Contemplating Polymathism and Connectorship in the Multidisciplinary Professional Environment
There was one moment in my early career that defined for me what multidisciplinary collaboration in a professional environment really meant. Back in 2006, I was working as a Sound Designer together with electrical engineers and visual artists on a music player that was solely controlled using hand gestures. This device had a lot of sensors, and there were therefore a lot of points where this device could break. The evening before our presentation of our creation to the management, we started up the device so we could do some tweaking on the user experience. Both the electrical engineer and I noticed there was a problem with the sound output. At the same time we said that frequency modulation was affecting the signal. Our mention of this problem came out of our intuition, yet our background and experience that shaped this intuition couldn’t be more divergent. In my case, due to many years of experience in distinguishing and deconstructing electronic sounds, I could instantly relate the abnormalities in the output to its possible cause, whereas for the electrical engineer frequency modulation was a mathematical concept and came to this conclusion by watching the numbers that were presented by a debugger.
Our solution to this problem was also as divergent as our background, but complementary in the process of realizing of our presentation. The electrical engineer suggested to make changes to the hardware: to replace the jittery sensors that caused the frequency modulation. Considering the time we had left, I suggested a software solution, namely to temporarily filter out the jitter by making some adjustments to the way that the data of this sensor was processed. This solution would also lower the risk of other things that could go wrong, and we could always modify the hardware after the crucial presentation. Both of these solutions have their basis in our experience of solving problems in our original domain of practice. The main concern for an electrical engineer is to deliver properly functioning hardware and the solutions he envisions therefore aim to contribute to that goal. Designers, on the other hand, view hardware on the merit of its capacities and limitations and envision solutions by changing the parameters that a piece of hardware allows at any given state of its functioning.
What is a Discipline composed of?
This experience provided me with the insight that there’s more to disciplinary skill than what one can ordinarily perceive on the surface of a discipline or the college textbooks from a university. Such skill is conveyed by a specific sharpness in the eye and according decision making abilities that can be applied from the gut feeling in make-or-break situations. I came to the following list of traits when deconstructing the particular skill set I witnessed during this crucial moment in this project:
- Attention to detail
- Embodied Knowledge
- Cognitive Style
- Discourse knowledge
Disciplinarity in Polymathism, Connectorship, and the Multidisciplinary Environment
The contemporary challenges in innovation, technology, research and science are best served by a professional environment that can optimally facilitate interactions between a multiplicity of disciplinary skill sets. These challenges have also sparked the creation of new professional fields, such as Educational Technologies, which is a field that firmly stands on the intersection of the learning sciences, creative design and engineering, and Neuroinformatics, which requires a deep knowledge of neuroscience and computer science. A popular managerial solution to the problem of making each disciplinary skill set interact with each other in the professional work environment is to hire “T-shaped” people. These are people that developed an expertise in one discipline in detail and have a working knowledge of the disciplines that surround them. This has turned out to work well in environments that highly depend on a high production output that is innovation-centered, but it is not the ideal solution when challenges need to be met on more fundamental levels, therefore requiring continuous switching between disciplinary skill sets. The people who have the capacity to switch between multiple disciplinary skill sets are called “polymaths”. This term is used to describe people who have developed an expertise in a large number of disciplines and are capable of drawing on these bodies of knowledge to solve specific challenges.
The person who plays a key role in the professional multidisciplinary environment is the connector. This is a role that bears several key leadership skills, such as the ability to listen, understand, communicate to individuals with a specific disciplinary focus and translate among them; and the ability to inspire people to work towards a common goal. It doesn’t necessarily need to be a separate role from the T-shaped and Polymathic individuals in a project team, but can best be performed by a polymath with strong interrelational skills.
What does a good conduct of Connectorship look like?
What a good conduct of connectorship in the role of a project leader can possibly entail, could be best explained by a bad conduct of connectorship. I had the unfortunate experience of being a researcher in a neuroinformatics project that suffered from a very ill-conceived and badly executed leadership. Neuroinformatics is a research field that is concerned with the development of tools and databases for the management and sharing of neuroscience data, the development of tools for the analysis and modelling of this data, and the development of computational models of neural processes (INCF, 2010). It stands on the intersection of neuroscience and engineering, and forms therefore an ideal platform for people with an multidisciplinary mindset.
