---
abstract: 'Learning is usually studied on the basis of binary distinctions like implicit vs. explicit learning, using instance vs. using rules, connectionist vs. symbolist, etc. In this thesis it is argued that many of these distinctions are not useful at all in understanding learning. This statement is supported by a large set of models in ACT-R, a cognitive architecture developed by J.R. Anderson. These models demonstrate that deeper understanding is often gained when the traditional distinctions are ignored.'
altloc:
  - http://tcw2.ppsw.rug.nl/~niels/thesis
  - http://www.ub.rug.nl/eldoc/dis/ppsw/n.a.taatgen/
chapter: ~
commentary: ~
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creators_id: []
creators_name:
  - family: Taatgen
    given: Niels
    honourific: ''
    lineage: ''
date: 1999-06
date_type: published
datestamp: 2000-10-16
department: Artificial Intelligence
dir: disk0/00/00/10/21
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editors_id: []
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eprint_status: archive
eprintid: 1021
fileinfo: /style/images/fileicons/application_pdf.png;/1021/3/thesis.pdf
full_text_status: public
importid: ~
institution: 'University of Groningen, Netherlands'
isbn: ~
ispublished: pub
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item_issues_comment: []
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keywords: 'cognitive modeling, ACT-R, skill acquisition, learning, implicit learning, explicit learning, scheduling, insight'
lastmod: 2011-03-11 08:54:25
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longitude: ~
metadata_visibility: show
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refereed: TRUE
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relation_type: []
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reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 16:36:03
subjects:
  - cog-psy
  - comp-sci-art-intel
  - comp-sci-complex-theory
  - comp-sci-mach-learn
  - dev-psy
succeeds: ~
suggestions: ~
sword_depositor: ~
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thesistype: PhD thesis
title: 'Learning without limits: from problem solving towards a Unified Theory of Learning'
type: thesis
userid: 398
volume: ~
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