1. Introduction
1.1. Language evolution in the laboratory
Early studies of the iterated learning paradigm were designed to learn about cultural transmission. In the field of language evolution, research took to the laboratory only in recent years since work by Kirby S, Cornish H, and Smith K. Evolutionary linguists design experiments in the laboratory to study the human cognitive capacities necessary for language and the emergence of new languages. Computational models such as agent-based simulations, as well as mathematical models are developed to represent and explore the plausible hypotheses about the historical origins of languages.
1.2. Research Significance
The origins of natural language cannot be observed directly. Language evolution experiments thus focus on the emergence of new languages and how they are used by human participants. A central theme in language evolution laboratory research is the transformation of individual-level behaviours into linguistic phenomena that occur at the level of an entire population. Linguistic phenomena that emerge cannot be explained only by reference to individual cognition, where the various forms of interaction that individuals engage in, such as cultural transmission and feedback, are as necessary in consideration.
2. Simulating Cultural Transmission of Language
The question of how language emerged concerns itself with both the biological evolution of the various cognitive capacities deemed necessary for language and the cultural evolution of languages, beginning from theorized protolanguages. It should be noted that cultural evolution will do well to not be considered in isolation from its biological counterpart, as the cognitive adaptations an individual is equipped with bear implications for social interaction and learning.
Upon establishment, a language requires to be learned by subsequent generations, each from a prior generation, in a cross-generational manner, also known as vertical cultural transmission. People learn a language from other people who once learned that language themselves. Studies on cultural transmission seek to explain the changes an emergent language system undergoes. The cultural transmission of a language can be said to give rise to design without intention and designer.
Exposure to linguistic behavior exhibited by members of one’s speech community induces one’s production of particular language properties. The resulting language used by one in turn translates to observable linguistic behavior which shapes the language of further members. Cultural evolution of the language is thus enabled by this cycle of repeated induction and elicitation of linguistic behavior.
Simulations of cultural transmission are based on the belief that when language is culturally transmitted, it develops structure, key design features unique to language over other communication systems, and maximized transmissibility (enhance learnability). These models include computational agent-based simulations, mathematical models, and most recently, laboratory experiments.
Pioneer work on agent-based simulations sought to explain how negotiation due to learners’ biases and interaction between them influence communication systems greatly. Subsequent studies focused largely on the development of linguistic structure as a byproduct of cultural learning (specifically iterated learning), despite poverty of the stimulus. Work on mathematical models followed, supplementing the findings of such agent-based simulations through mathematical characterizations of changes effected by cultural transmission. As an effort to reflect prior computational and mathematical models, laboratory experiments aim to demonstrate how cumulative, adaptive, and non-intentional the cultural evolution of language is.
2.1. Challenges
One overarching challenge faced by studies on cultural evolution remains to be the arguably reductionist approach taken. Any given language is constituted by thousands of language systems (capturing pragmatic, semantic, morphological, and phonological distinctions) and language strategies, of which all are intertwined. No model can hope to closely replicate all aspects of language evolution through simulation.
Despite availability of real-life observations of cultural transmission with the likes of Nicaraguan Sign Language, study of genuine emergence remains limited by the lack of direct, natural, data. Hence, only indirect evidence can be drawn.
3. Iterated Learning Process and 2 key paradigms
3.1. What is Iterated Learning?
As the key mechanism of the cultural evolution of language, the aforementioned models are largely based on iterated learning. It refers to the process by which an individual acquires a behavior by observing a similar behavior in another individual who acquired it in the same way. Spoken and signed languages, birdsong, and music are transmitted via iterated learning, as opposed to explicit teaching. One’s linguistic behavior is thus a product of one’s observation of others’ similar behavior, which was induced by that of those who came before. The chain of diffusion occurs in not only cultural transmission, but also horizontal negotiation of conventions between peers of the same generation. Iterated learning is thus manifested both along a cross-generational chain of different individuals and back-and-forth within a dyad.
Iterated learning has been found to have profound effects on linguistic structure. One of many methodologies involves the iterated learning of an artificial language where participants were organized into diffusion chains. Such studies have propounded that the process of iterated learning becomes an adaptive system, in which the linguistic behavior being transmitted changes to overcome key threats to its further transmission.
3.2. Agent-based simulation (Computational)
Agent-based modelling is a new analytical method for social sciences. It enables one to build models where individual entities and their interactions are directly represented. As compared to variable-based approaches or system-based approaches, agent-based simulation offers the possibility of modelling individual heterogeneity, representing agents’ decision rules explicitly, and situating agents in a geographical or another type of space. Basically, it allows one to represent multiple scales of analysis in a natural and efficient way.
