performance and development are based on hard work, experience and learning. learning how to change different behaviors is crucial to successful talent management plans. within the brain there are complex connected circuits that can identify threats. the brain reacts to change as a threat. there is also a collection of brain structures tied to a natural reward system that are involved in the re...

Some new constructions of parallelisms in PG(3, q) are given that produce parallelisms consisting of one Desarguesian spread and q + q derived Knuth semifield spreads and other types consisting of one Knuth semifield spread, one Hall spread and the remaining spreads are derived Knuth semifield spreads.

The goal of metalearning is to generate useful shifts of inductive bias by adapting the current learning strategy in a \useful" way. Our learner leads a single life during which actions are continually executed according to the system's internal state and current policy (a modiiable, probabilistic algorithm mapping environmental inputs and internal states to outputs and new internal states). An...

Employees’ attitude towards his/her job differs across organization’s sector. The present study aimed to find out the differences in employee’s attitude in public and private sector. For this purpose, managers’ reward power and coercive power were taken as independent variables and their impact on employees’ job satisfaction (dependent variable) was examined in both sectors. Sample of 130 respo...

Rewarding improves performance. Is it due to modulations of the output modules of the neural systems or are there mechanisms favoring more generous inputs? Some recent study included V1 in the the circuitry of reward-based modulations, but the effects of reward can easily be confused with effects of attention. Here we address this issue with a psychophysical dual task to control attention while...

We present and solve a real-world problem of learning to drive a bicycle. We solve the problem by online reinforcement learning using the Sarsa( )-algorithm. Then we solve the composite problem of learning to balance a bicycle and then drive to a goal. In our approach the reinforcement function is independent of the task the agent tries to learn to solve.