It is widely assumed that language in some form or other originated by piggybacking on pre-existing learning mechanism not dedicated to language. Using evolutionary connectionist simulations, we explore the implications of such assumptions by determining the effect of constraints derived from an earlier evolved mechanism for sequential learning on the interaction between biological and linguistic adaptation across generations of language learners. Artificial neural networks were initially allowed to evolve “biologically” to improve their sequential learning abilities, after which language was introduced into the population. We compared the relative contribution of biological and linguistic adaptation by allowing both networks and language to change over time. The simulation results support two main conclusions: First, over generations, a consistent head-ordering emerged due to linguistic adaptation. This is consistent with previous studies suggesting that some apparently arbitrary aspects of linguistic structure may arise from cognitive constraints on sequential learning. Second, when networks were selected to maintain a good level of performance on the sequential learning task, language learnability is significantly improved by linguistic adaptation but not by biological adaptation. Indeed, the pressure toward maintaining a high level of sequential learning performance prevented biological assimilation of linguistic-specific knowledge from occurring.
Clay Beckner, Richard Blythe, Joan Bybee, Morten H. Christiansen, William Croft, Nick C. Ellis, John Holland, Jinyun Ke, Diane Larsen‐Freeman & Tom Schoenemann
2009. Language Is a Complex Adaptive System: Position Paper. Language Learning 59:s1 ► pp. 1 ff.
Chater, Nick & Morten H. Christiansen
2010. Language evolution as cultural evolution: how language is shaped by the brain. WIREs Cognitive Science 1:5 ► pp. 623 ff.
Christiansen, Morten H. & Nick Chater
2015. The language faculty that wasn't: a usage-based account of natural language recursion. Frontiers in Psychology 6
Christiansen, Morten H., Florencia Reali & Nick Chater
2011. Biological Adaptations for Functional Features of Language in the Face of Cultural Evolution. Human Biology 83:2 ► pp. 247 ff.
Davis, Emily & Kenny Smith
2023. The learnability and emergence of dependency structures in an artificial language. Journal of Language Evolution 8:1 ► pp. 64 ff.
de Boer, Bart
2015. Biology, culture, evolution and the cognitive nature of sound systems. Journal of Phonetics 53 ► pp. 79 ff.
Ellis, Nick C.
2012. Formulaic Language and Second Language Acquisition: Zipf and the Phrasal Teddy Bear. Annual Review of Applied Linguistics 32 ► pp. 17 ff.
Folia, Vasiliki, Julia Uddén, Meinou De Vries, Christian Forkstam & Karl Magnus Petersson
2010. Artificial Language Learning in Adults and Children. Language Learning 60:s2 ► pp. 188 ff.
Goldberg, Adele & Laura Suttle
2010. Construction grammar. WIREs Cognitive Science 1:4 ► pp. 468 ff.
Gong, Tao, Yau W. Lam & Lan Shuai
2016. Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment. Frontiers in Psychology 7
Gong, Tao, Lan Shuai & Yicheng Wu
2018. Rethinking foundations of language from a multidisciplinary perspective. Physics of Life Reviews 26-27 ► pp. 120 ff.
Gong, Tao, Lan Shuai & Xiaolong Yang
2022. A simulation on coevolution between language and multiple cognitive abilities. Journal of Language Evolution 7:1 ► pp. 120 ff.
Hruschka, Daniel J., Morten H. Christiansen, Richard A. Blythe, William Croft, Paul Heggarty, Salikoko S. Mufwene, Janet B. Pierrehumbert & Shana Poplack
2009. Building social cognitive models of language change. Trends in Cognitive Sciences 13:11 ► pp. 464 ff.
Isbilen, Erin S. & Morten H. Christiansen
2020. Chunk‐Based Memory Constraints on the Cultural Evolution of Language. Topics in Cognitive Science 12:2 ► pp. 713 ff.
2014. Models of language evolution and change. WIREs Cognitive Science 5:3 ► pp. 281 ff.
Thomas, Michael S. C., Neil A. Forrester & Angelica Ronald
2016. Multiscale Modeling of Gene–Behavior Associations in an Artificial Neural Network Model of Cognitive Development. Cognitive Science 40:1 ► pp. 51 ff.
This list is based on CrossRef data as of 31 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
Any errors therein should be reported to them.