Pain and Learning Lab

Laboratoire apprentissage et douleur

The laboratory's research program combines human brain imaging with computational modelling and artificial intelligence to better understand how pain is perceived and modulated under both normal and pathological conditions.

School of Psychology, Université Laval, Centre for Interdisciplinary Research in Rehabilitation and Social Integration (Cirris)

Pain: a scientific and public health challenge

Why does chronic pain develop and persist in some people but not in others?

Pain is a universal human experience and a fundamental driver of behavior. However, its perception is subjective and strongly influenced by multiple factors such as experience, mood, and emotions. Although numerous studies have revealed a complex network of brain regions involved in the experience of pain and its modulation, our understanding of the specific mechanisms that generate and regulate pain perception within this network remains limited. Furthermore, the subjective nature of pain makes it difficult to measure in research and clinical settings, as it relies primarily on rating scales and questionnaires that are heavily influenced by multiple factors and only moderately reliable.

Understanding pain is not only a scientific challenge but also an urgent public health issue. Chronic pain is the leading reason people seek health care, the primary cause of health resource use, and the most significant source of disability among working-age adults in Canada. Despite the significant impacts of this condition, the causes of chronic pain remain unclear, and its treatment continues to pose a challenge for clinicians. Given our limited knowledge of the fundamental mechanisms underlying pain perception and modulation, we have a poor understanding of why chronic pain develops and persists in some individuals but not in others.

Our approach

Human learning and machine learning inform each other to understand and predict acute and clinical pain perception.Human learningMachine learningUnderstand and predictacute and clinical pain perceptionHuman learning and machine learning inform each other to understand and predict acute and clinical pain perception.HumanlearningMachinelearningUnderstand and predictacute and clinicalpain perception

Two research axes

See projects and publications

Axis 2: Neural oscillations and biomarkers of pain

This data-driven research axis aims to identify and interpret sensitive, specific, and generalizable brain biomarkers of pain perception that can predict pain intensity across individuals and pain modalities.

Take part in a study

We recruit volunteers for our studies on pain perception, in the lab or remotely. The stimulations we use are safe, well controlled, and always comply with current ethical standards.

The form opens in Google Forms.