Clinical reasoning plays a major role in the ability of doctors to make diagnoses and decisions. It is considered as the physician's most critical competence, and has been widely studied by physicians, educationalists, psychologists and sociologists. Since the 1970s, many theories about clinical reasoning in medicine have been put forward.
This paper aims at exploring a comprehensive approach: the “dual-process theory”, a model developed by cognitive psychologists over the last few years.
After 40 years of sometimes contradictory studies on clinical reasoning, the dual-process theory gives us many answers on how doctors think while making diagnoses and decisions. It highlights the importance of physicians’ intuition and the high level of interaction between analytical and non-analytical processes. However, it has not received much attention in the medical education literature. The implications of dual-process models of reasoning in terms of medical education will be discussed.
Dual process; analytical reasoning; expertise; professional intuition; hypothetico-deduction; pattern recognition; diagnostic errors
Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA.
First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker.
We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease.
We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).
Numerous studies have shown that diagnostic failure depends upon a variety of factors. Psychological factors are fundamental in influencing the cognitive performance of the decision maker. In this first of two papers, we discuss the basics of reasoning and the Dual Process Theory (DPT) of decision making. The general properties of the DPT model, as it applies to diagnostic reasoning, are reviewed. A variety of cognitive and affective biases are known to compromise the decision-making process. They mostly appear to originate in the fast intuitive processes of Type 1 that dominate (or drive) decision making. Type 1 processes work well most of the time but they may open the door for biases. Removing or at least mitigating these biases would appear to be an important goal. We will also review the origins of biases. The consensus is that there are two major sources: innate, hard-wired biases that developed in our evolutionary past, and acquired biases established in the course of development and within our working environments. Both are associated with abbreviated decision making in the form of heuristics. Other work suggests that ambient and contextual factors may create high risk situations that dispose decision makers to particular biases. Fatigue, sleep deprivation and cognitive overload appear to be important determinants. The theoretical basis of several approaches towards debiasing is then discussed. All share a common feature that involves a deliberate decoupling from Type 1 intuitive processing and moving to Type 2 analytical processing so that eventually unexamined intuitive judgments can be submitted to verification. This decoupling step appears to be the critical feature of cognitive and affective debiasing.
Patient safety; Cognitive biases; Decision making; Diagnostic errors
Age differences in affective/experiential and deliberative processes have important theoretical implications for cancer decision making as cancer is often a disease of older adulthood. We examine evidence for adult age differences in affective and deliberative information processes, review the sparse evidence about age differences in decision making and introduce how dual process theories and their findings might be applied to cancer decision making. Age-related declines in the efficiency of deliberative processes predict poorer-quality decisions as we age, particularly when decisions are unfamiliar and the information is numeric. However, age-related adaptive processes, including an increased focus on emotional goals and greater experience, can influence decision making and potentially offset age-related declines. A better understanding of the mechanisms that underlie cancer decision processes in our aging population should ultimately allow us to help older adults to better help themselves.
neoplasms; decision making; aged; judgment; affect; emotions; psychology; review
The improved support of complex medical decision making will require a greater understanding of the cognitive processes of physicians. Decision making in medicine often involves the careful weighing of uncertain and ill-structured information from various sources. In this paper a cognitive approach to analyzing complex intensive care decision making is outlined. The study described involved the presentation of case descriptions of systematically varied complexity, to two levels of physicians: intensive care residents (intermediates) and intensive care specialists (experts). Subjects were asked to "think aloud" in providing treatment and management decisions for the cases. The audiotaped protocols were then analyzed for the use of decision strategies and for key aspects of decision making. It was found that expert subjects tended to focus on developing a more refined situational analysis of the decision problem. The study results are being used in the design of a system for aiding physicians in making complex decisions in intensive care medicine.
This study analyzed nurses' perceptions of clinical decision making (CDM) in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with descriptive frequencies, t-tests, Chi-Square test, and linear regression. Nurses' decision making was categorized into analytic-systematic, intuitive-interpretive, and quasi-rational models of CDM. Most nurses reported the use of quasi-rational models during CDM thereby supporting the tenet that cognition most often includes properties of both analysis and intuition. Increased use of intuitive-interpretive models of CDM was associated with years in present job, further education, male gender, higher age, and working in predominantly surgical units.
