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Chan, Jenny Yun-Chen; Nagashima, Tomohiro; Closser, Avery H. – Cognitive Science, 2023
Given the recent call to strengthen collaboration between researchers and relevant practitioners, we consider participatory design as a way to advance Cognitive Science. Building on examples from the Learning Sciences and Human-Computer Interaction, we (a) explore "what," "why," "who," "when," and…
Descriptors: Cognitive Science, Learning Processes, Man Machine Systems, Cooperation
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
Trott, Sean; Jones, Cameron; Chang, Tyler; Michaelov, James; Bergen, Benjamin – Cognitive Science, 2023
Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models…
Descriptors: Models, Language Processing, Beliefs, Child Development
Vong, Wai Keen; Lake, Brenden M. – Cognitive Science, 2022
In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from…
Descriptors: Vocabulary Development, Cognitive Mapping, Problem Solving, Visual Aids

Langley, Pat – Cognitive Science, 1985
Examines processes by which general but weak search methods are transformed into powerful, domain-specific search strategies by classifying types of heuristics learning that can occur and components that contribute to such learning. A learning system--SAGE.2--and its structure, behavior in different domains, and future directions are explored. (36…
Descriptors: Artificial Intelligence, Computer Software, Design, Heuristics