🤖 AI Summary
This study addresses the limited understanding of how users form and update beliefs about AI across multiple tasks and use these beliefs to decide whether to delegate tasks, particularly when task-specific AI reliability varies substantially. Through a preregistered controlled simulation experiment involving grammar checking, travel planning, and visual question answering, the research employs Bayesian belief modeling and a binary delegation decision paradigm to demonstrate that users’ beliefs about general-purpose AI exhibit path dependence and cross-task transfer. Findings reveal that users do not reset beliefs between tasks; belief updates align directionally with Bayesian predictions but occur at a conservative rate (approximately 50%); and delegation decisions are primarily driven by subjective accuracy beliefs, with self-confidence playing only a secondary inhibitory role. These results challenge the conventional assumption of task independence and offer new foundations for designing multi-task AI systems.
📝 Abstract
Large language models (LLMs) increasingly support heterogeneous tasks within a single interface, requiring users to form, update, and act upon beliefs about one system across domains with different reliability profiles. Understanding how such beliefs transfer across tasks and shape delegation is therefore critical for the design of multipurpose AI systems. We report a preregistered experiment (N=240; 7,200 trials) in which participants interacted with a controlled AI simulation across grammar checking, travel planning, and visual question answering, each with fixed, domain-typical accuracy levels. Delegation was operationalized as a binary reliance decision: accepting the AI's output versus acting independently, and belief dynamics were evaluated against Bayesian benchmarks. We find three main results. First, participants do not reset beliefs between tasks: priors in a new task depend on posteriors from the previous task, with a 10-point increase predicting a 3-4 point higher subsequent prior. Second, within tasks, belief updating follows the Bayesian direction but is substantially conservative, proceeding at roughly half the normative Bayesian rate. Third, delegation is driven primarily by subjective beliefs about AI accuracy rather than self-confidence, though confidence independently reduces reliance when beliefs are held constant. Together, these findings show that users form global, path-dependent expectations about multipurpose AI systems, update them conservatively, and rely on AI primarily based on subjective beliefs rather than objective performance. We discuss implications for expectation calibration, reliance design, and the risks of belief spillovers in deployed LLM-based interfaces.