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At least 19 recordsLinked to original sources

PubMed1994

Heuristic processing can bias systematic processing: effects of source credibility, argument ambiguity, and task importance on attitude judgment.

High- and low-task-importance Ss read a strong or weak unambiguous message or an ambiguous message that was attributed to a high- or low-credibility source. Under low task importance, heuristic processing of the credibility cue was the sole determinant of Ss' attitudes, regardless of argument ambiguity or strength. When task importance was high and message content was unambiguous, systematic processing alone determined attitudes when this content contradicted the validity of the credibility heuristic; when message content did not contradict this heuristic, systematic and heuristic processing determined attitudes independently. Finally, when task importance was high and message content was ambiguous, heuristic and systematic processing again both influenced attitudes. Yet, source credibility affected persuasion partly through its impact on the valence of systematic processing, confirming that heuristic processing can bias systematic processing when evidence is ambiguous. Implications for persuasion and other social judgment phenomena are discussed.

Adult↗

PubMedDate not supplied

Heuristics reasoning in diagnostic judgment.

Heuristics inquiry is an exciting new approach to understanding diagnostic reasoning. Heuristics are short-cut mental strategies that streamline information. Although heuristics allow for faster processing of information than analytic methods, they can lead to errors because not all information is considered. This article describes three heuristics particularly relevant to diagnostic reasoning: accessibility, similarity, and anchoring and adjustment. Specific factors thought to influence heuristic reasoning are reviewed and analyzed. Last, interventions to be tested for both practice and education are presented.

Clinical Competence↗

PubMed1980

Algorithmic and heuristic processing of information by the nervous system.

Starting from the fact that the nervous system must discover the information it needs, the author describes the way it decodes the received message. The logical circuits of the nervous system, submitting the received signals to a process by means of which information brought is discovered step by step, participates in decoding the message. The received signals, as information, can be algorithmically or heuristically processed. Algorithmic processing is done according to precise rules, which must be fulfilled step by step. By algorithmic processing, one develops somatic and vegetative reflexes as blood pressure, heart frequency or water metabolism control. When it does not dispose of precise rules of information processing or when algorithmic processing needs a very long time, the nervous system must use heuristic processing. This is the feature that differentiates the human brain from the electronic computer that can work only according to some extremely precise rules. The human brain can work according to less precise rules because it can resort to trial and error operations, and because it works according to a form of logic. Working with superior order signals which represent the class of all inferior type signals from which they begin, the human brain need not perform all the operations that it would have to perform by superior type of signals. Therefore the brain tries to submit the received signals to intensive as possible superization. All informational processing, and especially heuristical processing, is accompanied by a certain affective color and the brain cannot operate without it. Emotions, passions and sentiments usually complete the lack of precision of the heuristical programmes. Finally, the author shows that informational and especially heuristical processes study can contribute to a better understanding of the transition from neurological to psychological activity.

Cybernetics↗

PubMed1985

Differential diagnosis and the competing-hypotheses heuristic. A practical approach to judgment under uncertainty and Bayesian probability.

Evaluating the same diagnostic information across the plausible competing diagnoses is a practical strategy (ie, heuristic) to guide decision making in the face of uncertainty. The prevalence of use of this competing-hypotheses heuristic by 89 first-year house officers was examined in three simulated patient cases. Results indicated that only a minority (24%) of the house officers selected optimal diagnostic information consistent with this Bayesian heuristic across all three cases. Almost all (97%) of the house officers selecting optimal diagnostic information were able to identify the most probable diagnosis specified by Bayes' theorem, while only a chance number (53%) of house officers selecting nonoptimal information were able to identify the most probable diagnosis. The competing-hypotheses heuristic is discussed within the context of diagnostic problem-solving models derived from the literature on medical decision making and clinicopathological conference case records. It is suggested that the heuristic, which does not necessitate any mathematical calculations, may be useful as a complement to clinical judgment.

Bayes Theorem↗

PubMed1991

Heuristic determination of quantitative data for knowledge acquisition in medicine.

Knowledge acquisition for medical knowledge bases can be aided by programs that suggest possible values for portions of the data. The paper presents an experiment which was used in designing a heuristic to help the process of knowledge acquisition. The heuristic helps to determine numerical data from stylized literature excerpts in the context of knowledge acquisition for the QMR medical knowledge base. Quantitative suggestions from the heuristics are shown to agree substantially with the data incorporated in the final version of the knowledge base. The experiment shows the potential of knowledge base specific heuristics in simplifying the task of knowledge base creation.

Artificial Intelligence↗

PubMed1996

Medical heuristics: the silent adjudicators of clinical practice.

Robust scientific conclusions are too sparse to inform fully most of the choices that physicians must make about tests and treatments. Instead, ad hoc rules of thumb, or "heuristics," must guide them, and many of these are problematic. Physicians extrapolate from the small samples studied by clinical trials to general populations, but they do so inconsistently. Many physicians live by rules that dictate "not treating the numbers," correcting abnormalities slowly, achieving diagnostic certainty, and operating now to avoid "greater" risk in the future. Yet in each case, historical trends or statistical realities suggest either doing the opposite or investing in more discriminating heuristics. The heuristics of medicine should be discussed, criticized, refined, and then taught. More uniform use of explicit and better heuristics could lead to less practice variation and more efficient medical care.

