Are Semantic Networks Associated with Idea Originality in Artificial Creativity? A Comparison with Human Agents

TL;DR

This study explores how semantic network structures relate to idea originality in AI and humans, revealing that AI can be more original despite more rigid networks, influenced by motivation and settings.

Contribution

It compares semantic networks and originality between AI and humans, highlighting factors influencing artificial creativity and proposing new evaluation methods.

Findings

ChatGPT-4o shows higher originality than lower creative humans.

AI's rigid networks can still produce more original ideas.

Motivational factors and hyperparameters affect AI creativity.

Abstract

The application of generative artificial intelligence in Creativity Support Tools (CSTs) presents the challenge of interfacing two black boxes: the user's mind and the machine engine. According to Artificial Cognition, this challenge involves theories, methods, and constructs developed to study human creativity. Consistently, the paper investigated the relationship between semantic networks organisation and idea originality in Large Language Models. Data was collected by administering a set of standardised tests to ChatGPT-4o and 81 psychology students, divided into higher and lower creative individuals. The expected relationship was confirmed in the comparison between ChatGPT-4o and higher creative humans. However, despite having a more rigid network, ChatGPT-4o emerged as more original than lower creative humans. We attributed this difference to human motivational processes and model hyperparameters, advancing a research agenda for the study of artificial creativity. In conclusion, we illustrate the potential of this construct for designing and evaluating CSTs.