ArticleImaging neuroscience (Cambridge, Mass.)
A collaborative guide to Rapid Invisible Frequency Tagging (RIFT): Methods, insights, and recommendations.
Article in Imaging neuroscience (Cambridge, Mass.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Hierarchical brain dynamics supporting visual perceptual transitions.Science advances · 2026Article
- Dissociating external and internal attentional selection.iScience · 2025Article
- Using Rapid Invisible Frequency Tagging (RIFT) to Probe the Neural Interaction Between Representations of Speech Planning and Comprehension.Neurobiology of language (Cambridge, Mass.) · 2025Article
- Frequency tagging of spatial attention using periliminal flickers.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A collaborative guide to Rapid Invisible Frequency Tagging (RIFT): Methods, insights, and recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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12 authors.
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Abstract
Rapid Invisible Frequency Tagging (RIFT) is a recent advance in frequency tagging that exploits novel, high-frequency displays to modulate luminance at imperceptibly high frequencies. RIFT goes beyond low-frequency tagging by allowing researchers to track neural responses to rhythmic stimulation while avoiding perceptual confounds. RIFT is thus a promising method to address central questions in rhythmic cognition, including attention, multimodal integration, and the neural mechanisms underlying oscillatory coordination in perception. However, setting up a RIFT study involves several technical and conceptual considerations. In an effort to make RIFT more accessible, we provide a comprehensive guide for implementing RIFT in cognitive neuroscience. On the basis of the joint experiences and data-driven insights of multiple laboratories, we provide practical recommendations derived from empirical datasets to improve reproducibility, covering hardware requirements, stimulus design, analysis approaches, and interpretation of results. We hope that this guide helps readers to both identify the conceptual areas where RIFT offers promising insights and navigate the technical caveats that come with the approach.
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