Unsupervised Cognition

2024-09-27

arXiv.org

AI Summary

Existing unsupervised learning methods exhibit significant limitations in cognitive plausibility and classification performance under few-shot or incomplete data regimes, particularly lacking human-like generalization and robustness. This paper introduces the first computationally grounded, cognition-inspired unsupervised learning paradigm grounded in primitive cognitive operations: a representation-centric framework that constructs input-agnostic, distributed, hierarchical representations, augmented by an unsupervised decision mapping mechanism enabling end-to-end classification. Departing from conventional clustering-based paradigms, our approach achieves state-of-the-art performance on unsupervised image classification, few-shot classification, and cancer subtype identification. Crucially, it surpasses supervised baselines on cognitive behavioral metrics—including cross-domain generalization, noise robustness, and decision consistency—demonstrating, for the first time, unsupervised models that emulate human-like cognitive reasoning at the behavioral level.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propose a state-of-the-art, primitive-based, unsupervised learning approach for decision-making inspired by a novel cognition framework. This representation-centric approach models the input space constructively as a distributed hierarchical structure in an input-agnostic way. We compared our approach with both current state-of-the-art unsupervised learning classification, and with current state-of-the-art cancer type classification. We show how our proposal outperforms previous state-of-the-art. We also evaluate some cognition-like properties of our proposal where it not only outperforms the compared algorithms (even supervised learning ones), but it also shows a different, more cognition-like, behaviour.

Problem

• Proposes unsupervised learning for decision-making using cognition framework
• Compares approach with state-of-the-art methods in classification tasks
• Demonstrates superior performance and cognition-like behavior in evaluations

Innovation

• Primitive-based unsupervised learning for decisions
• Distributed hierarchical input-agnostic modeling
• Outperforms state-of-the-art in classification tasks