How Occam’s Razor Guides Human and Machine Decision-Making
- Speaker
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Joshua Gold, Ph.D.Professor, Neuroscience, Perelman School of Medicine, University of Pennsylvania
Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.
Deciding something as potentially complicated as what to do next or as deceptively simple as where to look next requires our brains to deliberate; that is, to move beyond the rigidness and immediacy of sensory-motor reflexes and instead take time to process and weigh evidence in a flexible manner until arriving at a categorical judgment that guides behavior. Our understanding of this deliberation process, which represents a major building block of cognition, has benefited greatly from mathematically rigorous theories from some unexpected places.
In this lecture, Joshua Gold will describe two theoretical frameworks that support ongoing studies of deliberative decision-making in the brain, focusing on their historical origins. The first describes quantitatively the process by which uncertain evidence can be accumulated over time to balance the competing needs of maximizing decision accuracy while minimizing decision time. This framework is built on mathematical advances that Alan Turing and colleagues developed to decode messages sent via the Enigma machine during World War II. The second describes how biases can emerge in this information-accumulation process that can be helpful when considering options that differ in form and scope. This framework is a formalization of Occam’s razor, which states that all else being equal, simple solutions are better — an idea directly relevant to how biological and artificial brains can make effective decisions.
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