dataesr/scientific-paragraphs-categorization huggingface dataset:
The combination of several modalities maximized sensitivity, spatiotemporal resolution, and coverage, thereby providing stringent and comprehensive tests of the theories. Furthermore, each data type was collected by two (or three) independent laboratories to ensure generalization across populations, recording systems, and experimenters. Altogether, we aimed at fostering informativeness, reproducibility, and robustness of the results by (1) dissociating theory from data acquisition/analysis to prevent biases, (2) using a multimodal approach to test theories with an exquisite temporal and spatial precision, (3)   acquiring data in a large sample of subjects to increase statistical power, (4) using standardized and preregistered protocols  12  to evaluate theories under the same experimental framework and further minimize con rmatory bias  20  , and nally (5) combining an analysis optimization phase with a nal testing phase using independent parts of our dataset to corroborate the robustness of the results (Figure  1b )  27.
90%, SD=12. 29; false alarm rate M=4. 25%, SD=20. 17). We con rmed that subjects were conscious of the stimuli both in the task relevant and irrelevant trials in a separate experiment which included a surprise memory test (see supplementary). As part of our testing framework, after excluding a limited number of subjects due to data quality checks, we conducted an initial optimization phase on 1/3 of the MEG (N=32) and fMRI (N=35) datasets to evaluate data quality across sites and to optimize analysis pipelines. Following the optimization phase, pipelines were preregistered (https://osf. io/92tbg/), and applied to the novel datasets containing twice as much data (MEG, N=65 and fMRI, N=73). In what follows we report results obtained on the novel, unseen datasets (see methods for the strategy used for iEEG and text for numbers of subjects that entered in each analysis).
It would be surprising if participants' consciousness remained tied to each image for its full duration on every trial of this long experiment. It is also unclear whether participants were ever aware of stimulus orientation, which was always irrelevant. A new experiment, using quanti ed introspection 62 , will be needed to assess for how long participants maintained the visual image in consciousness. For the same reason, the absence of decodable activation at stimulus offset, while challenging, may simply indicate that participants never consciously attended to that event, which was always uninformative and irrelevant. Making stimulus offset more attractive, for instance by turning it into an occlusion event where an object hides behind a screen, could yield different results. For GNWT, the prefrontal code for a conscious mental object is thought to involve a vector code distributed over millions of neurons which, unlike in posterior regions, are not clustered but spatially intermingled  28, 71.
Ethics Statement The experiment was approved by the institutional ethics committees of each of the data-collecting labs were subsequently added to the preregistration as an additional amendment. These preregistered analyses were then run on the remaining 2/3 of the data (henceforth, Replication dataset) and constitute the data reported in the main study. For comparison, results from the optimization phase are reported in the supplementary material. This procedure was not used for the iEEG data due to the serendipitous nature of the recording and electrode placement, the rarity of this type of data and the increased di culty of data collection due to the COVID-19 pandemic. For the iEEG arm of the project, a total of 34 patients were recruited. stimulus duration (0. 5 s, 1. 0 s, 1. 5 s), and (5) task relevance (relevant targets, relevant non-targets, irrelevant).
Stimuli Four stimulus categories were used: faces, objects, letters and false fonts. These stimuli naturally fell into two clearly distinct groups: pictures (faces and objects) and symbols (letters and false fonts). These natural couplings were aimed at creating a clear difference between task relevant and task irrelevant stimuli in each trial block (see Procedure). All stimuli covered a squared aperture at an average visual angle of 6˚ by 6˚. Face stimuli were created with FaceGen Modeler 3. 1; letter and false fonts stimuli were generated with MAXON CINEMA 4D Studio (RC -R20) 20. 059; object stimuli were taken from the Object Databank 2. Stimuli were gray-scaled and equated for luminance and size. To facilitate face individuation, faces had different hairstyles and belonged to different ethnicities and genders.
