dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, we predicted that the effect of LoT complexity in the proposed language of thought would increase as the sequence gets longer. On the other hand, given the automaticity of statistical learning, we did not expect any difference in its contribution to long versus short sequences. After examining the adequacy of the language for predicting task performance for each of the different experiments (with different lengths), analyses combining the data from multiple experiments were finally conducted, first to better assess the influence of complexity, length and transition probabilities in sequence processing, and second to compare the proposed LoT complexity to other computational approaches to sequence complexity proposed in the literature.
0) = 6. 78, p <. 0001). However, one sequence appeared as an outlier in this experiment, with an average LISAS 3. 9 SD below the average of all sequences (i. e. indicating a much better performance): the AAAAAA sequence. In this case, performing the task requires no sequence learning, but merely remembering the identity of the A sound, and violation detection is therefore similar to a classic oddball paradigm. When this sequence was removed from the dataset (it was also excluded from further analyses), the inclusion of the complexity fixed factor did no longer improved model goodness of fit (χ 2 (1) = 0. 10, p =. 752). Indeed, the LoT complexity fixed effect was not significant in the full model (t(628. 0) = 0. 32, p =. 752), as opposed to the surprise fixed effect (t(628. 0) = 4. 07, p <.
We tested LoT chunk complexity as another potential predictor of behavioral performance. Model comparison. With the aim of arbitrating between previous models, we pooled data from all previous experiment with auditory sequences (using LISAS to index task performance), excluding super-deviants. Unfortunately, due to the nature of algorithmic complexity (derived from the output frequency for a pattern using small Turing machines, which decreases rapidly with sequence length), no values were available for the ten length-16 patterns that we used in experiment 1, as well as for one length-12 pattern used in experiment 2. Those sequences were therefore excluded from some analyses. The sequence AAAAAA from experiment 4 was also excluded. Consequently, a first pooled dataset, for which all 8 different predictors could be compared, included performance with 77 different auditory sequences (and 88 different participants), of length 6 (N = 31 sequences), length 8 (N = 35) and length 12 (N = 11), while a second one, for which 7 different predictors were compared, also included sequences of length 16 (N = 88 sequences, 113 participants).
Second, since, as we reported earlier, surprise derived from the learning of transition probabilities may strongly affect the performance in such violation detection task, all these models were estimated again, this time including surprise as a fixed effect covariate. Dataset with sequences of length 6, 8 and 12. Sixteen different mixed models were fitted using datasets with sequences of length 6, 8 and 12. As illustrated in Fig 8A, model fit, as indexed by the Δ(AIC) value, always improved when the surprise associated to the deviants was included in the model. This finding confirms that the effect due to transition probabilities needs to be taken into account when assessing responses to deviants in the violation detection paradigm. The improvement in model fit was smallest for the model with entropy.
However, while this appeared true here using statistical models partially controlling for sequence length (i. e. by including participant index as a random factor, since each participant performed the task with only one given sequence length), this measure appears inappropriate to predict complexity across different lengths. Indeed, when we computed the Pearson correlation of average LISAS per sequence for the pooled dataset (sequences of length 6, 8 and 12), we obtained a positive correlation value of. 39. Such positive correlation is in conflict with the presupposition that patterns containing more symmetries should be simpler. This is explained by the fact that the number of subsymmetries tends to increase with sequence length. These correlations were actually negative when each length was considered independently (r = -.
Dataset with sequences of length 6, 8, 12 and 16 Fourteen different mixed models (with participants as a random effect) were here fitted, using the same dataset as before to which was added data from 11 sequences for which algorithmic complexity value was not available (thus now with sequences of length 6, 8, 12 and 16). The same predictors as above were used, with the exception of algorithmic complexity. Here again, as illustrated in Fig 8B, goodness of fit systematically increased when surprise was included. LoT-chunk complexity and LoT complexity (with or without surprise as a covariate) were again the best predictors of performance (see Table  2 ). As opposed to the previous set of analyses in which the data from experiment 1 (length 16) was not included, the model with change complexity performed clearly better than the one with the number of subsymmetries.
Important variations in the correlation with complexity (especially for experiments with shorter sequences) were indeed observed across participants. When computed at the level of individual participants, the correlation with LoT complexity appeared on average stronger (mean r =. 31, SD =. 32) than the one with change complexity (mean r =. 23, SD =. 30; t(112) = 3. 54, p <. 001). With both datasets, two measures performed poorly, LZ complexity and chunk complexity. Contrary to our language, the LZ algorithm has the advantage to be able to quickly "parse" any sequence of any number of different characters, by building for each sequence its own vocabulary of substrings. Its adequacy to human behavior, however, appears limited since, when scanning the sequence from one item to the next, it does not necessarily take into consideration Each marker corresponds to one sequence.
