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Felicity F. Frinsel; Fabio Trecca; Morten H. Christiansen – Cognitive Science, 2024
In language learning, learners engage with their environment, incorporating cues from different sources. However, in lab-based experiments, using artificial languages, many of the cues and features that are part of real-world language learning are stripped away. In three experiments, we investigated the role of positive, negative, and mixed…
Descriptors: Feedback (Response), Language Acquisition, Mathematical Linguistics, Role Theory
Stefan Depeweg; Contantin A. Rothkopf; Frank Jäkel – Cognitive Science, 2024
More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial…
Descriptors: Visual Learning, Problem Solving, Cognitive Science, Artificial Intelligence
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