Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies

The European Journal of Neuroscience
Benjamin WilsonChristopher I Petkov

Abstract

Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the ...Continue Reading

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Citations

Jun 27, 2015·Cognition·Andrea RavignaniW Tecumseh Fitch
Nov 9, 2016·Scientific Reports·Alice E MilneChristopher I Petkov
Feb 18, 2017·PloS One·Eiichi Hoshino, Ken Mogi
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Jul 31, 2020·Topics in Cognitive Science·Benjamin WilsonArnaud Rey
Feb 10, 2021·Cognition·Juan M Toro, Paola Crespo-Bojorque
Feb 25, 2019·Hearing Research·Jennifer K Schiavo, Robert C Froemke

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