Failure Modes in Sequential Decisions
Neural activations are messy and complicated, so the behaviour of an agent often constrains a system’s computational model more tightly than measurements of its internals (Niv 2021). A large part of my research focuses on modelling the algorithm an agent is running from behaviour alone, and predicting its failure modes.
Abstract replay for efficient learning: How does a system consolidate days of learning into compositional knowledge that transfers to new tasks? The two halves of the question suggest two ingredients: compositionality requires reusable abstractions, and consolidation requires replaying - even simulating - experience offline. We proposed that the cortico-hippocampal circuits in the brain combines the two to learn efficiently and compress memories.
Spens E*, Gupta D*, Lewis E, Castegnaro A, Burgess N, Mrsic-Flogel T, Behrens TEJ (2026). Efficient learning through abstracted generative replay. Cosyne Abstracts 2026
Failure modes in sequential decisions: I showed that two failure modes of decision-making (history-dependent biases and attentional lapses) long treated as separate and unpredictable - are signatures of one underlying autoregressive computation, and can therefore be modelled and predicted (Nature Communications 2024; Best Paper Award at RLDM 2022). Along the way I showed that a widely used correction for slow drifts in decision variables distorts inference, and that the drift has to be modelled jointly with the updates instead (NBDT 2022).
