2020/12/28 by Luann C. Jung, Haiyan Wang, Jung, Luann C. +1
Computer Science · Mathematics · Psychology · Social Sciences · #Adaptation (eye) #Artificial intelligence #Categorization #Child and Animal Learning Development #Cognitive psychology #Cognitive science #Computational Engineering #Computer science #Concept learning #Domain (mathematical analysis) #FOS: Computer and information sciences #Finance #Language and cultural evolution #Linguistics #Mathematics #Natural language processing #Process (computing) #Psychology #Reading and Literacy Development #Set (abstract data type) #and Science (cs.CE) #cs.CE
paper · pdf · doi:10.48550/arxiv.2012.14400
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/12/28 · openalex publication_date 2020/12/28 · arxiv updated 2020/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For humans learning to categorize and distinguish parts of the world, the set of assumptions (overhypotheses) they hold about potential category structures is directly related to their learning process. In this work we examine the effects of two overhypotheses for category learning: 1) the bias introduced by the presence of linguistic labels for objects; 2) the conceptual 'domain' biases inherent in the learner about which features are most indicative of category structure. These two biases work in tandem to impose priors on the learning process; and we model and detail their interaction and effects. This paper entails an adaptation and expansion of prior experimental work that addressed label bias effects but did not fully explore conceptual domain biases. Our results highlight the importance of both the domain and label biases in facilitating or hindering category learning.