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Maurício D. Martins; Zoe Bergmann; Elena Leonova; Roberta Bianco; Daniela Sammler; Arno Villringer – Cognitive Science, 2025
Recursive hierarchical embedding allows humans to generate multiple hierarchical levels using simple rules. We can acquire recursion from exposure to linguistic and visual examples, but only develop the ability to understand "multiple-level" structures like "[[second] red] ball]" after mastering "same-level"…
Descriptors: Psychomotor Skills, Adults, Adult Learning, Learning Processes
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Fabian Tomaschek; Michael Ramscar; Jessie S. Nixon – Cognitive Science, 2024
Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences--and the relations between the elements they comprise--are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the…
Descriptors: Sequential Learning, Learning Processes, Serial Learning, Executive Function
Litchfield, Brenda C. – Performance and Instruction, 1990
Describes methods for item construction for computer-based instruction (CBI). Use of a rational set generator (RSG) in presenting an adaptive sequence of items to the learner is described; a model matrix of a computer-based RSG is presented; concrete and defined concepts are discussed; and rule using is explained. (Four references) (LRW)
Descriptors: Computer Assisted Instruction, Concept Teaching, Discrimination Learning, Generalization
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Baeyens, Frank; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Kerkhof, Ineke; De Ceulaer, Annick – Learning & Memory, 2005
Using a conditioned suppression task, we investigated extinction and renewal of Pavlovian modulation in human sequential Feature Positive (FP) discrimination learning. In Experiment 1, in context a participants were first trained on two FP discriminations, X[right arrow]A+/A- and Y[right arrow]B+/B-. Extinction treatment was administered in the…
Descriptors: Discrimination Learning, Classical Conditioning, Contingency Management, Sequential Learning