Abstract

The paper examines constraints ond preferences employed by people in learning decision rules from preclossified examples. Results from four experiments with human subiects were onolyzed ond compared with ortificiol intelligence (Al) inductive learning programs. The results showed the people’s rule inductions tended lo emphosize category validity (probability of some property, given o category) more than cue validity (probability that on entity is o member of o cote-gory given that it hos some property) to o greater extent than did the Al pro-groms. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, o single process model provided o good account of the data from each study. These observations ore used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus Al induction programs become condidote psychological process models ond results from inductive learning experiments con suggest new algorithms. More generally, the results show that humon induc-tive generolizotions tend toword greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toword specificity moy be due lo the fact that this criterion both maximizes inferences that moy be drown from category membership ond protects rule induction sys-tems from developing over-generolizotions.

Citations