Increasing data suggest that for medical school students the stress of academic and psychologicaldemands can impair social emotions that are a core aspect of compassion and ultimately physiciancompetence. Few interventions have proven successful for enhancing physician compassion inways that persist in the face of suffering and that enable sustained caretaker well-being. To addressthis issue, the current study was designed to (1) investigate the feasibility of cognitively-basedcompassion training (CBCT) for second-year medical students, and (2) test whether CBCT decreasesdepression, enhances compassion, and improves daily functioning in medical students. Comparedto the wait-list group, students randomized to CBCT reported increased compassion, and decreasedloneliness and depression. Changes in compassion were most robust in individuals reporting highlevels of depression at baseline, suggesting that CBCT may benefit those most in need by breakingthe link between personal suffering and a concomitant drop in compassion
Although kindness-based contemplative practices are increasingly employed by clinicians and cognitive researchers to enhance prosocial emotions, social cognitive skills, and well-being, and as a tool to understand the basic workings of the social mind, we lack a coherent theoretical model with which to test the mechanisms by which kindness-based meditation may alter the brain and body. Here, we link contemplative accounts of compassion and loving-kindness practices with research from social cognitive neuroscience and social psychology to generate predictions about how diverse practices may alter brain structure and function and related aspects of social cognition. Contingent on the nuances of the practice, kindness-based meditation may enhance the neural systems related to faster and more basic perceptual or motor simulation processes, simulation of another’s affective body state, slower and higher-level perspective-taking, modulatory processes such as emotion regulation and self/other discrimination, and combinations thereof. This theoretical model will be discussed alongside best practices for testing such a model and potential implications and applications of future work.
Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.
Together, these results suggest that T may influence social behavior by increasing the frequency of words related to aggression, sexuality, and status, and that it may alter the quality of interactions with an intimate partner by amplifying emotions via swearing.
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