Statistical learning shapes pain perception and prediction independently of external cues.

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Evaluation of a COVID-19 fundamental nursing care guideline versus usual care: The COVID-NURSE cluster randomized controlled trial.

AIM: To evaluate the impact of usual care plus a fundamental nursing care guideline compared to usual care only for patients in hospital with COVID-19 on patient experience, care quality, functional ability, treatment outcomes, nurses' moral distress, patient health-related quality of life and cost-effectiveness. DESIGN: Parallel two-arm, cluster-level randomized controlled trial. METHODS: Between 18th January and 20th December 2021, we recruited (i) adults aged 18 years and over with COVID-19, excluding those invasively ventilated, admitted for at least three days or nights in UK Hospital Trusts; (ii) nurses caring for them. We randomly assigned hospitals to use a fundamental nursing care guideline and usual care or usual care only. Our patient-reported co-primary outcomes were the Relational Aspects of Care Questionnaire and four scales from the Quality from the Patient Perspective Questionnaire. We undertook intention-to-treat analyses. RESULTS: We randomized 15 clusters and recruited 581 patient and 418 nurse participants. Primary outcome data were available for 570-572 (98.1%-98.5%) patient participants in 14 clusters. We found no evidence of between-group differences on any patient, nurse or economic outcomes. We found between-group differences over time, in favour of the intervention, for three of our five co-primary outcomes, and a significant interaction on one primary patient outcome for ethnicity (white British vs. other) and allocated group in favour of the intervention for the 'other' ethnicity subgroup. CONCLUSION: We did not detect an overall difference in patient experience for a fundamental nursing care guideline compared to usual care. We have indications the guideline may have aided sustaining good practice over time and had a more positive impact on non-white British patients' experience of care. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: We cannot recommend the wholescale implementation of our guideline into routine nursing practice. Further intervention development, feasibility, pilot and evaluation studies are required. IMPACT: Fundamental nursing care drives patient experience but is severely impacted in pandemics. Our guideline was not superior to usual care, albeit it may sustain good practice and have a positive impact on non-white British patients' experience of care. REPORTING METHOD: CONSORT and CONSERVE. PATIENT OR PUBLIC CONTRIBUTION: Patients with experience of hospitalization with COVID-19 were involved in guideline development and writing, trial management and interpretation of findings.

The effect of body image dissatisfaction on goal-directed decision making in a population marked by negative appearance beliefs and disordered eating.

Eating disorders are associated with one of the highest mortality rates among all mental disorders, yet there is very little research about them within the newly emerging and promising field of computational psychiatry. As such, we focus on investigating a previously unexplored, yet core aspect of eating disorders-body image dissatisfaction. We continue a freshly opened debate about model-based learning and its trade-off against model-free learning-a proxy for goal-directed and habitual behaviour. We perform a behavioural study that utilises a two-step decision-making task and a reinforcement learning model to understand the effect of body image dissatisfaction on model-based learning in a population characterised by high scores of disordered eating and negative appearance beliefs, as recruited using Prolific. We find a significantly reduced model-based contribution in the body image dissatisfaction task condition in the population of interest as compared to a healthy control. This finding suggests general deficits in deliberate control in this population, leading to habitual, compulsive-like behaviours (body checking) dominating the experience. Importantly, the results may inform treatment approaches, which could focus on enhancing the reliance on goal-directed decision making to help cope with unwanted behaviours.