and interpret language. Thismodel provides a linking function assigning a likelihood tothe speaker utterances and listener choices we observe oneach trial, given any latent lexicon. We adopt the probabilistic Rational Speech Act (RSA) framework, which has beensuccessful in recent years at capturing a broad array of pragmatic phenomena in language use (Goodman & Frank, 2016;
Franke & Jäger, 2016). This framework captures the Gricean
assumption of cooperativity: a pragmatic speaker S1 attempts
to be informative in context while a pragmatic listener L1 inverts their model of the speaker to infer the intended target.
The chain of recursive social reasoning grounds out in a literal listener L0 , which directly soft-maximizes its lexicon,
L t (w, o), to interpret a given utterance. This model can beformally specified as follows:
L0 (oi |w, L t ) ∝ exp{L t (w, oi )}S1 (w|oi , L t ) ∝ exp{ln L0 (oi |w, L t )}L1 (oi |w, L t ) ∝ S1 (w|oi , L t )P(oi )where oi is a chosen object and w an uttered word.
We use these pragmatic speaker and listener likelihood
functions to link latent lexica, represented as a matrix of realvalues `tw,o ∈ R, to behavior. This allows us to then use
Bayesian inference to back out each participant’s effective
lexicon from their trial-by-trial behavior. Because each trial
has only a single choice for each player, we pool statistics
within k epochs of the data (we choose k = 6 such that eachtarget appears exactly twice in each epoch). For each epoch,

we sample lexical entries from independent Gaussian priors:`ko,w ∼ N (0, 5)This prior is intended to regularize lexicon entries to be relatively close to 0, inducing a bias toward sparsity.
We approximate the posterior of this model separately
for each pair using mean-field variational inference, implemented in the probabilistic programming language WebPPL(Goodman & Stuhlmüller, electronic; Ritchie, Horsfall, &Goodman, 2016). The approximating family for each random variable is Gaussian. We approximate the joint posterior
over all lexical entries used in each epoch by each participant.

Validating post-test responses
We begin by showing that the lexical entries we infer for eachparticipant accurately predict their post-test responses. We
constructed a logistic classifier from our posterior on eachepoch: for each object-word pair (o, w) in the post-test response matrix, we computed the marginal posterior probability P(`o,w > 0.5|θo,w ), where θo,w are the corresponding
variational parameters (i.e. the mean and variance of the approximating Gaussian). This gives the posterior probability
that word w applies to object o. We evaluated the performance of this classifier by constructing an ROC curve thatshows the tradeoff between hits and false alarms as the discrimination criterion is varied. We found that the classifier
based on the final epoch predicts post-test responses with excellent accuracy (AUC: 0.98; see Fig. 4A). This indicates thatthe post-test lexicon is indeed linked to behavior as predicted
by RSA, validating both the post-test measure and the resultsof our Bayesian analysis.
Furthermore, we found that the corresponding posterior
predictives from earlier epochs predicted final post-test responses less well, even though they were learned from thesame number and type of behavioral observations (Fig. 4A).
Still, even the classifier based on the earliest epoch performs
above chance, indicating that some information about the final lexicon is available from the earliest trials. These patterns
are suggestive of a path-dependent process where the lexicon
gradually coalesces from initially arbitrary associations over

density

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Figure 4: Model-based results. (A) A logistic classifier based on inferred lexical entries accurately predicts post-test responses.
(B) Entropy of posterior word extensions show coalescence across epochs for each condition. (C) Mean change in entropy at
the word level from trial to trial (error bars are ±1 SE)
the course of interaction. We next turn to the earliest stagesof this process.

