How people work to obtain a reward depends on the context of the reward delivery, such as the presence/absence of competition and the contingency of reward delivery. Since resources are limited, winning a competition is critically important for organisms' obtaining a reward. People usually expect ordinary performance-reward contingency, with better performers obtaining better rewards. Unordinar...

The present research aimed at determining the role played by the amygdala in reward devaluation using transient inactivation induced by lidocaine microinfusions into the centromedial region. Two situations involving reward devaluation were tested in rats: consummatory successive negative contrast (cSNC) and anticipatory negative contrast (ANC). In cSNC, rats exposed to a downshift from 32% to 4...

Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL models are based on the hypothesis that dopamine ...

Markov-reward models, as extensions of continuous-time Markov chains, have received increased attention for the specification and evaluation of performance and dependability properties of systems. Until now, however, the specification of reward-based performance and dependability measures has been done manually and informally. In this paper, we change this undesirable situation by the introduct...

This paper is based on the context of electronic knowledge repositories (EKR). There are many problems in people using EKR to share knowledge. We can classify these problems into three types: 1) Free-riding situation, 2) Non-use situation, 3) Dormant situation. Many organizations use rewards to motivate people to contribute knowledge into EKR, but there has been no conclusive evidence of the ro...

In this research, we deal with VMI (Vendor Managed Inventory) problem where one supplier is responsible for managing a retailer’s inventory under unstable customer demand situation. To cope with the nonstationary demand situation, we develop a retrospective action-reward learning model, a kind of reinforcement learning techniques, which is faster in learning than conventional action-reward lear...

Defining a reward function that, when optimized, results in the rapid acquisition of an optimal policy, is one of the most challenging tasks involved in applying reinforcement learning algorithms. The behavior learned by agents is directly related to the reward function they are using. Existing works on Optimal Reward Problem (ORP) propose mechanisms to design reward functions that facilitate f...