Influence of Solution Efficiency and Valence of Instruction on Additive and Subtractive Solution Strategies in Humans and GPT-4

arXiv:2404.16692v1 Announce Type: new
Abstract: We explored the addition bias, a cognitive tendency to prefer adding elements over removing them to alter an initial state or structure, by conducting four preregistered experiments examining the problem-solving behavior of both humans and OpenAl's GPT-4 large language model. The experiments involved 588 participants from the U.S. and 680 iterations of the GPT-4 model. The problem-solving task was either to create symmetry within a grid (Experiments 1 and 3) or to edit a summary …

abstract arxiv behavior bias cognitive cs.ai cs.cl efficiency gpt gpt-4 humans influence problem-solving solution state strategies them type