dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The results of Study 2 replicated the conclusions of Study 1: there was a significant difference of FSIQ between WAIS-III and WAIS-IV, but it was exclusively driven by the five subtests depending on Gc; no difference appeared for Gv or Gf subtests. The same was true when combining the two datasets, and Bayesian evidence was clearly in favor of the null for Gv and Gf subtests, in line with a difference on subtests assessing culture-dependent declarative knowledge but incompatible with the existence of a general decline of intelligence or a negative Flynn effect. Further analyses investigating DIF indicated significant bias against a recent sample for many items of the five Gc subtests of the WAIS-III. DIF was relatively limited for the Similarities and Comprehension subtests, where item parameters could not be estimated for many items which lacked discriminating power in the current sample; but it was clearly apparent for the three other subtests where more items were available.
Examining item content suggested a number of things that were comparatively less well known in the recent sample -such as the names of famous people, topics of civic education, and how to perform a mental division. A rough estimation of summed bias at the test level suggested that this DIF was sufficient to account for most or all of the observed difference between WAIS-III and WAIS-IV for the various subtests. In other words, the lower performance on subtests depending on culture-based declarative knowledge was largely attributable, not to a lower ability in the recent sample, but to items being comparatively more difficult for a recent sample than for the normative sample (for an equal level of ability). One discrepancy with the results of Study 1 was the lack of a difference for Block Design, but since this difference existed only for one of four subgroups in Study 1, the significant difference in the other dataset may have been a spurious effect partly caused by the particular sampling with discrete age groups.
This point was also accounted for in analyses of DIF, where the normative sample was trimmed to match the composition of the current sample (see section 3. 1. 3), and where we used item response theory which estimates item parameters using subjects of all available ability levels. The limitation of this study was its relatively low sample size. For the first analysis testing which subtests demonstrated a difference between WAIS-III and WAIS-IV, this was not a major problem, given that Study 2 corroborated Study 1. Combining the datasets for Study 1 and Study 2 also partly answered this issue, although this was not a perfect strategy either (with the two samples being separated by a little under 10 years, even though their conclusions converged). However, sample size was still low for IRT-based analyses.
The risk of magnifying spurious effects is especially present when these corrections are large and when they are applied to a small dataset of N = 12 (in which graphing the data reveals significant departures from normality). All of this can lead to highly unstable estimates. The second problem is with the correction for restriction of range. It is not entirely clear whether this correction, which inflated the correlation by about 50%, was justified in this case (this approach makes assumptions about how the data were selected, and what the performance decline would have been with a different version of the WAIS), and besides, it was applied incorrectly. The authors divided the observed correlation by the ratio of the restricted and unrestricted standard deviations, whereas the actual correction is a bit more complex (see e.
We performed this analysis, using the estimate of performance decline computed on the combined Study 1 and Study 2 datasets (see partial eta squared effect sizes in Table  4 ; the results were very similar when considering only the Study 1 dataset). We used three different measures of cultural load: the subjective influence of culture on a subtest as assessed by expert consensus (retrieved from  Kan et al. , 2013) ; the average proportion of items that needed to be changed when adapting the WAIS-III to a different country (retrieved from  Georgas et al. , 2003 ; see also  Kan et al. , 2013)  3 ; and the effect size of socio-economic level on performance (expressed 3 Subjective cultural load and the average proportion of items that needed to be changed were not available for three subtests: Matrix Reasoning, Letter-Number Sequencing, and Symbol Search.
As noted by Woodley of  Menie and Dunkel (2015) , substantial genetic changes are unlikely over such a short timeframe, and their proposed explanation of immigration would be very difficult to defend in this particular case: there did not seem to be large differences in racial composition of the two WAIS samples, and the share of immigrants in the French population has increased by about 2 percentage points between 1999 and 2019, which seems much too small to create the large differences reported here (e. g. Figure  2b ). (2003)  and  Kan et al. (2013) ; the effect of socio-economic level was computed from the WAIS-III normative dataset; composite cultural load is the average of the three cultural load estimates after standardization.