3.2.1. Literature on Agent-based Simulation
It was started by Hurford to model the biological and cultural evolution of language. Earlier work in this area sought to explain the role of interaction and negotiation, or biases of learners in shaping communication systems, focusing mainly on the conditions under which communicatively optimal, socially learnt communication systems would emerge. Thereafter, researchers tried to find out how linguistic structure can arise from iterated learning. Emphasis was given on the role of bottleneck learning, which was thought to be the driving force behind the evolution of structure, since language learners must try to learn an infinitely expressive linguistic system on the basis of a small set of linguistic data.
A major finding is that compositional languages emerge from unstructured languages due to repeated transmission through the learning bottleneck – language structure appears as an adaptive response by language per se to the problem of being transmitted through a narrow bottleneck, since the presence of compositional rules enables a learner to infer from a small sample rules underpinning the whole language.
There is another model that represents the emergence of systematicity in phonological systems through communicative interaction and iterated learning. One example would be De Boer who looked at the cultural evolution of vowel systems, showing that the universal features of the organisation of all the vowels in the world can arise through repeated interaction between simulated agents under certain reasonable articulatory and perceptual constraints.
These variety of agent-based models give evidence to key design features of language being emerged from iterated learning.
3.3. Laboratory Experiments with Humans
Early studies of iterated learning that observed human behaviour in a laboratory setting were designed to learn about cultural transmission. One of the pioneer studies was Bartlett’s ‘serial reproduction’ paradigm, in which participants were exposed to some stimulus (such as drawings) and were then asked to reproduce the same material from memory. Their reproduced work served as the stimulus for a second participant, and so on. Bartlett observed that the material that was transmitted in this manner had changed as participants impressed their expectations about what they deemed was the right and appropriate content onto the material, thus causing it to be restructured. An interesting observation that came out of the study was that drawings could change toward conventional, prototypical forms of the object drawn. (Bartlett, 1932) This is an experimental demonstration of the predictions made by the mathematical analysis of iterated learning, which revealed that behavior transmitted by iterated learning evolve to reflect the biases of individuals involved in transmission.
The contemporary literature has acknowledged the presence and consequence of learner biases, demonstrating them in their studies. Several studies take known biases such as the learning of grammatical categories or functions from tasks that have been well-studied, and verify that cultural transmission through iterated learning yield behaviours which reflect those biases. An alternative approach is to use iterated learning as a means to discover individual biases, which include showing biases for keeping social information over non-social information.
A couple of studies have combined iterated learning techniques with artificial language learning or communication game paradigms in a bid to explore how languages and other communication systems evolve through learning and use. In language evolution, iterated learning has become a paradigm which involves experimentation with artificial languages. Human participants learn a set of items in the language, and then produce linguistic behavior which subsequent individuals learn from and so on. This was introduced by researchers Kirby S, Cornish H, and Smith K. A learning bottleneck is also imposed on transmission: participants are asked to learn a target language based on exposure to a smaller set of language items from the original set of stimuli, with the language produced by the nth participant. In this chain, the nth participant provides the input to participant n +1 and so forth.
3.3.1. Literature on Laboratory Experiments with Humans
In Kirby et al’s (2008) experiment, participants were asked to learn labels for coloured moving shapes, where the initial artificial language provided a randomly generated, unique label for the shapes. Their findings revealed a structured language that developed from the initial unstructured set of meaning-signal associations as a result of the iterated learning. By the 10th participant, each label had consisted of a prefix which specified colour (e.g. ne– for black, la– for blue), a stem for shape (e.g. –ho– for circle, -ki- for triangle) and an affix specifying motion (e.g. –plo for bouncing, –pilu for looping). As predicted by mathematical modelling results, languages developed over time to ones that facilitated generalisations and rules. Compositional languages also developed where components or sub-parts of each complex label or word specified components of the picture that the label had referred to.
In other experimental paradigms (see Galantucci, 2005; Verhoef, 2012), participants communicate using or learn of a novel medium, such as systematically distorted graphic scribbles, that show the emergence of combinatorial structure. So instead of the case as in compositional structures, complex signals are composed by the recombination of a smaller set of meaningless parts.
4. Research Limitations
The above models make very weak assumptions about the transmission process itself: no language is easier or harder for learners to acquire than any other. The need for fitness – being able to communicate effectively with others – can be said to be the driving force in the dynamics of, rather than learning.
In addition, computational models might not be able to match the abilities and biases of real human learners to a realistic extent. The use of modern humans is inevitable in laboratory experiments; it is hard for us to determine what humans were like at the point when language emerged. However, it is worth noting replicating the evolutionary history of language is not the objective of such experimental approaches.