The causal explanatory functions of medical diagnoses

Diagnoses in medicine are often taken to serve as explanations of patients’ symptoms and signs. This article examines how they do so. I begin by arguing that although some instances of diagnostic explanation can be formulated as covering law arguments, they are explanatory neither in virtue of their argumentative structures nor in virtue of general regularities between diagnoses and clinical presentations. I then consider the theory that medical diagnoses explain symptoms and signs by identifying their actual causes in particular cases. While I take this to be largely correct, I argue that for a diagnosis to function as a satisfactory causal explanation of a patient’s symptoms and signs, it also needs to be supplemented by understanding the mechanisms by which the identified cause produces the symptoms and signs. This mechanistic understanding comes not from the diagnosis itself, but rather from the theoretical framework within which the physician operates.     

The explanatory role of consistency requirements

Synthese - Is epistemic inconsistency a mere symptom of having violated other requirements of rationality—notably, reasons-responsiveness requirements? Or is inconsistency irrational on its...     

Conceptual role semantics and the explanatory role of content

Conclusion I've tried to argue that there is more to representational content than CRS can acknowledge. CRS is attractive, I think, because of its rejection of atomism, and because it is a plausible theory of targets. But those are philosopher's concerns. Someone interested in building a person needs to understand representation, because, as AI researchers have urged for some time, good representation is the secret of good performance. I have just gestured in the direction I think a viable theory of representation must take. I hope, however, to have created some advance sympathy for the gesture by distinguishing the problem of representation from the problem of targets on the one hand, and from the problem truth-conditions for the attitudes on the other.     

The role of causal models in analogical inference

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed.     

Grounding and the explanatory role of generalizations

According to Hempel’s (Aspects of scientific explanation and other essays. The Free Press, New York, 1965) influential theory of explanation, explaining why some a is G consists in showing that the truth that a is G follows from a law-like generalization to the effect that all Fs are G together with the initial condition that a is F. While Hempel’s overall account is now widely considered to be deeply flawed, the idea that some generalizations play the explanatory role that the account predicts is still often endorsed by contemporary philosophers of science. This idea, however, conflicts with widely shared views in metaphysics according to which the generalization that all Fs are G is partially explained by the fact that a is G. I discuss two solutions to this conflict that have been proposed recently, argue that they are unsatisfactory, and offer an alternative.     

The role of causal attributions in competitive situations

Previous data have shown that successful women are judged to have masculine characteristics [J. A. Doyle (1989), The Male Experience(2nd ed.), Dubuque, IA: Wm. C. Brown; D. L. Gill (1986), Psychological Dynamics of Sport,Champaign, IL: Human Kinetics; D. J. Murphy (1988), “Sport and Gender,” in W. M. Leonard II (Ed.), A Sociological Perspective of Sport(3rd ed.), New York: Macmillan; P. Willis (1994), “Women in Sport in Ideology,” in S. Birrell and C. L. Cole (Eds.), Women, Sport, and Culture,Champaign, IL: Human Kinetics]. Two experiments were conducted to test two different explanations for this effect, the appraisal and attribution hypotheses. In Experiment 1, male participants who lost to a female confederate on a masculine-relevant task rated her as having more masculine and fewer feminine characteristics than when the task was described as feminine-relevant. However, heart rate and blood pressure data failed to support the appraisal hypothesis that these reactions were due to the stress of losing to the female, or defensiveness. The alternative hypothesis that these ratings were due to standard attributional processes was more consistent with the data. Consequently, this hypothesis was tested in Experiment 2 using an attributional paradigm. Consistent with Experiment 1, the attribution hypothesis fared better than did the appraisal hypothesis.     

A probabilistic contrast model of causal induction

Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased.     

The causal role of interpretive bias in anxiety reactivity

Elevated anxiety vulnerability is associated with a tendency to interpret ambiguous stimuli as threatening, but the causal basis of this relationship has not been established. Recently, procedures have been developed to systematically manipulate interpretive bias, but the impact of such manipulation on anxiety reactivity to a subsequent stressor has not yet been examined. In the present study, training procedures were used to induce interpretive biases favoring the threatening or nonthreatening meanings of ambiguous information in a sample of 48 undergraduate students. Following this interpretive training, participants' emotional reactions to a stressful video were assessed. The finding that the manipulation of interpretive bias modified emotional reactivity supports the hypothesis that interpretive bias can indeed play a causal role in anxiety vulnerability.     

The role of causal discourse structure in narrative writing

All writers produce text content and ideally connect it together according to discourse conventions. We investigate whether a particularly strong discourse convention, the need for causal coherence in narratives, can predict the kind of text writers will produce. Causality has been found to be a significant discourse factor in reading comprehension and hence can be expected to determine also what writers produce during composition. In Experiment 1, writers composed short continuations at various points throughout a simple narrative, whereas in Experiment 2, writers composed continuations to complete several narratives. The results indicate that causality indeed plays a major role in composition. Writers tend to produce new text in such a way that it is causally connected to the prior text. Furthermore, writers favored causal relations of necessity or of necessity and sufficiency while largely avoiding relations of sufficiency alone, which suggests a general discourse constraint to be maximally informative (e.g., Grice, 1975).     

