What is optimal in adaptive decision-making? A complexity-based perspective.
Description
Our brain uses past experiences, integrated over multiple timescales, to shape how it makes decisions in our uncertain and dynamic world. These adaptive processes can take many forms, across both conditions and individuals, with different combinations of costs (like processing time) and benefits (like flexibility) that can make them difficult to compare and benchmark. Here I will describe our recent efforts to characterize the effectiveness of decision processes with respect to the complexity of model they use to convert past observations into useful predictions that can guide choices. I will show that this approach: 1) has a solid theoretical foundation using concepts drawn from physics and other fields; 2) can account for substantial individual variability of human subjects performing certain decision tasks; and 3) leads to quantitative predictions about the most efficient and effective solutions to a host of decision problems according to a fundamental “law of diminishing returns” relating accuracy to complexity. I will then show that these notions of complexity can be encoded in pupil-linked arousal systems that, in turn, may reflect the influence of neuromodulatory systems like the locus coeruleus-norepinephrine system on coordinated neural dynamics that can affect how information is integrated over time.
Speaker Bio
Dr. Joshua Gold is a Professor of Neuroscience, Chair of the Neuroscience Graduate Group, and Co-Director of the Computational Neuroscience Initiative at the University of Pennsylvania. His primary research interest is to understand the neural mechanisms responsible for perception, decision-making, and learning in uncertain environments. His laboratory uses a combination of approaches that include animal and human studies and computational modeling and theory. He obtained his Bachelor’s degree in Neural Sciences from Brown University in 1991, where he studied under the supervision of Drs. Mark Bear and Leon Cooper. He earned his PhD in Neuroscience from Stanford University 1992–1997 under the supervision of Dr. Eric Knudsen, while at the same time working part-time as a research scientist in the Advanced Technology Group at Apple Computer, Inc. He was a postdoctoral fellow at the University of Washington 1998–2002 with Dr. Michael Shadlen, after which he began his career at Penn. He is currently a Senior Editor at eLife.