Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
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Vera and Simon [Cognitive Science, 17 (1993) 117] complain that the situational approach to cognition is insufficiently operationalized. This paper presents an interpreted system of diagrams that permit descriptions of tasks in both symbolic and situative terms, allowing differences to be operationally defined. It is argued that the basic language for describing psychological phenomena, shared by both behavioral and cognitive approaches, that of stimulus and response, is not adequate for the situational approach. Two examples, one drawn from published research in animal learning and one illustrating a re-analysis of standard tasks used in studies of problem solving, demonstrate differences between characterizing tasks in traditional stimulus-response terms as opposed to characterizing tasks in terms of situations, deeds, and observations. Connections are made to Reinforcement Learning and the study of Partially Observable Markov Decision Processes (POMDPs).