The helpfulness of category labels in semi-supervised learning depends on category structure

Autor: Amy Perfors, Wai Keen Vong, Daniel J. Navarro
Rok vydání: 2019
Předmět:
Adult
Male
Adolescent
Experimental and Cognitive Psychology
Semi-supervised learning
Models
Psychological

computer.software_genre
Rational planning model
050105 experimental psychology
Young Adult
03 medical and health sciences
0302 clinical medicine
Arts and Humanities (miscellaneous)
Concept learning
Developmental and Educational Psychology
Humans
Learning
Frame (artificial intelligence)
0501 psychology and cognitive sciences
Set (psychology)
Aged
business.industry
05 social sciences
Extension (predicate logic)
PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Concepts and Categories
Middle Aged
Models
Theoretical

Classification
bepress|Social and Behavioral Sciences|Psychology|Cognitive Psychology
PsyArXiv|Social and Behavioral Sciences
ComputingMethodologies_PATTERNRECOGNITION
Categorization
Helpfulness
bepress|Social and Behavioral Sciences
PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology
Female
Artificial intelligence
business
Psychology
computer
030217 neurology & neurosurgery
Natural language processing
Popis: The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people’s responses are driven by the specific set of labels they see. We present an extension of Anderson’s Rational Model of Categorization that captures this effect.
Databáze: OpenAIRE