The Parallel Distributed Processing Approach to modeling cognition challenges the view that the brain uses explicit symbolic representations to capture the knowledge underlying our natural cognitive abilities, including our ability to perceive, understand, and produce language, our intuitions about the properties of objects, and even our intuitions about physical principles, such as the role of weight and distance from a fulcrum in determining which side of a balance scale will go down. According to the PDP approach, the knowledge underlying these natural cognitive abilities is stored in the strengths of connections among simple processing units, and learning occurs through a process of connection adjustment, driven by experience. Knowledge stored in this way is not accessible as such for overt report, but can govern quite complex intuitions in all of the indicated domains, as cognitive modeling and machine learning research makes clear. But can this approach tell us anything about how people solve problems in mathematics, or how children acquire mathematical abilities? In a new research direction, I have begun to explore this topic. This new direction is beginning to bear fruit, as I will explain by describing three new research projects from my laboratory, and an ambitious project that is just getting under way in my laboratory. I will also briefly mention other current directions being explored by other groups using machine learning methods that share common assumptions with the PDP framework.
|Date||29 September 2014|
|Time||16:00 - 17:00|