Transmission to new learners, communicative use, interactions among speakers in a community, and the structure of the world, all leave their imprint on the structure of languages (see Figure 2 for a description of two structural design features of language that have been claimed to be adaptations arising from cultural evolution: combinatoriality and compositionality). The headline conclusion so far is that language evolves to maximise expressivity under pressures for communication whilst minimising complexity under pressure to be learnable. Evidence in support of this conclusion comes from experiments [11••], computational and mathematical models [12] and data from a wide range of languages [13].
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## Current Opinion in Psychology

Volume 8, April 2016, Pages 37-43

# The cultural evolution of language

Author links open overlay panelMonicaTamariz, SimonKirby

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## Highlights

- •
  Key design features of language can be explained as the result of cultural evolution.

- •
  Experiments have found that structure emerges through transmission and communication.

- •
  Cultural transmission leads to compressible representations.

- •
  Communicative use leads to expressive languages.

Human language has unusual structural properties that enable open-ended communication. In recent years, researchers have begun to appeal to cultural evolution to explain the emergence of these structural properties. A particularly fruitful approach to this kind of explanation has been the use of laboratory experiments. These typically involve participants learning and interacting using artificially constructed communication systems. By observing the evolution of these systems in the lab, researchers have been able to build a bridge between individual cognition and population-wide emergent structure. We review these advances, and show how cultural evolution has been used to explain the origins of structure in linguistic signals, and in the mapping between signals and meanings.

## Introduction

Language is arguably the defining characteristic of our species, and the evolution of language is an area of intense interdisciplinary interest [1, 2, 3, 4, 5, 6]. The term ‘language evolution’, however, has three common interpretations: biological evolution, language change, and the cultural emergence of linguistic structure (Figure 1). This review will focus on the third interpretation. We will examine research looking at whether the way language is transmitted and used can explain the origins and evolution of key design features of language. These are the features which mark language out as special when compared to the vast number of communication systems in the natural world, and enable us to communicate about an open-ended range of meanings.

Transmission to new learners, communicative use, interactions among speakers in a community, and the structure of the world, all leave their imprint on the structure of languages (see Figure 2 for a description of two structural design features of language that have been claimed to be adaptations arising from cultural evolution: combinatoriality and compositionality). The headline conclusion so far is that language evolves to maximise expressivity under pressures for communication whilst minimising complexity under pressure to be learnable. Evidence in support of this conclusion comes from experiments [11••], computational and mathematical models [12] and data from a wide range of languages [13].

We will focus in this review on experimental approaches to cultural evolution as they have been applied to language (Table 1). There is a long history of using transmission chain experiments to look at how behaviour evolves culturally [14, 15, 16, 17, 18], see also Whiten et al., this volume. Kirby et al. [19••] emphasise the applicability of this method to language, which they argue undergoes a process they call iterated learning [20, 21, 22, 23], emphasising the way in which individuals learn from other learners during transmission and usage. Figure 3 gives various different ways in which iterated learning of language has been explored experimentally.

## Section snippets

## Signals

Why is human language combinatorial (Figure 2)? One answer might be that it arises in response to the need for a large vocabulary. Hockett [24] argues that once a system has too many meanings to be efficiently encoded by distinct non-combinatorial forms, a combinatorial system becomes advantageous. More recently, an alternative hypothesis has been proposed [25] that combinatoriality emerges through cultural transmission under biases favouring simplicity. In Verhoef's [25, 26••] cultural

## Mappings between signals and meanings

Linguistic signals fulfil their communicative function because they have conventionally associated meanings which are shared by a community of speakers. A number of experiments have explored how signal-meaning mappings come to be shared (however, see [6] for the limitations of treating languages as mappings). Fay et al. [35••] used a graphical communication design in which a microsociety of participants (Figure 3) played naming games based on a ‘pictionary’ task: a participant had to draw a

## Conclusions and future directions

Linguistic structure evolves culturally under pressure from learning and communication. Languages adapt to the first pressure by becoming compressible; to the second, by maintaining relevant distinctions. Both together lead inevitably to the characteristic structural design features of language such as combinatoriality and compositionality.

We have focussed in this review on cultural evolution as it applies to the emergence of fundamental features of language. However, returning to Figure 1, we

## Conflict of interest

The authors declare no conflict of interest

## References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

- • of special interest

- •• of outstanding interest

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## References (73)

- T. Scott-Phillips et al.

### Language evolution in the laboratory

### Trends Cogn Sci

(2010)

- S. Kirby et al.

### Compression and communication in the cultural evolution of linguistic structure

### Cognition

(2015)

- T. Regier et al.

### Word meanings across languages support efficient communication

### Handb Lang Emerg

(2015)

- S. Kirby et al.

### Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language

### Proc Natl Acad Sci U S A

(2008)

- T. Verhoef

### The origins of duality of patterning in artificial whistled languages

### Lang Cogn

(2012)

- B. De Boer

### Self-organization in vowel systems

### J Phon

(2000)

- A.B. Wedel

### Lexical contrast maintenance and the organization of sublexical contrast systems

### Lang Cogn

(2012)

- H. Cornish et al.

### Systems from sequences: a iterated learning account of the emergence of systematic structure in a non-linguistic task

- M. Tamariz et al.

### Cultural selection drives the evolution of human communication systems

### Proc Biol Sci

(2014)

- S. Goldin-Meadow et al.

