The emergence of multiple learning systems
Year: 2006
Type: dataset
Abstract: Multiple learning systems models hold that separate learning systems, often organized around discrepant principles, combine their outputs to support human categorization. Rather than propose a complex model, we adopt a complex systems ’ viewpoint and propose that multiple learning systems emerge from a flexible and adaptive clustering mechanism’s interactions with the environment. The model, CLUSTer Error Reduction (CLUSTER), retains the flexibility characteristic of human learning by building knowledge structures as needed to support a learner’s goals. Importantly, CLUS-TER can apply ostensibly different procedures to different parts of the stimulus space, a hallmark of multiple systems models. We describe a simulation of a human learning study in which CLUSTER develops different cluster representations for different item types. Rule-following items are captured by clusters that are broadly tuned and focused on rule-relevant stimulus aspects, whereas exceptions (especially those that violate high-frequency rules) are captured by narrowly tuned clusters that focus on item-specific stimulus qualities. We end by considering the relation between CLUSTER
Source: PsycEXTRA Dataset
Authors: Bradley C. Love, Matt Jones
Institution: The University of Texas at Austin
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2622232467