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A Bias-variance Tradeoff in Human Decision-making

Wednesday, April 18, 2018

4:00 PM-5:30 PM

BIOMED Seminar

A Bias-variance Tradeoff in Human Decision-making

Joshua Gold, PhD
Department of Neuroscience
University of Pennsylvania

Decisions often benefit from learned expectations about the sequential structure of the evidence upon which the decisions are based. In this talk, I will present behavioral and theoretical findings indicating that individual variability in this learning process can reflect different implicit assumptions about sequence complexity, which leads to measurable performance tradeoffs.

Specifically, for a task requiring decisions about noisy and changing evidence, human subjects with more flexible, history-dependent choices (low bias) had greater trial-to-trial choice variability (high variance). In contrast, subjects with more history-independent choices (high bias) were more predictable (low variance). We accounted for this range of behaviors using models in which assumed complexity was encoded by the size of the hypothesis space over the latent rate of change of the evidence.

The most parsimonious model used a biologically plausible sampling algorithm in which the range of sampled hypotheses governed an information bottleneck that gave rise to a bias-variance tradeoff. This tradeoff, which is well known in machine learning, may also have broad applicability to human decision-making.

Joshua Gold, PhD, is a Professor in the Department of Neuroscience, Co-Director of the Computational Neuroscience Initiative, and Chair of the Neuroscience Graduate Group at the University of Pennsylvania.

Dr. Gold received his undergraduate degree from Brown University, where he worked with Dr. Mark Bear. He received his PhD in Neuroscience from Stanford University, where he worked with Dr. Eric Knudsen. He then completed his postdoctoral work at the University of Washington in the lab of Dr. Mike Shadlen. At Penn, Dr. Gold's lab continues to study the neural basis of decision-making, with a focus on understanding how the brain learns to use past experiences to optimize decisions in order to achieve specific goals. 

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Papadakis Integrated Sciences Building (PISB), Room 120


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