Cognitive models of test-item effects in human category learning

Xiaojin Zhu, Bryan R. Gibson, Kwang Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Imagine two identical people receive exactly the same training on how to classify certain objects. Perhaps surprisingly, we show that one can then manipulate them into classifying some test items in opposite ways, simply depending on what other test items they are asked to classify (without label feedback). We call this the Test-Item Effect, which can be induced by the order or the distribution of test items. We formulate the Test-Item Effect as online semi-supervised learning, and extend three standard human category learning models to explain it.

Original languageEnglish (US)
Title of host publicationICML 2010 - Proceedings, 27th International Conference on Machine Learning
Pages1247-1254
Number of pages8
StatePublished - 2010
Externally publishedYes
Event27th International Conference on Machine Learning, ICML 2010 - Haifa, Israel
Duration: Jun 21 2010Jun 25 2010

Publication series

NameICML 2010 - Proceedings, 27th International Conference on Machine Learning

Conference

Conference27th International Conference on Machine Learning, ICML 2010
Country/TerritoryIsrael
CityHaifa
Period6/21/106/25/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Education

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