, 2017;  this is a variant of investment theory and of explanations assuming genotype-environment covariance; e. g. Kan et al. , 2013). However, this idea would be almost impossible to falsify, and it would be difficult to reconcile with the facts that the correlation with heritability was non-significant and that there was no decline at all for the Gf and Gv subtests, which tend to have high heritability (e. g. Kan et al. , 2013; Rijsdijk et al. , 2002; van Leuuwen et al. , 2008) , and which would be expected to decrease before effects on Gc could be observable. There is also a lack of plausible biological mechanisms that could create such a large decline in the dataset in such a short timeframe. All this converges to suggest a role of cultural changes as the most parsimonious interpretation of the data.
g. Beaujean & Osterlind, 2008; Beaujean & Sheng, 2010; Kaufman, 2010; Nugent, 2006; Pietschnig et al. , 2013; Rodgers, 1998; Weiss et al. , 2015). As an example,  Flieller (1988)  reached the same conclusion in a French dataset over three decades ago;  Brand and colleagues (1989)  also found a similar result of decreasing scores due to changes of items difficulty, which they illustrated with an understandable decline of the proportion of correct answers for the item "What is a belfry?" between 1961 and 1984. This conclusion is also in line with studies arguing for the role of cultural environment and culture-based knowledge in Flynn-like fluctuations of intelligence over time (e. g. Bratsberg & Rogeberg, 2018). Note that drifts of item difficulty are only one aspect of such cultural changes; changes of test-taking pattern behavior, such as increased guessing, are another example (e.
This analysis is critical to inform risk communication strategies that aim to be effective in achieving the public health targets. This research responds to calls  15  to understand the psychological factors underlying individuals' response to this pandemic, mindful that even after the availability of COVID-19 vaccines, behavioral, non-pharmaceutical protective measures remain crucial  16  -but largely dependent on voluntary compliance. Our primary data was collected to examine whether health policy analysis should consider not just governance-level guidelines, but also individual-level decision making as a relevant dimension to understand compliance with policy measures. Policy guidelines may be curtailed if these fail to effectively communicate the relevant risks or, as our data will show, focus its communication on the wrong risks.
Materials and methods Study design and data collection. This cross-sectional study is part of the global Psycorona project (https:// psyco rona. org) which focuses on how people feel and think about the coronavirus epidemic and its economic consequences. This study complied with ethical regulations for research on human subjects and all participants gave informed consent, as approved by the Institutional Review Board at New York University Abu Dhabi (protocol HRPP-2020-42) and the Ethics Committee of Psychology at Groningen University (protocol PSY-1920-S-0390). Personal identifiers were removed from all sections of the manuscript, including supplementary information and public dataset. Survey responses were collected through Qualtrics' panel management service, except in China where data was collected by WJX, following a similar methodological approach.
The company's methodology involves obtaining responses from invited internet users drawn from its panel of over 90 million people worldwide. Data was collected in 24 countries: Argentina, Australia, Brazil, Canada, China, France, Germany, Greece, Indonesia, Italy, Japan, Netherlands, Philippines, Romania, Russia, Saudi Arabia, Serbia, South Africa, South Korea, Spain, Turkey, United Kingdom, Ukraine, and the United States of America. These countries cover various levels of economic development as well as different temporal stages of the COVID-19 pandemic. National proportional quota samples were collected with a 3% margin of error and 95% confidence level, representative of the country's population in terms of gender and age (age representativeness was less achieved in China, Greece, Saudi Arabia, Indonesia and the Philippines, where people aged 55+ were less present in the survey).
