Forming Concepts of Mozart and Homer Using Short-Term and Long-Term Memory: A Computational Model Based on Chunking

A fundamental issue in cognitive science concerns the mental processes that underlie the formation and retrieval of concepts in the short-term and long-term memory (STM and LTM respectively). This study advances Chunking Theory and its computational embodiment CHREST to propose a single model that accounts for significant aspects of concept formation in the domains of literature and music. The proposed model inherits CHREST’s architecture with its integrated
STM/LTM stores, while also adding a moving attention window and an “LTM chunk activation” mechanism. These additions address the overly destructive nature of primacy effect in discrimination network based architectures and
expand Chunking Theory to account for learning, retrieval and categorisation of complex sequential symbolic patterns – like real-life text and written music scores. The model was trained through exposure to labelled stimuli and learned to categorise classical poets/writers and composers. The model categorised
previously unseen literature pieces by Homer, Chaucer, Shakespeare, Walter Scott, Dickens and Joyce, as well as unseen sheet music scores by Bach, Mozart, Beethoven and Chopin. These findings offer further support to mechanisms
proposed by Chunking Theory and expand it into the psychology of music.