Carl Jackson, PhD

My undergraduate degree was in physics at the University of Nottingham, from which I graduated in 2003. During this time, I spent a year on exchange at the University of Toronto.

For my PhD I stayed in Nottingham, although I switched disciplines and studied experimental psychology. Specifically, I worked on the statistical processing of visuomotor distributions and brain imaging of the learning of predictable and random force sequences. Along the way, I was involved in the development of a novel device for providing forces to the human finger inside the MRI scanner. I was supervised by Professor Stephen Jackson in the School of Psychology and Professor Peter Morris in the Sir Peter Mansfield Magnetic Resonance Centre.

In 2006 I started postdoctoral work with Professor Chris Miall at the University of Birmingham. I worked mainly on behavioural studies using the vBOT robotic manipulandum to investigate state-dependent reaching movements. I successfully defended my PhD in February 2008 and graduated in July 2008.

In October 2009 I moved to Kingston, Ontario, Canada to work with Professor Stephen Scott at Queen's University. My work here consists of further behavioural and clinical studies, including electromyography recordings, alongside building neuronal network models of human arm control. I was awarded a two-year NeuroDevNet postdoctoral fellowship to extend my clinical work to populations of children with fetal alcohol spectrum disorder.

Starting in November 2012, I took a change in career direction and left full-time research. I spent several months travelling the world, making money by freelancing, performing pre-submission editing of academic papers of scientists without English as their first language.

I moved to Toronto in September 2013, and I am currently freelancing in a variety of fields: editing, content writing, coding, tutoring, and music.

During my nine years as a PhD student and postdoctoral fellow, I engaged in a wide variety of research into human motor control.

Behavioural

Much of my motor control research was on behavioural experiments in humans. I was primarily interested in reaching movements; it is a complex and difficult task to activate all the muscles in the human arm in just the right order to attain a goal, and yet it is something that we do so naturally that we do not even think about it. Goal-directed reaches are a huge part of human interactions with the world, and how we learn to both control our actions and predict their sensory consequences is an interesting problem.

In this arena, I explored how we learn perturbations on one arm that depend on the state of the other arm (Jackson & Miall, 2008) as well as investigating a novel sensory illusion that arises following a voluntary arm movement (Jackson & Miall, 2010). I also studied the impact of proprioception on visual attention (Jackson, Miall and Balslev, 2010) and was involved with the development of a novel device to provide force feedback during fMRI experiments (Jackson et al., 2008).

Computational

I also explored some computational models of motor control (e.g., Haith et al., 2008). The primary issues involved in creating a computational model centre around explanatory power and physiological realism. Just as it would be pointless to laboriously build a physiologically realistic model that did not work, so too nobody wants a model that can explain everything but that cannot possibly be implemented in the brain.

Neuronal network models allow the amount of realism and explanatory power to be carefully tuned depending on the nature of the problem. The great advantages of using networks are that they are intrinsically good at generalising what they have learned in one configuration to other configurations. It’s been exciting to see the progress of machine learning using deep neural networks over the last few years.

Clinical

The use of robotic technology in rehabilitation following stroke is well-established. However, the use of robotic technology for the assessment of impairments following stroke is still in its infancy. In t his domain, I developed an intuitive bimanual robotic task with the aim of finding parameters that can be used to give an objective assessment of impairments in coordination (Lowrey et al., 2014). This task is now being used to identify similar impairments in children with fetal alcohol spectrum disorder (Williams et al., 2014).