This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.

A fundamental issue in cognitive science concerns the mentalprocesses that underlie the formation and retrieval of conceptsin the short-term and long-term memory (STM and LTMrespectively). This study advances Chunking Theory and itscomputational embodiment CHREST to propose a singlemodel that accounts for significant aspects of conceptformation in the domains of literature and music. The proposedmodel inherits CHREST’s architecture with its integratedSTM/LTM stores, while also adding a moving attentionwindow and an “LTM chunk activation” mechanism. Theseadditions address the overly destructive nature of primacyeffect in discrimination network based architectures andexpand Chunking Theory to account for learning, retrieval andcategorisation of complex sequential symbolic patterns – likereal-life text and written music scores. The model was trainedthrough exposure to labelled stimuli and learned to categoriseclassical poets/writers and composers. The model categorisedpreviously unseen literature pieces by Homer, Chaucer,Shakespeare, Walter Scott, Dickens and Joyce, as well asunseen sheet music scores by Bach, Mozart, Beethoven andChopin. These findings offer further support to mechanismsproposed by Chunking Theory and expand it into thepsychology of music.

Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models.