Speaker: Danielle Williams, Ph.D.
Title: Computational Models as Liminal Models in Neuroscience
Short Bio: Danielle is a philosopher of mind and science whose research examines the role of computation in our theories and explanations of the mind and brain. Her work brings together philosophy of mind, cognitive science, neuroscience, and artificial intelligence, with particular interests in computational theories of mind, computational modeling, and the relationship between different explanatory and modeling strategies in the mind sciences. She also investigates philosophical questions raised by contemporary AI, including the nature of opacity in computational models and its implications for explanation in cognitive science. Alongside this work, she is a historian of cognitive science and AI, examining how the development of computer technology has shaped our conceptions and models of the mind and brain.
Abstract: Scientists rely on various types of models to make sense of their phenomena of interest. This has led philosophers to take up a range of semantic, ontological, and epistemological questions about how models represent, what kinds of things models are, what we learn from models, and how they explain. One type of model that has been of particular interest to philosophers is the computational model. Computational models are commonplace in the mind sciences in general and neuroscience in particular, with many asking how computational modeling can help us understand the nervous system and its contribution to cognition and behavior. These questions are particularly pressing in neuroscience, as computational models have become a dominant framework for making sense of cognition. Thus, old questions about model-based science have re-emerged within the context of neuroscience, with many philosophers looking to established frameworks to understand how computational models represent, explain, and contribute to theory development in neuroscience. These established frameworks, however, characterize computational models as a homogeneous type which cannot capture actual modeling practices in neuroscience. In this talk, I argue that computational models in neuroscience are best typed as a kind of liminal model, a type I introduce to capture how the status of a computational model is determined by the epistemic context in which it is deployed. Indeed, the category to which a computational model belongs shifts according to the role it plays within a given context.
ASL-English interpreters have been requested. Light refreshments will be provided.
Source: www.rit.edu