Examining early time course
One advantage of the statistical approach we develop here is
the ability to make descriptive inferences about the meanings
being used in settings where we don’t ask participants for explicit judgements—in particular, in early trials of our games.
Our primary measure of interest is the entropy of the extension of words over the eight objects. The entropy of aparticular word is near zero when its meaning is peaked on asingle object, and is maximized when could apply equally toall objects (e.g. for a novel word that has not yet been used).
We expect abstract terms to lie in between these extremes. We
obtain the extension distribution for each word by running it
through our L0 model, essentially asking how likely it is torefer to each of the eight objects.3 We use the MAP estimateof the lexicon. The resulting distribution of estimated word
entropies, aggregated for each epoch and condition, is shownin Fig. 4B. Abstract terms begin to form early (epoch 2) inthe coarse condition, and remain stable throughout the game.
In contrast, specific terms are relatively slow-forming (epoch4-5) in the other two conditions. The peak near an entropy of
3 reflects the inferred ambiguity of words that were not used
or used randomly.
Because these distributions are aggregated across words,
however, they leave open the possibility that lexica are notstabilizing or coalescing but simply cycling through different
words each epoch. We address these dynamics more thoroughly at the word level by computing the difference in eachword’s entropy from epoch to epoch (Fig. 4C). For all conditions, we found that the entropy of individual words changed
less over later epochs (i.e. the difference scores approached
3 Using L , rather than L or L , gives us a notion of word exten0
1sion that is close to the underlying lexicon while influenced by nonidentifiability of parameters. For instance, L has an overall scaling
per row that doesn’t influence behavior.

zero), indicating that meanings gradually stabilized. There
are also differences across conditions: words in the mixed
and fine condition began with high entropy reduction (becoming more specific) which continued through the final epochs,
while words in the coarse condition actually seemed to increase in entropy across the game on average.
These preliminary results, then, may reflect a combinationof narrowing and broadening depending on condition. Unknown words can initially refer to any of the objects and only
acquire more informative meanings as agents learn throughinteraction. Yet in the coarse condition where agents arequick to adopt meanings, the rest of the game may be spentparing down the lexicon instead.

Discussion
How and why do abstractions emerge in local interactions?
We hypothesized that although communicative contexts requiring fine distinctions would favor one-to-one object-word
mappings, pressures for efficiency would allow abstractions
to emerge in coarser contexts. By manipulating context statistics in a real-time experiment, we found evidence for thesepragmatic influences on interactive convention formation.
Our results may help to illuminate the relationship between
our concepts and words, which are often treated interchangeably. While our mental taxonomies are adaptive to the natural
perceptual structure of the world (Mervis & Rosch, 1981) it
is far from inevitable that all levels of these conceptual hierarchies become conventionalized as lexical items. There aremany perfectly natural concepts that are not represented by
distinct words in the English language: for instance, we do
not have words for each tree in our yards, or for ad-hoc concepts (Barsalou, 1983). Indeed, English speakers are often
fascinated by foreign words like the Danish “hygge” (a specific notion of coziness) or Scottish “tartle” (hesitating whenintroducing someone because you’ve forgotten their name)that are difficult to express in English. Our results highlight
communicative needs to distinguish, in context, as a force be-hind the choice to lexicalize some fine-grained concepts. A
related direction for future work is to explore the relationshipbetween communicative need and basic-level structure.
While we showed how abstract words emerge from efficiency even in a task requiring only reference to individual
objects, there are other clear functional advantages to having
abstract terms in the lexicon. For one, they allow speakers
to efficiently refer to large, potentially infinite, sets of things,
and make generalizations about categories, e.g. “Dogs bark”(Tessler & Goodman, 2016). Future work should explore thisas an additional pressure toward abstract, nested nouns. Similarly, the option to refer to more specific concepts with compound terms (e.g. “spotted dog”), which was not available inour experiment, may impact final conventions. We expect thatlabels will become lexicalized when the cost incurred by frequently using a compositional construction exceeds the costof adding an additional word to the lexicon. Future work
should also explore these hypotheses about how lexicalization of nominal terms trades off with compositionality.
Finally, although we implemented a purely statistical
Bayesian data analysis model to infer lexica, it is also possible to consider a cognitive model of participants’ own lexicalinferences. Indeed, our findings are consistent with a recent
cognitive model of convention formation which explained therapid coordination on efficient but informative lexical terms
as a process of mutual lexical learning (Hawkins et al., 2017).
In this model, each agent assumes their partner is rationallyproducing cooperative utterances under some latent lexicon;
given initial uncertainty over the contents of that lexicon,
agents can invert their model of their partner to infer theirlexicon from observable behavior. The different dynamics weobserved across conditions, then, may be the consequence ofdifferent lexical inferences in different local contexts. Further, while we used RSA as a linking function in our statistical model, a cognitive model would allow us to test to what
extent pragmatic reasoning is necessary to explain behavior.
Our shared lexical conventions are richly structured systems with meanings at multiple levels of abstraction. There is
now abundant evidence that languages adapt to the needs oftheir users, and the context-sensitive emergence of abstractions demonstrated in this paper suggests that the driver ofthis adaptation may lie in the remarkably rapid adaptabilityof agents themselves. We are constantly supplementing ourexisting language with local conventions as we need them.
Our separate minds may organize the world into meaningfulconceptual hierarchies but our shared language only evolves
to reflect this structure when it is communicatively relevant.

