Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment
Figure 9
Effects of multiple timescales.
Learning error for basic pattern and novel pattern training for various slow context time constant values are shown. Differences in timescale are described by the ratio of τ values in the fast and slow context units (τ-slow/τ-fast). Bars in the graph correspond to mean values over 5 learning trials for each parameter setting. Error bars indicate the degree of standard deviation. Asterisks indicate significant differences in mean values between the standard setting (τ-ratio = 14.0) and other settings. The significance of these differences was examined using a randomized test. Both in basic pattern training and in additional training, performance for the case of small τ-ratio was significantly worse than the standard setting. These results suggest that multiple timescales in the fast and slow context units was an essential factor leading to the emergence of hierarchical functional differentiation.