Evidence map›Paper›PMID 40800424›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2024

Perceptual foundation and extension to phase tagging for rapid invisible frequency tagging (RIFT).

Eelke Spaak, Floortje G Bouwkamp, Floris P de Lange

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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  4. Learning Modulates Early Encephalographic Responses to Distracting Stimuli: A Combined SSVEP and ERP Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Eelke SpaakDonders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
Floortje G BouwkampDonders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.
Floris P de LangeDonders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have seen the emergence of a visual stimulation protocol called Rapid Invisible Frequency Tagging (RIFT) in cognitive neuroscience. In RIFT experiments, visual stimuli are presented at a rapidly and sinusoidally oscillating luminance, using high refresh rate projection equipment. Such stimuli result in strong steady-state responses in visual cortex, measurable extracranially using EEG or MEG. The high signal-to-noise ratio of these neural signals, combined with the alleged invisibility of the manipulation, make RIFT a potentially promising technique to study the neural basis of visual processing. In this study, we set out to resolve two fundamental, yet still outstanding, issues regarding RIFT; as well as to open up a new avenue for taking RIFT beyond frequency tagging per se. First, we provide robust evidence that RIFT is indeed subjectively undetectable, going beyond previous anecdotal reports. Second, we demonstrate that full-amplitude luminance or contrast manipulation offer the best tagging results. Third and finally, we demonstrate that, in addition to frequency tagging, phase tagging can reliably be used in RIFT studies, opening up new avenues for constructing RIFT experiments. Together, this provides a solid foundation for using RIFT in visual cognitive neuroscience.

Indexed as

magneto-encephalography (MEG)phase taggingrapid invisible frequency tagging (RIFT)steady-state visual evoked potential/field (SSVEP/F)subjective invisibility

Identifiers

PMID40800424
PMCPMC12272182

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.