Learning and retrieval of hierarchically organized information in a simple, one-layered RNN

How is it possible for an autonomous agent to learn and retrieve hierarchically organized information? The question is particularly interesting if there is not a simple, tree-like hierarchy, but when low-level items may belong to several superordinate elements. In this article we propose a solution for this problem following the ideas of O´Connor et al., which are guided by the observation that children learn superordinate concepts from implicitly given information. For the simulation, we use a simple, one-layered RNN consisting of IC units and a very simple learning rule based on teacher forcing. We assume the capability of figure-ground separation as being given. Furthermore, the agent is assumed to owe sensory systems. There are, however, no explicit, preformulated concepts given like `Red´. Further, the agent is assumed to be able to record words. Each object presented to the agent is separately and individually learned and represented in the RNN forming an episodic memory. We show that, using this simple approach, learning and retrieval of hierarchically organized information is possible, although this information is not given explicitly and no hierarchical structure can be found in the network. Learning is very fast. The net shows top-down generalisation and bottom-up activation. Furthermore, asymmetric priming effects can be observed similar to those fond in human subjects. The agent is able to chunk different sensory inputs to represent the same object in memory, but nevertheless being able to distinguish between the different stimuli if, during learning, a supervisor labels the different stimuli with the same name.