Richard Martin's aim in this paper is to present a critical method of making ethical decisions in a medical context. He feels that such a reflective method provides the best means of making the appropriate decisions in given situations. It is based on Dr Martin's experience in applying ethical theory while collaborating with physicians in the daily course of clinical practice. Through his giving of a functional definition of medical ethics, his descriptions of an analytical model, the significance of values for clinical decision-making and the advocacy role of medical ethicists and their relationships with clinicians, Richard Martin sets out his own value-intention as regards an ideal decision process. He stresses that his argument is of particular importance to his fellow ethicists who should continuously and vigorously examine the creative interaction of faith and fact in their own inquiry and action. Dr Martin concludes by stating that physicians and ethicists can work together to accomplish their common aim, which is, of course, the health and well-being of the patient.
A better theoretical base for understanding professional behaviour change is needed to support evidence-based changes in medical practice. Traditionally strategies to encourage changes in clinical practices have been guided empirically, without explicit consideration of underlying theoretical rationales for such strategies. This paper considers a theoretical framework for reasoning from within psychology for identifying individual differences in cognitive processing between doctors that could moderate the decision to incorporate new evidence into their clinical decision-making.
Parallel dual processing models of reasoning posit two cognitive modes of information processing that are in constant operation as humans reason. One mode has been described as experiential, fast and heuristic; the other as rational, conscious and rule based. Within such models, the uptake of new research evidence can be represented by the latter mode; it is reflective, explicit and intentional. On the other hand, well practiced clinical judgments can be positioned in the experiential mode, being automatic, reflexive and swift. Research suggests that individual differences between people in both cognitive capacity (e.g., intelligence) and cognitive processing (e.g., thinking styles) influence how both reasoning modes interact. This being so, it is proposed that these same differences between doctors may moderate the uptake of new research evidence. Such dispositional characteristics have largely been ignored in research investigating effective strategies in implementing research evidence. Whilst medical decision-making occurs in a complex social environment with multiple influences and decision makers, it remains true that an individual doctor's judgment still retains a key position in terms of diagnostic and treatment decisions for individual patients. This paper argues therefore, that individual differences between doctors in terms of reasoning are important considerations in any discussion relating to changing clinical practice.
It is imperative that change strategies in healthcare consider relevant theoretical frameworks from other disciplines such as psychology. Generic dual processing models of reasoning are proposed as potentially useful in identifying factors within doctors that may moderate their individual uptake of evidence into clinical decision-making. Such factors can then inform strategies to change practice.
Dual-process approaches of decision-making examine the interaction between affective/intuitive and deliberative processes underlying value judgment. From this perspective, decisions are supported by a combination of relatively explicit capabilities for abstract reasoning and relatively implicit evolved domain-general as well as learned domain-specific affective responses. One such approach, the somatic markers hypothesis (SMH), expresses these implicit processes as a system of evolved primary emotions supplemented by associations between affect and experience that accrue over lifetime, or somatic markers. In this view, somatic markers are useful only if their local capability to predict the value of an action is above a baseline equal to the predictive capability of the combined rational and primary emotional subsystems. We argue that decision-making has often been conceived of as a linear process: the effect of decision sequences is additive, local utility is cumulative, and there is no strong environmental feedback. This widespread assumption can have consequences for answering questions regarding the relative weight between the systems and their interaction within a cognitive architecture. We introduce a mathematical formalization of the SMH and study it in situations of dynamic, non-linear decision chains using a discrete-time stochastic model. We find, contrary to expectations, that decision-making events can interact non-additively with the environment in apparently paradoxical ways. We find that in non-lethal situations, primary emotions are represented globally over and above their local weight, showing a tendency for overcautiousness in situated decision chains. We also show that because they tend to counteract this trend, poorly attuned somatic markers that by themselves do not locally enhance decision-making, can still produce an overall positive effect. This result has developmental and evolutionary implications since, by promoting exploratory behavior, somatic markers would seem to be beneficial even at early stages when experiential attunement is poor. Although the model is formulated in terms of the SMH, the implications apply to dual systems theories in general since it makes minimal assumptions about the nature of the processes involved.
dual system decision-making; affect; decision chains; dynamic decision-making; somatic marker hypothesis; discrete-time Markov chains
Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology and machine learning, to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and which give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection.
Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.
Dual process models of decision-making suggest that behavior is mediated by a spontaneous behavior selection process or by a more deliberative evaluation of behavioral options. We examined whether the deliberative system moderates the influence of spontaneous cognition on HIV-risk behaviors. A measure of spontaneous sex-related associations (word association), a measure of deliberative working memory capacity (operation span), and two measures of sexual behavior (condom use and multiple partners) were assessed in a cross-sectional study among 490 adult drug offenders. Significant effects were observed among men but not among women in two latent interaction models. In a novel finding, the accessibility of spontaneous safe sex-related associations was significantly more predictive of condom use among men with higher working memory capacity than among men with lower capacity. These results have implications for the design of interventions to promote safe sex practices.
