Thinking fast or slow? Functional magnetic resonance imaging reveals stronger connectivity when experienced neurologists diagnose ambiguous casesvan den Berg, B., de Bruin, A., Marsman, J-B. C., Lorist, M., Schmidt, H., Aleman, A. & Snoek, J., 2-Mar-2020, In : Brain Communications.
Research output: Contribution to journal › Article › Academic › peer-review
For almost 40 years, thinking about reasoning has been dominated by dual process theories. This model, consisting of two distinct types of human reasoning, one fast and effortless, the other slow and deliberate, has also been applied to medical diagnosis. Medical experts are trained to diagnose patients based on their symptoms. When symptoms are prototypical for a certain diagnosis, practitioners may rely on fast, recognition-based reasoning. However, if they are confronted with ambiguous clinical information slower, analytical reasoning is required. To examine the neural underpinnings of these two hypothesized forms of reasoning, sixteen highly experienced clinical neurologists were asked to diagnose two types of medical cases, straightforward and ambiguous cases, while functional magnetic resonance imaging was being recorded. Compared to reading control sentences, diagnosing cases resulted in increased activation in brain areas typically found to be active during reasoning such as the caudate nucleus, and frontal and parietal cortical regions. In addition, we found vast increased activity in the cerebellum. Regarding the activation differences between the two types of reasoning, no pronounced differences were observed in terms of regional activation. Notable differences were observed, though, in functional connectivity: cases containing ambiguous information showed stronger connectivity between specific regions in the frontal, parietal, and temporal cortex in addition to the cerebellum. Based on these results we propose that the higher demands in terms of controlled cognitive processing during analytical medical reasoning may be subserved by stronger communication between key regions for detecting and resolving uncertainty.
|Publication status||E-pub ahead of print - 2-Mar-2020|