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

TL;DR

This paper introduces CogAct, a cognitive model that uses chunking to adaptively learn various concept types from simple to complex, including natural concepts, and accounts for individual subjectivity in human concept learning.

Contribution

The paper presents a novel cognitive model, CogAct, that demonstrates adaptive concept learning across diverse domains and incorporates subjective human judgments, advancing psychological theories.

Findings

CogAct effectively learns categories from simple to complex in multiple domains.

It captures individual subjective judgments in music without pre-built knowledge.

Compared to deep learning, CogAct offers a more flexible and human-like approach.

Abstract

A key issue in cognitive science concerns the fundamental psychological processes that underlie the formation and retrieval of multiple types of concepts in short-term and long-term memory (STM and LTM, respectively). We propose that chunking mechanisms play an essential role and show how the CogAct computational model grounds concept learning in fundamental cognitive processes and structures (such as chunking, attention, STM and LTM). First are the in-principle demonstrations, with CogAct automatically adapting to learn a range of categories from simple logical 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 modelling artificial categories and (non-GPT) models based on deep learning requiring task-specific changes to the architecture. Secondly, we offer novel ways of designing human benchmarks for concept learning experiments and simulations accounting for subjectivity, ways to control for individual human experiences, all while keeping to real-life complex categories. We ground CogAct in simulations of subjective conceptual spaces of individual human participants, capturing humans subjective judgements in music, with the models learning from raw music score data without bootstrapping to pre-built knowledge structures. The CogAct simulations are compared to those obtained by a deep-learning model. These findings integrate concept learning and adaptation to complexity into the broader theories of cognitive psychology. Our approach may also be used in psychological applications that move away from modelling the average participant and towards capturing subjective concept space.