Clinical Competence↗

PubMed1990

Indeterminacy of the isomorphism heuristic.

We are less prone today than in Köhler's time to believe in a unified science. The Gestalt program could therefore safely abandon the psychophysical isomorphism heuristic if it wished to. Indeterminacy of the heuristic might be a reason for doing so. The determination of the components involved in the isomorphism of the visual field as well as the electrocortical events would have to occur simultaneously. In the Gestalt program, however, components are determined by their position in the whole. They can therefore not be compared in different contexts, so no independent test of a candidate heuristic is possible.

Arousal↗

PubMed1994

Children's theory of mind: Fodor's heuristics examined.

The study reported in this manuscript examined Fodor's (1992) argument that standard false belief tasks used in developmental research seriously underestimate young children's understanding of false belief. The problem of these tasks according to Fodor is that always a unique, actual state of affairs (e.g., chocolate is now in cupboard B) is contrasted with a believed state of affairs (e.g., chocolate is still in cupboard A). Fodor argued that this uniqueness feature may be critical because young children with limited computational resources have to trade reliability of behavioral prediction for computational simplicity and, therefore, may rely on simple heuristics such as "Predict that the agent will act in a way that will satisfy his desires". In the standard false belief task such a heuristic will result in a unique, but incorrect (reality-based) prediction. Fodor's expectation is that when young children are not misled into applying such heuristics by the possibility of unique, reality-based prediction, then their true competence for belief-based reasoning will become evident. The present study contrasted for two different belief tasks a traditional unique version with a non-unique version, but found no support for Fodor's expectation as both 3- and 4-year-old children did not improve in the non-unique version.

Child, Preschool↗

PubMed1991

A heuristic approach to predicting the tertiary structure of bovine somatotropin.

A combination of a heuristic approach and energy minimization was used to predict the three-dimensional structure of bovine somatotropin (bSt), also known as bovine growth hormone, a protein of 191 amino acids. The starting points for energy minimizations were generated from the following two types of inputs: (a) the amino acid sequence and (b) the heuristic inputs, which were derived according to physical, chemical, and biological principles by piecing together all useful information available. The predicted 3-D structure of the bSt molecule has all the features observed in four-helix bundle proteins. The four alpha-helices in bSt are intimately packed to form an assembly with an approximately square cross section. All the adjacent alpha-helices are antiparallel, with a somewhat tilted angle between each of the adjacent pairs so that the assembly of the four helices looks like a left-handed twisted bundle. There are two disulfide bonds in the bSt structure: one "hooking" the middle of a long loop with helix 4 so as to pull the long loop onto the surface of the helix bundle and the other "hooking" the C-terminal segment with the same helix so as to force the C-terminal segment to bend toward the helix bundle. As a consequence, a considerable part of the surface of the four-helix bundle is closely packed or intimately embraced by the loop segments. The predicted bSt structure has a hydrophobic core and a hydrophilic exterior surface. The energetic analysis of the predicted bSt structure indicates that the interaction between helices and loops plays a dominant role in stabilizing the four-helix bundle structure from the viewpoint of both electrostatic and nonbonded interactions. A technique called FOLD was meanwhile developed, by which one can fold a polypeptide chain into any shape as desired. This tool proved to be very useful during the heuristic model-building process.

Algorithms↗

PubMed1985

Heuristic approaches to decision-making in the delivery of primary health care within developing regions.

The delivery of primary health care (PHC) services is now recognized as a crucial element in the development of low-income regions. Effective delivery of these services requires the ability to solve a variety of policy decision problems. Research has demonstrated the utility of operations research/management science (OR/MS) models and methodologies in the analysis and solution of such problems. However, this approach may be limited in some regions by the data and computational requirements of the models. Intuitive approaches in the spirit of OR/MS models, termed heuristics, can provide an effective alternative in such cases. The current paper proposes a general heuristic procedure for solving problems of PHC delivery in developing regions. The heuristic is applied, in detail, to the problem of identifying "best" community financing schemes for PHC services in low-income sections of Rio de Janeiro, Brazil.

Decision Making↗

PubMed1989

[Psychotherapeutic activity as a realization of therapeutic heuristics. A process comparison of 3 therapy forms from a new perspective].

A new research perspective for analyzing the psychotherapeutic process is discussed. Instead of the usual method-oriented thinking, a heuristic understanding of psychotherapy is presented that focuses on the goals of therapeutic activity. The instrument developed for this purpose, the Heuristic Rating Scales, is described. Using data from a comparative treatment study, the suitability of these rating scales for analyzing therapeutic activity is tested statistically. Finally, the three therapy forms examined are described in terms of the realization in terms of the realization of therapeutic goals (heuristics) and compared with each other.

Adaptation, Psychological↗

PubMed1994

The influence of experience on community health nurses' use of the similarity heuristic in diagnostic reasoning.

