Bariatric surgery and brain health: A longitudinal observational study investigating the effect of surgery on cognitive function and gray matter volume

Dietary modifications leading to weight loss have been suggested as a means to improve brain health. In morbid obesity, bariatric surgery (BARS)—including different procedures, such as vertical sleeve gastrectomy (VSG), gastric banding (GB), or Roux-en-Y gastric bypass (RYGB) surgery—is performed to induce rapid weight loss. Combining reduced food intake and malabsorption of nutrients, RYGB might be most effective, but requires life-long follow-up treatment. Here, we tested 40 patients before and six months after surgery (BARS group) using a neuropsychological test battery and compared them with a waiting list control group. Subsamples of both groups underwent structural MRI and were examined for differences between surgical procedures. No substantial differences between BARS and control group emerged with regard to cognition. However, larger gray matter volume in fronto-temporal brain areas accompanied by smaller volume in the ventral striatum was seen in the BARS group compared to controls. RYGB patients compared to patients with restrictive treatment alone (VSG/GB) had higher weight loss, but did not benefit more in cognitive outcomes. In sum, the data of our study suggest that BARS might lead to brain structure reorganization at long-term follow-up, while the type of surgical procedure does not differentially modulate cognitive performance

When self-humanization leads to algorithm aversion: What users want from decision support systems on prosocial microlending platforms

Decision support systems are increasingly being adopted by various digital platforms. However, prior research has shown that certain contexts can induce algorithm aversion, leading people to reject their decision support. This paper investigates how and why the context in which users are making decisions (for-profit versus prosocial microlending decisions) affects their degree of algorithm aversion and ultimately their preference for more human-like (versus computer-like) decision support systems. The study proposes that contexts vary in their affordances for self-humanization. Specifically, people perceive prosocial decisions as more relevant to self-humanization than for-profit contexts, and, in consequence, they ascribe more importance to empathy and autonomy while making decisions in prosocial contexts. This increased importance of empathy and autonomy leads to a higher degree of algorithm aversion. At the same time, it also leads to a stronger preference for human-like decision support, which could therefore serve as a remedy for an algorithm aversion induced by the need for self-humanization. The results from an online experiment support the theorizing. The paper discusses both theoretical and design implications, especially for the potential of anthropomorphized conversational agents on platforms for prosocial decision-making