### The natural order of events: how speakers of different languages represent events nonverbally

### Proc Natl Acad Sci U S A

(2008)

N. Khetarpal et al.

### Spatial terms across languages support near-optimal communication: evidence from Peruvian Amazonia, and computational analyses

C. Kemp et al.

### Kinship categories across languages reflect general communicative principles

### Science

(2012)

G. Roberts et al.

### How communication changes when we cannot mime the world: experimental evidence for the effect of iconicity on combinatoriality

### Cognition

(2015)

N. Claidiere et al.

### Cultural evolution of systematically structured behaviour in a non-human primate

### Proc R Soc B Biol Sci

(2014)

J. Greenberg

### Some universals of grammar with particular reference to the order of meaningful elements

T.W. Fitch

### The Evolution of Language

(2010)

J.R. Hurford et al.

### Origins of Language: A Slim Guide

(2014)

A.D.M. Smith

### Models of language evolution and change

### WIREs Cogn Sci

(2014)

M. Tallerman et al.

### The Oxford Handbook of Language Evolution

(2012)

T. Scott-Phillips

### Speaking Our Minds: Why Human Communication is Different, and How Language Evolved to Make it Special

(2014)

N. Ritt

### Selfish Sounds and Linguistic Evolution: A Darwinian Approach to Language Change

(2004)

W. Croft

### Explaining Language Change: An Evolutionary Approach

(2000)

G.J. Baxter et al.

### Utterance selection model of language change

### Phys Rev E Stat Nonlin Soft Matter Phys

(2006)

M. Dunn et al.

### Evolved structure of language shows lineage-specific trends in word-order universals

### Nature

(2011)

H. Brighton et al.

### Language as an evolutionary system

### Phys Life Rev

(2005)

F.C. Bartlett

### Remembering: An Experimental and Social Study

(1932)

E.A. Esper

### A technique for the experiment investigation of associative interference in artificial linguistic material

### Lang Monogr

(1925)

A. Whiten et al.

### Conformity to cultural norms of tool use in chimpanzees

### Nature

(2005)

A. Mesoudi et al.

### A bias for social information in human cultural transmission

### Br J Psychol

(2006)

C.A. Caldwell et al.

### Experimental models for testing hypotheses about cumulative cultural evolution

### Evol Hum Behav

(2008)

S. Kirby

### Spontaneous evolution of linguistic structure—an iterated learning model of the emergence of regularity and irregularity

### IEEE Trans Evol Comput

(2001)

S. Kirby et al.

### Innateness and culture in the evolution of language

### Proc Natl Acad Sci U S A

(2007)

T.L. Griffiths et al.

### Language evolution by iterated learning with Bayesian agents

### Cogn Sci

(2007)

S. Kirby et al.

### Iterated learning and the evolution of language

### Curr Opin Neurobiol

(2014)

C.F. Hockett

### The origins of language

### Sci Am

(1960)

T. Verhoef et al.

### Emergence of combinatorial structure and economy through iterated learning with continuous acoustic signals

### J Phon

(2014)

View more references

## Cited by (86)

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Citation Excerpt :

Language is optimized for human communication. It is an efficient vector of information transmission, shaped under cultural and environmental constraints to meet physical, technological, and social needs (e.g., Kirby et al., 2015; Lupyan and Dale, 2016; Millikan, 2005; Scott-Phillips, 2015; Tamariz and Kirby, 2016). Language is also adapted to thinking and interpretation, playing a scaffolding role in cognition (e.g., Carruthers, 2002; Chomsky, 2014; Clark, 2006; Jackendoff, 1996; Reboul, 2015), and is crucial in meta-cognition including self-evaluation, self-regulation and autonoetic consciousness (Alderson-Day and Fernyhough, 2015; Perrone-Bertolotti et al., 2014).

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Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults ( InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand ( flexibility) or several factors such as age or gender ( variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.

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Citation Excerpt :

This can be seen as a timescale related to ‘social interest’, much in the same way that Michel and collaborators [1] analyzed the decreasing levels of interest in events of the past and showed that, in recent years, interest decline to half of the initial level after only 10 years. Beyond the cognitive factors that regulate word cycles, a number of cultural and biological forces are involved in language change at different levels, such as individual learning [7,24,25], changes in vocal anatomy [26] and adaptations that decrease the effort of language production and understanding [27,28], among others. Decreased effort refers to the optimization of the trade-off between accurate and efficient communication, which has been put forward as a possible explanation of Zipf's law [29].

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The analysis of thousands of time series in different languages reveals that word usage presents oscillations with a prevalence of 16-year cycles, mounted on slowly varying trends. These components carry different information: while similar oscillatory patterns gather semantically related words, similar trends group together keywords representative of cultural and historical periods. We interpreted the regular oscillations as cycles of interest and saturation, whose behavior could be captured using a simple mathematical model. Driving the model with the empirical trends, we were able to explain word frequency traces across multiple languages throughout the last three centuries. Our results suggest that word frequency usage is poised at dynamical criticality, close to a Hopf bifurcation which signals the emergence of oscillatory dynamics. Crucially, our model explains the oscillatory synchronization observed within groups of words and provides an interpretation of this phenomenon in terms of the cultural context driving collective cognition. These findings contribute to unravel how our use of language is shaped by the interplay between human cognition and sociocultural forces.

The frequency with which words are used presents regular oscillations of 16 years. We propose that these oscillations arise from a basic cognitive mechanism common to other cultural objects with life cycles, such as fashion. The words that belong to a topic of interest increase their frequency, which is then inhibited by saturation until interest is regained. Here we set up a simple mathematical model for the interaction of this cognitive mechanism and the sociocultural context, which explains the occurrence frequencies of thousands of words in different languages during the past three centuries. We show that oscillations are tuned to a critical point and are synchronized within word communities.

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Copyright © 2015 Elsevier Ltd. All rights reserved.

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