Research article summary (published 29 Apr 2001):

Does learning a complex task have to be complex? A study in learning decomposition.

Full Abstract

Many theories of skill acquisition have had considerable success in addressing the fine details of learning in relatively simple tasks, but can they scale up to complex tasks that are more typical of human learning in the real world? Some theories argue for scalability by making the implicit assumption that complex tasks consist of many smaller parts, which are learned according to basic learning principles. Surprisingly, there has been rather sparse empirical testing of this crucial assumption. In this article, we examine this assumption directly by decomposing the learning in the Kanfer-Ackerman Air-Traffic Controller Task (Ackerman, 1988) from the learning at the global level all the way down to the learning at the keystroke level. First, we reanalyze the data from Ackerman (1988) and show that the learning in this complex task does indeed reflect the learning of smaller parts at the keystroke level. Second, in a follow-up eye-tracking experiment, we show that a large portion of the learning at the keystroke level reflects the learning even at a lower, i.e., attentional level. Copyright 2001 Academic Press.

Author information

Author/s: Lee, F J (FJ); Anderson, J R (JR);

Affiliation: Department of Psychology, Carnegie Mellon University, Pittsburg, PA 15213-3890, USA. fjl+(-atsign-)cmu.edu

Journal and publication information

Publication Type: Clinical Trial; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Cognitive psychology (Cogn Psychol), published in United States. (Language: eng)

Reference: 2001-May; vol 42 (issue 3) : pp 267-316

Dates: Created 2001/04/18; Completed 2001/07/05; Revised 2009/01/16;

PMID: 11305884, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Attention Aviation Computer Simulation Eye Movements Humans Learning

Learning Models, Psychological Motor Skills Practice (Psychology) Reaction Time Task Performance and Analysis

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