This study explored the diagnostic reasoning of community health nurses, examining the association between nursing experience and use of the cognitive heuristic similarity. Two types of similarity reasoning were examined, similarity to a single prototype (SSP) and similarity to a population prototype (SPP). The hypothesis of the study, derived from heuristics theory and ACT theory predicted that experts would be more likely to make diagnoses by similarity assessment than less experienced nurses. A random sample of 214 community health nurses was studied. Each subject completed eight diagnostic problems included in the Clinical Inference Questionnaire (CIQ) that was developed for this study to measure judging by similarity. Most of the nurses in this study, regardless of experience, used similarity assessment as a basis for some diagnoses. Experts were more likely than less experienced nurses to judge by similarity in population prototype problems but not in single prototype problems. These findings suggest that the diagnostic process includes similarity reasoning and imply that this process cannot be well understood without further exploration of the role of cognitive heuristics.

Cognition↗

PubMed2025

MRDtarget: A heuristic Gaussian approach for optimizing targeted capture regions to enhance Minimal Residual Disease detection.

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.

Humans↗

PubMed1986

Heuristics, normative models, and the value of serial test patterns.

This paper deals with living systems at the level of the human organism, including all subsystems. It argues that, in medicine, conclusions about the adequacy of clinical heuristics, vis-à-vis normative models, may be suspect until the models are more adequately designed and the heuristics better defined. Through a theoretical analysis, it shows that dynamic information, ordinarily ignored in normative models yet often present in biological systems, can have a profound impact on the cost-effectiveness of test use. Empirically, it also shows that explicit clinical policies ignoring dynamic test patterns can lead to substantial loss in diagnostic information. These results motivate research on clinical strategies for using dynamic information. They also suggest unmet needs in evaluating tests, providing decision support, and educating physicians in cost-effective test use.

Cost-Benefit Analysis↗

PubMed1988

Automatic classification of two-dimensional gel electrophoresis pictures by heuristic clustering analysis: a step toward machine learning.

The interpretation of two-dimensional gel electrophoresis (2-DGE) profiles can be facilitated by artificial intelligence and machine learning programs. We have incorporated into our 2-DGE computer analysis system (termed MELANIE-Medical Electrophoresis Analysis Interactive Expert system) a program which automatically classifies 2-DGE patterns using heuristic clustering analysis. This program is a step toward machine learning. In this publication, we describe the classification method and the preliminary results obtained with liver biopsy electrophoretograms. Heuristic clustering is also compared to other classification techniques.

Algorithms↗

PubMed1987

Validation of the first step of the heuristic refinement method for the derivation of solution structures of proteins from NMR data.

A new method for the analysis of NMR data in terms of the solution structure of proteins has been developed. The method consists of two steps: first a systematic search of the conformational space to define the region allowed by the initial set of experimental constraints, and second, the narrowing of this region by the introduction of additional constraints and optional refinement procedures. The search of the conformational space is guided by heuristics to make it computationally feasible. The method is therefore called the heuristic refinement method and is coded in an expert system called PROTEAN. The paper describes the validation of the first step of the method using an artificial NMR data set generated from the known crystal structure of sperm whale carbon monoxymyoglobin. It is shown that the initial search procedure yields a low-resolution structure of the myoglobin molecule, accurately reproducing its main topological features, and that the precision of the structure depends on the quality of the initial data set.

Expert Systems↗

PubMed1990

The use of heuristic strategies in the interpretation of pronouns.

The aim of the two experiments reported here was to distinguish between two heuristic strategies that have been proposed to account for the assignment of pronouns: the subject assignment strategy and the parallel function strategy. According to the subject assignment strategy, a pronoun is assigned to a preceding subject noun phrase, whereas according to the parallel function strategy, a pronoun is assigned to a previous noun phrase in the same grammatical position as the pronoun. These two strategies were tested by examining the interpretation of single object pronouns, first in a reading task and second in an assignment task. In both experiments, there was a strong preference for assigning an object pronoun to the preceding subject noun phrase, thus supporting the subject assignment strategy. However, this was only the case for pronouns that were linguistically ambiguous. When assignment was constrained by gender, there was no effect of either strategy. It is suggested that heuristic strategies are only used in the absence of other strong cues to assignment.

Attention↗

PubMed1989

Heuristic determination of relevant diagnostic procedures in a medical expert system for gynecology.

Many professions including medicine have standard operating procedures for the performance of their tasks. In the construction of expert systems, knowledge engineers have exploited this fact in devising heuristic rules that mimic the standard practice among such personnel (i.e., experts). This article suggests that the expert system designer should not stop at the level of the standard operating procedure heuristic but should instead investigate the reasons that the standard procedures have become standard. Because the experts in a field often do not understand the reasons for the standard operating procedures of their profession, this effort not only rewards the system designer but the expert as well. Because medical training does not always emphasize the logical reasoning underlying certain standard operating procedures, the ability to perform this reasoning is especially important in medicine. Further, a medical expert system for consultation or education would make a valuable impact by incorporating such knowledge and inference rules. This article investigates the development of a computerized medical expert system that applies the principles of artificial intelligence by limiting the number of questions and tests to find the solution for an ill-defined complex problem. Finally, we describe a logic program that tests the basic ideas.

Artificial Intelligence↗