The Script Concordance Tests (SCTs) are an examination modality introduced by decree in the French National Ranking Exam for medical students in 2024. Their objective is to evaluate clinical reasoning in situations of uncertainty. In practice, SCTs assess the impact of new information on the probability of a hypothesis formulated a priori based on an authentic clinical scenario. This approach resembles probabilistic (or Bayesian) reasoning. Due to the uncertainty associated with the explored clinical situation, SCTs do not compare the student\'s response to an expected one in a theoretical knowledge reference. Instead, the distribution of responses from a panel of experienced physicians is used to establish the question\'s scoring scale. Literature data suggest that physicians, even experienced ones, like most humans, often exhibit biased intuitive probabilistic reasoning. These biases raise questions about the relevance of using expert panel responses as scoring scales for SCTs.

A recent study by Johnston, Brecht, and Nieder (2023, Current Biology, 33, 3238-3243) finds that carrion crows associate varying rates of reinforcement with novel arbitrary stimuli and make optimal decisions when they must later choose between stimulus pairs. These results demonstrate that crows are capable of not only storing information about reward probabilities in their memory but also making optimal choices based on this information even a month later.

Improving scientific reasoning enables students to navigate the challenges of learning science. Teachers use Lawson\'s classroom test of scientific reasoning (LCTSR) to measure scientific reasoning. The LCTSR is a two-tiered assessment that uses content-based questions and explanation statements. Researchers have found that if a student answers a knowledge-based question correctly but selects an incorrect explanation statement, there may be an element of guessing or an established misconception. Misconceptions are beliefs that students hold that are not based on scientific evidence. The present study added a confidence variable to the LCTSR, which measures how confident students regarded their responses in both tiers. Selecting a correct response to a knowledge-based question while providing an incorrect explanation and having a high confidence rating indicates an established misconception. The confidence variable is, therefore, a measure of an established scientific misconception and is the basis of the present study. The present study analyzed the responses of 71 first-year university students enrolled in an introductory physics course. The LCTSR results indicate that students performed the best in the conservation reasoning dimension and the worst in the proportional reasoning dimension. In all scientific reasoning dimensions, more than half the students chose the incorrect explanation for each context question. Students\' confidence responses surpassed their performance in three of the 14 LCTSR items. The low frequency of correct answers and the statistically significant correlation between LCTSR items and confidence suggest possible misconceptions in students\' scientific reasoning skills.

Objective: Obesity is a significant risk factor for adverse outcomes following coronavirus infection (COVID-19). However, BMI fails to capture differences in the body fat distribution, the critical driver of metabolic health. Conventional statistical methodologies lack functionality to investigate the causality between fat distribution and disease outcomes. Methods: We applied Bayesian network (BN) modelling to explore the mechanistic link between body fat deposition and hospitalisation risk in 459 participants with COVID-19 (395 non-hospitalised and 64 hospitalised). MRI-derived measures of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat were included. Conditional probability queries were performed to estimate the probability of hospitalisation after fixing the value of specific network variables. Results: The probability of hospitalisation was 18% higher in people living with obesity than those with normal weight, with elevated VAT being the primary determinant of obesity-related risk. Across all BMI categories, elevated VAT and liver fat (>10%) were associated with a 39% mean increase in the probability of hospitalisation. Among those with normal weight, reducing liver fat content from >10% to <5% reduced hospitalisation risk by 29%. Conclusion: Body fat distribution is a critical determinant of COVID-19 hospitalisation risk. BN modelling and probabilistic inferences assist our understanding of the mechanistic associations between imaging-derived phenotypes and COVID-19 hospitalisation risk.

Findings on children\'s proportional reasoning abilities strongly vary across studies. This might be due to the different contexts that can be used in proportional problems: fair-sharing, mixtures, and probability. A review of the scientific literature suggests that the context of proportional problems may not only impact the difficulty of the problem, but that it also plays an important role in how children approach the problems. In other words, different contexts might elicit different (erroneous) thinking strategies. The aim of the present study was to investigate the role of context in third graders\' (n = 305) proportional reasoning abilities. Results showed that children performed significantly better in a fair-sharing context compared to a mixture and a probability context. No evidence was found for a difference in performance on the mixture and the probability context. However, the kind of erroneous answers that were given in the mixture and probability context differed slightly, with more additive answers in the mixture context and more one-dimensional answers in the probability context. These findings suggest that the type of answers elicited by proportional problems might depend on the specific context in which the problem is presented.

