[Left to right] Sharon, Flavia, Swati, Carl, and Xuanci

We are a multidisciplinary research team led by Dr Flavia Mancini, at Computational and Biological Learning (CBL), Department of Engineering, University of Cambridge. 

Chronic pain affects 1 in 5 people and is the leading cause of disability in the world. In many cases, it is not clear why pain persists for long periods of time and why some people are more vulnerable to developing chronic pain than others. The brain does not passively receive information from the nerves but rather interprets these signals based on what it already knows, anticipating and trying to adjust its responses to what will happen next. 

Our primary objective in conducting research is to uncover the mechanisms by which the human brain carries out essential functions during periods of pain. This research holds great significance as the brain’s interpretation of pain signals directly influences its capacity to regulate responses to pain, and conceivably even determines the intensity of pain experienced. By undertaking this research, we aspire to enhance the prevention, treatment, and overall management of chronic pain. Our lab works on projects related to statistical learning and contextual inference in the human brain. We have a particular focus on learning of aversive states, as this has a strong clinical significance for chronic pain and mental health disorders. Our team collaborates with theoretical and experimental colleagues in Cambridge, Oxford, and abroad.

Lab member collecting data through fNIRS

Nox Lab has undertaken projects to understand:

(a) The Temporal structure of pain

(b) Neural correlates of Pain Uncertainty in the Transition to Chronic Back Pain

(c) Optical brain monitoring to understand mechanisms of learning,

and many others. Please see publications for more details. 


As a comprehensive methodology, we employ a fusion of statistical learning tasks in human participants, computational modeling techniques utilizing Bayesian inference, reinforcement learning, deep learning methodologies, and neural networks. We integrate these approaches with neuroimaging methods, including the utilization of 7T functional Magnetic Resonance Imaging (fMRI). This multi-faceted approach allows us to investigate the intricate workings of the human brain and gain deeper insights into the mechanisms underlying cognitive processes, decision-making, and information processing. By combining these advanced techniques, we aim to find the complex interplay between neural activity, behavior, and cognitive functions, leading to a more comprehensive understanding of pain in the brain.