@inproceedings{dash-etal-2026-persona,
    title = "Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning",
    author = "Dash, Saloni  and
      Reymond, Am{\textbackslash}'elie  and
      Spiro, Emma  and
      Caliskan, Aylin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.585/",
    pages = "12043--12069",
    ISBN = "979-8-89176-395-1",
    abstract = "Reasoning in humans is prone to biases due to underlying motivations like identity protection, that undermine rational decision-making and judgment. This motivated reasoning at a collective level can be detrimental to society when debating critical issues such as human-driven climate change or vaccine safety, and can further aggravate political polarization. Prior studies have reported that large language models (LLMs) are also susceptible to human-like cognitive biases, however, the extent to which LLMs selectively reason toward identity-congruent conclusions remains largely unexplored. Here, we investigate whether assigning 8 personas across 4 political and socio-demographic attributes induces motivated reasoning in LLMs. Testing 8 LLMs (open source and proprietary) across two reasoning tasks from human-subject studies {---} veracity discernment of misinformation headlines and evaluation of numeric scientific evidence {---} we find that persona-assigned LLMs have up to 9{\%} reduced veracity discernment relative to models without personas. Political personas specifically are up to 90{\%} more likely to correctly evaluate scientific evidence on gun control when the ground truth is congruent with their induced political identity. Prompt-based debiasing methods are largely ineffective at mitigating these effects. Taken together, our empirical findings are the first to suggest that persona-assigned LLMs exhibit human-like motivated reasoning that is hard to mitigate through conventional debiasing prompts {---} raising concerns of exacerbating identity-congruent reasoning in both LLMs and humans."
}