Literature DB >> 29435770

Visual artificial grammar learning by rhesus macaques (Macaca mulatta): exploring the role of grammar complexity and sequence length.

Lisa A Heimbauer1, Christopher M Conway2, Morten H Christiansen3, Michael J Beran2, Michael J Owren4.   

Abstract

Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.

Entities:  

Keywords:  Artificial grammar learning; Rhesus macaques; Sequence learning; Statistical learning

Mesh:

Year:  2018        PMID: 29435770     DOI: 10.1007/s10071-018-1164-4

Source DB:  PubMed          Journal:  Anim Cogn        ISSN: 1435-9448            Impact factor:   3.084


  8 in total

1.  The relationship between a combinatorial processing rule and a continuous mate preference function in an insect.

Authors:  Camille Desjonquères; Rebecca R Holt; Bretta Speck; Rafael L Rodríguez
Journal:  Proc Biol Sci       Date:  2020-09-16       Impact factor: 5.349

2.  Working Memory for Spatial Sequences: Developmental and Evolutionary Factors in Encoding Ordinal and Relational Structures.

Authors:  He Zhang; Yanfen Zhen; Shijing Yu; Tenghai Long; Bingqian Zhang; Xinjian Jiang; Junru Li; Wen Fang; Mariano Sigman; Stanislas Dehaene; Liping Wang
Journal:  J Neurosci       Date:  2021-12-03       Impact factor: 6.709

3.  Nonhuman primates learn adjacent dependencies but fail to learn nonadjacent dependencies in a statistical learning task with a salient cue.

Authors:  Maisy Englund; Will Whitham; Christopher M Conway; Michael J Beran; David A Washburn
Journal:  Learn Behav       Date:  2021-09-28       Impact factor: 1.986

4.  Recursive sequence generation in monkeys, children, U.S. adults, and native Amazonians.

Authors:  Stephen Ferrigno; Samuel J Cheyette; Steven T Piantadosi; Jessica F Cantlon
Journal:  Sci Adv       Date:  2020-06-26       Impact factor: 14.136

Review 5.  Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution.

Authors:  Christopher I Petkov; Carel Ten Cate
Journal:  Top Cogn Sci       Date:  2019-07-29

6.  Gray Matter Variation in the Posterior Superior Temporal Gyrus Is Associated with Polymorphisms in the KIAA0319 Gene in Chimpanzees (Pan troglodytes).

Authors:  William D Hopkins; Nicky Staes; Michele M Mulholland; Steven J Schapiro; Madeleine Rosenstein; Cheryl Stimpson; Brenda J Bradley; Chet C Sherwood
Journal:  eNeuro       Date:  2021-12-14

7.  Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta-Analysis Approach.

Authors:  Antony S Trotter; Padraic Monaghan; Gabriël J L Beckers; Morten H Christiansen
Journal:  Top Cogn Sci       Date:  2019-09-08

8.  Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials.

Authors:  Yonatan I Fishman; Wei-Wei Lee; Elyse Sussman
Journal:  Neuroimage       Date:  2020-10-21       Impact factor: 7.400

  8 in total

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