The role of autopecking in behavioral contrast

Four groups of four pigeons each were studied on two different multiple schedules. The cues correlated with the schedule components were localized on the response key for two groups and were not localized for the others. Two groups worked on multiple schedules with variable interval 15-sec in both components, and variable interval 15-sec in one component and extinction in the other. The other two groups had identical procedures except that food was presented on a response-independent variable-time schedule. Variable-interval birds with localized stimuli showed marked behavioral contrast; variable-interval birds with non-localized stimuli showed no behavioral contrast. Variable-time birds with key-light stimuli acquired high rates of autopecking, which changed as treatment changed in a manner that paralleled rate changes, resulting in behavioral contrast for variable-interval birds. Variable-time birds with non-localized stimuli key pecked only at a low rate. The findings indicate that behavioral contrast in pigeons may result from the autopecking that is obtained with stimulus-contingent food presentation.     

Causal powers and preventers: An explanatory account of cue interaction effects in human causal judgement

According to the causal powers theory, all causal relations are understood in terms of causal powers of one thing producing an effect by acting on liability of another thing. Powers can vary in strength, and their operation also depends on the presence of preventers. When an effect occurs, there is a need to account for the occurrence by assigning sufficient strength to produce it to its possible causes. Contingency information is used to estimate strengths of powers and preventers and the extent to which they account for occurrences and nonoccurrences of the outcome. People make causal judgements from contingency information by processes of inference that interpret evidence in terms of this fundamental understanding. From this account it is possible to derive a computational model based on a common set of principles that involve estimating strengths, using these estimates to interpret ambiguous information, and integrating the resultant evidence in a weighted averaging model. It is shown that the model predicts cue interaction effects in human causal judgement, including forward and backward blocking, second and third order backward blocking, forward and backward conditioned inhibition, recovery from overshadowing, superlearning, and backward superlearning.     

The role of mechanism and covariation information in causal belief updating

The present study is aimed at identifying how prior causal beliefs and covariation information contribute to belief updating when evidence, either compatible or contradictory with those beliefs, is provided. Participants were presented with a cover story with which it was intended to activate or generate a causal belief. Variables related to the prior belief (the type of information, the strength of the cause-effect causal link, and how confident the participant was that the link existed) were assessed. Subsequently, participants were presented with covariational information and were asked to update their beliefs in light of the new evidence. Information reliability, prior belief's causal influence magnitude, and the cause-effect level of contingency portrayed by the new information--but not the type of the prior belief--are shown to directly determine belief updating.     

The role of causal connections in the retrieval of text

In two experiments, subjects read a series of passages, each containing two target concepts that appeared in widely separated positions within the passage. Following each passage, the time to retrieve each of these concepts was recorded. Several measures from both the Kintsch and van Dijk (1978) model and Trabasso and Sperry’s (1985) causal analysis were derived to predict retrieval time. Regression analyses showed that substantial proportions of variance were accounted for by measures derived from a causal analysis. Neither physical position nor measures based on the Kintsch and van Dijk model accounted for significant proportions of variance. Following Experiment 2, a reanalysis of O’Brien’s (1987) results revealed that the number of causal connections to an antecedent was a significant predictor of antecedent search time. Results are discussed in terms of a spread of activation through an integrated text representation in which causal connections play a major role.     

The relationship of explanatory flexibility to explanatory style

Traditional cognitive vulnerability-stress models regarding the etiology of depression emphasize the content of the depressed individual’s thoughts. One important cognitive content index, explanatory style, represents the habitual way that individuals assign causes to events that occur in their lives. A more contemporary model, however, emphasizes the cognitive process by which these attributions are made and to what extent the individual can make different attributions depending on the particular context of the event. This process is referred to as explanatory flexibility. Given that both indices of causal explanation are derived from the same assessment instrument, the Attributional Style Questionnaire, the current investigation sought to examine the extent to which the two variables can be differentiated from one another. Results indicated that explanatory style (a measure of cognitive content) and explanatory flexibility (a cognitive process measure) are empirically related, but distinct, constructs.     

The role of causal models in multiple judgments under uncertainty

Two studies examined a novel prediction of the causal Bayes net approach to judgments under uncertainty, namely that causal knowledge affects the interpretation of statistical evidence obtained over multiple observations. Participants estimated the conditional probability of an uncertain event (breast cancer) given information about the base rate, hit rate (probability of a positive mammogram given cancer) and false positive rate (probability of a positive mammogram in the absence of cancer). Conditional probability estimates were made after observing one or two positive mammograms. Participants exhibited a causal stability effect: there was a smaller increase in estimates of the probability of cancer over multiple positive mammograms when a causal explanation of false positives was provided. This was the case when the judgments were made by different participants (Experiment 1) or by the same participants (Experiment 2). These results show that identical patterns of observed events can lead to different estimates of event probability depending on beliefs about the generative causes of the observations.