Clark Glymour

"Goodness of Fit": Clinical Applications from Infancy through Adult Life. By Stella Chess & Alexander Thomas. Brunner/Mazel, Philadelphia, PA, 1999. pp. 229. pound24.95 (hb). Chess and Thomas's pioneering longitudinal studies of temperamental individuality started over 40 years ago (Thomas et al., 1963). Their publications soon became and remain classics. Their concept of "goodness of fit" emerges out of this monumental work but has had a long gestation period. In their new book, the authors distinguish between behaviour disorders that are reactive to the child's life circumstances, including life events, and which are self-correcting or responsive to the relevant changes in their environment, and more serious disorders

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception.

Time series of macroscopic quantities that are aggregates of microscopic quantities, with unknown one‐many relations between macroscopic and microscopic states, are common in applied sciences, from economics to climate studies. When such time series of macroscopic quantities are claimed to be causal, the causal relations postulated are representable by a directed acyclic graph and associated probability distribution—sometimes called a dynamical Bayes net. Causal interpretations of such series imply claims that hypothetical manipulations of macroscopic variables have unambiguous effects on variables “downstream” in the graph, and such macroscopic variables may be predictably produced or altered even while particular microstates are not. This paper argues that such causal time series of macroscopic aggregates of microscopic processes are the appropriate model for mental causation.

Reverse inference in cognitive neuropsychology has been characterized as inference to ‘psychological processes’ from ‘patterns of activation’ revealed by functional magnetic resonance or other scanning techniques. Several arguments have been provided against the possibility. Focusing on Machery’s presentation, we attempt to clarify the issues, rebut the impossibility arguments, and propose and illustrate a strategy for reverse inference. 1 The Problem of Reverse Inference in Cognitive Neuropsychology2 The Arguments2.1 The anti-Bayesian argument3 Patterns of Activation4 Reverse Inference Practiced5 Seek and Ye Shall Find, Maybe6 Conclusion

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 neuropsychology 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.

The claims of science and the claims of probability combine in two ways. In one, probability is part of the content of science, as in statistical mechanics and quantum theory and an enormous range of "models" developed in applied statistics. In the other, probability is the tool used to explain and to justify methods of inference from records of observations, as in every science from psychiatry to physics. These intimacies between science and probability are logical sports, for while we think science aims to say what happens, what has happened, what will happen, what would happen if other things were to happen, what could and could not happen, what will nearly happen, or what will approximate what will happen, probability claims say none of these things, or at least none of them about the phenomena with which science is concerned. On that, at least, almost all philosophical interpreters of probability since DeMoivre agree, with whatever reluctance. Consider some examples.