Title :
Arousal performance interactions in neural networks: the Yerkes-Dodson Law revisited
Author :
Schreter, Zoltan
Author_Institution :
Dept. of Psychol., Tasmania Univ., Hobart, Tas., Australia
Abstract :
A neural network model of the Yerkes-Dodson Law is described. The network´s learning performance varies as a function of simulated arousal and of task difficulty, in the way described by the Yerkes-Dodson Law: the arousal-performance relationship is of an inverted-U form and optimal arousal is higher for easier tasks.
Keywords :
neural nets; physiological models; Yerkes-Dodson Law; arousal performance interactions; inverted-U form; learning performance; neural networks; simulated arousal; task difficulty; Animals; Biological neural networks; Brain modeling; Heart rate; Intelligent networks; Nerve fibers; Neural networks; Neurons; Psychology; Rats;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.714251
