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  1. 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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  • Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning.Samuel Schmid, Douglas Saddy & Julie Franck - 2023 - Cognitive Science 47 (1):e13242.
    In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling (...)
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  • Knowledge transfer, templates, and the spillovers.Chia-Hua Lin - 2022 - European Journal for Philosophy of Science 12 (1):1-30.
    Mathematical models and their modeling frameworks developed to advance knowledge in one discipline are sometimes sourced to answer questions or solve problems in another discipline. Studying this aspect of cross-disciplinary transfer of knowledge objects, philosophers of science have weighed in on the question of whether knowledge about how a mathematical model is previously applied in one discipline is necessary for the success of reapplying said model in a different discipline. However, not much has been said about whether the answer to (...)
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