Conditioned facilitation of brain reward function after repeated cocaine administration.

Cocaine lowers brain reward thresholds, reflecting increased brain reward function. The authors investigated whether, similar to acute cocaine administration, cocaine-predictive conditioned stimuli would lower intracranial self-stimulation (ICSS) thresholds. Rats received a saline injection for 5 days, a cocaine injection (10 mg/kg) for 20 consecutive days, then saline for 5 additional days. Thresholds were measured immediately before and 10 min after each injection. The initial 5 saline injections had no effect on thresholds, whereas cocaine significantly lowered thresholds for 20 days. There was no tolerance or sensitization to this effect of cocaine over days. During the last 5 days when cocaine administration was substituted with saline, rats demonstrated a conditioned lowering of thresholds during the 2nd daily ICSS session. These data demonstrate that cocaine-predictive conditioned stimuli induce a conditioned facilitation of brain reward function. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Delay discounting of real and hypothetical rewards.

The degree to which real and hypothetical rewards were discounted across delays ranging from 6 hr to 1 year was explored in a within-subjects design. An adjusting-amounts procedure was used to estimate the subjective value of real and hypothetical rewards at each delay. A hyperbolic discounting function provided a significantly better fit to individual participants' preferences than did an exponential function. No significant effect of reward type on degree of hyperbolic discounting or area under the discounting curves was detected. These findings offer some support for the validity of using hypothetical rewards to estimate discounting rates in substance-abusing and other populations, but caution is suggested because this support is gleaned from a failure to detect an effect of reward type. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Promise is Debt, Threat Another Matter: The Effect of Credibility on the Interpretation of Conditional Promises and Threats.

Evidence from reasoning tasks shows that promises and threats both tend to receive biconditional interpretations. They also both display high speaker control. On the face of it, the only difference seems to be the positive or negative signing of the consequent. In a promise, the speaker tries to persuade the hearer to do something by holding out the prospect of a particular reward; in a threat, the speaker tries to refrain the hearer from doing something by holding out the prospect of a particular punishment. This paper investigates the respects in which conditional promises and threats differ further by means of an inference task. The credibility of the consequent was manipulated in order to examine whether the acceptability ratings of inferences based on promises and on threats would be equally affected. The results of the inference task and an analysis of the reasons people give for their answers suggest that the credibility of promises is less affected by the use of excessive consequents than the credibility of threats. In other words, promise remains debt, whereas threat is another matter. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reward optimization of a repairable system

This paper analyzes a system subject to repairable and non-repairable failures. Non-repairable failures lead to replacement of the system. Repairable failures, first lead to repair but they lead to replacement after a fixed number of repairs. Operating and repair times follow phase type distributions (PH-distributions) and the pattern of the operating times is modelled by a geometric process. In this context, the problem is to find the optimal number of repairs, which maximizes the long-run average reward per unit time. To this end, the optimal number is determined and it is obtained by efficient numerical procedures.     

Oxytocin Signaling as a Target to Block Social Defeat-Induced Increases in Drug Abuse Reward

There is huge scientific interest in the neuropeptide oxytocin (OXT) due to its putative capacity to modulate a wide spectrum of physiological and cognitive processes including motivation, learning, emotion, and the stress response. The present review seeks to increase the understanding of the role of OXT in an individual’s vulnerability or resilience with regard to developing a substance use disorder. It places specific attention on the role of social stress as a risk factor of addiction, and explores the hypothesis that OXT constitutes a homeostatic response to stress that buffers against its negative impact. For this purpose, the review summarizes preclinical and clinical literature regarding the effects of OXT in different stages of the addiction cycle. The current literature affirms that a well-functioning oxytocinergic system has protective effects such as the modulation of the initial response to drugs of abuse, the attenuation of the development of dependence, the blunting of drug reinstatement and a general anti-stress effect. However, this system is dysregulated if there is continuous drug use or chronic exposure to stress. In this context, OXT is emerging as a promising pharmacotherapy to restore its natural beneficial effects in the organism and to help rebalance the functions of the addicted brain.     

临桂县小型农田水利奖补政策实践与思考

以奖代补政策是临桂县人民政府为扶持农村水利建设而出台的一项惠民政策,该政策自2011年出台以来已3年多,在给百姓带来诸多收益的同时,也暴露出了许多问题,为此,总结3年以来政策执行情况和实践经验,分析存在问题,提出改进建议。     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

实施《实验技术成果奖》后的成效与体会

实验技术成果奖的申报、评定办法的实施,极大地调动了实验室工作人员的积极性和创造性,发展了实验技术,是推动实验室建设的有效激励机制和竞争机制．     

Is compulsive internet use related to sensitivity to reward and punishment,and impulsivity?

Aim of the present study was to examine whether the personality correlates sensitivity to reward and to punishment, and impulsivity predict compulsive internet use (CIU). Furthermore, the predictive value of these personality correlates was compared to the predictive value of factors relating to psychosocial wellbeing. The results showed that particularly rash spontaneous impulsivity predicts CIU and that this personality factor is more important than psychosocial wellbeing factors. Sensitivity to reward, which is supposed to play a role in craving processes associated with substance abuse and eating disorders, could not be related to CIU. The data suggest that internet users who are characterized by an impulsive personality feature, are less able to control their use of the internet, which makes them more vulnerable to develop CIU.     

A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning

We present a neural network software architecture, guided by that of the human and more generally primate brain, for the construction of an autonomous cognitive system (which we have named GNOSYS). GNOSYS is created so as to be able to attend to stimuli, to conceptualise them, to learn their predicted reward value and reason about them so as to attain those stimuli in the environment with greatest predicted value. We apply this software system to an embodied version in a robot, and describe the activities in the various component modules of GNOSYS, as well as the overall results. We briefly compare our system with some others proposed to have cognitive powers, and finish by discussion of future developments we propose for our system, as well as expanding on the arguments for and against our approach to creating such a software system.