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Naïve and Robust: Class-Conditional Independence in Human Classification Learning

Jarecki, JB, Meder, B and Nelson, JD (2017) Naïve and Robust: Class-Conditional Independence in Human Classification Learning Cognitive Science.

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Abstract

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence-independence structure and category learning model, DISC-LM) that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments — designed using optimal experimental design principles — were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks.

Item Type: Article
Subjects : Psychology
Divisions : Faculty of Health and Medical Sciences > School of Psychology
Authors :
NameEmailORCID
Jarecki, JBUNSPECIFIEDUNSPECIFIED
Meder, BUNSPECIFIEDUNSPECIFIED
Nelson, JDj.d.nelson@surrey.ac.ukUNSPECIFIED
Date : 2 June 2017
Identification Number : 10.1111/cogs.12496
Copyright Disclaimer : This is the peer reviewed version of the following article: Jarecki, J. B., Meder, B. and Nelson, J. D. (2017), Naïve and Robust: Class-Conditional Independence in Human Classification Learning. Cogn Sci. doi:10.1111/cogs.12496, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/cogs.12496/full. This article may be used for personal use only.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 04 Apr 2017 15:34
Last Modified : 08 Sep 2017 12:47
URI: http://epubs.surrey.ac.uk/id/eprint/813939

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