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

Lookup NU author(s): Dr Ben Wilson, Professor Christopher Petkov

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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 non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans.


Publication metadata

Author(s): Wilson B, Smith K, Petkov CI

Publication type: Article

Publication status: Published

Journal: European Journal of Neuroscience

Year: 2015

Volume: 41

Issue: 5

Pages: 568-578

Print publication date: 01/03/2015

Online publication date: 01/03/2015

Acceptance date: 15/12/2014

Date deposited: 30/06/2015

ISSN (print): 0953-816X

ISSN (electronic): 1460-9568

Publisher: Wiley-Blackwell Publishing Ltd.

URL: http://dx.doi.org/10.1111/ejn.12834

DOI: 10.1111/ejn.12834


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