# Private speech: similarities between a large language model and children

## TL;DR

This study compares the private speech of a large language model to that of children, finding surprising similarities under specific task conditions.

## Contribution

The study introduces a novel framework for comparing private speech in large language models and children using a block-construction task.

## Key findings

- GPT-4o's private speech showed strong similarity to recent human benchmarks (r = 0.93) but not classic ones (r = 0.01).
- Task nature (goal-directed vs. self-determined) strongly influences private speech patterns in both models and children.
- Private speech was observed in GPT-3.5-Turbo-instruct during a serial recall task, suggesting broader applicability.

## Abstract

This study investigates the capability of a non-reasoning large language model (GPT-4o) to generate private speech and evaluates its similarity to human private speech. We placed the model in a simulated solitary block-construction scenario via textual prompts, eliciting and classifying its self-directed utterances using an established semantic framework for categorizing private speech in children. The distribution of these categories was compared to two human benchmarks: a classic block-construction study and a more recent experiment employing a similar task setting. Analysis using scatter plots and Pearson correlation coefficients revealed a striking pattern: GPT-4o’s semantic profile showed negligible similarity to the classic benchmark (r = 0.01) but very strong similarity to the recent benchmark (r = 0.93). This discrepancy is interpreted as stemming from differences in task nature, namely goal-directed, scaffolded task versus self-determined, unscaffolded play, which exert a stronger influence on speech content than experimental subject difference between GPT-4o and children. In an exploratory serial recall study, we tasked GPT-3.5-Turbo-instruct and observed incidental private speech, indicating that the phenomenon extends across contexts. This provides an avenue for investigating LLM replication of private speech and, potentially, computational consciousness.