Parallel distributed processing: Implications for cognition and development. Technical report
This paper provides a brief overview of the connectionist or parallel distributed processing framework for modeling cognitive processes, and considers the application of the connectionist framework to problems of cognitive development. Several aspects of cognitive development might result from the process of learning as it occurs in multi-layer networks. This learning process has the characteristic that it reduces the discrepancy between expected and observed events. As it does this, representations develop on hidden units which dramatically change both the way in which the network represents the environment from which it learns and the expectations that the network generates about environmental events. The learning process exhibits relatively abrupt transitions corresponding to stage shifts in cognitive development. These points are illustrated using a network that learns to anticipate which side of a balance beam will go down, based on the number of weights on each side of the fulcrum and their distance from the fulcrum on each side of the beam. The network is trained in an environment in which weight more frequently governs which side will go down. It recapitulates the states of development seen in children, as well as the stage transitions, as it learns to represent weight and distance information.
- Research Organization:
- Carnegie-Mellon Univ., Pittsburgh, PA (USA). Artificial Intelligence and Psychology Project
- OSTI ID:
- 6860151
- Report Number(s):
- AD-A-219063/5/XAB; AIP-47
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
CHILDREN
LEARNING
COMPUTERIZED SIMULATION
DATA PROCESSING
DISTANCE
DISTRIBUTED DATA PROCESSING
ENVIRONMENT
LAYERS
PARALLEL PROCESSING
PROGRESS REPORT
TRAINING
AGE GROUPS
DOCUMENT TYPES
PROCESSING
PROGRAMMING
SIMULATION
990200* - Mathematics & Computers