A Bateson-Inspired Model for the Generation of Semantic Concepts from Sensory Data

Although neural-symbolic techniques for symbol grounding in sensory data have shown significant effectiveness, they still require substantial training. This article revisits symbolic-only approaches by introducing a new algorithm for creating hierarchical concept structures from spatial sensory data. The method is based on Bateson’s idea of difference as the fundamental element of concept formation. By leveraging this principle, the algorithm extracts atomic features from raw data through basic sequential comparisons within a stream of multivariate numerical values. Experiments carried out on a set of common objects indicate that the method can successfully discriminate and assimilate categories as needed without training. The results show that the model improves on the neural networks it has been tested against, which required more than 400 training examples for the same task. The results also show that the model can generate rich conceptual structures and human-like representations, which (i) facilitate high composability, (ii) support formal reasoning, (iii) inherently enable generalisation, and (iv) possess potential for generative behaviour. Consequently, this approach offers a compelling contribution to symbol grounding and neural-symbolic research by providing a seamless algorithm to bridge perception and conceptual knowledge.