Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Emergence and Categorization of Coordinated Visual Behavior ThroughEmbodied Interaction
Machine Learning - Special issue on learning in autonomous robots
Neural learning of embodied interaction dynamics
Neural Networks - Special issue on neural control and robotics: biology and technology
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Neural Networks - Special issue on organisation of computation in brain-like systems
Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive mixtures of local experts
Neural Computation
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.01 |
The relationship between generalization and differentiation fluctuates depending on the ongoing context, which is extracted by the current adaptive capability of the learner. In the present report, we numerically examine the relationship between generalization and differentiation using a novel connectionist model. The simulation results of incremental learning indicate that the newly added sequence modifies the previously learned internal representations in a different manner, depending on the inconsistency with the preceding task. This observation supports our assertion that it is fundamentally important to investigate how the transition dynamics of learning toward a goal affects the finally acquired structure of the learner.