I have worked on this project under an employer who viewed himself as someone who was able to switch between the different modes of thinking that make up the relevant disciplines in this project. He is a radiologist and a computer scientist. Despite his claim of having a multidisciplinary aptitude, these are disciplines that are highly related in terms of the type of mind set and cognitive skill that is needed to operate in these disciplines and address their challenges. Both disciplines depend on machine-based processes and require a type of attention to detail that is ideally suited to break down a concept into a process schematic depicting all discrete interacting components. These disciplines also greatly benefit from a cognitive style that is intolerant of ambiguity: any kind of tolerance of ambiguity can lead to improper functioning of computer software or false diagnoses using tomography equipment. Due to the discreteness in the delineation of these processes, a research question or problem statement is always being described within closed-ended constraints: there is a clear start and end point that define the process. In the case of PET neuroimaging, the injection of a radiotracer in a patient defines the starting point, and the presentation of the resulting image on a computer screen is the end point.
Neuroscience, on the other hand, requires a mindset that is highly tolerant of ambiguity. On the basis of the current state of evidence, it can be stated that the human nervous system has its starting point in our senses, and has its end point in movement. However, the discreteness in the delineation of functional processes in the human nervous system starts to break down when trying to point to the starting and ending point of concepts in cognition, such as reasoning or imagination, or concepts in memory, such as the location of individual long term memories and the process of consolidation of experiences in the long term memory system. Cognitive Neuroscience started off as a field where investigations in brain functions were centered around the idea that behaviours we observe in the cultural environment of a subject could possibly be associated with specific brain regions. Slowly, however, the view emerged that the information processes in the brain operate differently from the way that it was assumed: the brain is a probabilistic complex system, where simple neuronal structures produce the complex behaviours that we observe. In order to understand, for example, how and whether creativity contributes to the consolidation of experiences and insights in long-term memory, it requires a meticulous envisioning of possible neuronal architectures and their evolutionary histories to come to an understanding on how the relevant information processes operate. In my own work in understanding the aforementioned problem for my MPhil dissertation, my first attempt focused largely on the neuronal processing as it occurs in the neocortex, which is highly involved in higher functions such as sensory perception, generation of motor commands, spatial reasoning, conscious thought and language (Lui et. al. 2011), and which I envisioned as having a certain directionality in its processing: going from the back area of the neocortex, which predominantly deals with sensory perception, to the pre-frontal areas, which deals with functions such as planning and decision-making. This particular view, however, did not lead to an understanding of how a drive for creativity will eventually lead to the consolidation of insights. Viewing the brain from a completely different angle, from the perspective of the brainstem, via the relevant neuronal pathways, into the higher cognitive functions, did bring me to an understanding of how this process operates.
On the basis of these experiences, one’s ability of engaging in multiple disciplinary modes in this particular field of Neuroinformatics is founded on one’s capacity to switch between a style of discerning and appraising a subject that is deconstructive, intolerant of ambiguity, and within clearly defined constraints; and a style that appraises how interrelated components conduce behaviour, that is tolerant of the various configurations from which these behaviours can emerge, and is tolerant of lack of clearly set boundaries that allows for the consideration of factors that lie outside the studied system, such as particular stimuli in the environment, or the subject’s culture, upbringing, the evolutionary history of a species, or even the relationship between different species, or gender and age roles within a species, throughout their evolutionary history. At the core of each of these styles lies either a deep appreciation of the natural, biological phenomena in our world in the neurosciences, or a drive to construct or synthesize the means with which we enhance our human abilities and cultural beliefs in engineering.
When it comes to being a connector among these disciplinary styles as a project leader, a clear awareness of striking a balance between the modes of deep appreciation and the drive to synthesize is a prerequisite to bring a multidisciplinary project to a success. In this particular Neuroinformatics project, the leadership style was too much slanted towards the drive to synthesize to the degree that we as researchers were viewed as instruments in the larger plan to create software solutions for the challenges found in neuroscience. Not only did this prevent us from bring our own polymathic skillset to the issues where it was most needed, it also brought the project to a grinding halt in failing to come up with the means to support original research. A connecting leadership style that is therefore based on false belief of having a multidisciplinary aptitude can be very detrimental to the successful completion of a project, or even the growth of an institution and the professionalisation of its workers.
INCF (2010). “INCF Strategy Overview 2008-2010″
Lui, J. H., Hansen, D. V., & Kriegstein, A. R. (2011). Development and evolution of the human neocortex. Cell, 146(1), 18-36.
August 28, 2014