MEG data acquisition MEG was acquired using a 306-sensor TRIUX MEGIN system, comprising 204 planar gradiometers and 102 magnetometers in a helmet-shaped array. The MEG gantry was positioned at 68 degrees for optimal coverage of frontal and posterior brain areas. Simultaneous EEG was recorded using an integrated EEG system and a 64-channel electrode cap (EEG data is not reported here, but is included in the shared dataset). During acquisition, MEG and EEG data were bandpass ltered (0. 01 and 330 Hz) and sampled at 1000 Hz. The location of the head ducials, the shape of the head, the positions of the 64 EEG electrodes and the head position indicator (HPI) coil locations relative to anatomical landmarks were collected with a 3-D digitizer system (Polhemus Isotrack). ECG was recorded with a set of bipolar electrodes placed on the subject's chest.
MEG preprocessing The MEG data were converted to BIDS 17 using MNE-BIDS 18 , and preprocessed following the FLUX Pipeline  19  in MNE-Python v0. 24. 0 6. Preprocessing steps included MEG sensor reconstruction using a semi-automatic detection algorithm and Signal-Space Separation (SSS)  20  to reduce environmental artifacts. FastICA 21 was used to detect and remove cardiac and ocular components from the data for each subject (M=2. 90 components, SD=0. 92). Prior to ICA, data were segmented, and segments containing muscle artifacts were removed. After preprocessing, data were epoched into a 3. 5 s segment (1 s pre-stimulus to 2. 5 s post-stimulus onset). Trials where gradiometers values exceeded 5000 fT/cm, magnetometers exceeded 5000 fT, and/or contained muscle artifacts were rejected from the MEG dataset.
e. , sum) covariance matrix was used with the forward model to create a common spatial lter. Data were spatially prewhitened using the covariance matrix from the baseline interval to combine gradiometer and magnetometer data  25. fMRI Preprocessing Source DICOM data were converted to BIDS using BIDScoin v3. 6. 3  26. This includes converting DICOM data to NIfTI using dcm2niix  27  and creating event les using custom Python codes. BIDS compliance of the resulting dataset was controlled using BIDS-Validator. Subsequently, MRI data quality control was performed using MRIQC 28 and custom scripts for data rejection. All (f)MRI data were preprocessed using fMRIPrep 20. 2. 3 29 , based on Nipype 1. 6. 1  30. For further details on the fMRIprep pipeline, see preregistration.
Each of the 12 regressors of interest was contrasted against an implicit baseline (used in the putative NCC analysis). Additionally, we obtained contrast of parameter estimates for 'relevant faces vs. relevant objects', 'relevant letters vs. relevant false fonts', 'irrelevant faces vs. irrelevant objects', 'irrelevant letters vs. irrelevant false fonts' (used for the de nition of decoding ROIs), 'relevant and irrelevant faces vs. relevant and irrelevant objects' and 'all stimuli vs. baseline' (used for the de nition of seeds for the generalized psychophysiological interaction analysis). Data were averaged across runs per subject using FSL's xed effects analysis and subsequently averaged across participants using FSL's FLAME1 mixed effect analysis. Gaussian random-eld cluster thresholding was used to correct for multiple comparisons, using the default settings of FSL, with a cluster formation threshold of one sided p < 0.
Eye-tracking analyses Eyelink, gaze and pupil data were segmented, and missing data were excluded. Blinks were detected using the Hershman algorithm 36 , and removed with 200 ms padding  37. The Eyelink standard parser The overall decoding strategy for fMRI was similar to that used on the iEEG and MEG data, yet with some differences. A Multi-Variate Pattern Analysis (MVPA) approach was used on the pattern of BOLD activity over voxels. A non-spatially-smoothed parameter estimate map was obtained by tting a GLM per event with that event as the regressor of interest and all the other remaining events as one regressor of no interest  43  as implemented in NiBetaSeries 0. 6. 0 package. The model also included the 24 nuisance regressors described in the fMRI preprocessing section. Decoding was performed using a whole-brain approach and an ROI-based approach.
The whole-brain analysis was performed using a searchlight approach with 4 mm radius. For ROI-based decoding, decoding ROIs were de ned based on functional fMRI contrasts (see fMRI preprocessing section) and constrained with pre-de ned anatomical ROIs (see Extended Data Table  2 : Anatomical Regions-of-interest (ROIs)). One-sample permutation test was used to determine if decoding signi cantly exceeds chance level within each ROI. FDR was used to correct for multiple comparisons across ROIs. For whole-brain decoding, a cluster-based permutation test was used to evaluate the decoding statistical signi cance across subjects (p < 0. 05). Additionally, stimulus vs. baseline searchlight decoding was performed using leave-one-run out cross validation and the resultant decoding accuracy maps were used as input for the multivariate putative NCC analysis (see below).
