Is a good bot better than a mediocre human?: Chatbots as alternative sources of social connection
Bibliographic record
Abstract
Around the world, hundreds of millions of people have used social chatbots designed to provide companionship to their users. But can people reap genuine feelings of social connection and happiness from interacting with chatbots? In Studies 1-4 (all pre-registered; N = 1201), participants shared good news with an interaction partner whom they believed was either a chatbot or a human. The conversation partner responded in either a highly responsive or less responsive manner. Interacting with a highly responsive chatbot was more rewarding than interacting with a less responsive human. Participants who believed they interacted with a highly responsive chatbot felt more rapport, were more socially connected, felt better about their own positive experience, and were in a better mood than participants who interacted with a less responsive human. In a final pre-registered study (n = 401), we identified a critical boundary condition by examining whether participants derived similar benefits when the chatbot partners shared their own experiences. Taken together, our results suggest that despite their inherent lack of agency, chatbots that are programmed to respond in an optimal manner may deliver greater social benefits than suboptimal human conversation partners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".