Job Title: UAF in Health Engineering
My research focuses on learning and decision-making. I take principles and experimental approaches from cognitive science and use them to address questions related to human performance in the real world. My postgraduate work used electroencephalogram recordings to identify the neural basis of sub-optimal cognitive decision-making. During post-doctoral training I investigated the relationship perceptuomotor decision errors in minimally invasive surgery. I now focus on applying knowledge from cognitive decision science to a variety of surgical disciplines including Laparoscopic, Robotic, Ophthalmic and Dental surgery.
Surgery is fascinating for a cognitive science perspective because it is a high-stakes endeavour that places large demands on cognitive and perceptuomotor systems. For example, laparoscopic surgery limits and transforms visual and haptic information used to guide skilled movements. The surgeon sees a 2-D representation (on a monitor) of a 3-D abdominal cavity and must manipulate tissue skilfully using instruments that impair dexterity and tactile sensation without many of the usual visual depth cues available during open surgery. In such environments, errors are commonplace and put patient safety at risk. In order to maximise performance and minimise the risk of patient harm, optimal preparation before surgery is required.
As University Academic Fellow in Health Engineering my research programme examines the utility of combining technology (e.g. virtual reality and patient specific simulation) with knowledge from cognitive science to prepare surgeons in order to improve intraoperative decision-making and surgical performance.
I co-ordinate the Cognitive Research Informed Surgical Techniques @ Leeds (CRIST@L) group, post (sporadically) on Twitter and can be found on ResearchGate and Academia.Edu.Contact