Economic approaches to decision making assume that people attach values to prospective
goods and act to maximize their obtained value. Neuroeconomics strives to observe these …

To behave adaptively, we must learn from the consequences of our actions. Studies using
event-related potentials (ERPs) have been informative with respect to the question of how …

Evidence from imaging and anatomical studies suggests that the midcingulate cortex (MCC)
is a dynamic hub lying at the interface of affect and cognition. In particular, this neural system …

In order to understand the exploitation/exploration trade-off in reinforcement learning,
previous theoretical and empirical accounts have suggested that increased uncertainty may …

In this report, we provide the first evidence that mood and anxiety dimensions are associated
with unique aspects of EEG responses to reward and punishment, respectively. We …

Computational models have been used to analyze the data from behavioral experiments.
One objective of the use of computational models is to estimate model parameters or …

Recent work has suggested that reward prediction errors elicit a positive voltage deflection
in the scalp-recorded electroencephalogram (EEG); an event sometimes termed a reward …

Understanding the neurophysiological mechanisms of learning is important for both
fundamental and clinical neuroscience. We present a neurophysiologically inspired …

Learning signals during reinforcement learning and cognitive control rely on valenced
reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven …

In order to improve our understanding of the components that reflect functionally important
processes during reward anticipation and consumption, we used principle components …