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Stephen Ferrigno; Samuel J. Cheyette; Susan Carey – Cognitive Science, 2025
Complex sequences are ubiquitous in human mental life, structuring representations within many different cognitive domains--natural language, music, mathematics, and logic, to name a few. However, the representational and computational machinery used to learn abstract grammars and process complex sequences is unknown. Here, we used an artificial…
Descriptors: Sequential Learning, Cognitive Processes, Knowledge Representation, Training
Eva Viviani; Michael Ramscar; Elizabeth Wonnacott – Cognitive Science, 2024
Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of…
Descriptors: Symbolic Learning, Learning Processes, Artificial Intelligence, Prediction
Kempe, Vera; Gauvrit, Nicolas; Panayotov, Nikolay; Cunningham, Sheila; Tamariz, Monica – Cognitive Science, 2021
Iterated language learning experiments that explore the emergence of linguistic structure in the laboratory vary considerably in methodological implementation, limiting the generalizability of findings. Most studies also restrict themselves to exploring the emergence of combinatorial and compositional structure in isolation. Here, we use a novel…
Descriptors: Learning Processes, Language Acquisition, Language Research, Auditory Stimuli

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