Decoding for the different predictions To and IIT decoding predictions, stimulus category (faces vs. objects and letters vs. false fonts) was decoded separately for the task relevant and task irrelevant conditions (within-task category decoding) while orientation (front view vs. left view vs. right view) was decoded on the combined data from the two task conditions. In addition, cross-task category decoding from task relevant to task irrelevant condition and vice versa was performed to test generalization by training classi ers on one condition and testing on the other condition. Both within-task category and orientation decoding were performed in a leave-one-run-out cross validation scheme for fMRI and in an k-fold cross validation scheme for MEG and iEEG. For category decoding, trials from each task condition (i.
Following Benavoli and colleagues  47  , the prior distribution of the mean difference in decoding scores between two classi er models was modeled as a Normal-gamma distribution conjugate to a normal likelihood, and the posterior distribution was obtained as a normal distribution. This posterior distribution was utilized to calculate the probability of one classi cation model being better than, worse than, or equivalent to the other model. As this estimation approach is applied using resampled datasets (e. g. , using 5-fold cross-validation), the performance of the model becomes dependent on the folds, and thus a variance corrected t-distribution was used 46. We also tested this prediction on the fMRI data. To select features to be used for both analyses, the face vs. object contrast for each subject was masked by a prede ned anatomical posterior ROIs as well as a PFC anatomical ROIs, de ned the same way as described above.
Bayesian Integration Criterion (BIC) was used to de ne the winning model. Models for iEEG were tted per electrode on the prede ned ROIs, using the HG (AUC), alpha (8-13 Hz, obtained through Morlet wavelets, f=8-13 Hz, in 1 Hz steps; f/2 cycles, AUC), and ERPs (peak to peak) as signal, separately for task relevant and irrelevant condition. MEG models were tted to source data on the prede ned ROIs, using the gamma (60-90 Hz) and alpha band (8-13 Hz) as signal, separately for task relevant and irrelevant conditions. Time-frequency analyses were performed on source-data using Morlet wavelets (f=8-13 Hz, in 1 Hz steps; f/2 cycles; f=60-90 Hz, in 2 Hz steps, f/4 cycles), and were baseline corrected. Spectral activity was computed for each vertex, baseline corrected and then averaged across trials within each parcel included in the ROIs, yielding a unique time-course per ROI parcel.
The stars and arrows on the brain (left) depict the different predictions about the expected synchrony patterns (green: GNWT; blue: IIT). b. The scienti c framework under which the Cogitate Consortium (Collaboration On GNW and IIT: Testing Alternative Theories of Experience), tested IIT and GNWT included: adversarial collaboration, (theoryneutral) team science, invasive and non-invasive multimodal data (iEEG, fMRI, MEG), large samples (>250 subjects), standardized protocols across multiple laboratories, built-in preregistered replication, and open methods, data and code. c. Conscious experience is multifaceted in content. Looking at the image of Mona Lisa by Leonardo da Vinci underscores the fact that conscious experiences are rich: The painting is experienced as occupying a location in space, pertaining to a given category (i.
Acknowledgements Special thanks to  Dawid Potgieter  for spearheading the  ARC program ; to  Daniel Kahneman  for guidance to navigate adversarial collaborations; to  Heather Berlin ,  William Jaworski ,  Hakwan Lau  and  Cyriel Pennartz  for insightful discussions during the two-day meeting organized by the  Templeton World Charity Foundation  at the  Allen Institute, Seattle  in March 2018; to  Hakwan Lau  for helping conceptualize the proposed experiments; to  Orrin Devinsky ,  Werner Doyle ,  Patricia Dugan  and  Daniel Friedman  for supporting the recruitment and patient care at  NYU ; to  Essa Yacoub ,  Michael Kahana ,  Peter Zeidman ,  Karl Friston ,  Jean-Remi King ,  Michael Cohen ,  Fosca Al Roumi ,  Shlomit Yuval-Greenberg  and  Dejan Draschkow  for guidance on diverse data analysis; to  Caspar Schwiedrzik  for insightful discussions and feedback throughout the different phases of this study (conceptualization, data analysis and initial draft); to  Sarah Brendecke  and  Felix Bernoully  for help with gures and stimulus materials; to  Monique Smulders  and  Sarah Kusch  for help with fMRI data acquisition; to the patients and their families for generously supporting this study.
