Neuronal activation patterns during self-referential pain imagination.

In clinical assessments and pain therapy, patients are asked to imagine themselves in pain. However, the underlying neuronal processes remain poorly understood. Prior research has focused on empathy for pain or reported small sample sizes. Thus, the present study aimed to promote the neurobiological understanding of self-referential pain imagination. We hypothesised to find activation contrasts (pain vs. no pain) across pain-related areas and expected two of the most prominent predictors of chronic pain, pain sensitivity (PS) and locus of control (LoC), to be moderators. In an fMRI study, N = 82 participants completed a pain imagination task, in which they were asked to imagine themselves in painful and non-painful situations presented in the form of pictures and texts. After each trial, they were instructed to give painfulness ratings. As a laboratory measure of PS, electrical pain thresholds were assessed. A questionnaire was completed to measure LoC. Across presentation modes we found activity contrasts in previously pain-related regions, such as the prefrontal, supplementary motor, primary motor, somatosensory and posterior parietal cortices, and the cerebellum. We found positive associations of PS and external LoC with painfulness ratings, and a negative correlation between PS and internal LoC. Despite our hypotheses, neither PS nor internal LoC were significant predictors of the BOLD-signal contrasts. Though future studies are needed to draw further conclusions, our results provide preliminary evidence of a potential neuronal imagination-perception overlap in pain.

Do patients prefer a human doctor, artificial intelligence, or a blend, and is this preference dependent on medical discipline? Empirical evidence and implications for medical practice.

Today the doctor-patient relationship typically takes place in a face-to-face setting. However, with the advent of artificial intelligence (AI) systems, two further interaction scenarios are possible: an AI system supports the doctor's decision regarding diagnosis and/or treatment while interacting with the patient, or an AI system could even substitute the doctor and hence a patient interacts with a chatbot (i.e., a machine) alone. Against this background, we report on an online experiment in which we analyzed data from N = 1,183 people. The data was collected in German-speaking countries (Germany, Austria, Switzerland). The participants were asked to imagine they had been suffering from medical conditions of unknown origin for some time and that they were therefore visiting a health center to seek advice from a doctor. We developed descriptions of patient-doctor interactions (referred to as vignettes), thereby manipulating the patient's interaction partner: (i) human doctor, (ii) human doctor with an AI system, and (iii) an AI system only (i.e., chatbot). Furthermore, we manipulated medical discipline: (i) cardiology, (ii) orthopedics, (iii) dermatology, and (iv) psychiatry. Based on this 3 × 4 experimental within-subjects design, our results indicate that people prefer a human doctor, followed by a human doctor with an AI system, and an AI system alone came in last place. Specifically, based on these 12 hypothetical interaction situations, we found a significant main effect of a patient's interaction partner on trust, distrust, perceived privacy invasion, information disclosure, treatment adherence, and satisfaction. Moreover, perceptions of trust, distrust, and privacy invasion predicted information disclosure, treatment adherence, and satisfaction as a function of interaction partner and medical discipline. We found that the situation in psychiatry is different from the other three disciplines. Specifically, the six outcome variables differed strongly between psychiatry and the three other disciplines in the "human doctor with an AI system" condition, while this effect was not that strong in the other conditions (human doctor, chatbot). These findings have important implications for the use of AI in medical care and in the interaction between patients and their doctors.

No increased prevalence of prosopagnosia in aphantasia: Visual recognition deficits are small and not restricted to faces.

Aphantasia and prosopagnosia are both rare conditions with impairments in visual cognition. While prosopagnosia refers to a face recognition deficit, aphantasics exhibit a lack of mental imagery. Current object recognition theories propose an interplay of perception and mental representations, making an association between recognition performance and visual imagery plausible. While the literature assumes a link between aphantasia and prosopagnosia, other impairments in aphantasia have been shown to be rather global. Therefore, we assumed that aphantasics do not solely exhibit impairments in face recognition but rather in general visual recognition performance, probably moderated by stimulus complexity. To test this hypothesis, 65 aphantasics were compared to 55 controls in a face recognition task, the Cambridge Face Memory Test, and a corresponding object recognition task, the Cambridge Car Memory Test. In both tasks, aphantasics performed worse than controls, indicating mild recognition deficits without face-specificity. Additional correlations between imagery vividness and performance in both tasks were found, suggesting that visual imagery influences visual recognition not only in imagery extremes. Stimulus complexity produced the expected moderation effect but only for the whole imagery-spectrum and only with face stimuli. Overall, the results imply that aphantasia is linked to a general but mild deficit in visual recognition.

The Role of Personality, Political Attitudes and Socio-Demographic Characteristics in Explaining Individual Differences in Fear of Coronavirus: A Comparison Over Time and Across Countries.

Since the emergence of the SARS-CoV-2 pandemic in December 2019 about 500,000 people died within the first 6 months. The virus itself, as well as the related political decisions, intensified an increasing feeling of fear in billions of people worldwide. However, while some people remained unperturbed, others experienced panic over the current situation. In order to investigate individual differences in the perceptions, emotions and behaviors in response to the Coronavirus pandemic, an online survey was conducted between 6th and 27th of March 2020. Participants included 7309 individuals from 96 countries, who provided information on socio-demographics, personality, political orientation and general life satisfaction. To determine the specificity of fear of Coronavirus, we also investigated fear related to two other current political issues: the refugee and the climate crises. Overall, in parallel with the escalation of the pandemic, fear of Coronavirus increased significantly over the 22-day period, with the strongest predictors being the personality variable neuroticism, as well as education, sex and being an at-risk person. A detailed longitudinal analysis of the largest sample, Germany, revealed that political orientation was also an important predictor of fear of Coronavirus. Specifically, conservatives were more afraid of Coronavirus than liberals. However, as the perceived threat of the virus increased, the influence of political orientation disappeared, whereas personality remained a stable predictor. The pattern of results regarding the perceived threat of the refugee and climate crises painted a different picture: political orientation was by far the best predictor, more important even than personality. Conservatives were more worried about the refugees, and liberals about climate change. Cross-cultural analyses showed pronounced differences between countries, dependent on the crisis. Nonetheless, the importance of personality for the prediction of fear of Coronavirus remained stable over time and across the world within the investigated 22-day period.