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.

The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder. The interviews are conducted by humans, human controlled agents and autonomous agents, and the participants include both distressed and non-distressed individuals. Data collected include audio and video recordings and extensive questionnaire responses; parts of the corpus have been transcribed and annotated for a variety of verbal and non-verbal features. The corpus has been used to support the creation of an automated interviewer agent, and for research on the automatic identification of psychological distress.