( see supplementary for details). All volunteers and patients provided oral and written informed consent before participating in the study. All study procedures were carried out in accordance with the Declaration of Helsinki. Epilepsy patients were also informed that clinical care was not affected by participation in the study. Participants Healthy volunteers and patients with pharmaco-resistant focal epilepsy participated in this study. The datasets reported here consist of: (1) Behaviour, eye tracking and invasive electroencephalogram (iEEG) data collected at the Comprehensive Epilepsy Center at New York University (NYU) Langone Health, Brigham and Women's Hospital, Boston Children's Hospital (Harvard), and University of Wisconsin School of Medicine and Public Health (WU). (2) Behaviour, eye tracking, magnetoencephalographic (MEG) data collected at the Centre for Human Brain Health (CHBH) of the University of Birmingham (UB), and at the Center for MRI Research of Peking University (PKU).
(3) Behaviour, eye tracking and functional magnetic resonance (fMRI) data collected at Yale Magnetic Resonance Research Center (MRRC) and at the Donders Centre for Cognitive Neuroimaging (DCCN), of Radboud University Nijmegen. For both the MEG and fMRI datasets, a 1/3 of the data that passed quality tests (henceforth, Optimization dataset; see preregistration for details about quality test criteria) were used to optimize the analysis methods, which.
Two patients were excluded due to incomplete data. Demographic, medical and neuropsychological scores for each patient, when available, are reported in Supplementary Table25. Three iEEG patients whose behavior fell slightly short of the prede ned behavioral criteria (i. e. hits < 70%, FA > 30%) were nonetheless included given the di culty to obtain additional iEEG data. A total of 97 healthy subjects were included in the MEG sample (mean age 22. 79 ± 3. 59 years, 54 females, all right-handed), 32 of those datasets were included in the optimization phase (mean age 22. 50 ± 3. 43 years, 19 females, all right-handed), and 65 in the replication sample (mean age = 22. 93 ± 3. 66, 35 females, all right-handed). Five additional subjects were excluded from the MEG dataset: two due to failure to meet prede ned behavioral criteria (i.
e. , hits < 80%, and/or FA > 20%), two due to excessive noise from sensors, and one due to incorrect sensor reconstruction. A total of 108 healthy participants were included in the fMRI sample (mean age 23. 28 ± 3. 46 years, 70 females, 105 right-handed), 35 of those datasets were included in the optimization sample (mean age 23. 26 ± 3. 64 years, 21 females, 34 right-handed), and 73 in the replication sample (mean age = 23. 29 ± 3. 37, 49 females, 71 right-handed). Twelve additional subjects were excluded from the fMRI dataset: eight due to motion artifacts, two due to insu cient coverage, and two due to incomplete data. Experimental procedure Experimental design To test critical predictions of the theories, ve experimental manipulations were included in the experimental design: (1) stimulus category (faces, objects, letters and false fonts), (2) stimulus identity (20 different exemplars per stimulus category), (3) stimulus orientation (front, left and right view), (.
The modelling is performed using the intermediate complexity model iLOVECLIM in the standard global and interactively downscaled over the Europe version. The combined effects of dynamical downscaling and bias correction resulted in significantly stronger agreement between the simulated results and pollen-based biome reconstructions (BIOME6000) for the pre-industrial (0. 18 versus 0. 44) and mid-Holocene (MH) (0. 31 versus 0. 40). Higher correlation is also observed between statistically modelled global gridded potential natural distribution and modelled biomes (0. 36 versus 0. 41). Similarly, we find higher correlation between the reconstructed and the modelled temperatures for the MH (0. 02 versus 0. 21). No significant difference is found for the Last Glacial Maximum when using temperature reconstructions, due to the low number of data points available.
