A Neural Network Model for Schemas Based On Pattern Completion

Abstract

Abstract Recent developments in neuroscience have provided us with a wealth of the basic knowledge and tools which are required for neurobiological understanding of the psychological concepts. This advantage enables contemporary scientists to suggest and test brain models for psychological concepts, theories, and methods. Considering the current dominance of biological ideas in psychiatry and psychology, such models are essential in confirmation of the psychological theories of mind. In this article a brain model for schemas as essential to cognitive theory is proposed. Schemas are seen as patterns which are recognized and memorized through the training phase of an autoassociative neural network. Then, these patterns are used to complete ambiguous aspects of future experiences through thalamo and hippocampal-cortical pathways. In relation to the self or the outside world when a pattern with unknown, noisy, or vague aspects is encountered, those aspects are completed by the principal components of previously learned patterns (schema). This process is to help the observer acquire a better understanding of the environment or the self. However, the patterns which are used to complete the uncertainties about the self or the environment are sometimes not good estimates of the reality and lead the person/patient to an illusionary perception of the self/environment. In this article, the role of the mirror neuron system in pattern recognition is also explained. Psychological and biological therapeutic implications of this model are discussed and the importance of a link between dynamic and cognitive therapies is rationalized.