SAIL Room, 111 Levin Building (425 S. University Ave.)
CNI Co-Director, Department of Neuroscience, UPenn
A bias-variance trade-off is central to human inference
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 trade-offs. 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 trade-off. This trade-off, which is well known in machine learning, may also have broad applicability to human decision-making.
A pizza lunch will be served.