Asymmetries in predictive and diagnostic reasoning

In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way.     

Heuristics and biases in diagnostic reasoning: I. Priors,error costs,and test accuracy

In three experiments, undergraduate subjects were asked to evaluate or choose between hypothetical medical tests. Subjects were told the subjective prior probability of a hypothetical disease, the hit rate and false-alarm rate of each test, and the relative subjective cost of the two possible errors that might be made. By varying priors, cost, and test accuracy, we could measure the influence of each parameter on subjects' responses. Subjects overweighed costs relative to both priors and test accuracy. In single-test cases in which the choice was whether to test or do something else (treat or withhold treatment), priors were not systematically misweighed relative to accuracy. When two tests were compared, priors were underweighed relative to accuracy. Justifications agreed with the conclusions reached by analysis of the preferences. When evaluating a test, subjects do not seem to understand that high priors make hit rates more relevant, while low priors make false-alarm rates more relevant. Subjects do, however, understand that a large cost of not treating diseased patients makes hit rates more relevant, while a large cost for treating nondiseased patients makes false-alarm rates more relevant. The overweighing of costs seems to result from the use of a heuristic in which the subject tends to minimize the probability of the worst kind of error, regardless of Other parameters.     

Classification as diagnostic reasoning

An ongoing goal in the field of categorization has been to determine how objects’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed.     

Causal reasoning and the diagnostic process

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context. Procedures: The analysis will discuss different conceptions of causal reasoning in medical diagnosis, discriminating primarily between narrow causal diagnosis and more thorough causal explanation. The theory of causes as non-redundant factors in effective causal complexes is used as an analytical background. Causal explanations are performed according to different causal models. Such models of diagnosis are assumptions concerning structure and mechanisms, which cannot be directly or immediately observed. Conceptions and results of causal search strategies differ, according to the focus of the searcher. Causal reasoning is also seen in diagnosis in a more extensive meaning: the pin-pointing of factors responsible for the condition of the patient at any time during the course of disease. Conclusion: Causal reasoning and diagnosis go well in hand, especially if both concepts are widened. The theory of causes as non-redundant components in effective causal complexes, modulated by what is referred to as the stop problem and causal fields, is valuable for explaining the many aspects of causal reasoning in medical diagnosis.     

Memory indexing of sequential symptom processing in diagnostic reasoning

In diagnostic reasoning, knowledge about symptoms and their likely causes is retrieved to generate and update diagnostic hypotheses in memory. By letting participants learn about causes and symptoms in a spatial array, we could apply eye tracking during diagnostic reasoning to trace the activation level of hypotheses across a sequence of symptoms and to evaluate process models of diagnostic reasoning directly. Gaze allocation on former locations of symptom classes and possible causes reflected the diagnostic value of initial symptoms, the set of contending hypotheses, consistency checking, biased symptom processing in favor of the leading hypothesis, symptom rehearsal, and hypothesis change. Gaze behavior mapped the reasoning process and was not dominated by auditorily presented symptoms. Thus, memory indexing proved applicable for studying reasoning tasks involving linguistic input. Looking at nothing revealed memory activation because of a close link between conceptual and motor representations and was stable even after one week.     

Memory activation of multiple hypotheses in sequential diagnostic reasoning

In sequential diagnostic reasoning, observed evidence activates hypotheses about possible causes in memory. These memory activations have been previously examined with a probe reaction task for problems with a single correct diagnosis. We applied this process tracing method to ambiguous problems with multiple compatible hypotheses. When participants reasoned about the causes of ambiguous symptom sequences, they were prompted to respond to probes representing hypotheses. The response time to a probe was shorter if the current support for the respective hypothesis was stronger indicating that the processing of compatible hypotheses can be traced. For sequences with two equally supported hypotheses, the initial hypothesis was more often chosen as the final diagnosis (a primacy effect). Probe reaction times suggest that the initial hypothesis has been activated more strongly already early, when it was finally chosen as the diagnosis. Nevertheless, substantial variance in response times limits the task's applicability for process tracing.     

Memory activation and the availability of explanations in sequential diagnostic reasoning

In the field of diagnostic reasoning, it has been argued that memory activation can provide the reasoner with a subset of possible explanations from memory that are highly adaptive for the task at hand. However, few studies have experimentally tested this assumption. Even less empirical and theoretical work has investigated how newly incoming observations affect the availability of explanations in memory over time. In this article we present the results of 2 experiments in which we address these questions. While participants diagnosed sequentially presented medical symptoms, the availability of potential explanations in memory was measured with an implicit probe reaction time task. The results of the experiments were used to test 4 quantitative cognitive models. The models share the general assumption that observations can activate and inhibit explanations in memory. They vary with respect to how newly incoming observations affect the availability of explanations over time. The data of both experiments were predicted best by a model in which all observations in working memory have the same potential to activate explanations from long-term memory and in which these observations do not decay. The results illustrate the power of memory activation processes and show where additional deliberate reasoning strategies might come into play.     

From symptoms to causes: Diversity effects in diagnostic reasoning

A single causal agent can often give rise to a cascade of consequences that can be envisioned as a branching pathway in which symptoms are the terminal nodes. In three studies, we investigated whether reasoning about root causes on the basis of such symptoms would conform to a diversity effect analogous to that found in inductive reasoning about properties of hierarchically organized categories. A strong diversity effect was found both for reasoning about medical diseases that drew on existing background knowledge and for reasoning that did not. Specifically, the presence of a root cause was more likely to be induced when the symptoms present were further apart in the branching structure.     

