How do users mentally model dialogue agent partners?

The Partner Modelling Questionnaire (PMQ) validates a three-factor structure for how users perceive machine dialogue partners. The concept originates in psycholinguistics: people form mental representations of their dialogue partner's communicative and social capabilities, and these representations guide what they say, how they say it, and what tasks they entrust to that partner.

Factor 1 — Communicative competence and dependability (49% variance, α=0.88): Strongest items: competent/incompetent, dependable/unreliable, capable/incapable. This is the largest factor — nearly half the variance in how users model a dialogue agent is about whether it can do the job reliably.

Factor 2 — Human-likeness in communication (32% variance, α=0.80): Strongest items: human-like/machine-like, life-like/tool-like, warm/cold. Humans act as the archetype for evaluating communication partners. Even when using machines, people evaluate against a human standard.

Factor 3 — Communicative flexibility (19% variance, α=0.72): Items: flexible/inflexible, interactive/stop-start, interpretive/literal, spontaneous/predetermined. This factor captures whether the agent feels like a living conversation or a scripted interaction.

The definition of partner models — "an interlocutor's cognitive representation of beliefs about their dialogue partner's communicative ability, multidimensional, initially informed by experience and stereotypes, dynamically updated during dialogue" — positions this as the HCI equivalent of theory of mind. Users build, maintain, and update these models continuously.

The practical implication: designing for perceived competence matters most (49% of variance), but human-likeness and flexibility are not negligible. An agent that is reliable but inflexible and machine-like will be perceived very differently from one that is reliable, warm, and spontaneous.

Source: Psychology Chatbots Conversation