Sources & Limits — Why Can We Feel Understood by an AI?
This note shows which sources support the essay, what they establish, and where the evidence stops. It is part of the essay’s free evidence layer.
1. Removing the human witness lowers the wall around disclosure
Used for: People revealed more, feared judgment less and managed their self-presentation less when they believed a virtual health interviewer was fully automated rather than human-operated.
Best available evidence: Gale M. Lucas, Jonathan Gratch, Aisha King & Louis-Philippe Morency (2014), “It’s only a computer: Virtual humans increase willingness to disclose”, Computers in Human Behavior, 37, 94–100.
What it supports: Participants who believed no human was involved reported lower fear of being evaluated, showed less impression management and disclosed more.
Where the evidence stops: A laboratory setting with a virtual health interviewer, not a modern text-based AI companion. Direction of effect, not everyday scale.
Source status: Peer-reviewed journal article.
2. Feeling understood grows from responsive answers
Used for: Psychologists have long argued that closeness grows when a disclosure is met with a response that feels understanding, validating and caring.
Best available evidence: Harry T. Reis, Margaret S. Clark & John G. Holmes (2004), “Perceived Partner Responsiveness as an Organizing Construct in the Study of Intimacy and Closeness”, in Handbook of Closeness and Intimacy (Mashek & Aron, eds.), Lawrence Erlbaum Associates. No stable open online record was located for this print chapter; the standard citation is given so readers can find it in any academic library.
What it supports: The theoretical construct of perceived partner responsiveness: intimacy is supported when responses to self-disclosure feel understanding, validating and caring.
Where the evidence stops: A theory of human relationships. It was not written about machines; the essay applies the mechanism, not the authors’ conclusions, to AI.
Source status: Peer-reviewed academic book chapter.
3. Short AI-companion conversations can reduce loneliness in the moment
Used for: Controlled studies found momentary reductions in loneliness after short AI-companion interactions, sometimes comparable to talking with another person, with “feeling heard” as the strongest reported explanation.
Best available evidence: Julian De Freitas, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp & Stefano Puntoni, “AI Companions Reduce Loneliness”, Journal of Consumer Research 52(6), April 2026 (online 25 June 2025; corrected/typeset 24 September 2025).
What it supports: Momentary loneliness reduction after short interactions; in one comparison the reduction was comparable to a conversation with a person; feeling heard was the principal identified mechanism.
Where the evidence stops: Short-term effects measured around single sessions. The study does not show a durable treatment for loneliness, and it does not show AI replacing human relationships. No correction, retraction or expression of concern was located.
Source status: Peer-reviewed journal article.
4. People apply social rules to machines while denying that they do
Used for: Classic experiments in which people were politer to a computer that asked about its own performance, and returned favours to helpful machines, while explicitly denying they treated computers socially.
Best available evidence: Clifford Nass & Youngme Moon (2000), “Machines and Mindlessness: Social Responses to Computers”, Journal of Social Issues 56(1), 81–103.
What it supports: Behavioural evidence that social responses to machines can coexist with accurate explicit beliefs about what the machine is.
Where the evidence stops: Desktop-era experiments about immediate social responses, not about long-term attachment to conversational AI.
Source status: Peer-reviewed journal article.
5. Attachment is not automatic: novelty can wear off
Used for: A longitudinal study in which feelings of friendship toward a pre-LLM chatbot declined across seven conversations over three weeks.
Best available evidence: Emmelyn A. J. Croes & Marjolijn L. Antheunis (2021), “Can we be friends with Mitsuku?”, Journal of Social and Personal Relationships 38(1), 279–300.
What it supports: 118 participants; seven interactions over three weeks; friendship perceptions and social processes decreased.
Where the evidence stops: Mitsuku was far more limited than today’s systems; the finding may not transfer cleanly. It serves as a corrective, not a prediction.
Source status: Peer-reviewed journal article.
6. Reported attachment tends to arise in particular circumstances
Used for: Early qualitative work in which users describing genuine attachment to an AI companion often said it arose amid distress, isolation or thin human company.
Best available evidence: Tianling Xie & Iryna Pentina (2022), “Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika”, Proceedings of the 55th Hawaii International Conference on System Sciences.
What it supports: Qualitative interviews with fourteen existing Replika users; attachment described under conditions such as distress and limited human companionship.
Where the evidence stops: A small, self-selected sample of existing users. No causal or population-wide conclusion.
Source status: Peer-reviewed conference proceedings.
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Last checked: 18 July 2026