# Psychological and behavioural responses in human-agent vs. human-human interactions: a systematic review and meta-analysis

arXiv: https://arxiv.org/abs/2509.21542 · HTML: https://mlsanity.com/paper/2509.21542/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 127 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Large language models surpass human experts in predicting neuroscience results](https://mlsanity.com/paper/2403.03230/) (2024)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [An Approach to Technical AGI Safety and Security](https://mlsanity.com/paper/2504.01849/) (2025)
- [ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents](https://mlsanity.com/paper/2311.03220/) (2023)
- [From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interaction](https://mlsanity.com/paper/2405.15051/) (2024)
- [UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library](https://mlsanity.com/paper/2202.09895/) (2022)
- [As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making](https://mlsanity.com/paper/2501.12868/) (2025)
- [Does GPT-4 surpass human performance in linguistic pragmatics?](https://mlsanity.com/paper/2312.09545/) (2023)
- [The AI Agent Index](https://mlsanity.com/paper/2502.01635/) (2025)
- [Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas](https://mlsanity.com/paper/2402.17270/) (2024)
- [Software-Based Dialogue Systems: Survey, Taxonomy and Challenges](https://mlsanity.com/paper/2106.10901/) (2021)
- [Small sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models](https://mlsanity.com/paper/1601.01981/) (2016)
- [Human-Robot Collaboration: From Psychology to Social Robotics](https://mlsanity.com/paper/1705.10146/) (2017)
- [Agency in the Age of AI](https://mlsanity.com/paper/2502.00648/) (2025)
- [Human-AI Interaction Design Standards](https://mlsanity.com/paper/2503.16472/) (2025)

## Built on by (most cited first, 2 of 2)

- [Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests](https://mlsanity.com/paper/2601.17617/) (2026)
- [Game AI Not Fun? A Scoping Review and Meta-Analysis on the Differences in Enjoyment between Human and Computer Opponents](https://mlsanity.com/paper/2607.24749/) (2026)

## Abstract (arXiv)

Interactive intelligent agents are being integrated across society. Despite achieving human-like capabilities, humans' responses to these agents remain poorly understood, with research fragmented across disciplines. We conducted a first systematic synthesis comparing a range of psychological and behavioural responses in matched human-agent vs. human-human dyadic interactions. A total of 162 eligible studies (146 contributed to the meta-analysis; 468 effect sizes) were included in the systematic review and meta-analysis, which integrated frequentist and Bayesian approaches. Our results indicate that individuals exhibited less prosocial behaviour and moral engagement when interacting with agents vs. humans. They attributed less agency and responsibility to agents, perceiving them as less competent, likeable, and socially present. In contrast, individuals' social alignment (i.e., alignment or adaptation of internal states and behaviours with partners), trust in partners, personal agency, task performance, and interaction experiences were generally comparable when interacting with agents vs. humans. We observed high effect-size heterogeneity for many subjective responses (i.e., social perceptions of partners, subjective trust, and interaction experiences), suggesting context-dependency of partner effects. By examining the characteristics of studies, participants, partners, interaction scenarios, and response measures, we also identified several moderators shaping partner effects. Overall, functional behaviours and interactive experiences with agents can resemble those with humans, whereas fundamental social attributions and moral/prosocial concerns lag in human-agent interactions. Agents are thus afforded instrumental value on par with humans but lack comparable intrinsic value, providing practical implications for agent design and regulation.