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.     

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.     

Expertise and error in diagnostic reasoning

An investigation is presented in which a computer simulation model (DIAGNOSER) is used to develop and test predictions for behavior of subjects in a task of medical diagnosis. The first experiment employed a process-tracing methodology in order to compare hypothesis generation and evaluation behavior of DIAGNOSER with individuals at different levels of expertise (students, trainees, experts). A second experiment performed with only DIAGNOSER identified conditions under which errors in reasoning in the first experiment could be related to interpretation of specific data items. Predictions derived from DIAGNOSER's performance were tested in a third experiment with a new sample of subjects. Data from the three experiments indicated that (1) form of diagnostic reasoning was similar for all subjects trained in medicine and for the simulation model, (2) substance of diagnostic reasoning employed by the simulation model was parable with that of the more expert subjects, and (3) errors in subjects' reasoning were attributable to deficiencies in disease knowledge and the interpretation of specific patient data cues predicted by the simulation model.     

Medical diagnostic reasoning: Epistemological modeling as a strategy for design of computer-based consultation programs

The complexity of cognitive emulation of human diagnostic reasoning is the major challenge in the implementation of computer-based programs for diagnostic advice in medicine. We here present an epistemological model of diagnosis with the ultimate goal of defining a high-level language for cognitive and computational primitives. The diagnostic task proceeds through three different phases: hypotheses generation, hypotheses testing and hypotheses closure. Hypotheses generation has the inferential form of abduction (from findings to hypotheses) constrained under the criterion of plausibility. Hypotheses testing is achieved by a deductive inference (from generated hypotheses to expected findings), followed by an eliminative induction, constrained under the criterion of covering, which matches expected findings against patient's findings to select the best explanation. Hypotheses closure is a deductive-inductive type of inference very similar to the inferences operating in hypotheses testing. In this case induction matches the consequences of the generated hypotheses against the patient's characteristics or preferences under the criterion of utility. By using the language exploited in this epistemological model, it is possible to describe the cognitive tasks underlying the most influential knowledge-based diagnostic systems.     

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.     

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.     

Common-sense reasoning as proto-scientific agent activity

We wish to model common-sense reasoning in situations where it contains some of the ingredients typical of proto-scientific reasoning, with a view to future elaboration and proof of concept. To model this proto-scientific narrative, we employ the integrative formal computational machinery we have been developing and implementing for rational cooperative epistemic agents. In our logic-based framework, agents can update their own and each other's theories, which are comprised of knowledge, active rules, integrity constraints, queries, abducibles, and preferences; they can engage in abductive reasoning involving updatable preferences; set each other queries; react to circumstances; plan and carry out actions; and revise their theories and preferences by means of concurrent updates on self and others.     

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.     

Belief-desire reasoning as a process of selection

Human learning may depend upon domain specialized mechanisms. A plausible example is rapid, early learning about the thoughts and feelings of other people. A major achievement in this domain, at about age four in the typically developing child, is the ability to solve problems in which the child attributes false beliefs to other people and predicts their actions. The main focus of theorizing has been why 3-year-olds fail, and only recently have there been any models of how success is achieved in false-belief tasks. Leslie and Polizzi (Inhibitory processing in the false-belief task: Two conjectures. Developmental Science, 1, 247-254, 1998) proposed two competing models of success, which are the focus of the current paper. The models assume that belief-desire reasoning is a process which selects a content for an agent's belief and an action for the agent's desire. In false belief tasks, the theory of mind mechanism (ToMM) provides plausible candidate belief contents, among which will be a 'true-belief.' A second process reviews these candidates and by default will select the true-belief content for attribution. To succeed in a false-belief task, the default content must be inhibited so that attention shifts to another candidate belief. In traditional false-belief tasks, the protagonist's desire is to approach an object. Here we make use of tasks in which the protagonist has a desire to avoid an object, about which she has a false-belief. Children find such tasks much more difficult than traditional tasks. Our models explain the additional difficulty by assuming that predicting action from an avoidance desire also requires an inhibition. The two processing models differ in the way that belief and desire inhibitory processes combine to achieve successful action prediction. In six experiments we obtain evidence favoring one model, in which parallel inhibitory processes cancel out, over the other model, in which serial inhibitions force attention to a previously inhibited location. These results are discussed in terms of a set of simple proposals for the modus operandi of a domain specific learning mechanism. The learning mechanism is in part modular--the ToMM--and in part penetrable--the Selection Processor (SP). We show how ToMM-SP can account both for competence and for successful and unsuccessful performance on a wide range of belief-desire tasks across the preschool period. Together, ToMM and SP attend to and learn about mental states.     