VECODE is a reduced-form dynamic global vegetation model (DGVM), which describes vegetation dynamics using two plant functional types (PFTs): trees and grass  (Brovkin et al 1997). iLOVECLIM has been previously successfully applied to simulate the climate of the Holocene  (Arthur et al 2023 , 2016 , Zhang et al 2018 , Li et al 2019) , the LGM  (Lhardy et al 2021) , the last deglaciation  (Quiquet et al 2021)  and the last interglacial  (Li et al 2020). In order to obtain higher spatial resolution climate model outputs, we make use of the online interactive downscaling method embedded in iLOVECLIM, first described by  Quiquet et al (2018). It is a physical downscaling procedure that replicates the processes governing the computation of climatic parameters on a spatially refined grid to explicitly take into account the sub-grid topography in the computation of heat and precipitation.
During the transient simulations of 1750-2014 astronomical parameters were kept constant, under the assumption that at this time scale the changes were so minor that there was no significant effect on the climate. The concentrations of the atmospheric trace gases were derived from the consolidated datasets of historical atmospheric concentrations for the Climate Model Intercomparison Project (CMIP6)  (Meinshausen et al 2017). To be consistent with the length of the observation dataset, 38 years were extracted from each simulation: 1976-2014 for the transient PD simulation and 38 years at the end of the spin-up for each of the equilibrium simulations. For the analysis, we used climatological mean of the simulations. To ensure that the selection of a 38 year average does not distort our analysis, we tested sensitivity of our method to low-frequency climate variability within the iLOVECLIM model.
The results (data not shown) indicated no significant impact of low-frequency climate variability in iLOVECLIM on studied variables following the CDF-t application. Table  1. Experimental set-up. The name of each experiment consists of an abbreviation of a studied period (PI, MH, LGM), and a grid name (T21, hi-res). T21 simulations are performed globally (g), hi-res simulations-over Europe (EU). For the LGM experiment, the ice-sheet forcing is the GLAC-1D reconstruction taken from the PMIP-4 protocol  (Kageyama et al 2017). For a complete description of the experimental setup of the LGM simulation, the reader is referred to  Lhardy et al (2021). For equilibrium (E) simulations, 3000 years of spinup were performed, and the last 38 years have been used for the analysis. For transient (T) simulations, 3000 years of spinup were performed, and then transient simulations for 264 years were performed, of which the last 38 years have been used for the analysis.
Simulation name Resolution ( For consistency with the EWEMBI dataset, for bias correction we have extracted the 1979-2014 timespan from the 1750-2014 model simulation, both at T21 and on the high-resolution grid. The EWEMBI dataset grid has been regridded to the iLOVECLIM T21 and hi-res grids using the first-order conservative remapping approach  (Jones 1999)  to ensure that each iLOVECLIM grid cell time series corresponds to an EWEMBI grid cell. This approach allowed us to perform bias correction on a gridcell-by-gridcell basis.
Subsequently, the values are generated based on the F Rp in chronological agreement with simulated climate data. Previous studies with BIOME models  (Prentice et al 1998 , Harrison and Prentice 2003 , Wohlfahrt et al 2004)  had proposed an anomaly procedure to remove the inherent model biases for diagnostic purposes. In the anomaly procedure, the vegetation models are run using modern (baseline) climatology (e. g. CRU) with the added modelled climate change (anomaly) between the past and PI periods. Both CDF-t and anomaly methodologies preserve the climate change between two time periods. The main difference between CDF-t and the anomaly procedure is that using CDF-t, the temporal chronology (i. e. the sequence of events in its own variability) simulated by a climate model is preserved while in the anomaly procedure, chronology remains identical to reference (baseline) climate dataset variability.
Application In this study, we used CDF-t to correct three climatic variables simulated by iLOVECLIM and needed for the vegetation computations: near-surface air temperature, precipitation, and relative humidity. The calibration of the bias correction was based on these variables from the PD simulations 1979-2014 and the observation dataset for the same period. This bias correction was then applied to PI, MH, and LGM. Seasonality of the geological past of the Earth differs from modern seasonality  (Guiot et al 2000 , Wu et al 2007 , Koutavas and Joanides 2012)  due to the change in the solar input's to the Earth over the year following long-term change of its orbit around the sun. Therefore, we tested our methodology by applying bias correction both on a monthly and yearly basis to evaluate the consequence of the choice made.