Data quality control was conducted by (1) examining IP addresses to detect potential duplicate responders; and (2) removing participants from the database whose answers indicated random responses. Data was collected online between 10th April and May 11th 2020. Measures. All measures are fully described in the Supplementary Table  1. The main predictors were the perceived likelihood to get infected with coronavirus, the perceived likelihood to suffer economic consequences due to the coronavirus, and their interaction effect. A total of six outcomes were predicted. The primary outcomes were compliance with preventive health behaviors, including frequent hand washing, avoiding crowded spaces and social isolation (i. e. , no contact with friends and family other than household members). The secondary outcomes were the support for strict health measures, namely support for mandatory coronavirus vaccination, mandatory quarantine for those that have coronavirus and those that have been exposed to the virus, and reporting of suspected coronavirus cases.
We estimated the Intraclass Correlation Coefficient (ICC) to describe the correlation among observations within the countries. The ICC is also equivalent to the variance partition coefficient, which can be interpreted as the proportion of variation that is due to a variation between countries  27. We also applied hierarchical models  28  to understand the effects of controlling for person-level predictors taking into account the random variations across nations. In preparation to run these models, we eliminated the missing values from the entire dataset (n = 592), considering all the variables together. If a subject had missing values, all variables from the subject were eliminated. We detected the multivariate outliers using Mahalanobis' distance and chi-square distribution (ɑ =. 95) with a total of 2282 eliminated.
The total sample used in the models was N = 22,561, constant across models. The predictors from the individual-level were group-mean-centering by country (and scaling is done by dividing the (centered) columns of x by their standard deviations). Countrylevel variables used grand-mean-centering, given these have a single value for each country. The models were implemented using R and the package lme4  29, 30. To predict each of the six outcomes, a total of three nested models were selected from a range of 15 models (using ANOVA approach for between model comparison). The selected models vary in increasing complexity. All models controlled for COVID-19 case-fatality rate: total COVID-19 deaths per million/ total COVID-19 cases per million. Model 0: Model 0 or empty model provided unadjusted rates for the behavior response (outcome) that accounted for clustering.
Acknowledgements This research received support from the  New York University Abu Dhabi  (VCDSF/75-71015-attributed to J. J. B. ), the  University of Groningen (Sustainable Society & Ubbo Emmius Fund -attributed to N. P. L), and the  Instituto de Salud Carlos III  ( COV20/00086 ), co-funded by the  European Regional Development Fund (ERDF)  "A way to make Europe. " (attributed to M. M. ).
Thies et al. [37]  investigated prominence marking in PD patients considering the effect of medication. Acoustic measures and articulatory data of tongue movements were analyzed. Three prominence marking conditions were considered: prominence marking of given information (unaccented), broad focus, and contrastive focus. They reported that PD patients are able to distinguish the three conditions by producing longer vowel durations and higher F0 rises in broad, and more so, in contrastive focus. Importantly, Thies and colleagues also show that the effects of drug intake may depend on the kind of measure considered, as they found an effect of medication intake in the agility of tongue movement in the articulatory measure, which however had no counterpart in the acoustic measures, namely, in the acoustic vowel space.
Then, we focused on the groups of patients to investigate the effects of medication and disease duration by means of a mixed-ANOVA. If a main effect of medication (or an interaction) was found, the Wilcoxon Signed Ranks Test was used to further examine the effects of medication within each group of patients. Finally, the patients' data were further analyzed by means of generalized linear mixed models in SPSS (IBM SPSS Statistics, version 26. 0), with the goal of examining effects of medication, disease duration, and presence of motor fluctuations while considering individual variation. We controlled for participants in the random effect structure, used a Satterthwaite approximation to account for differences, and robust estimation of the covariances to account for small sample sizes. Fixed effects were medication (OFF, ON, as within-subject factor), motor fluctuations (yes, no), and disease duration (as a continuous variable).
