Title :
A neural network which learns psychological internal representations
Author_Institution :
Dept. of Electr. & Electron. Eng., Adelaide Univ., SA, Australia
Abstract :
A neural network model is described which learns psychological internal representations of a set of objects in its environment. The model adopts the `Psychological Space´ theoretical construct as an appropriate model of human mental representational structure. By considering mental representations as environmental adaptations, the model internally derives the indices of psychological similarity required to construct Psychological Spaces from information provided by the environment
Keywords :
learning (artificial intelligence); neural nets; Psychological Spaces; environmental adaptations; human mental representational structure; mental representations; neural network; psychological internal representations; psychological similarity; Animal structures; Australia; Humans; Information processing; Multidimensional systems; Neural networks; Psychology; Sensor arrays; Sensor phenomena and characterization; Signal processing;
Conference_Titel :
Intelligent Information Systems, 1996., Australian and New Zealand Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-3667-4
DOI :
10.1109/ANZIIS.1996.573928
