Anderson/Cognition: BBS Call for Commentators

[Stevan Harnad]

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 at clarity.princeton.edu  or harnad at 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.
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