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Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or 'cognitive architecture', from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods--even granting their assumptions--in resolving uncertainties about that architecture. With some idealization, the question of the capacities of various experimental designs in cognitive neuro-psychology to uncover cognitive architecture can be reduced to comparatively simple questions about the prior assumptions investigators are willing to make. This paper presents some of simplest of those reductions.
2 Theories as functional diagrams and graphs
4 Discovery problems and success
5 Some examples
6 Resource/PDP models
Neuropsychology has relied on a variety of methods to obtain information about human 'cognitive architecture' from the profiles of capacities and incapacities presented by normal and abnormal subjects. The nineteenth-century neuropsychological tradition associated with Broca, Wernicke, Meynert, and Lichtheim attempted to correlate abnormal behaviour with loci of brain damage, and thus to found syndrome classification ultimately on neuroanatomy. At the same time, they aimed to use the data of abnormal cognitive incapacities to found inferences to the functional architecture of the normal human cognitive system. Contemporary work in neuropsychology involves statistical studies of the correlation of behaviour with physical measures of brain activity in both normal and abnormal subjects, statistical studies of the correlations of behavioural abnormalities in groups of subjects, and studies of behavioural abnormalities in particular individuals, sometimes in conjunction with information about the locations of lesions.(2) The goal of identifying the functional structure of normal cognitive architecture remains as it was in the 19th century.
The fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from any of the kinds of data just mentioned, the assumptions upon which the reliability of such methods are premised, and the limits of such methods--even granting their assumptions--in resolving uncertainties about that architecture. These questions have recently been the subject of intense debate occasioned by a series of articles by Caramazza and his collaborators: these articles have prompted a number of responses, including at least one book. As the issues have been framed in these exchanges, they concern:
1. whether studies of the statistical distribution of abnormalities in
groups of subjects selected by syndrome, by the character of brain
lesions, or by other means, are relevant evidence for determining
2. whether the proper form of argument in cognitive neuropsychology
is 'hypothetico-deductive'--in which a theory is tested by deducing
from it consequences whose truth or falsity can be determined more
or less directly--or 'bootstrap testing'--in which theories are tested
by assuming parts of them and using those parts to deduce (non-
circularly) from the data instances of other parts of the theory;
3. whether associations of capacities, or cases of dissociation in which
one of two normally concurrent capacities is absent, or double
dissociations in which of two normally concurrent capacities, A
and B, one abnormal subject possesses capacity A but not B, while
another abnormal subject possesses B but not A, are the 'more
important' form of evidence about normal cognitive architecture.
Bub and Bub  object that Caramazza's arguments against group studies assume a 'hypothetico-deductive' picture of theory testing in which a hypothesis is confirmed by a body of data if from the hypothesis (and perhaps auxiliary assumptions) a description of the data can be deduced. They suggest that inference to cognitive architecture from neuropsychological data follows instead a 'bootstrap' pattern much like that described by Glymour .(3) They, and also Shallice , reassert that double dissociation data provide especially important evidence for cognitive architecture. Shallice argues that if a functional module underlying two capacities is a connectionist computational system of which one capacity requires more computational resources than another, then injuries to the module that remove one of these capacities may leave the other intact. The occurrence of subjects having one of these capacities and lacking the other (dissociation) therefore will not permit a decision as to whether or not there is a functional module required for the first capacity but not required for the second. Double dissociations, Shallice claims, do permit this decision.
The main issue in these disputes is this: by what methods, and from what sorts of data, can the truth about various questions of cognitive architecture be found, whatever the truth may be? There is a tradition in computer science and in mathematical psychology that provides a means for resolving such questions. Work in this tradition characterizes mathematically whether or not specific questions can be settled in principle from specific kinds of evidence. Positive results are proved by exhibiting some method and demonstrating that it can reliably reach the truth; negative results are proved by showing that no possible method can do so. There are results of these kinds about the impossibility of predicting the behavior of a 'black box' with an unknown Turing machine inside; about the possibility …