  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 (Experiments 2 and 4). As hypothesized, we found
that overall, the addition bias was present. Solution efficiency (Experiments 1
and 2) and valence of the instruction (Experiments 3 and 4) played important
roles. Human participants were less likely to use additive strategies when
subtraction was relatively more efficient than when addition and subtraction
were equally efficient. GPT-4 exhibited the opposite behavior, with a strong
addition bias when subtraction was more efficient. In terms of instruction
valence, GPT-4 was more likely to add words when asked to "improve" compared to
"edit", whereas humans did not show this effect. When we looked at the addition
bias under different conditions, we found more biased responses for GPT-4
compared to humans. Our findings highlight the importance of considering
comparable and sometimes superior subtractive alternatives, as well as
reevaluating one's own and particularly the language models' problem-solving
behavior.
