Biological basis of language

Neurology, evolution and ecology of our memes

January 11, 2018 — September 16, 2021

communicating
grammar
language
machine learning
NLP
stringology
Figure 1

A placeholder for squishy brain stuff about language. If I wanted to think about it from a multi-agent perspective, I might consider how a social brain playing language games. Or, I might wonder about predictive coding and how that would relate. We could also wonder if semantics has something to do with biology. My old lectured Ana Wierzbicka would say yes.

1 Neurology of language

See (Petersson, Folia, and Hagoort 2012; Pylkkänen 2019) for a review of the neural basis of language in humans. I’m quite charmed by how well the neural basis of syntax is studied in finches, e.g. Jin (2009).

2 Analogy with artificial neural networks

Mapping NLP to neurology is surely a thing, but I do not know much about it.

3 Evolution of language

See language games.

4 Computational complexity

See syntax. Or, these days, ignore all the classical theory and just think about how transformer do great at NLP in defiance of classic hopelessness.

5 Meaning

See semantics.

6 Incoming

“They’re using phrase-structure grammar, long-distance dependencies. FLN recursion, at least four levels deep and I see no reason why it won’t go deeper with continued contact. […] It doesn’t have a clue what I’m saying.”

“What?”

“It doesn’t even have a clue what it’s saying back,” she added.

Peter Watts, Blindsight

7 References

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———. 1988. Identifying Languages from Stochastic Examples.” No. YALEU/DCS/RR-614.
Berwick, Okanoya, Beckers, et al. 2011. Songs to Syntax: The Linguistics of Birdsong.” Trends in Cognitive Sciences.
Blazek, and Lin. 2020. A Neural Network Model of Perception and Reasoning.” arXiv:2002.11319 [Cs, q-Bio].
Bolhuis, Tattersall, Chomsky, et al. 2014. How Could Language Have Evolved? PLoS Biol.
Bottou. 2011. From Machine Learning to Machine Reasoning.” arXiv:1102.1808 [Cs].
Cancho, and Solé. 2003. Least Effort and the Origins of Scaling in Human Language.” Proceedings of the National Academy of Sciences.
Christiansen, and Chater. 2008. Language as Shaped by the Brain.” Behavioral and Brain Sciences.
Elman. 1991. Distributed Representations, Simple Recurrent Networks, and Grammatical Structure.” Machine Learning.
———. 1993. Learning and Development in Neural Networks: The Importance of Starting Small.” Cognition.
———. 1995. “Language as a Dynamical System.”
Fitch. 2006. The Biology and Evolution of Music: A Comparative Perspective.” Cognition.
Hart. 2021. Drug Use for Grown-Ups: Chasing Liberty in the Land of Fear.
Hauser, and Watumull. 2016. The Universal Generative Faculty: The Source of Our Expressive Power in Language, Mathematics, Morality, and Music.” Journal of Neurolinguistics.
Jin. 2009. Generating Variable Birdsong Syllable Sequences with Branching Chain Networks in Avian Premotor Nucleus HVC.” Physical Review E.
Jin, and Kozhevnikov. 2011. A Compact Statistical Model of the Song Syntax in Bengalese Finch.” PLoS Comput Biol.
Katahira, Suzuki, Okanoya, et al. 2011. Complex Sequencing Rules of Birdsong Can Be Explained by Simple Hidden Markov Processes.” PLoS ONE.
Kirby. 1998. Learning, Bottlenecks and the Evolution of Recursive Syntax.” In.
———. 2003. Language Evolution.
Marcus, Marblestone, and Dean. 2014. The atoms of neural computation.” Science.
Mcclelland, Botvinick, Noelle, et al. 2010. Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition.” Trends in Cognitive Sciences.
Nowak, and Krakauer. 1999. “The Evolution of Language.” Proceedings of the National Academy of Sciences of the United States of America.
Petersson, Folia, and Hagoort. 2012. What Artificial Grammar Learning Reveals about the Neurobiology of Syntax.” Brain and Language, The Neurobiology of Syntax,.
Plotkin, and Nowak. 2000. Language Evolution and Information Theory.” Journal of Theoretical Biology.
Pylkkänen. 2019. The Neural Basis of Combinatory Syntax and Semantics.” Science.
Salakhutdinov. 2015. Learning Deep Generative Models.” Annual Review of Statistics and Its Application.
Sasahara, Cody, Cohen, et al. 2012. Structural Design Principles of Complex Bird Songs: A Network-Based Approach.” PLoS ONE.
Scarle. 2009. Implications of the Turing completeness of reaction-diffusion models, informed by GPGPU simulations on an XBox 360: cardiac arrhythmias, re-entry and the Halting problem.” Computational Biology and Chemistry.
Solé, Corominas-Murtra, Valverde, et al. 2010. Language Networks: Their Structure, Function, and Evolution.” Complexity.