Thinking about THOG: Sources of error in a deductive reasoning problem

Summary Three experiments are reported which investigated performance on a number of variants of the THOG problem. Collectively, these experiments indicated that subjects are more accurate (a) when the problem is phrased in a way that clearly separates the properties of the positive instance provided from the properties about which hypotheses must be generated; and (b) when subjects are required to generate hypotheses about what might be the chosen properties. It is suggested that people have little difficulty in carrying out the various thought processes that are required to reach the correct solution to the standard THOG problem, but that it is beyond most people to carry out all of these processes simultaneously.     

Heuristics and biases in diagnostic reasoning: II. Congruence,information, and certainty

Six experiments were carried out to examine possible heuristics and biases in the evaluation of yes-or-no questions for the purpose of hypothesis testing. In some experiments, the prior probability of the hypotheses and the conditional probabilities of the answers given each hypothesis were elicited from the subjects; in other experiments, they were provided. We found the following biases (systematic departures from a normative model), and interviews and justifications suggested that each was the result of a corresponding heuristic: Congruence bias. Subjects overvalued questions that have a high probability of a positive result given the most likely hypothesis. This bias was apparently reduced when alternative hypotheses or probabilities of negative results are explicitly stated. Information bias. Subjects evaluated questions as worth asking even when there is no answer that can change the hypothesis that will be accepted as a basis for action. Certainty bias. Subjects overvalued questions that have the potential to establish, or rule out, one or more hypotheses with 100% probability. These heuristics are explained in terms of the idea that people fail to consider certain arguments against the use of questions that seem initially worth asking, specifically, that a question may not distinguish likely hypotheses or that no answer can change the hypothesis accepted as a basis for action.     

The Expertise Question Revisited: Horizontal and Vertical Expertise

This article deals with the developments taking place in the field of family therapy from the expertise perspective, and as an expert practice. This development is looked at as a dialectical movement between two types of expertise, namely vertical and horizontal expertise. This spatial metaphor is put forward as a complementary alternative to the prevailing modern/postmodern debate. A brief account of the development of the study of science and of expertise is provided. The author suggests that the richness of current family therapeutic thinking owes something to both these different modes of expertise, the division not being as exclusive as some postmodernist authors suggest.     

Clinical psychologists' theory-based representations of mental disorders predict their diagnostic reasoning and memory

The theory-based model of categorization posits that concepts are represented as theories, not feature lists. Thus, it is interesting that the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) established atheoretical guidelines for mental disorder diagnosis. Five experiments investigated how clinicians handled an atheoretical nosology. Clinicians' causal theories of disorders and their responses on diagnostic and memory tasks were measured. Participants were more likely to diagnose a hypothetical patient with a disorder if that patient had causally central rather than causally peripheral symptoms according to their theory of the disorder. Their memory for causally central symptoms was also biased. Clinicians are cognitively driven to use theories despite decades of practice with the atheoretical DSM.     

Associative and causal reasoning accounts of causal induction: Symmetries and asymmetries in predictive and diagnostic inferences

Associative and causal reasoning accounts are probably the two most influential types of accounts of causal reasoning processes. Only causal reasoning accounts predict certain asymmetries between predictive (i.e., reasoning from causes to effects) and diagnostic (i.e., reasoning from effects to causes) inferences regarding cue-interaction phenomena (e.g., the overshadowing effect). In the experiments reported here, we attempted to delimit the conditions under which these asymmetries occur. The results show that unless participants perceived the relevance of causal information to solving the task, predictive and diagnostic inferences were symmetrical. Specifically, Experiments 1A and 1B showed that implicitly stressing the relevance of causal information by having participants review the instructions favored the presence of asymmetries between predictive and diagnostic situations. In addition, Experiment 2 showed that explicitly stressing the relevance of causal information by stating the importance of the causal role of events after the instructions were given also favored the asymmetry.     

Watching diagnoses develop: Eye movements reveal symptom processing during diagnostic reasoning

Finding a probable explanation for observed symptoms is a highly complex task that draws on information retrieval from memory. Recent research suggests that observed symptoms are interpreted in a way that maximizes coherence for a single likely explanation. This becomes particularly clear if symptom sequences support more than one explanation. However, there are no existing process data available that allow coherence maximization to be traced in ambiguous diagnostic situations, where critical information has to be retrieved from memory. In this experiment, we applied memory indexing, an eye-tracking method that affords rich time-course information concerning memory-based cognitive processing during higher order thinking, to reveal symptom processing and the preferred interpretation of symptom sequences. Participants first learned information about causes and symptoms presented in spatial frames. Gaze allocation to emptied spatial frames during symptom processing and during the diagnostic response reflected the subjective status of hypotheses held in memory and the preferred interpretation of ambiguous symptoms. Memory indexing traced how the diagnostic decision developed and revealed instances of hypothesis change and biases in symptom processing. Memory indexing thus provided direct online evidence for coherence maximization in processing ambiguous information.     

Understanding Expertise

It is sometimes said that experts know and decide 'in the moment', not by theoretical or propositionally articulated reflection. What differentiates expert from novice is not that the former know a lot more than the latter, but that their knowledge and the way they use it is qualitatively different. Although this idea is common in the education literature, especially the literature on professional education, it has received little sustained philosophical treatment. I shall argue that the idea of a distinct expert epistemology is not warranted. I argue that what differentiates the epistemic standpoint of experts is not what or how they know, let alone how they deploy knowledge in decision-making, but their capacity for learning. This capacity for learning is plausibly a function of their epistemic station broadly conceived, in particular the nature of their capacities for attention.