Model based on Bayesian cue combination shows that procedural motor learning is driven by perceptual error in localizing one's effector.

Humans are biased in their assessment of sensory information by their own preceding categorical judgment in an attempt to remain self-consistent.

Perceptual decision making, even in simple scenarios, is affected by sensory and decisional choice biases.

The rational application of heuristic learning strategies and satisficing goals accounted for near-optimal decisions that combined reward and noisy visual information by well-trained monkeys.

Confidence-dependent reinforcement learning is active and produces trial-to-trial choice updating even in well-learned perceptual decisions without explicit reward biases, across species and sensory modalities.

When choosing between stimuli, the effort required to act on the resulting decision influences the processing of the stimuli themselves.

Choice history signals bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation.

Residual activity from previous trials in a biophysical decision network model causes biases in choice behavior such that a previous response is more likely to be repeated.

In monkeys making decisions that balance noisy evidence and reward expectation, frontal cortical and caudate activity reflect different computational components that are related to the monkeys' strategy.

In a forced decision between two identical percepts, subjects preferentially rely on inferred internally generated over veridically seen percepts.