When pooling datasets from experiments with different sequence lengths, the linear mixed models with surprise and complexity as predictors fitted the data better than models including one predictor alone, indicating that those two predictors captured distinct aspects of the data. However, one may note that the size of the surprise effect varied across experiments. Surprise and complexity showed opposite patterns, with a stronger effect of complexity for longer sequences than shorter ones and, conversely, a strong effect of surprise only with the shortest sequences. Given the evidence that we just cited, showing that transition probabilities are constantly being computed unconsciously, the most likely interpretation is probably that task difficulty increased with sequence length and resulted in longer response times, thus masking the contribution of statistical learning.
Procedure Participants were seated in front of a computer in a quiet room and were wearing headphones. Stimuli were delivered using the Psychophysics Toolbox 3  [134, 135]  running on Matlab R2016a (Mathworks Inc. , Natick, MA, USA). Before starting the experiment, participants listened to a sample of stimuli (different sequences from the ones used in the main experiment) and the sound volume was adjusted if necessary. In the first part of experiment 1, participants performed the complexity rating task. They were asked to judge each sequence on a scale going from "1: very simple" to "9: very complex", by pressing the corresponding key on the keyboard following sequence presentation. They were informed that "each sequence contains two different beeps, presented according to a more or less complex order" and listened two examples, presented as a "rather simple" (AAB-BAABBAABBAABB), and as "rather complex" (ABAAABABAABBBABB).
This score was computed for each sequence, each deviant type in each subject, according to the following formula: ¼ RT c þ MR � S RT S MR , where RT c refers to the average response time (of correct responses), MR to the miss rate, S RT to participant's overall RT standard deviation and S MR to the participant's overall MR standard deviation. These scores were computed after removing extreme response times (2. 5 standard deviations (SD) above or below the median in each condition and subject, 2. 0% of data). Participants with excessive average miss rate over the entire session (i. e. 2. 5 SD above group median), average response time and/or average number of false alarms were excluded (three participants). All data analyzes were performed in R 3. 6. 0 [136]. We performed statistical analyses using a mixed model in which the dependent variable was the LISAS for each participant and each cell of the design; participants were the random factor, and LoT complexity and deviant type (sequence deviants vs.
visual) in experiment 5. Extreme response times were removed (using the same procedure as in experiment 1), and represented 1. 2% of all RTs in experiment 2, 1. 6% of RTs in experiment 3, 1. 6% in experiment 4 and 2. 4% in experiment 5. One participant was excluded in experiment 2 (average number of false alarms per sequence more than 2. 5 SD above the group median), one in experiment 3 (average number of false alarms per sequence more than 2. 5 SD above the group median), one in experiment 4 (average miss rate more than 2. 5 SD above the group median) and one in experiment 5 (average miss rate and number of false alarms more than 2. 5 SD above the group median). Data collected in all five experiments is available in S1 and S2 Data (average performance for each participant and sequence).
11), which was the next highest after the correlations with neuroticism and conscientiousness). Questionnaires and Procedure. The survey was created with Limesurvey (www. limesurvey. com) and was performed online by the participants. On the first page of the online survey, the participants provided informed consent. The participants were then required to report demographic information such as age, gender, native language, and their educational level. Next, three questionnaires were self-administered: the PVD questionnaire, the social touch questionnaire and the BIG-5. The BIG-5 was always completed second, and the PVD and the social touch questionnaire were completed in first or third position depending on the participants. The PVD and the social touch questionnaires were first translated into French by the authors and then checked by a professional native-speaking translator of English.
However, this type of threat can be accentuated by specific situations such as a pandemic. At the time of data collection, the World was witnessing a pandemic caused by a novel virus, SARS-CoV-2, which causes the disease known as COVID-19. Thus, unlike most previous studies, which have focused on "hypothetical" pathogen threats, one strength of Study 2 is that we report people's psychological responses to an actual threat (for previous studies on the impact of a real pathogen threat see, for example: Blakey and Abramowitz, 2017;  Blakey et al. , 2015; Schaller et al. , 2017). Critically, our study makes it possible to test further a key tenet of the BIS according to which pathogen avoidance attunes people to increasing pathogen threat. In Study 2, the relationships between PVD, social touch, and personality traits were examined during an actual pandemic period.
From the initial pool of 200 adults, 61 participants took part in Study 2. (There were eleven participants who took part in Study 2 but were not involved in Study 1. Their data were removed from the analyses. ) Questionnaires and Procedure. We used exactly the same questionnaires as those used in Study 1, and we followed exactly the same procedure as described in Study 1. The participants were told that the different surveys were the same when as they first took part but that it was important to answer all the questions spontaneously even though the questions had been answered before.