Dual process; Implicit cognition; HIV risk; Decision making; Working memory capacity
Human judgment and decision making (JDM) has substantial room for improvement, especially among adolescents. Increased technological and social complexity “ups the ante” for developing impactful JDM interventions and aids. Current explanatory advances in this field emphasize dual processing models that incorporate both experiential and analytic processing systems. According to these models, judgment and decisions based on the experiential system are rapid and stem from automatic reference to previously stored episodes. Those based on the analytic system are viewed as slower and consciously developed. These models also hypothesize that metacognitive (self-monitoring) activities embedded in the analytic system influence how and when the two systems are used. What is not included in these models is the development of an intersection between the two systems. Because such an intersection is strongly suggested by memory and educational research as the basis of wisdom/expertise, the present paper describes an Integrated Judgment and Decision-Making Model (IJDM) that incorporates this component. Wisdom/expertise is hypothesized to contain a collection of schematic structures that can emerge from the accumulation of similar episodes or repeated analytic practice. As will be argued, in comparisons to dual system models, the addition of this component provides a broader basis for selecting and designing interventions to improve adolescent JDM. Its development also has implications for generally enhancing cognitive interventions by adopting principles from athletic training to create automated, expert behaviors.
Judgment and decision making (JDM); Self-regulation; Metacognition; Interventions for JDM; Analytically-created schemas
According to an influential dual-process model, a moral judgment is the outcome of a rapid, affect-laden process and a slower, deliberative process. If these outputs conflict, decision time is increased in order to resolve the conflict. Violations of deontological principles proscribing the use of personal force to inflict intentional harm are presumed to elicit negative affect which biases judgments early in the decision-making process. This model was tested in three experiments. Moral dilemmas were classified using (a) decision time and consensus as measures of system conflict and (b) the aforementioned deontological criteria. In Experiment 1, decision time was either unlimited or reduced. The dilemmas asked whether it was appropriate to take a morally questionable action to produce a “greater good” outcome. Limiting decision time reduced the proportion of utilitarian (“yes”) decisions, but contrary to the model’s predictions, (a) vignettes that involved more deontological violations logged faster decision times, and (b) violation of deontological principles was not predictive of decisional conflict profiles. Experiment 2 ruled out the possibility that time pressure simply makes people more like to say “no.” Participants made a first decision under time constraints and a second decision under no time constraints. One group was asked whether it was appropriate to take the morally questionable action while a second group was asked whether it was appropriate to refuse to take the action. The results replicated that of Experiment 1 regardless of whether “yes” or “no” constituted a utilitarian decision. In Experiment 3, participants rated the pleasantness of positive visual stimuli prior to making a decision. Contrary to the model’s predictions, the number of deontological decisions increased in the positive affect rating group compared to a group that engaged in a cognitive task or a control group that engaged in neither task. These results are consistent with the view that early moral judgments are influenced by affect. But they are inconsistent with the view that (a) violation of deontological principles are predictive of differences in early, affect-based judgment or that (b) engaging in tasks that are inconsistent with the negative emotional responses elicited by such violations diminishes their impact.
moral decision-making; moral judgment; dual process; emotion
Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social–cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions.
decision making; aggressive behavior; social information processing; child development; social behavior
To review physician, patient, and contextual factors that affect treatment decision-making in older adults diagnosed with cancer and relate these factors to theoretical models of decision-making.
PubMed (1966-April 2010), PsycINFO (1967-April 2010) and CINAHL (1982-April 2010) databases were searched to access relevant medical, psychological and nursing literature.
Physician factors in treatment decisions include physician personal beliefs and values, expertise, practice type, perception of lowered life expectancy, medical factors, power, and communication style. Patient factors include personal beliefs and values, ethnicity, decisional control preferences, previous health-related experience, perception of the decision-making process, and personal factors. Contextual factors include availability of caregiver, lack of insurance, poor financial status, and geographical barrier. The interplay of physician, patient, and contextual factors are not well understood. Existing models of decision-making are not sufficient to explicate TDM process in older adults diagnosed with cancer.
Clinical studies in older adult patient population using a longitudinal and prospective design are needed to examine real-time interplay of patient, physician, and contextual factors and to better understand how these divergent factors influenced actual treatment decisions.
Implications for Nursing
Oncology nurses can advocate for a patient’s autonomy during TDM by coaching them to seek evidence-based discussion of various treatment options, benefits and risks assessments, and truthful discussion of the probability of success for each treatment option from their physicians. Oncology nurses must promote an informed treatment decisions that are consistent with a patient’s personal preference and values within the limits of the patient’s personal contexts.
Decision making; Geriatric oncology; Ethics; Patient education
This study investigates the medical decision making process in both expert and novice physicians in an attempt to identify specific weaknesses in this decision making process which might be targeted for medical decision support. Two groups of physicians, practicing gastroenterologists and third and fourth year medical students, were given simulated patient management problems in a paper-and-pencil format. The two problems used were both from the same medical domain, liver disease, and consisted of an initial clinical scenario followed by two opportunities to order and obtain laboratory test results. The results were analyzed using a hypothetico-deductive model of decision making as a basis for evaluation. It is assumed that weaknesses of the novices relative to the experts in one of the four primary decision making phases described by this model may indicate a possible need for decision support.
Recent research in neuroeconomics suggests that social economic decision-making may be best understood as a dual-systems process, integrating the influence of deliberative and affective subsystems. However, most of this research has focused on young adults and it remains unclear whether our current models extend to healthy aging. To address this question, we investigated the behavioral and neural basis of simple economic decisions in 18 young and 20 older healthy adults. Participants made decisions which involved accepting or rejecting monetary offers from human and non-human (computer) partners in an Ultimatum Game, while undergoing functional magnetic resonance imaging (fMRI). The partners’ proposals involved splitting an amount of money between the two players, and ranged from $1 to $5 (from a $10 pot). Relative to young adults, older participants expected more equitable offers and rejected moderately unfair offers ($3) to a larger extent. Imaging results revealed that, relative to young participants, older adults had higher activations in the left dorsolateral prefrontal cortex (DLPFC) when receiving unfair offers ($1–$3). Age group moderated the relationship between left DLPFC activation and acceptance rates of unfair offers. In contrast, older adults showed lower activation of bilateral anterior insula in response to unfair offers. No age group difference was observed when participants received fair ($5) offers. These findings suggest that healthy aging may be associated with a stronger reliance on computational areas subserving goal maintenance and rule shifting (DLPFC) during interactive economic decision-making. Consistent with a well-documented “positivity effect”, older age may also decrease recruitment of areas involved in emotion processing and integration (anterior insula) in the face of social norm violation.
decision-making; aging; dorsolateral prefrontal cortex; insula
Many important decisions involve outcomes that are either probabilistic or delayed. Based on similarities in decision preferences, models of decision making have postulated that the same psychological processes may underlie decisions involving probabilities (i.e., risky choice) and decisions involving delay (i.e., intertemporal choice). Equivocal behavioral evidence has made this hypothesis difficult to evaluate. However, a combination of functional neuroimaging and behavioral data may allow identification of differences between these forms of decision making. Here, we used functional magnetic resonance imaging (fMRI) to examine brain activation in subjects making a series of choices between pairs of real monetary rewards that differed either in their relative risk or their relative delay. While both sorts of choices evoked activation in brain systems previously implicated in executive control, we observed clear distinctions between these forms of decision making. Notably, choices involving risk evoked greater activation in posterior parietal and lateral prefrontal cortices, whereas choices involving delay evoked greater activation in the posterior cingulate cortex and the striatum. Moreover, activation of regions associated with reward evaluation predicted choices of a more-risky option, whereas activation of control regions predicted choices of more-delayed or less-risky options. These results indicate that there are differences in the patterns of brain activation evoked by risky and intertemporal choices, suggesting that the two domains utilize at least partially distinct sets of cognitive processes.
risk; delay; utility; posterior parietal cortex; intraparietal sulcus; choice
The concept of risk thresholds has been studied in medical decision making for over 30 years. During that time, physicians have been shown to be poor at estimating the probabilities required to use this method. To better assess physician risk thresholds and to more closely model medical decision making, we set out to design and test a method that derives thresholds from actual physician treatment recommendations. Such an approach would avoid the need to ask physicians for estimates of patient risk when trying to determine individual thresholds for treatment. Assessments of physician decision making are increasingly relevant as new data are generated from clinical research. For example, recommendations made in the setting of ocular hypertension are of interest as a large clinical trial has identified new risk factors that should be considered by physicians. Precisely how physicians use this new information when making treatment recommendations has not yet been determined.
We derived a new method for estimating treatment thresholds using ordinal logistic regression and tested it by asking ophthalmologists to review cases of ocular hypertension before expressing how likely they would be to recommend treatment. Fifty-eight physicians were recruited from the American Glaucoma Society. Demographic information was collected from the participating physicians and the treatment threshold for each physician was estimated. The method was validated by showing that while treatment thresholds varied over a wide range, the most common values were consistent with the 10-15% 5-year risk of glaucoma suggested by expert opinion and decision analysis.
This method has advantages over prior means of assessing treatment thresholds. It does not require physicians to explicitly estimate patient risk and it allows for uncertainty in the recommendations. These advantages will make it possible to use this method when assessing interventions intended to alter clinical decision making.
We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have significant advantages over standard decision analysis. We compare MDPs to standard Markov-based simulation models by solving the problem of the optimal timing of living-donor liver transplantation using both methods. Both models result in the same optimal transplantation policy and the same total life expectancies for the same patient and living donor. The computation time for solving the MDP model is significantly smaller than that for solving the Markov model. We briefly describe the growing literature of MDPs applied to medical decisions.
Markov decision processes; decision analysis; Markov processes
How humans think and make decisions is important in understanding behaviour. Hence an understanding of cognitive processes among physicians may inform our understanding of behaviour in relation to evidence implementation strategies. A personality theory, Cognitive-Experiential Self Theory (CEST) proposes a relationship between different ways of thinking and behaviour, and articulates pathways for behaviour change. However prior to the empirical testing of interventions based on CEST, it is first necessary to demonstrate its suitability among a sample of healthcare workers.
To investigate the relationship between thinking styles and the knowledge and clinical practices of doctors directly involved in the management of acute coronary syndromes.
Self-reported doctors' thinking styles (N = 74) were correlated with results from a survey investigating knowledge, attitudes, and clinical practice, and evaluated against recently published acute coronary syndrome clinical guidelines.
Guideline-discordant practice was associated with an experiential style of thinking. Conversely, guideline-concordant practice was associated with a higher preference for a rational style of reasoning.
Findings support that while guidelines might be necessary to communicate evidence, other strategies may be necessary to target discordant behaviours. Further research designed to examine the relationships found in the current study is required.
The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decision-making. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.
decision-making; diffusion model; optimality; neurons; social insects; sequential probability ratio test
In the diagnostic reasoning process medical students and novice physicians need to be made aware of the diagnostic values of the clinical findings (including history, signs, and symptoms) to make an appropriate diagnostic decision. Diagnostic reasoning has been understood in light of two paradigms on clinical reasoning: problem solving and decision making. They advocate the reasoning strategies used by expert physicians and the statistical models of reasoning, respectively. Evidence-based medicine (EBM) applies decision theory to the clinical diagnosis, which can be a challenging topic in medical education.
This theoretical article tries to compare evidence-based diagnosis with expert-based strategies in clinical diagnosis and also defines a novel concept of category-oriented likelihood ratio (LR) to propose a new model combining both aforementioned methods.
Evidence-based medicine advocates the use of quantitative evidence to estimate the probability of diseases more accurately and objectively; however, the published evidence for a given diagnosis cannot practically be utilized in primary care, especially if the patient is complaining of a nonspecific problem such as abdominal pain that could have a long list of differential diagnoses. In this case, expert physicians examine the key clinical findings that could differentiate between broader categories of diseases such as organic and non-organic disease categories to shorten the list of differential diagnoses. To approach nonspecific problems, not only do the experts revise the probability estimate of specific diseases, but also they revise the probability estimate of the categories of diseases by using the available clinical findings.
To make this approach analytical and objective, we need to know how much more likely it is for a key clinical finding to be present in patients with one of the diseases of a specific category versus those with a disease not included in that category. In this paper, we call this value category-oriented LR.
In this article, we review the brain and cognitive processes underlying the development of arithmetic skills. This review focuses primarily on the development of arithmetic skills in children, but it also summarizes relevant findings from adults for which a larger body of research currently exists. We integrate relevant findings and theories from experimental psychology and cognitive neuroscience. We describe the functional neuroanatomy of cognitive processes that influence and facilitate arithmetic skill development, including calculation, retrieval, strategy use, decision making, as well as working memory and attention. Building on recent findings from functional brain imaging studies, we describe the role of distributed brain regions in the development of mathematical skills. We highlight neurodevelopmental models that go beyond the parietal cortex role in basic number processing, in favor of multiple neural systems and pathways involved in mathematical information processing. From this viewpoint, we outline areas for future study that may help to bridge the gap between the cognitive neuroscience of arithmetic skill development and educational practice.