Predictive processing, creativity and mental health

Research project description

PhD will investigate predictive processing as an underlying dimension relevant for mental health, as well as creativity assessment and its link to psychological well-being. 

Predictive processing is a new approach to perception and action based on the idea that the brain is not a passive organ, passively waiting for sensory input in order to process it, but an active one, actively building perception and action based on past experiences. Since it was proposed, this approach has gained increasing attention, and descriptions of virtually all mental-health related conditions in terms of predictive processing have emerged in recent years. In this context, we have developed and extensively tested both in healthy and clinical populations a computer task specifically designed to evaluate predictive processing in the context of emotion perception and social evaluation. Likewise, creativity is a construct that has received increasing attention in relation to mental health, although its assessment remains limited. On an online study, we are currently starting to gauge the potential relationship between predictive processing and creativity and envision new potential ways of measuring it.  

The present research project aims to advance our understanding of predictive processing as an underlying dimension relevant for mental health, as well as creativity assessment and its link to psychological well-being. The project proposes a groundbreaking shift from traditional computer tasks and behavioral studies to the inclusion of Large Language Models (LLMs) and advanced AI techniques, in a truly transdisciplinary effort bringing together experts in psychology and neuroscience and experts in artificial intelligence and data science.

The project has important implications both at the scientific level, by potentially providing a deeper understanding of mental health and creativity in relation to novel theories of human cognition, as well at the applied level, by having the potential to lead to better assessments of predictive processing and creativity, which could open the door to the development of diagnostic or therapeutic elements with relevance for applied psychological fields, including, importantly, the clinical field.

Academic background / Skills

Candidates must hold a degree that allows admission to the official doctoral programme at UAB.  

Additional requirements for a stronger application are: 

• A Masters degree in Psychology, Neuroscience, Cognitive Sciences or related fields is required. Additional coursework or background in Statistics, Computer Science or Artificial Intelligence will be highly valued. 
• Provable experience in research settings, whether through significant academic projects, publications, or a Masters dissertation. 
• Interest in dimensional, transdiagnostic approaches to mental health and transdisciplinary work. 
• Experience in handling, analyzing, and visualizing large behavioral datasets will be valued. 
• Collaborative team spirit, effective communication aptitude, good level of written and spoken English. 

Research group/s description

The present project is a truly transdisciplinary proposal involving two groups of research: 

1) The AI and Data Science Research Group is a multidisciplinary team of experts at the Computer Vision Center (UAB) and the University of Barcelona united to harness tech for societal benefit. The team includes specialists in artificial intelligence, machine learning, data analysis, and mental health. They specialize in creating AI solutions for predictive tasks, text analysis, and tailored treatment approaches, seeking to enhance mental health care using AI-driven methods. 

2) The Cognitive and Affective Science Laboratory, led by Lorena Chanes, is a research group at the Dept. of Clinical and Health Psychology-Institute of Neurosciences. Their research focuses on cognitive and affective processes that ultimately lead to conscious experience and how they relate to mental health. They have a multidisciplinary approach including behavior, noninvasive brain stimulation and neuroimaging.