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Constraints and Preferences in Inductive Learning: An Experimental Study of Human and Machine Performance
Author(s) -
Medin Douglas L.,
Wattenmaker William D.,
Michalski Ryszard S.
Publication year - 1987
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog1103_3
Subject(s) - property (philosophy) , inductive bias , inductive reasoning , artificial intelligence , embodied cognition , constraint (computer aided design) , process (computing) , computer science , machine learning , simplicity , rule induction , concept learning , cognitive psychology , psychology , multi task learning , mathematics , task (project management) , epistemology , philosophy , geometry , management , economics , operating system
The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations.