Decades of reasoning and decision-making research have established that human judgment is often biased by intuitive heuristics. Recent "error" or bias detection studies have focused on reasoners' abilities to detect whether their heuristic answer conflicts with logical or probabilistic principles. A key open question is whether there are individual differences in this bias detection efficiency. Here we present three studies in which co-registration of different error detection measures (confidence, response time and confidence response time) allowed us to assess bias detection sensitivity at the individual participant level in a range of reasoning tasks. The results indicate that although most individuals show robust bias detection, as indexed by increased latencies and decreased confidence, there is a subgroup of reasoners who consistently fail to do so. We discuss theoretical and practical implications for the field.

D-dimer is an indirect marker of fibrinolysis and fibrin turnover; this molecule exhibits unique properties as a biological marker of hemostatic abnormalities as well as an indicator of intravascular thrombosis. D-dimer is a soluble fibrin degradation product that results from the systematic degradation of vascular thrombi through the fibrinolytic mechanism. Because of this, the D-dimer serves as a valuable marker of activation of coagulation and fibrinolysis in a number of clinical scenarios. Most commonly, D-dimer has been extensively investigated for excluding the diagnosis of venous thromboembolism (VTE) and is used routinely for this indication. In addition, D-dimer has been evaluated for determining the optimal duration of anticoagulation in VTE patients, for diagnosing and monitoring disseminated intravascular coagulation, and for monitoring other conditions in which the patient is at high risk of bleeding or thrombosis. Limitations of the assay include D-dimer elevation in a constellation of clinical scenarios (age, pregnancy, and cancer) and lack of clinical standardization.

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Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian inferences relative to normalized formats (e.g., probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e., transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct vs. incorrect reasoners depart, and how individual differences might influence this time point.

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