Brief exposure increases mind perception to ChatGPT and is moderated by the individual propensity to anthropomorphize
Bibliographic record
Abstract
The rapid proliferation of advanced AI chatbots and large language models (LLMs), such as ChatGPT, have coincided with increased calls to use psychological tools to understand how people perceive interactions with these AI systems. Decades of research into how people perceive minds and anthropomorphize non-humans have led to the development of several prominent frameworks for investigating how people perceive minds in AI systems. Yet, due to the novelty of programs like ChatGPT, mind perception frameworks have not been applied to better understand how exposure to ChatGPT influences the perception of mind in AI. Furthermore, there is an absence of knowledge regarding how individual differences may moderate changes in mind perception as a function of exposure. Here, we reveal the results of a brief exposure manipulation to ChatGPT and its subsequent effect on changing mind perception ratings. We find that even brief exposure significantly increased people’s perceptions of agency and experience in ChatGPT. Moreover, individuals with a higher propensity to anthropomorphize were also more likely to show changes to experiential attributions to ChatGPT (i.e., its ability to feel). These findings suggest that as LLMs like ChatGPT grow in popularity, and people are exposed to them to a greater extent, the degree to which people attribute qualities of mind to AI systems will also increase. This study expands the field’s current understanding of how exposure to LLMs and individual differences may influence attribution of mind to AI.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".