harnad@elbereth.rutgers.edu (Stevan Harnad) (09/15/90)
Below is the abstract of a forthcoming target article to appear in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal that provides Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be current BBS Associates or nominated by a current BBS Associate. To be considered as a commentator on this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please send email to: harnad@clarity.princeton.edu or harnad@pucc.bitnet or write to: BBS, 20 Nassau Street, #240, Princeton NJ 08542 [tel: 609-921-7771] To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you are selected as a commentator. ____________________________________________________________________ IS HUMAN COGNITION ADAPTIVE? John R. Anderson Psychology Department Carnegie Mellon University Pittsburgh,PA 15213-3890 ABSTRACT: Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience to develop stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. (1) Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in (2) categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in (3) casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in (4) problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. -- Stevan Harnad INTERNET: harnad@confidence.princeton.edu harnad@princeton.edu srh@flash.bellcore.com harnad@elbereth.rutgers.edu harnad@princeton.uucp BITNET: harnad@pucc.bitnet CSNET: harnad%princeton.edu@relay.cs.net (609)-921-7771