Flexible shaping: how learning in small steps helps.
Humans and animals can perform much more complex tasks than they can acquire using pure trial and error learning. This gap is filled by teaching. One important method of instruction is shaping, in which a teacher decomposes a complete task into sub-components, thereby providing an easier path to learning. Despite its importance, shaping has not been substantially studied in the context of computational modeling of cognitive learning. Here we study the shaping of a hierarchical working memory task using an abstract neural network model as the target learner. Shaping significantly boosts the speed of acquisition of the task compared with conventional training, to a degree that increases with the temporal complexity of the task. Further, it leads to internal representations that are more robust to task manipulations such as reversals. We use the model to investigate some of the elements of successful shaping.
Krueger
Kai A
KA
Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, United Kingdom. kai.krueger@ucl.ac.uk
Dayan
Peter
P
D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer; D012193:Reversal Learning