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

2025-12-21

AI Summary

A core challenge in cognitive science is elucidating the psychological mechanisms underlying natural concept formation and retrieval in short- and long-term memory. To address this, we propose CogAct—a computational model grounded in chunking theory and attentional mechanisms—that unifies subjective conceptual space modeling with individual experiential modulation. For the first time, CogAct directly learns logical, artificial, and complex concepts adaptively from raw, cross-domain natural data—including musical scores, literary texts, and chess positions—without preprocessing or task-specific architectural modifications. The model integrates dynamic interactions between short- and long-term memory systems and is validated against human behavioral benchmarks. Simulation results closely align with human subjective judgments; moreover, CogAct demonstrates complexity-adaptive learning across diverse real-world concepts. This work establishes a psychologically interpretable, unified computational framework for concept acquisition.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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.

Problem

• Modeling automatic adaptation to concept complexity and subjective natural concepts
• Grounding concept learning in chunking, attention, and memory processes
• Designing benchmarks for subjective human concept learning without pre-built knowledge

Innovation

• Chunking-based computational model adapts to concept complexity automatically
• Simulates subjective conceptual spaces using raw data without pre-built knowledge
• Novel human benchmark design controls for individual experiences in experiments