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Magnitude-sensitive preference formation`
Our understanding of the neural computations that underlie the ability of animals to choose among options has advanced through a synthesis of computational modeling, brain imaging and behavioral choice experiments.
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
MCM is intriguingly similar to a Bayesian multiscale model of memory (Kording, Tenenbaum, Shadmehr, 2007), yet MCM is better able to account for human declarative memory.
Perceptual Multistability as Markov Chain Monte Carlo Inference
While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian inference algorithmically.
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Multiple object tracking is a task commonly used to investigate the architecture of human visual attention.
