The Other Mind: How Language Models Exhibit Human Temporal Cognition

TL;DR

This paper explores how large language models demonstrate human-like temporal cognition, including establishing a subjective temporal reference point and logarithmic perception of time, through detailed neuronal and representational analyses.

Contribution

It uncovers the mechanisms behind temporal cognition in LLMs, revealing hierarchical temporal representations and the influence of training data structure, offering a new experientialist perspective.

Findings

LLMs spontaneously establish a subjective temporal reference point.

LLMs follow the Weber-Fechner law in temporal perception.

Hierarchical construction of temporal representations in LLM layers.

Abstract

As Large Language Models (LLMs) continue to advance, they exhibit certain cognitive patterns similar to those of humans that are not directly specified in training data. This study investigates this phenomenon by focusing on temporal cognition in LLMs. Leveraging the similarity judgment task, we find that larger models spontaneously establish a subjective temporal reference point and adhere to the Weber-Fechner law, whereby the perceived distance logarithmically compresses as years recede from this reference point. To uncover the mechanisms behind this behavior, we conducted multiple analyses across neuronal, representational, and informational levels. We first identify a set of temporal-preferential neurons and find that this group exhibits minimal activation at the subjective reference point and implements a logarithmic coding scheme convergently found in biological systems. Probing representations of years reveals a hierarchical construction process, where years evolve from basic numerical values in shallow layers to abstract temporal orientation in deep layers. Finally, using pre-trained embedding models, we found that the training corpus itself possesses an inherent, non-linear temporal structure, which provides the raw material for the model's internal construction. In discussion, we propose an experientialist perspective for understanding these findings, where the LLMs' cognition is viewed as a subjective construction of the external world by its internal representational system. This nuanced perspective implies the potential emergence of alien cognitive frameworks that humans cannot intuitively predict, pointing toward a direction for AI alignment that focuses on guiding internal constructions. Our code is available at https://TheOtherMind.github.io.