Stress is known to influence learning in a complex fashion. The present study aimed to examine, in how far feedback-based behavioral adaptation and electrophysiological correlates of error and feedback processing during this process are altered after acute stress. To this end, a learning task involving conditions with contingent and non-contingent monetary feedback was applied to 40 healthy you...

The extent to which motor learning is generalized across the limbs is typically very limited. Here, we investigated how two motor learning hypotheses could be used to enhance the extent of interlimb transfer. According to one hypothesis, we predicted that reinforcement of successful actions by providing binary error feedback regarding task success or failure, in addition to terminal error feedb...

In this paper, a simple but effective method for compensation of the quadrature error in MEMS vibratory gyroscope is provided. The proposed method does not require any change in the sensor structure, or additional circuit in the feedback path. The mathematical relations of the proposed feedback readout system were analyzed and the proposed solution assures good rejection capabilities. Based on ...

Neural network based controller is used for controlling a mobile robot system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a mobile robot system controlled by a FEL neural network and PD controllers. This feedback error-learning controller benefits from both classic and adaptive controller proper...

Recent research uncovers that goal directed sensorimotor behaviour is governed by negative feedback of positional error, and by feedforward through inverse modelling of the limb’s dynamics. Thereby, forward models seem to provide the kinematic state of the limb. The question addressed in the paper is, how the neural network representing the inverse model can be trained. Because in this case an ...

To efficiently learn from feedback, the cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error back-propagation. It has been successfully used in both machine learning and modelling of the brain’s cognitive functions. However, in the back-propagation algorithm, ...

In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO...

Background. Several researchers have studied the effects of type of feedback on learning motor skills, but there are few studies on the interaction between personality traits and the type of feedback. Objectives. This study aimed at investigating the effect of type of feedback on intrinsic motivation and learning volleyball jump serve in students with neuroticism. Methods. A total of 59 femal...