As a student and in my career, I have sought to work with and learn from the best of minds. I have had exceptional academic mentors: George Fouriezos, Claude Lamontagne and Aaron Sloman. The opportunity to work with Jim Roche (now head of Stratford Managers) led me out of academia into the Newbridge Newbridge Networks spin-off ecosystem (Tundra Semiconductor Corporation and Abatis Systems Corp.), where I worked with three of R.O.B.’s Y2000 “Top 40 under 40”  and several other truly top-caliber Canadian high tech people.

My research aims to discover the workings of the most brilliant, cognitively productive minds. CogZest products are meant to make great minds produce even better. One important feature that I have observed, which the expertise literature doesn’t sufficiently emphasize, is simply that these minds tend to get it right. That means they can tell what is a wrong view, objective or approach and they progressively correct towards better ones.  As scholars, they move us towards a better understanding. As product developers, they identify, characterize and resolve problems well that others didn’t necessarily know they had (more precisely, that others didn’t fully understand). As business people, they clearly see a path to multidimensional value and they inspire people to help them take that path. Sure, they have a high IQ. But having a high IQ does not guarantee that one will tackle the right problem nor that one won’t settle for a subpar solution.

I am delighted to present a paper on expert learning at A symposium in Honour of Aaron Sloman: from animals to robots and back:  Reflections on hard problems in the study of cognition (Sept 12-13, 2011; in Birmingham, England). If you visit Aaron Sloman’s web site, you will discover the work of an extremely original, zestful and productive thinker. At 75, he continues to produce substantial scholarly content at a rate that even most younger academics can’t match. Aaron is a broad, clear and rigorous thinker. When presented with one option he sees a space of requirements and a space of possible solutions — with trade-offs amongst them; he is quick to notice when a proposition is wrong, in the many ways that they can be wrong. Aaron Sloman has a B.Sc. in mathematics and a  Ph.D. in philosophy. He is a Rhodes Scholar and a musician. He has extensive AI computer programming and design experience—not a typical philosopher! (But check out his free book on how philosophy should be done, much of which applies to how psychology as a science should be conducted.) He has written ground breaking papers in too many very different areas of Artificial Intelligence to list here.

Here is the preface to my paper for Aaron Sloman’s festschrift that I will be presenting at the symposium. While the paper is “academic”, it expresses several key ideas about expertise and cognitive productivity that I am applying at CogZest. In workshops and upcoming documents, I communicate them in plainer terms.

Flush with scholarships and graduate school opportunities in 1990, having researched the Commonwealth for the most fertile ground in cognitive science, I heeded Dr. Claude Lamontagne’s advice to study with a brilliant scholar whom he had known at the School of Artificial Intelligence of the University of Edinburgh (1972-3). Lamontagne praised Aaron Sloman’s penetrating mind, one which always offered insightful comments, criticisms and suggestions aimed at the heart of the matter. Lamontagne also knew that Sloman (and Sussex University) fully embraced theoretical, computational cognitive science. Lamontagne was right. Sloman is—as all who know him well will attest—a productive thinker of unsurpassed caliber and the wise steward of his beautiful mind.

This somewhat Escherian paper weaves five themes from cognitive science in my quest to understand and help enhance experts’ cognitive productivity:

1. Productive learning and expertise (Bereiter & Scardamalia, 1993; Wertheimer, 1959). Sloman introduced me to the work of Wertheimer (e.g., Sloman, 1978), who seemed to capture the essence of productive thinking, though, ironically, Wertheimer’s understanding, like Freud’s, was stunted by concepts from physics.
2. Motive processing, from a designer stance (Sloman, 1993). The stance itself affords productive thinking.
3. Conceptual analysis (Sloman, 1978), which also are helpful thinking tools.
4. Potent psychological principles for productive learning (too many to list here).
5. Self-regulated learning with technology (Beaudoin & Winne, 2009; Winne, 2006).

In my quest, I seek to wrest the mechanisms underlying “testing effects” (Kuo & Hirshman, 1996) from Ebbinghaus’s undying grasp on cognitive psychology, to polish them and to hand them over for theoretical and practical study to the scions of Immanuel Kant, Max Whertheimer, Frederic Bartlett and Warren McCulloch. I hope this paper will inspire others to address the difficult problems I have raised.

I am fortunate to still be in touch with Aaron Sloman about our respective projects.

— Update on 2011-09-20

Here’s a photo of the people whose tenure at Sussex University overlapped with Aaron’s and that were still present at the symposium on its second day. I was fortunate to be a DPhil student at Sussex’s School of Cognitive and Computing Sciences when it was one of the top cognitive science schools in the world (1990-1991). There was a lot of talent there (not that there isn’t now!) Aaron Sloman moved to the University of Birmingham in Oct. 1991 to be Head of Computer Science (now one of the top ranked in the UK) and involved in the Cognitive Science program, so I followed him there.