We planned for half of the infants to see the Contingent condition, and half to see the Non-contingent condition. Additionally, to control for possible effects of handedness when addressing the question of arm differentiation, in the Contingent condition, the arm (left or right) where we attached the connected bracelet was counterbalanced across the subjects. We attributed the left and right arms alternately to the infants as they were recruited. Our main interest was to test for the effect of the contingency between the movement of one arm and the resulting audiovisual stimulation. We took a gravity-corrected acceleration measure (see section "Acceleration Data Processing") for each arm as a measure of arm activity and as the dependent variable.
Participants Sixty infants were recruited from a database of interested local middle to upper-middle class families. Before participating in the experiment, caregivers gave their written informed consent. The experimental protocol was approved by the University Paris Descartes ethics committee. The data for 23 infants had to be rejected: in the Contingent test condition, 10 infants did not complete the entire experiment (for at least 5 continuous minutes) because they cried (5) or became fussy for at least 30 s (5), and 5 were excluded because of problems with data recording; in the Non-contingent control condition, 4 infants did not complete the entire experiment because they cried (3) or became fussy (1), data for 2 infants were incomplete due to technical error, 1 infant was excluded as preterm, and 1 infant was excluded because the caregiver did not follow instructions and restrained the infant's arms.
Data analysis was thus based on 37 infants aged 6 months, 20 in the Contingent condition (9 females and 11 males, mean age = 184. 5 days, SD = 7. 5 days, range = 172-200 days) and 17 in the Non-contingent condition (8 females and 9 males, mean age = 181. 9 days, SD = 7. 2 days, range = 174-201 days).
This difference also seemed to start to appear very early, already in the first minute of the experiment. Comparison With  Jacquey et al. (2020b)  Our hypothesis in this work was that the changes we made in the paradigm would make it easier for infants to detect the contingency as compared to  Jacquey et al. (2020b). Fortunately we had available the raw data of the original experiment of  Jacquey et al. (2020b) , and were thus able to use it for a reanalysis. In order to make the comparison, we ran the same analyses on the same age group from  Jacquey et al. (2020b) , using the same pre-processing of acceleration values that we used for our own data (see section "Acceleration Data Processing"). This data included 18 infants in Jacquey et al. 's (2020b) Contingent condition and 16 infants in their Non-contingent condition (the data of the 17th infant was a far outlier and was excluded).
ACKNOWLEDGMENTS We wish to thank  Mark Wexler  for the help with designing and programming the present experiment and  Lisa Jacquey ,  Jacqueline Fagard ,  Rana Esseilly ,  Eszter Somogyi ,  Gianlucca Baldassarre ,  Vieri Giulano Santucci ,  Francesco Mannella , and  Valerio Sperati  for their extensive comments and/or useful discussions.
To control the distances between the tables, lines were drawn on the floor (see Fig. 3 ). The AB and AC distances were 2 and 3. 5 m, respectively. These two distances were chosen so that the hand condition required less time than the tool condition for the three smallest quantities of rolls (i. e. , 4, 8, and 12 rolls), and vice versa for the three largest quantities of rolls (i. e. , 16, 20, and 24 rolls) (see Table  3 ). A computer equipped with the OpenSesame 3. 0. 7 software  52  was placed on table A so as not to disturb the movements of the participants. The OpenSesame software was used to record participants' movement durations (via Bluetooth keyboards) and to follow the movements of the participants.
Data analysis plan All the analyses and the results reported in this study are original. Statistical analyses were performed with JASP software (version 0. 14. 0. 0). Clinical, bodily, neuropsychological, movement durations, and speed gain data were tested for normal distribution with skewness and kurtosis indexes. No normal distribution was considered when absolute values for skewness and kurtosis were greater than 3 and 20, respectively  53. Except the duration measurement during the revised shopping test, all measures for each experimental condition were below these values. Student's t tests were performed for data with a normal distribution, and Mann-Whitney tests for data with a non-normal distribution. The first analysis compared sociodemographic, bodyweight, clinical, and neuropsychological data between individuals with schizophrenia and healthy control participants (Table  1 ).