Data availability. The key climatological outputs of the simulations are stored in the PANGAEA database: https://doi. pangaea. de/ 10. 1594/PANGAEA. 930422 (Toumoulin et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-341-2022-supplement. Author contributions. YD, AT and JBL conducted the modeling experiments. AT compiled proxy data, analyzed the model results, generated figures and tables, and drafted the paper. AT, YD, DT and JBL discussed the paleoclimate results. LK contributed to the paleobotanical context and AL and GDN to the paleogeographic and geological context. All authors have provided critical feedback and contributed to the final paper. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests.
Acknowledgements. We thank the  CEA/CCRT  for providing access to the HPC resources of TGCC under the allocation  2018-A0030102212 , and  2019-A0050102212  made by  GENCI. Agathe Toumoulin  and  Guillaume Dupont-Nivet  acknowledge the support of the  ERC MAGIC  under grant  649081. Yannick Donnadieu  and  Delphine Tardif  acknowledge support from  ANR AMOR  (grant no. ANR-16-CE31-0020 ). The authors acknowledge  Cheng-Sen Li  for sharing a translation of the reference Budantsev (1997). We acknowledge the use of Ferret (https://ferret. pmel. noaa. gov/Ferret/,last access: 10 November 2021) and RStudio software (https://www. rstudio. com/,last access: 10 November 2021) for analysis and figures in this paper. We sincerely thank  Alberto Reyes  for his editorial handling and two anonymous reviewers for their interesting comments that improved the quality of this article.
Funding statements Study of Health in Pomerania (SHIP and SHIP-Trend): SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network 'Greifswald Approach to Individualized Medicine (GANI_MED)' funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania.
Rotterdam Study (RS): The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific  Research NWO Investments (nr. 175. 010. 2005. 011, 911-03-012) , the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. This work has been performed as part of the CoSTREAM project (www.
The Austrian Stroke Prevention Familiy Study (ASPS-Fam): The research reported in this article was funded by the Austrian Science Fond (FWF) grant number P20545-P05 and P13180 and supported by the Austrian National Bank Anniversary Fund, P15435, the Austrian Ministry of Science under the aegis of the EU Joint Programme-Neurodegenerative Disease Research (JPND)-www. jpnd. eu and by the Austrian Science Fund P20545-B05. The Medical University of Graz supports the databank of the ASPS-Fam.
Imaging Resting-state fMRI was performed on 1. 5 or 3 Tesla scanners at all study sites. The phenotypes of interest mean and maximal frame-wise displacement (mFD, maxFD) were calculated according to Power et al. (Power et al. , 2012). All but one study used FSL's function MCFLIRT to extract 6 motion parameters (three rotational and three translational) which model the frame-to-frame head motion. In SHIP, AFNI's 3dvolreg was used to calculate these parameters. Then, framewise displacement was calculated from these parameters according to  (Power et al. , 2012). We provided a publicly available custom script to all research groups (https://github. com/fBeyer89/life_followup_preproc/blob/master/qa/resting/qa_pipeline/util s. py). Information on scanner manufacturers, acquisition protocols and motion correction tools are provided in Supplementary Table  1.
Quality control of single study association results Summary statistics of all studies were checked and harmonized with the software EasyQC. We discarded SNPs not in the reference panel (1000 Genomes phase 3, version 5, European ancestry), mismatching alleles or mismatching chromosomal position with respect to the reference. Moreover, we removed SNPs with missing allele information (effect allele, effect allele frequency), missing association statistics (beta estimates, standard errors) or missing imputation quality score. SNPs were filtered for weighted minor allele frequency (MAF) >1%. Genotyped SNPs were filtered for call rate >97% and p-value of Hardy-Weinberg test >10 -6. Imputed SNPs were filtered for imputation quality score >0. 5 and for deviation from reference allele frequency <20%.
Imaging and physiological data acquisition Resting state fMRI was acquired with the same echo-planar-imaging sequence as in the baseline assessment (repetition time, 2 s; echo time, 30 ms; flip angle, 90°; image matrix, 64 × 64; 30 slices; field of view, 192 × 192 × 144 mm3, no multiband, iPAT acceleration: 1, voxel size of 3 mm × 3 mm, slice thickness of 4 mm, slice gap of 0. 8 mm; 300 volumes; total acquisition time, 10:04 minutes). Physiological parameters were recorded during the scan using Siemens proprietary hardware (breathing belt and an oximeter attached to the index finger). The sampling rate was 50 Hz for both devices.
