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  1.  16
    Structured Sequence Learning: Animal Abilities, Cognitive Operations, and Language Evolution.Christopher I. Petkov & Carel ten Cate - 2020 - Topics in Cognitive Science 12 (3):828-842.
    Human language is a salient example of a neurocognitive system that is specialized to process complex dependencies between sensory events distributed in time, yet how this system evolved and specialized remains unclear. Artificial Grammar Learning (AGL) studies have generated a wealth of insights into how human adults and infants process different types of sequencing dependencies of varying complexity. The AGL paradigm has also been adopted to examine the sequence processing abilities of nonhuman animals. We critically evaluate this growing literature in (...)
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  2.  29
    From evolutionarily conserved frontal regions for sequence processing to human innovations for syntax.Benjamin Wilson & Christopher I. Petkov - 2018 - Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies / Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies 19 (1-2):318-335.
    Empirical advances have been made in understanding how human language, in its combinatorial complexity and unbounded expressivity, may have evolved from the communication systems present in our evolutionary ancestors. However, a number of cognitive processes and neurobiological mechanisms that support language may not have evolved specifically for communication, but rather from abilities that support perception and cognition more generally. We review recent evidence from comparative behavioural and neurobiological studies on structured sequence learning in human and nonhuman primates. These studies support (...)
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    Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches.Carel ten Cate, Judit Gervain, Clara C. Levelt, Christopher I. Petkov & Willem Zuidema - 2020 - Topics in Cognitive Science 12 (3):804-814.
    Artificial grammar learning (AGL) is used to study how human adults, infants, animals or machines learn various sorts of rules defined over sounds or visual items. Ten Cate et al. introduce the topic and provide a critical synthesis of this important interdisciplinary area of research. They identify the questions that remain open and the challenges that lie ahead, and argue that the limits of human, animal and machine learning abilities have yet to be found.
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