Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding

TL;DR

This paper introduces CognitiveBench, a benchmark for multi-dimensional cognitive state modeling in LLMs, identifies the challenge of cognitive crowding, and proposes HyCoLLM with hyperbolic space modeling to improve performance.

Contribution

It presents the first unified benchmark for multi-dimensional cognitive states, analyzes the hierarchical structure of cognitive states, and introduces hyperbolic modeling to enhance LLM capabilities.

Findings

LLMs perform well on single-dimension tasks but struggle with joint modeling.

CognitiveBench exhibits a strong hierarchical structure.

HyCoLLM improves multi-dimensional understanding, outperforming GPT-4o.

Abstract

Modeling human cognitive states is essential for advanced artificial intelligence. Existing Large Language Models (LLMs) mainly address isolated tasks such as emotion analysis or stance detection, and fail to capture interactions among cognitive dimensions defined in psychology, including emotion, thinking style, stance, and intention. To bridge this gap, we construct CognitiveBench, the first benchmark with unified annotations across the above four dimensions. Experiments on CognitiveBench show that although LLMs perform well on single dimension tasks, their performance drops sharply in joint multi-dimensional modeling. Using Gromov $\delta$-hyperbolicity analysis, we find that CognitiveBench exhibits a strong hierarchical structure. We attribute the performance bottleneck to ``Cognitive Crowding'', where hierarchical cognitive states require exponential representational space, while the Euclidean space of LLMs grows only polynomially, causing representation overlap and degraded performance. To address this mismatch, we propose HyCoLLM, which models cognitive states in hyperbolic space and aligns LLM representations via Hyperbolic Guided Alignment Tuning. Results show that HyCoLLM substantially improves multi-dimensional cognitive understanding, allowing 8B parameter model to outperform strong baselines, including GPT-4o.