Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hyperscanning refers to obtaining simultaneous neural recordings from more than one person (Montage et al., 2002 [[1]Montague P.R. Berns G.S. Cohen J.D. McClure S.M. Pagnoni G. Dhamala M. et al.Hyperscanning: simultaneous fMRI during linked social interactions.Neuroimage. 2002; 16: 1159-1164Crossref PubMed Scopus (441) Google Scholar]), that can be used to study interactive situations. In particular, hyperscanning with Electroencephalography (EEG) is becoming increasingly popular since it allows researchers to explore the interactive brain with a high temporal resolution. Notably, there is a 40-year gap between the first instance that simultaneous measurement of EEG activity was mentioned in the literature (Duane and Behrendt, 1965 [[2]Duane T.D. Behrendt T. Extrasensory electroencephalographic induction between identical twins.Science. 1965; 150: 367Crossref PubMed Scopus (81) Google Scholar]), and the first actual description of an EEG hyperscanning setup being implemented (Babiloni et al., 2006 [[3]Babiloni F. Cincotti F. Mattia D. Mattiocco M. De Vico Fallani F. Tocci A. et al.Hypermethods for EEG hyperscanning.Conf. Proc. IEEE Eng. Med. Biol. Soc. 2006; 1: 3666-3669Crossref PubMed Scopus (94) Google Scholar]). To date, specific EEG hyperscanning devices have not yet been developed and EEG hyperscanning setups are not usually described with sufficient detail to be easily reproduced. Here, we offer a step-by-step description of solutions to many of these technological challenges. Specifically, we describe and provide customized implementations of EEG hyperscanning setups using hardware and software from different companies: Brain Products, ANT, EGI, and BioSemi.•Necessary details to set up a functioning EEG hyperscanning protocol are provided.•The setups allow independent measures and measures of synchronization between the signals of two different brains.•Individual electrical Ground and Reference is obtained in all discussed systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it