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.     

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.     

Relations as transformations: Implications for analogical reasoning

We present two experiments assessing whether the size of a transformation instantiating a relation between two states of the world (e.g., shrinks) is a performance factor affecting analogical reasoning. The first experiment finds evidence of transformation size as a significant factor in adolescent analogical problem solving while the second experiment finds a similar effect on adult analogical reasoning using a markedly different analogical completion paradigm. The results are interpreted as providing evidence for the more general framework that cognitive representations of relations are best understood as mental transformations.     

Relations as transformations: implications for analogical reasoning

We present two experiments assessing whether the size of a transformation instantiating a relation between two states of the world (e.g., shrinks) is a performance factor affecting analogical reasoning. The first experiment finds evidence of transformation size as a significant factor in adolescent analogical problem solving while the second experiment finds a similar effect on adult analogical reasoning using a markedly different analogical completion paradigm. The results are interpreted as providing evidence for the more general framework that cognitive representations of relations are best understood as mental transformations.     

Analogical reasoning as a tool of epidemiological investigation

Few, if any, scientific inquiries are conducted against a background of complete knowledge, a background in which inquirers are in possession of the full facts that relate to a particular question or issue. More often than not, scientists are compelled to conduct their deliberations in contexts of epistemic uncertainty, in which partial knowledge or even a total absence of knowledge characterise inquiry. Nowhere is this epistemic uncertainty more evident, or indeed more successfully controlled, than in the branch of scientific inquiry called epidemiology. In this paper, I examine how epidemiologists overcome the unique challenges to inquiry that are posed by epistemic uncertainty. In specific terms, I contend that epidemiologists employ analogical reasoning strategies in an attempt to advance their inquiries in situations that are epistemically uncertain. The context for my claims will be the early inquiries that were conducted into the emergence of HIV/AIDS in the United States. I argue that early scientific work in relation to HIV/AIDS was directly premised upon epidemiological investigations in which analogical reasoning with hepatitis B had featured significantly. I conclude that epidemiological investigations of AIDS exemplify the capacity of analogical reasoning to advance inquiry under conditions of epistemic uncertainty. To this extent, analogical reasoning should be a concern both to those who address practical problems of uncertainty management and to those who pursue theoretical debates within argumentation studies and epistemology.     

The latent variable approach as applied to transitive reasoning

We discuss the limitations of hypothesis testing using (quasi-) experiments in the study of cognitive development and suggest latent variable modeling as a viable alternative to experimentation. Latent variable models allow testing a theory as a whole, incorporating individual differences with respect to developmental processes or abilities in the model. Experiments, in contrast, aim at testing hypotheses that refer to a specific part of a theory; also they ignore individual differences or model the individual differences using age group as a proxy for developmental stage. Drawing on a sample of 409 5–13-year olds, we demonstrate the advantages of latent variable models in the area of transitive reasoning. A comparison of three models showed that the latent variable model that represented fuzzy trace theory had a better fit than the models representing Piaget's theory or linear ordering theory.     

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.     

Attitudes,values and moral reasoning as predictors of delinquency

Attitudes to institutional authority, strength of support for moral values and maturity of socio‐moral reasoning have all been identified as potential predictors of adolescent delinquency. In a sample of 12–15‐year‐old boys (N = 789), after checking for effects of age, IQ, social background and ethnicity, self‐reported delinquency was significantly and independently predicted by attitudes to authority (r = ?.47) and moral values (r = ?.27), but not by a structural measure of moral reasoning level (r = ?.04). The findings argue for closer attention to the developmental determinants of attitudes to institutional authority and strength of moral value commitments than these have received hitherto. On the other hand, lack of an association between moral reasoning level and delinquent conduct does leave unanswered questions about the consequences of this kind of moral competence.     

Response options and presentation format as contributors to conditional reasoning

The present study examines whether students' inability to solve conditional reasoning problems, shown in previous studies, is at least partially attributable to having to choose among logically incorrect response options. In two experiments, students evaluated conclusions to conditional reasoning problems where one of several response options was either the standard, Sometimes true, or the more logically appropriate, Could be true. Decision accuracy was related to the logical appropriateness of the response options available. This relationship was replicated across different problem types and formats.