Acknowledgments
This work was supported by ONR grant N000141310341 and a
Sloan Foundation fellowship to NDG. RXDH was supported by the
Stanford Graduate Fellowship and the National Science Foundation
Graduate Research Fellowship under Grant No. DGE-114747. This
project has received funding from the European Research Council(ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 681942, held by KS).

References
Barsalou, L. W. (1983). Ad hoc categories. Memory & cognition, 11(3), 211–227.
Brown, R. (1958). How shall a thing be called? Psychological review, 65(1), 14.
Clark, H. H., & Wilkes-Gibbs, D. (1986). Referring as acollaborative process. Cognition, 22(1), 1–39.
Franke, M., & Jäger, G. (2016). Probabilistic pragmatics,
or why bayes’ rule is probably important for pragmatics.
Zeitschrift für Sprachwissenschaft, 35(1), 3–44.
Gibson, E., Futrell, R., Jara-Ettinger, J., Mahowald, K.,
Bergen, L., Ratnasingam, S., . . . Conway, B. R. (2017).
Color naming across languages reflects color use. Proceedings of the National Academy of Sciences, 114(40), 1078510790.
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in
Cognitive Sciences, 20(11), 818 - 829.
Goodman, N. D., & Stuhlmüller, A. (electronic). The design and implementation of probabilistic programming languages.
Graf, C., Degen, J., Hawkins, R. X. D., & Goodman, N. D.
(2016). Animal, dog, or dalmatian? level of abstraction in
nominal referring expressions. In Proceedings of the 38th
annual conference of the Cognitive Science Society.
Hawkins, R. X. D. (2015). Conducting real-time multiplayer experiments on the web. Behavior Research Methods, 47(4), 966-976.
Hawkins, R. X. D., Frank, M. C., & Goodman, N. D. (2017).
Convention-formation in iterated reference games. In Proceedings of the 39th annual meeting of the cognitive science society.
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015).
Compression and communication in the cultural evolutionof linguistic structure. Cognition, 141, 87–102.
Mervis, C. B., & Rosch, E. (1981). Categorization of naturalobjects. Annual Review of Psychology, 32(1), 89-115.
Partee, B. (1995). Lexical semantics and compositionality. In
An invitation to cognitive science, part i: Language. Cambridge, MA: MIT Press.
Regier, T., Carstensen, A., & Kemp, C. (2016). Languages
support efficient communication about the environment:
Words for snow revisited. PloS one, 11(4), e0151138.
Regier, T., Kemp, C., & Kay, P. (2015). Word meanings
across languages support efficient communication. The
handbook of language emergence, 237–263.
Ritchie, D., Horsfall, P., & Goodman, N. D. (2016).
Deep amortized inference for probabilistic programs.
arXiv:1610.05735.
Tessler, M. H., & Goodman, N. D. (2016). A pragmatic theory of generic language. arXiv preprint arXiv:1608.02926.
Winters, J., Kirby, S., & Smith, K. (2014). Languages adapt
to their contextual niche. Language and Cognition, 1–35.
Xu, F., & Tenenbaum, J. B. (2007). Word learning as
bayesian inference. Psychological review, 114(2), 245.