Automatic Adaptation to Concept Complexity and Subjective Natural Concepts: A Cognitive Model based on Chunking

A key issue in cognitive science concerns the fundamental psychological processes that underlie the formation and retrieval of multiple types ofconcepts in short-term and long-term memory (STM and LTM, respectively). We propose that chunking mechanisms play an essential role and show how theCogAct computational model grounds concept learning in fundamental cognitive processes and structures (such as chunking, attention, STM and LTM). Thisis done in two ways. First are the in-principle demonstrations, with CogAct automatically adapting to learn a range of categories – from simplelogical functions, to artificial categories, to natural raw (as opposed to natural pre-processed) concepts in the dissimilar domains of literature,chess and music. This kind of adaptive learning is difficult for most other psychological models, e.g., with cognitive models stopping at modellingartificial categories and (non-GPT) models based on deep learning requiring task-specific changes to the architecture. Secondly, we offer novel waysof designing human benchmarks for concept learning experiments and simulations accounting for subjectivity, ways to control for individual humanexperiences, all while keeping to real-life complex categories. We ground CogAct in simulations of subjective conceptual spaces of individual humanparticipants, capturing humans subjective judgements in music, with the models learning from raw music score data without bootstrapping to pre-builtknowledge structures. The CogAct simulations are compared to those obtained by a deep-learning model. These findings integrate concept learning andadaptation to complexity into the broader theories of cognitive psychology. Our approach may also be used in psychological applications that move awayfrom modelling the average participant and towards capturing subjective concept space.