A particle filtering account of selective attention during learning

A growing literature has highlighted a role for selective attention in shaping representation learning of relevant task features, yet little is known about how humans learn what to attend to. Here we model the dynamics of selective attention as a memory-augmented particle filter. In a task where participants had to learn from trial and error which of nine features is more predictive of reward, we show that trial-by-trial attention to features measured with eye-tracking is better fit by the particle filter, compared to a reinforcement learning mechanism that had been proposed in the past. This is because inference based on a single particle captures the sparse allocation and rapid switching of attention better than incremental error-driven updates. However, because a single particle maintains insufficient information about past events to switch hypotheses as efficiently as do participants, we show that the data are best fit by the filter augmented with a memory buffer for recent observations. This proposal suggests a new role for memory in enabling tractable, resource-efficient approximations to normative inference.