rsfMRI and physiological data preprocessing We used FSL's function MCFLIRT to extract 6 motion parameters from the rsfMRI scan (three rotational and three translational) which model the frame-to-frame head motion. Framewise displacement was calculated from these parameters according to  (Power et al. , 2012)  using a publicly available custom script (https://github. com/fBeyer89/life_followup_preproc/blob/master/qa/resting/qa_pipeline/util s. py). We used log10-transformed average mFD and frame-to-frame FD for analysis. We also calculated DVARS, a measure of BOLD fluctuations from frame to frame which allows us to quantify the effect of head motion/physiological parameters on the acquired signal  (Power et al. , 2015). Standardized DVARS was calculated from the raw fMRI scan with the ComputeDVARS function from the nipype.
algorithms. confounds module in this script. We used PhysIO toolbox ("Created: 2011-08-01") in Matlab version 9. 3 to preprocess the physiological data  (Kasper et al. , 2017). We performed automated pulse detection for detecting cardiac phase with default parameters for heartbeat duration outliers. Heart rate was estimated by averaging heart beat duration in a sliding window of 6 seconds (3 TR)  (Chang et al. , 2009). Respiration maximal and minimal amplitude (corresponding to respiratory cycles) was also automatically detected. Then, respiratory volume per time (RVT) was calculated by dividing the amplitude between adjacent breathing minima and maxima by the interpolated time interval between them  (Birn et al. , 2006). We normalized RVT by dividing it by the maximal RVT value for each participant and calculated the average and standard deviation of the normalized RVT.
Statistical analysis All statistical analyses were done using R version 3. 6. 1. The code for the statistical analyses is publicly accessible at https://github. com/fBeyer89/Mediators_of_HeadMotion. As pre-registered, we performed mediation analyses for respiratory and psychological variables. BMI was the independent predictor, mFD was the outcome and all models were adjusted for age and sex (male as reference category). Mediating factors were RR, mean RVT and HR for the physiological and BIS_motor, BIS_selfcontrol, BIS_total for the psychological models. The mediation models were implemented in the R package lavaan 0. 6. 7 and estimated using maximum likelihood. The standard errors and confidence intervals were derived from 1000 bootstraps. Depending on the number of tests per group, we used Bonferronicorrected p-values (physiological: p<0.
, 2017). Thus, it is likely that no single variant explains enough variance to be a genome-wide predictor of this polygenic trait. Even though head motion was determined similarly across studies, it is possible that study-specific patient positioning and fixation influenced the magnitude of head motion and inter-participant variation, and with that, decreased our power due to increased heterogeneity between studies. In the UKBB, the largest sample in our analysis, the correlation of BMI and mean FD was comparable to previous reports (Pearson's r ~ 0. 5, data not shown). Thus, in data acquisition are unlikely to explain the null result. Previous studies indicated that physiological rather than psychological factors are major determinants of head motion  (Ekhtiari et al. , 2019; Makowski et al.
In order to provide participants with both an obtainable target and a "reach" goal, we kept the 4-fold and 8-fold tracks for the 2020 challenge. One frequent feedback on the 2019 challenge was on generalizability: despite the size of the data set, all of the data and results were from studies performed on MRI scanners from a single vendor at a single institution. To address this, we created the new Transfer track at 4-fold acceleration (Transfer 4X). For the Transfer track, participants were asked to run their models on data from vendors outside the main fastMRI data set. There was a caveat: we also restricted participants in the Transfer track to train their models only using available fastMRI data to ensure evaluation of transfer capability. At the time of the 2020 challenge announcement, we stated that these data would come "from another vendor" but did not specify further.
B. Data Set For the 2020 challenge we used brain MRI data. The neuroimaging subset of the fastMRI data has been described in an updated version of the arXiv paper  [25] , with further information included in the supplemental material for this paper. It includes 6,970 scans (3,001 at 1. 5 T, 3,969 at 3 T) collected at NYU Langone Health on Siemens scanners using T1, T1 post-contrast, T2, and FLAIR acquisitions. Unlike the knee challenge, this data set exhibits a wide variety of reconstruction matrix sizes. A summary of the data for the two main track splits is shown in Table  I. Of these 6,970 scans, 565 were withheld for evaluation in the challenge. In addition to standard HIPAA-compliant anonymization practices, all scans were cropped at the level of the orbital rim, preserving only the top part of the head.
For the challenge, the 565 scans were augmented further by 329 non-Siemens scans for the Transfer track. GE data were collected at NYU Langone Health and Philips data were collected on volunteers by clinical partner sites of Philips Healthcare of North America. Since the Philips data was collected on volunteers, this subsplit had no post-contrast imaging. One difficulty of the Transfer track was the fact that the GE data did not contain frequency oversampling. The lack of frequency oversampling was due to automatic removal during the analog-to-digital conversion process on the GE scanner. In total the 2020 challenge had 6,405 scans available for training and validation (train, val, test) and there were 894 total scans for evaluation in the final challenge phase. This marked a substantial increase in scale from the 2019 challenge.