Does Cognitive Load Affect Human Accuracy in Detecting Voice-Based Deepfakes?

2026-01-15

AI Summary

This study systematically investigates, for the first time, the impact of cognitive load on human accuracy in detecting voice deepfakes, aiming to better simulate real-world multitasking scenarios on social media. Using established psychological experimental paradigms to manipulate cognitive load, the authors conducted a controlled experiment with 30 participants exposed to voice deepfake samples under varying cognitive demands. Contrary to the intuitive assumption that cognitive load invariably impairs detection performance, the results reveal that low cognitive load does not universally degrade accuracy; rather, under specific conditions, concurrently presented auxiliary stimuli significantly enhance participants’ ability to identify deepfakes. These findings challenge prevailing assumptions about the detrimental effects of cognitive load and offer novel empirical evidence for understanding how humans discern synthetic speech in complex, information-rich environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Deepfake technologies are powerful tools that can be misused for malicious purposes such as spreading disinformation on social media. The effectiveness of such malicious applications depends on the ability of deepfakes to deceive their audience. Therefore, researchers have investigated human abilities to detect deepfakes in various studies. However, most of these studies were conducted with participants who focused exclusively on the detection task; hence the studies may not provide a complete picture of human abilities to detect deepfakes under realistic conditions: Social media users are exposed to cognitive load on the platform, which can impair their detection abilities. In this paper, we investigate the influence of cognitive load on human detection abilities of voice-based deepfakes in an empirical study with 30 participants. Our results suggest that low cognitive load does not generally impair detection abilities, and that the simultaneous exposure to a secondary stimulus can actually benefit people in the detection task.

Problem

• cognitive load
• deepfakes
• voice-based deepfakes
• human detection
• disinformation

Innovation

• cognitive load
• voice-based deepfakes
• human detection
• empirical study
• multitasking