How do changes in the brain lead to learning? To answer this question, consider an artificial
neural network (ANN), where learning proceeds by optimizing a given objective or cost …

Classical models of perceptual decision-making assume that subjects use a single,
consistent strategy to form decisions, or that decision-making strategies evolve slowly over …

Decision-making strategies evolve during training and can continue to vary even in well-
trained animals. However, studies of sensory decision-making tend to characterize behavior …

Tracking an odour plume to locate its source under variable wind and plume statistics is a
complex task. Flying insects routinely accomplish such tracking, often over long distances, in …

In probabilistic and nonstationary environments, individuals must use internal and external
cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the …

Commonly in reinforcement learning (RL), rewards are discounted over time using an
exponential function to model time preference, thereby bounding the expected long-term …

Reinforcement learning (RL) models are used extensively to study human behavior. These
rely on normative models of behavior and stress interpretability over predictive capabilities …

We present a novel approach to use curricula to identify principles by which a system learns.
Previous work in curriculum learning has focused on how curricula can be designed to …

Foraging animals must use decision-making strategies that dynamically adapt to the
changing availability of rewards in the environment. A wide diversity of animals do this by …

To unveil how the brain learns, ongoing work seeks biologically-plausible approximations of
gradient descent algorithms for training recurrent neural networks (RNNs). Yet, beyond task …