Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning

Zuidema, Willem; French, Robert M.; Alhama, Raquel G.; Ellis, Kevin; O’Donnell, Timothy J.; Sainburg, Tim; Gentner, Timothy Q.

doi:10.1111/tops.12474

Cited by 9 publications

(13 citation statements)

References 34 publications

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“…As the present paper has also shown, another research area with important implications for language evolution is research on the changes in the language use of individuals across the life-span. After being neglected for a long time, this aspect has recently been receiving increasing attention in diachronic linguistics ( Lastly, whereas the scenario outlined here was evidence-based but at the level of a verbal theory, this model would surely profit from further theory building informed by computational modelling of the interplay of cognitive and interactional mechanisms operating on individual (proto)language users as well as populations of utterances within a community across different timescales (Guest and Martin, 2020;van Rooij and Baggio, 2021;Zuidema et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Interactional and Cognitive Mechanisms in the Evolution of (Proto)language(s)

Pleyer¹

2021

Preprint

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This paper discusses the role of interactional and cognitive mechanisms in the emergence of (proto-)linguistic structures and the evolution of (proto)language(s) from the perspective of usage-based and constructionist approaches. Both the social, interactive nature of human communication and the enchronic, interactional timescale have received increasing attention in investigations of how structure emerges in the complex adaptive system of language, which operates across multiple timescales and is shaped by multiple different factors. This has also led to an increasing focus on the mechanisms involved in the dialogic co-construction of structure and meaning in interaction. These include ad hoc constructionalization, interactive alignment, conceptual pacts, reuse and modification, and local forms of entrenchment, routinisation and schematisation. Interactional and cognitive mechanisms like these not only play a crucial role in the emergence of structure in modern languages. They can also help explain how the first (proto)constructions came into being in hominin interaction. Frequently re-occuring, temporary, local (proto)constructions acquired increasing degrees of entrenchment, which led to their subsequent diffusion throughout hominin communities. They were then subject to processes of conventionalisation and cumulative cultural evolution. This process is hypothesised to eventually have led to the gradual transition from protolanguage to language.

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Section: Resultsmentioning

confidence: 99%

Interactional and Cognitive Mechanisms in the Evolution of (Proto)language(s)

Pleyer¹

2021

Preprint

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“…These neural-network-based algorithms can be used to sample directly from the learned representational spaces described in Section 3. A simple example is autoencoder-based synthesis ( Figure 6 ) (Sainburg et al, 2018a ; Zuidema et al, 2020 ). Autoencoders can be trained on spectral representations of vocal data, and systematically sampled in the learned latent space to produce new vocalizations.…”

Section: Synthesizing Vocalizationsmentioning

confidence: 99%

“…One advantage of GAN-based models is that their loss is not defined directly by reconstruction loss, resulting in higher-fidelity syntheses (Larsen et al, 2016 ). Typically, approaches for synthesizing vocalizations based on neural networks rely on treating magnitude spectrogram like an image, training a neural network architecture in the same manner as one would an image, and finally inverting the sampled spectrogram into a waveform (Sainburg et al, 2020b ; Zuidema et al, 2020 ; Pagliarini et al, 2021 ). When synthesizing vocalizations from neural networks trained on the magnitude spectrogram, the estimation of phase is necessary to invert the spectrogram into a waveform signal for playback.…”

Section: Synthesizing Vocalizationsmentioning

confidence: 99%

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Sainburg

Gentner

2021

Front. Behav. Neurosci.

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Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

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“…Computational modelling can help formalise theories of learning and evolution in order to yield empirical predictions and a better understanding of the dynamics involved (Guest and Martin 2020;Zuidema et al 2019). Here we are interested in the effect of a developmental interdependence between language and perspective-taking on a cultural evolutionary timescale.…”

Section: Computational Models Of Word Learning and Language Evolutionmentioning

confidence: 99%

A computational model of the cultural co-evolution of language and mindreading

2020

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Several evolutionary accounts of human social cognition posit that language has co-evolved with the sophisticated mindreading abilities of modern humans. It has also been argued that these mindreading abilities are the product of cultural, rather than biological, evolution. Taken together, these claims suggest that the evolution of language has played an important role in the cultural evolution of human social cognition. Here we present a new computational model which formalises the assumptions that underlie this hypothesis, in order to explore how language and mindreading interact through cultural evolution. This model treats communicative behaviour as an interplay between the context in which communication occurs, an agent’s individual perspective on the world, and the agent’s lexicon. However, each agent’s perspective and lexicon are private mental representations, not directly observable to other agents. Learners are therefore confronted with the task of jointly inferring the lexicon and perspective of their cultural parent, based on their utterances in context. Simulation results show that given these assumptions, an informative lexicon evolves not just under a pressure to be successful at communicating, but also under a pressure for accurate perspective-inference. When such a lexicon evolves, agents become better at inferring others’ perspectives; not because their innate ability to learn about perspectives changes, but because sharing a language (of the right type) with others helps them to do so.

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Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning

Cited by 9 publications

References 34 publications

Interactional and Cognitive Mechanisms in the Evolution of (Proto)language(s)

Interactional and Cognitive Mechanisms in the Evolution of (Proto)language(s)

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

A computational model of the cultural co-evolution of language and mindreading

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