Computational modelling, Experimental psychology, Creative arts (architecture)
In a creative discipline such as architecture, one of the keys to the problem solving is the ability to mentally visualise, manipulate and create in three dimensions. An architecture student may find it relatively easy to imagine a particular view of a three dimensional structure, but will often have difficulty in transforming from two dimensional plans or designs to mental manipulation of three dimensional structures. Often the moment people manage to overcome this cognitive bottleneck and transform their viewpoint is a recognizable shift in thinking - a “Eureka” moment.
This qualitative change in thinking will generally not occur as a result of a simple linear process, starting with a problem that is consciously worked upon, with options iteratively explored until a solution is found. Often, solutions can only be found during a period of time spent off task. For example, in the case of architectural design, students will be able to describe a clear ‘before’ and ‘after’ in their design process, but often have little awareness or conscious recognition of how their cognitive shift has occurred, or how it could be repeated or supported.
The aim of this project will be to examine the divergent processes of creative thought in unconscious problem solving. It will start by observing and interviewing architecture students in order to design a more controlled neuropsychological experiment focusing on unconscious aspects of problem solving. A combination of subliminal priming techniques combined with cognitive neuroimaging will be used to probe unconscious processing to examine the temporal properties of information processing. This will include the selection and rejection of information sources and the creation of new representations during problem solving. These investigations will be used to examine the effectiveness of different off-task distraction strategies, such as relaxation, exercise, sleep, or engagement in secondary problem solving. These understanding will then inform the design of a spiking neuron model of self-generation of a solution, as an extension of a successful model of instructed Stimulus-Response associative learning (Bugmann, Goslin & Duchamp-Viret, 2013).
Guido Bugmann, Jeremy Goslin, Katharine Willis (Plymouth University)