Reasoning is a process of inference from given premises to new conclusions. Deductive reasoning is truth-preserving and conclusions can only be either true or false. Probabilistic reasoning is based on degrees of belief and conclusions can be more or less likely. While deductive reasoning requires people to focus on the logical structure of the inference and ignore its content, probabilistic reasoning requires the retrieval of prior knowledge from memory. Recently, however, some researchers have denied that deductive reasoning is a faculty of the human mind. What looks like deductive inference might actually also be probabilistic inference, only with extreme probabilities. We tested this assumption in an fMRI experiment with two groups of participants: one group was instructed to reason deductively, the other received probabilistic instructions. They could freely choose between a binary and a graded response to each problem. The conditional probability and the logical validity of the inferences were systematically varied. Results show that prior knowledge was only used in the probabilistic reasoning group. These participants gave graded responses more often than those in the deductive reasoning group and their reasoning was accompanied by activations in the hippocampus. Participants in the deductive group mostly gave binary responses and their reasoning was accompanied by activations in the anterior cingulate cortex, inferior frontal cortex, and parietal regions. These findings show that (1) deductive and probabilistic reasoning rely on different neurocognitive processes, (2) people can suppress their prior knowledge to reason deductively, and (3) not all inferences can be reduced to probabilistic reasoning.

Decision-making often entails the accumulation of evidence. Previous studies suggested that people with obsessive-compulsive disorder (OCD) process decision-making differently from healthy controls. Both their compulsive behavior and obsessive thoughts may influence the evidence accumulation process, yet the previous studies disagreed on the reason. To address this question, we employed a probabilistic reasoning task in which subjects made two alternative forced choices by viewing a series of visual stimuli. These stimuli carried probabilistic information toward the choices. While the OCD patients achieved similar accuracy to the control, they took longer time and accumulated more evidence, especially in difficult trials in which the evidence strength was low. We further modeled the subjects\' decision making as a leaky drifting diffusion process toward two collapsing bounds. The control group showed a higher drifting rate than the OCD group, indicating that the OCD group was less sensitive to evidence. Together, these results demonstrated that the OCD patients were less efficient than the control at transforming sensory information into evidence. However, their evidence accumulation was comparable to the healthy control, and they compensated for their decision-making accuracy with longer reaction times.

Cyberchondria is characterized by excessive health-related online search behavior associated with an unfounded escalation of concerns about common symptomatology. It often co-occurs with health anxiety. We investigated whether base-rate neglect-the cognitive bias to ignore a priori probabilities (e.g., of serious diseases)-plays a significant role in cyberchondria and health anxiety. 368 participants were randomly assigned to eight experimental conditions, manipulating the base-rate (30 % vs. 70 %), the judgment domain (health-neutral versus health-related), and the salience of base-rate information (low vs. high) in a 2×2×2 between-subjects design when asking them for probability judgments with versus without disease relevance. We found that high salience decreased base-rate neglect in participants with low, but not in those with elevated levels of either cyberchondria or health anxiety. Under low salience conditions, however, both cyberchondria and health anxiety severity were uncorrelated with base-rate neglect. These effects were independent of whether health-related or health-neutral problems were evaluated. Our findings suggest a domain-general probabilistic reasoning style that may play a causal role in the pathogenesis of cyberchondria and health anxiety.

In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions.
We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual\'s behavior, could predict treatment response to Metacognitive Training using machine learning.
We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level.
Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.

Delusions in schizophrenia are false beliefs that are assigned certainty and not afforded the scrutiny that normally gives rise to doubt, even under conditions of weak evidence. The goal of the current functional magnetic resonance imaging (fMRI) study is to identify the brain network(s) involved in gathering information under conditions of weak evidence, in people with schizophrenia experiencing delusions. fMRI activity during probabilistic reasoning in people with schizophrenia experiencing delusions (n = 29) compared to people with schizophrenia not experiencing delusions (n = 41) and healthy controls (n = 41) was observed when participants made judgments based on evidence that weakly or strongly matched (or mismatched) with the focal hypothesis. A brain network involved in visual attention was strongly elicited for conditions of weak evidence for healthy controls and patients not experiencing delusions, but this increase was absent for patients experiencing delusions. This suggests that the state associated with delusions manifests in fMRI as reduced activity in an early visual attentional process whereby weak evidence is incorrectly stamped as conclusive, manifestating as a feeling of fluency and misplaced certainty, short-circuiting the search for evidence, and providing a candidate neural process for \'seeding\' delusions.