In I used Turing machine arguments to show that computers can recognize humanly recognizable patterns in principle. In 1978 James D. Heffernan has expressed some doubts about such arguments. He does not question the propositions that I defend in the paper, nor the specific arguments in their support. What he does criticize are certain background assumptions.

In his article “On Mechanical Recognition” R. J. Nelson brings to bear a branch of mathematical logic called automata theory on problems of artificial intelligence. Specifically he attacks the anti-mechanist claim that “[i]nasmuch as human recognition to a very great extent relies on context and on the ability to grasp wholes with some independence of the quality of the parts, even to fill in the missing parts on the basis of expectations, it follows that computers cannot in principle be programmed (...) to recognize or learn to recognize all patterns”, p. 24). Nelson proposes, contrary to this claim, that “gestalt recognition is not beyond digital automata”. in particular, he claims, he will establish by what he calls “Turing machine arguments” that the following four theses are true: automata can recognize different pattern types in one and the same set of instances;automata can recognize the “same” pattern in different sets of instances;automata can recognize incomplete, degraded patterns having missing or indeterminate parts;automata can recognize family resemblances. (shrink)

A representation is a remnant of previous experience that allows that experience to affect later behavior. This paper develops a metatheoretical view of representation and applies it to issues concerning representation in animals. To describe a representational system one must specify the following: thedomainor range of situations in the represented world to which the system applies; thecontentor set of features encoded and preserved by the system; thecodeor transformational rules relating features of the representation to the corresponding features of the represented (...) world; themedium, or the representation's physical instantiation; and thedynamics, or how the system changes with time. In part because of the behaviorist assumption that the hypothetical, covert changes occurring in an organism during learning correspond to the overt physical changes that are observed, issues of representation in animal behavior have been largely ignored as irrelevant or misleading. However, it can be inferred that representations, acting as models of environmental regularities, operate at many levels of behavioral functioning, both cognitive and noncognitive. Objections to the use of this concept in explanations of animal behavior, based on the claim that it is indeterminate and on behaviorist considerations of parsimony, can be answered. Animal representations may be specialized in terms of tasks and species. Data from tasks involving spatial memory, delayed matching-to-sample, and sequence learning suggest some foundations for a general theory of animal representations. (shrink)