The understanding gap: when functionality looks like understanding

Abstract

The rapid proliferation of conversational AI systems has led many users to attribute understanding, emotional sensitivity, and experiential qualities to AI chatbots. While these systems produce highly fluent and contextually appropriate responses, whether such attributions reflect genuine understanding or conflate functional performance with cognitive capacity remains unclear. This paper examines this attribution by integrating a rigorous philosophical framework with empirical findings from chatbot users. We ground our analysis in an expanded ‘knowledge of causes’ view, according to which genuine understanding requires knowledge of dependence relations and the cognitive capacity to manipulate them through analogical, counterfactual, and abductive reasoning. On this account, understanding presupposes an underlying cognitive architecture, not merely correct outputs or behavioral adequacy. We surveyed 122 adult chatbot users about their perceptions of their chatbots’ understanding and their own epistemic beliefs. Participants rated AI functionality significantly higher than AI understanding, and cognitive capacities higher than affective ones, with particularly low ratings for affective understanding. Hierarchical regressions revealed domain-sensitive drivers: cognitive understanding attribution tracked perceived cognitive functionality and a ‘functionality implies understanding’ stance, whereas affective understanding was uniquely predicted by epistemic stance and attitudes toward AI subjective experience, beyond perceived affective functionality. These findings illuminate an understanding gap: user attributions are partly guided by epistemic orientation and experiential ascriptions that make sophisticated simulation appear as understanding, especially in affective interaction, raising urgent questions about epistemic trust, relational vulnerability, and the ethics of AI companionship.