The time of reward and the temporal structure of reward occurrence fundamentally influence behavioral reinforcement and decision processes [1-11]. However, despite knowledge about timing in sensory and motor systems [12-17], we know little about temporal mechanisms of neuronal reward processing. In this experiment, visual stimuli predicted different instantaneous probabilities of reward occurre...

Motivational manipulations, such as the presence of performance-contingent reward incentives, can have substantial influences on cognitive control. Previous evidence suggests that reward incentives may enhance cognitive performance specifically through increased preparatory, or proactive, control processes. The present study examined reward influences on cognitive control dynamics in the AX-Con...

Distinct psychological processes have been proposed to unfold in decision-making. The time course of neural mechanisms supporting these processes has not been fully identified. The present MEG study examined spatio-temporal activity related to components of decision-making proposed to support reward valuation, reward prediction, and outcome evaluation. Each trial presented information on reward...

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queuei...

We introduce a notion of real-valued reward testing for probabilistic processes by extending the traditional nonnegative-reward testing with negative rewards. In this richer testing framework, the may and must preorders turn out to be inverses. We show that for convergent processes with finitely many states and transitions, but not in the presence of divergence, the real-reward must-testing pre...

At the working heart of policy iteration algorithms commonly used and studied in discounted setting reinforcement learning, evaluation step estimates value states with samples from a Markov reward process induced by following decision process. We propose simple efficient estimator called loop that exploits regenerative structure processes without explicitly estimating full model. Our method enj...

We consider a discrete time, nite state Markov reward process that depends on a set of parameters. In earlier work, we proposed a class of (stochastic) gradient descent methods that tune the parameters in order to optimize the average reward, using a single (possibly simulated) sample path of the process of interest. The resulting algorithms can be implemented online, and have the property that...

Sensitivity to time, including the time of reward, guides the behaviour of all organisms. Recent research suggests that all major reward structures of the brain process the time of reward occurrence, including midbrain dopamine neurons, striatum, frontal cortex and amygdala. Neuronal reward responses in dopamine neurons, striatum and frontal cortex show temporal discounting of reward value. The...