---
type: source
id: chen-2026-humans-ai-deepfake-teaming
title: 'How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming'
authors:
- Xinyi Chen
- et al.
year: 2026
venue: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
volume: null
pages: null
doi: 10.1177/10711813261487968
pmid: null
pmcid: null
arxiv_id: null
openalex_id: W7221715535
oa_url: null
peer_review: peer-reviewed-conference
evidence_tier: B
design: Eye-tracking and EEG comparison of human and AI attention allocation in deepfake detection, across levels of AI literacy
modality:
- video
stimuli: Deepfake media manipulating lip movements, facial expressions and voices (details not in abstract)
n_participants: null
n_note: N not reported in the abstract; OpenAlex lists Jing Yang (University at Buffalo) as last author; full author list not confirmed
topics:
- human-detection
- human-ai-teaming
- neural
summary: Humans and AI detectors relied on different cues; eye-tracking and EEG suggested holistic, flexible human viewing that catches some inconsistencies AI misses, and people with more AI exposure matched or exceeded AI detectors in some contexts (abstract; no numbers reported).
results:
- metric: Cue use
  value: humans and AI rely on different cues and reasoning strategies
  note: qualitative
  source_location: abstract
- metric: Human vs AI detector
  value: people with more AI exposure may match or exceed AI detectors in some contexts
  note: no figures in abstract
  source_location: abstract
- metric: Processing style
  value: holistic, flexible visual processing
  note: eye-tracking and EEG evidence
  source_location: abstract
moderators:
- AI literacy / AI exposure
limitations:
- Abstract reports no N or effect sizes
- Short proceedings paper
- Full text not read (closed access)
verification:
  status: verified-abstract
  confidence: medium
  checked: 2026-10-19
  via: OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968
url: null
cited_in:
- MR-0001
updated: '2026-10-19'
---

# How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming

> **Source record** `chen-2026-humans-ai-deepfake-teaming` · 2026 · Proceedings of the Human Factors and Ergonomics Society Annual Meeting  
> **Evidence tier:** B (peer-reviewed-conference) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Humans and AI detectors relied on different cues; eye-tracking and EEG suggested holistic, flexible human viewing that catches some inconsistencies AI misses, and people with more AI exposure matched or exceeded AI detectors in some contexts (abstract; no numbers reported).

## Citation {#citation}

Xinyi Chen, et al. (2026). How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming. *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*. https://doi.org/10.1177/10711813261487968

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#chen-2026-humans-ai-deepfake-teaming](/data/citations.json#chen-2026-humans-ai-deepfake-teaming)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Eye-tracking and EEG comparison of human and AI attention allocation in deepfake detection, across levels of AI literacy |
| Modality | video |
| Stimuli | Deepfake media manipulating lip movements, facial expressions and voices (details not in abstract) |
| Participants (N) | not recorded |
| N detail | N not reported in the abstract; OpenAlex lists Jing Yang (University at Buffalo) as last author; full author list not confirmed |
| Topics | human-detection, human-ai-teaming, neural |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Cue use | humans and AI rely on different cues and reasoning strategies | qualitative |
| Human vs AI detector | people with more AI exposure may match or exceed AI detectors in some contexts | no figures in abstract |
| Processing style | holistic, flexible visual processing | eye-tracking and EEG evidence |

## Moderators {#moderators}

- AI literacy / AI exposure

## Limitations {#limitations}

- Abstract reports no N or effect sizes
- Short proceedings paper
- Full text not read (closed access)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.