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Induction and interaction in the evolution of language and conceptual structure
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| 22 |
Co-evolution of language and mindreading: a computational exploration
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Abstract:
Language relies on mindreading: in order to use it successfully we need to be able to entertain and recognise communicative intentions. Mindreading abilities in turn profit from language, as language provides a means for expressing mental states explicitly, and for transmitting our knowledge of mental states to others. Given this interdependence, it has been hypothesised that language and mindreading have co-evolved. In this thesis I formalise the relationship between language and mindreading in a computational model, in order to explore under what circumstances a co-evolutionary dynamic between the two skills could have gotten off the ground. In Chapter 3 I present an agent-based model which combines referential signalling with perspective-taking, where perspective-taking instantiates a very simple form of mindreading. In this model, agents’ communicative behaviour is probabilistically determined by an interplay between their language and their perspective on the world. The literal variant of these agents (explored in Chapters 3 and 4) consists of speakers who produce utterances purely based on their own language and perspective, and listeners who interpret these utterances using what they’ve learned about the speaker’s perspective through interaction. The pragmatic variant of these agents in contrast (explored in Chapters 5 and 6) consists of speakers who optimise their utterances by maximising the probability that the listener will interpret them correctly (assuming the listener shares their perspective), and listeners who interpret these utterances by reasoning about such a speaker, again using what they’ve learned about the speaker’s perspective through interaction. Learning is not straightforward however, because agents’ languages and perspectives are private (i.e. not directly observable to other agents). Instead, the Bayesian learners in this model only get to observe a speaker’s utterances in context, from which they have to simultaneously infer the speaker’s language and perspective. Simulation results show that learners can overcome this joint inference problem by bootstrapping one from the other, but that the success of this process depends on how informative the speaker’s language is. This leads to an evolutionary question: If the co-development between language-learning and perspective-learning relies on agents being exposed to an informative language, how could a population of such agents evolve an informative language from scratch? I address this question with an iterated learning version of the model described above, combined with different selection pressures. Simulation results with literal agents (presented in Chapter 4) show that an informative language emerges not just if the population is subjected to a selection pressure for communication, but also under selection for accurate perspective-inference. Under both pressures, the emergence of an informative language leads not just to more successful communication, but also to more successful perspective-inference. This is because sharing an informative language with others provides agents with information about those others’ perspectives (note that agents’ innate ability to learn about others’ perspectives does not change over generations). Simulation results with pragmatic agents (presented in Chapter 5) show the same co-evolutionary dynamics as literal agents, with the difference that they can achieve equally high levels of success at communicating and inferring perspectives with much more ambiguous languages, because they can compensate for suboptimal languages using their pragmatic ability. Finally, in Chapter 6 I explore under what circumstances such pragmatic agents could have evolved; that is, under what circumstances being a pragmatic communicator provides an evolutionary advantage over being a literal communicator. Taken together, the model results presented in this thesis suggest firstly that co-evolution between language and mindreading could have gotten off the ground under any circumstances which created a need for either improved communication or improved insight into others’ minds. Secondly, the results suggest that such a co-evolutionary dynamic could have been driven largely by cultural evolution; where mindreading improves by virtue of evolving a language.
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Keyword:
agent-based model; co-developmental; language and mindreading; perspective-inference; pragmatic communicator; selection pressures
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URL: http://hdl.handle.net/1842/35944
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| 23 |
Cross-modal associations and synesthesia: Categorical perception and structure in vowel–color mappings in a large online sample
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| 24 |
High-fidelity copying is not necessarily the key to cumulative cultural evolution: a study in monkeys and children
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In: Proc Biol Sci (2019)
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| 25 |
The emergence of spatial modulation in artificial sign languages ...
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| 26 |
Challenges in detecting evolutionary forces in language change using diachronic corpora ...
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| 27 |
Quantifying the dynamics of topical fluctuations in language ...
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| 28 |
Topical advection as a baseline model for corpus-based lexical dynamics ...
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| 30 |
Simplifying linguistic complexity: culture and cognition in language evolution
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| 32 |
Topical advection as a baseline model for corpus-based lexical dynamics
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In: Proceedings of the Society for Computation in Linguistics (2018)
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| 33 |
Animacy Distinctions Arise from Iterated Learning
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In: Open Linguistics, Vol 4, Iss 1, Pp 552-565 (2018) (2018)
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| 35 |
Evolutionary psycholinguistic approach to the pragmatics of reference
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| 36 |
Artificial sign language learning: a method for evolutionary linguistics
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| 37 |
Direction and directedness in language change: an evolutionary model of selection by trend-amplification
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| 39 |
Minimal requirements for the cultural evolution of language
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