Year: 2024
Type: article
Abstract: Machine creativity is on the rise. Recent studies find that large language models achieve human performance on common psychological tests of creativity, which often pose a given problem and ask for novel or unusual solutions. But can AI go beyond producing solutions for given problems, to creatively propose new problems? We present the Task Task, a novel test that asks participants to come up with creative problems. In this test, we assess the ability of humans and GPT-4 to design challenge tasks for a game show. We evaluated proposed tasks using crowdsourced subjective creativity ratings, as well as computational measures of linguistic complexity and semantic content. We found that GPT-4 achieves similar scores as humans on creativity, originality, and judgments of how fun or difficult the tasks are. However, model-generated output tends to be shorter and connect more semantically distant concepts. We discuss implications and future directions for the psychology of creativity.
Authors: Chu, Junyi, Hu, Jennifer, Ullman, Tomer D.
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7006208159