Perceptually Grounded Language Learning: Part 1—A Neural Network Architecture for Robust Sequence Association

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Abstract
In this two-part series, we explore how a perceptually based foundation for natural language semantics might be acquired, via association of sensory/motor experiences with verbal utterances describing those experiences. In Part 1, we introduce a novel neural network architecture, termed Katamic memory, that is inspired by the neurocircuitry of the cerebellum and that exhibits (a) rapid/robust sequence learning/recogmtion and (b) allows integrated learning and performance. These capabilities are due to novel neural elements, which model dendritic structure and function in greater detail than in standard connectionist models. In Part 2, we describe the DETE system, a massively parallel proceduraljneural hybrid model that utilizes over 50 Katamic memory modules to perform two associative learning tasks: (a) verbal-to-visual / motor association—given a verbal sequence, DETE learns to regenerate a neural representation of the visual sequence being described and/or to carry out motor commands; and (b) visual/motor-to-verbal association—given a visual/motor sequence, DETE learns to produce a verbal sequence describing the visual input. DETE can learn verbal sequences describing spatial relations and motions of 2D 'blob-like objects; in addition, the system can also generalize to novel inputs. DETE has been tested successfully on small, restricted subsets of English and Spanish—languages that differ in inflectional properties, word order and how they categorize perceptual reality.