Synthese 16 (3-4):344 - 380 (1966)
Abstract |
It is argued that current attempts to model human learning behavior commonly fail on one of two counts: either the model assumptions are artificially restricted so as to permit the application of mathematical techniques in deriving their consequences, or else the required complex assumptions are imbedded in computer programs whose technical details obscure the theoretical content of the model. The first failing is characteristic of so-called mathematical models of learning, while the second is characteristic of computer simulation models. An approach to model building which avoids both these failings is presented under the title of a black-box theory of learning. This method permits the statement of assumptions of any desired complexity in a language which clearly exhibits their theoretical content.Section II of the paper is devoted to the problem of testing and comparing alternative learning theories. The policy advocated is to abandon attempts at hypothesis testing. It is argued that, in general, we not only lack sufficient data and sufficiently powerful techniques to test hypotheses, but that the truth of a model is not really the issue of basic interest. A given model may be true in the sense that on the basis of available evidence we cannot statistically reject it, but not interesting in the sense that it provides little information about the processes underlying behavior. Rather, we should accept or reject models on the basis of how much information they provide about the way in which subjects respond to environmental structure. This attitude toward model testing is made precise by introducing a formal measure of the information content of a model. Finally, it is argued that the statistical concept of degrees-of-freedom is misleading when used in the context of model testing and should be replaced by a measure of the information absorbed from the data in estimating parameters.
|
Keywords | No keywords specified (fix it) |
Categories | (categorize this paper) |
DOI | 10.1007/BF00485088 |
Options |
![]() ![]() ![]() ![]() |
Download options
References found in this work BETA
A Study of Thinking.Jerome S. Bruner, Jacqueline J. Goodnow & George A. Austin - 1958 - Philosophy and Phenomenological Research 19 (1):118-119.
Toward a Statistical Theory of Learning.William K. Estes - 1950 - Psychological Review 57 (2):94-107.
The Conduct of Inquiry: Methodology for Behavioral Science.J. L. Mackie - 1966 - Philosophical Quarterly 16 (65):404.
A Mathematical Model for Simple Learning.Robert R. Bush & Frederick Mosteller - 1951 - Psychological Review 58 (5):313-323.
Probability Learning and a Negative Recency Effect in the Serial Anticipation of Alternative Symbols.Murray E. Jarvik - 1951 - Journal of Experimental Psychology 41 (4):291.
View all 6 references / Add more references
Citations of this work BETA
Explanation, Prediction, Description, and Information Theory.Joseph F. Hanna - 1969 - Synthese 20 (3):308 - 334.
On Transmitted Information as a Measure of Explanatory Power.Joseph F. Hanna - 1978 - Philosophy of Science 45 (4):531-562.
Statistics, Induction, and Lawlikeness: Comments on Dr. Vetter's Paper.Jaakko Hintikka - 1969 - Synthese 20 (1):72 - 83.
Theory Construction in Psychology: The Interpretation and Integration of Psychological Data.Gordon M. Becker - 1981 - Theory and Decision 13 (3):251.
Philosophy of Science (Wissenschaftstheorie) in Finland.Jaakko Hintikka - 1970 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 1 (1):119-132.
View all 6 citations / Add more citations
Similar books and articles
Bayesian Model Learning Based on Predictive Entropy.Jukka Corander & Pekka Marttinen - 2006 - Journal of Logic, Language and Information 15 (1-2):5-20.
An Integrative Approach to the Modeling of Behavior.William Timberlake, Norman Pecoraro & Matthew Tinsley - 2000 - Behavioral and Brain Sciences 23 (2):268-268.
Models in Science.Roman Frigg & Stephan Hartmann - 2006 - In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford.
Bottoms-Up! A Refreshing Change in Models.Charles T. Snowdon - 2000 - Behavioral and Brain Sciences 23 (2):266-267.
Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing.Axel Cleeremans - 1993 - MIT Press.
Computational Models in the Philosophy of Science.Paul Thagard - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:329 - 335.
Analytics
Added to PP index
2009-01-28
Total views
31 ( #368,176 of 2,504,848 )
Recent downloads (6 months)
1 ( #417,030 of 2,504,848 )
2009-01-28
Total views
31 ( #368,176 of 2,504,848 )
Recent downloads (6 months)
1 ( #417,030 of 2,504,848 )
How can I increase my downloads?
Downloads