Investigating Motivated Inference in Large Language Models (2025)

Abstract: Our desires often influence our beliefs and expectations. Humans tend to think good things are more likely to happen than they actually are, while believing bad things are less likely. This tendency has been referred to as wishful thinking in research on coping strategies. With large language models (LLMs) increasingly being considered as computational models of human cognition, we investigate whether they can simulate this distinctly human bias. We conducted two systematic experiments across multiple LLMs, manipulating outcome desirability and information uncertainty across multiple scenarios including probability games, natural disasters, and sports events. Our experiments revealed limited wishful thinking in LLMs. In Experiment 1, only two models showed the bias, and only in sports-related scenarios when role-playing characters. Models exhibited no wishful thinking in mathematical contexts. Experiment 2 found that explicit prompting about emotional states (being hopeful) was necessary to elicit wishful thinking in logical domains. These findings reveal a significant gap between human cognitive biases and LLMs’ default behavior patterns, suggesting that current models require explicit guidance to simulate wishful thinking influences on belief formation.

## introduction advances in large language models (llms) have motivated researchers to explore their potential for modeling human cognition and simulating human behaviors @xcite @xcite . for effective behavioral simulation, llms must model emotional behaviors, a fundamental aspect of human psychology. while researchers have explored various emotional tasks in llms @xcite @xcite @xcite , one important aspect of emotion that has received less attention is coping, cognitive and behavioral efforts to regulate emotions by modifying the situation and the relationship between the individual and their environment @xcite . few studies have examined coping behaviors in llms, with those that have producing mixed results @xcite . this work addresses this gap by investigating wishful thinking, a common emotion-focused coping strategy @xcite . wishful thinking can be modeled as overestimating positive outcomes while underestimating negative events, allowing people to regulate emotions when facing uncertainty by aligning beliefs with desired rather than objective reality @xcite @xcite . for instance, sports fans often believe their team has a higher probability of winning than the current situation objectively indicates (figure 1 ). while wishful thinking, and related concepts like motivated inference and motivated reasoning (thagard and @xcite , is a common cognitive phenomenon in humans, they have not been explored in detail in llms. we examine how wishful thinking affects belief formulation when processing information about po-tentially desirable or undesirable outcomes, identifying patterns where models assign higher probabilities to favorable outcomes and lower probabilities to unfavorable ones compared to neutral conditions. building on human research identifying outcome desirability and information uncertainty as key influencing factors @xcite , we systematically explore both variables across two experiments. across two experiments testing multiple llms across varied domains, we found limited but specific instances of wishful thinking, primarily in sports contexts and when characters were explicitly described as hopeful. our work contributes a framework for studying wishful thinking in llms and advances understanding of their capabilities and limitations in simulating human behaviors and serving as cognitive models. ## experiment 1 in the first experiment, we systematically investigated wishful thinking in llms along two key dimensions: information uncertainty and outcome desirability. ## experiment 2 experiment 1 revealed limited wishful thinking patterns, particularly in the urn domain where models frequently emphasized objectivity and performed explicit probability calculations. this suggested that certain domains predispose models toward logical rather than wishful responses, prompting us to investigate whether alternative prompting strategies could elicit wishful thinking without explicitly referencing the concept. ## discussion our findings reveal significant limitations in llms' ability to naturally simulate wishful thinking behaviors. in experiment 1, only domain-specific instances emerged, sonnet 3.7 in football and deepseek v3 in quidditch, suggesting that sports contexts facilitate wishful thinking more readily than mathematical domains like urn problems. this contrasts with human studies where wishful thinking appears in abstract probability scenarios @xcite . models showed no sensitivity to simulation trial numbers, indicating this uncertainty manipulation was ineffective. experiment 2 demonstrated that prompting to explicitly consider hopeful emotional state can elicit wishful thinking in mathematical domains, but only for some models. these results suggest that within our tested domains and prompting strategies, current llms do not spontaneously exhibit human-like wishful thinking. this echoes findings where models maintain their capabilities even when roleplaying characters who should lack them, likely due to assistantoriented training @xcite . such behavior suggests current limitations in llms' capacity to fully simulate naturalistic human behaviors. our current study focused on only binary probability assessments with simulation-based uncertainty presentation. future work should explore linguistic uncertainty expressions, incomplete information, alternative information formats instead of simulations, and additional domains. another interesting direction is investigating naturalistic wishful thinking, such as models overestimating their own accuracy or underestimating task difficulty. to further understand models' internal representations, future work could examine token-level probabilities and whether they align with their textual outputs. different prompting strategies may be needed for different models to effectively elicit biased reasoning. beyond belief formation, investigating belief updating under wishful thinking and scenarios with conflicting information sources (relating to confirmation bias) would provide deeper insights. in conclusion, this work provides systematic evidence and contribute to our understanding of current llms' capabilities and limitations in simulating wishful thinking behaviors.