Learning is typically characterized as the acquisition of new beliefs, and an assumption that is usually made is that these beliefs can be acquired one at a time. To the extent that we learn in this atomistic fashion, this poses a threat to models of learning that use fully distributed neural nets. However, fully distributed neural nets are effective at learning to perform certain tasks that have been intractable in classical AI, e.g., tasks relating to perception and motor control. The goal of the research proposed here is to investigate the properties of these two types of learning and their relation to each other. We intend to do so by identifying the appropriate task domains of atomistic and holistic learning, by characterizing atomistic and holistic increases or decreases in knowledge, and by working toward a better understanding of whether and how atomistic and holistic learning are related.
This research is important both in psychology and the philosophy of science. Many psychological explanations of cognitive processes assign a central role to learning. If we are to eventually understand these explanations in neural terms, we must be able to say not only how, in general, knowledge is acquired, e.g., whether this acquisition is holistic or atomistic (or a combination thereof), but under what conditions this acquisition takes place. Our research proposes to advance current naturalistic understanding of science by detailing the relation between science, which is a paradigmatic example of an atomistic learner, and its cognitive basis, which, if our previously funded research is to be believed, is only partly capable of atomistic learning. We hope this investigation will shed light on some issues in epistemology since we cannot hope to even articulate an epistemology appropriate to neurally based cognition until we have a viable account both of its proprietary forms of learning and of their relation.
