David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Theory and Decision 6 (August):249-67 (1975)
In this paper it is argued that certain stimulus-response learning models which are adequate to represent finite automata (acceptors) are not adequate to represent noninitial state input-output automata (transducers). This circumstance suggests the question whether or not the behavior of animals if satisfactorily modelled by automata is predictive. It is argued in partial answer that there are automata which can be explained in the sense that their transition and output functions can be described (roughly, Hempel-type covering law explanation) while their behaviors are in principle not predictable short of possession of their complete histories or of information concerning present internal states by indirect observation
|Keywords||Animal Automata Behaviorism Education Finite Functionalism Learning Linguistics Metaphysics Prediction Psychology Response Stimulus Fodor, J Hempel Moore, E Putnam, H Suppes, P|
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