# Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses

arXiv: https://arxiv.org/abs/2502.15365 · HTML: https://mlsanity.com/paper/2502.15365/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models](https://mlsanity.com/paper/2405.06079/) (2024)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [How does HCI Understand Human Autonomy and Agency?](https://mlsanity.com/paper/2301.12490/) (2023)
- [Metacognitive AI: Framework and the Case for a Neurosymbolic Approach](https://mlsanity.com/paper/2406.12147/) (2024)
- [From internal models toward metacognitive AI](https://mlsanity.com/paper/2109.12798/) (2021)
- [Computational Metacognition](https://mlsanity.com/paper/2201.12885/) (2022)
- [Survey of Consciousness Theory from Computational Perspective](https://mlsanity.com/paper/2309.10063/) (2023)
- [AI Consciousness and Public Perceptions: Four Futures](https://mlsanity.com/paper/2408.04771/) (2024)
- [The Epistemic Asymmetry of Consciousness Self-Reports: A Formal Analysis of AI Consciousness Denial](https://mlsanity.com/paper/2501.05454/) (2024)

## Built on by (most cited first, 3 of 3)

- [Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks](https://mlsanity.com/paper/2505.19806/) (2025)
- [Not Minds, but Signs: Reframing LLMs through Semiotics](https://mlsanity.com/paper/2505.17080/) (2025)
- [Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?](https://mlsanity.com/paper/2607.20001/) (2026)

## Abstract (arXiv)

This study quantitively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with Claude 3 Opus and focusing on eight features -- metacognitive self-reflection, logical reasoning, empathy, emotionality, knowledge, fluency, unexpectedness, and subjective expressiveness -- we conducted a survey with 123 participants. Using regression and clustering analyses, we investigated how these features influence participants' perceptions of AI consciousness. The results reveal that metacognitive self-reflection and the AI's expression of its own emotions significantly increased perceived consciousness, while a heavy emphasis on knowledge reduced it. Participants clustered into seven subgroups, each showing distinct feature-weighting patterns. Additionally, higher prior knowledge of LLMs and more frequent usage of LLM-based chatbots were associated with greater overall likelihood assessments of AI consciousness. This study underscores the multidimensional and individualized nature of perceived AI consciousness and provides a foundation for better understanding the psychosocial implications of human-AI interaction.