A reservoir of foraging decision variables in the mouse brain

TL;DR: Researchers demonstrate that trial history biases and apparent lapses in decision-making arise from a common cognitive process, optimal under mistaken beliefs of a changing environment, and test this model using datasets from male rats in two distinct decision-making tasks.

TL;DR: In this paper , the authors used hidden Markov models applied to behavioral choices during sensory discrimination tasks, and found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states.

TL;DR: It is found that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states, and it is suggested that mice regulate their arousal during optimal performance.

TL;DR: Results demonstrate that the FOF is a critical node in the neural circuit for the dynamic representation of action values for choice under risk, and indicates that the FOF encodes the lottery value on each trial.

TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

TL;DR: In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features.

TL;DR: This book discusses statistical decision theory and sensory processes in signal detection theory and psychophysics and describes how these processes affect decision-making.

TL;DR: A novel algorithm for solving pomdps off line and how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP is outlined.