This study is the first to provide an integrated view on the body of knowledge of artificial
intelligence (AI) published in the marketing, consumer research, and psychology literature …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

Research on cognitive control and executive function has long recognized the relevance of
motivational factors. Recently, however, the topic has come increasingly to center stage, with …

Why has computational psychiatry yet to influence routine clinical practice? One reason may
be that it has neglected context and temporal dynamics in the models of certain mental …

Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …

Midbrain dopamine signals are widely thought to report reward prediction errors that drive
learning in the basal ganglia. However, dopamine has also been implicated in various …

We review the current state of knowledge on the computational and neural mechanisms of
reinforcement-learning with a particular focus on fronto-striatal circuits. We divide the …

Recent behavioral studies in both humans and rodents have found evidence that
performance in decision-making tasks depends on two different learning processes; one …

To better understand the reward circuitry in human brain, we conducted activation likelihood
estimation (ALE) and parametric voxel-based meta-analyses (PVM) on 142 neuroimaging …