These data, standardised, are described in main publications or on French Depending on the additional tests that have been spontaneously performed during the consultation, the patient will be rescheduled to perform the other tests in order to complete the data collection. The patient will also be scheduled for a second appointment to perform the functional neuroimaging evaluation (index test). Finally, a semidirected interview will be conducted with the patient and his caregiver, to specifically explore the question of the ageing in place and its consequences. In order to harmonise the clinician's evaluation and to avoid personal bias, the clinical records of each participant will be presented and validated by a medical team including the geriatricians and neurologists involved in this study.
Index test: functional neuroimaging The assessment of the clinician's decision-making ability will be compared with the activation of the brain areas of interest involved in decision-making, during the performance of a decision-making task under risk, the BART. Behavioural data from this decision-making task will not be interpreted in the primary analysis. The paradigm used will be derived from the Lejuez paradigm,  28  and will consist of three sessions of 20 blocks, comparing control balloons (no active decision) and balloons with a random probability of explosion, representing decision-making under uncertainty. The period of interest will represent the decision phase to continue the inflation of the balloon or to put virtual money into a virtual kitty. fMRI predetermined regions of interest (ROIs) involved in decision-making ability  31  will include, namely ► The dorsolateral prefrontal cortex.
Open access Neurofunctional analyses will be blinded to the clinical and neuropsychological data used as the basis for the standard reference. Whenever possible, patients will also have a highresolution electroencephalography (EEG-HR) examination. Indeed, while fMRI is distinguished by its high spatial resolution, EEG-HR studies more specifically the temporality in cognitive functioning. Many studies point to the impact of the slowing down of the processing speed in cognitive ageing, which makes the realisation of a task in EEG-HR a relevant complementary data. Nevertheless, in case of fatigue or difficulty, fMRI will be preferred, and EEG-HR will only be used as complementary data. In EEG-HR, patients will perform a validated decision task (IGT) adapted to EEG-HR, and simplified to a binary decision.
Analysis Statistical methods description Quantitative data will be described in the following way: number, mean, SD, quartiles and extreme values. Qualitative and semiquantitative data will be described with the frequency and proportion of each modality. The main statistical analysis will consist of a descriptive analysis of the concordance/discordance data between the standardised global gerontological assessment (clinical consultation supplemented by neuropsychological tests to establish the clinical diagnosis of decision-making ability) and the objectification of the activation areas by functional imaging. ► Concordance: corresponds to situations where the clinical assessment is consistent with the fMRI activation pattern. A distinction is made between: -Positive concordance corresponds to a positive neuropsychological evaluation (able patients=no cognitive impairment impacting decision-making) and activation of ROI on fMRI.
Statistical strategy McNemar's χ 2 test and Cohen's κ will be used to study the correlation between fMRI, EEG and standardised neuropsychological and gerontological consultations (2*two crosses: decision-able/doubtful decision-able). Activation data from functional imaging (fMRI, EEG) will be coded as two-modality categorical variables (ability/doubt about ability) and will be linked to neuropsychological tests of executive skills, social cognition, apathy assessment, decision-making and depression tests using regression models according to their distribution. Normality of the data will be tested using the Shapiro-Wilk test. If this is not verified, non-parametrical tests will be used. Student's t-test or the non-parametrical Wilcoxon test will allow to compare respectively the means or the ranks of each test (mentioned above) according to the aptitude result from the functional imaging.
Analysis of missing and/or incomplete data The analysable population of the study will consist of all included patients who meet the eligibility criteria (inclusion and non-inclusion) and who have not refused the use of their data. Patients who have been withdrawn from the study will not be analysed. No additional enrolment is planned; patients for whom imaging results are not usable will be substituted after agreement with the principal investigator. For the primary outcome, the statistical analysis will be based on complete data (no statistical procedure for replacing missing data). All the data filled in at the different times of the study relating to the analysable population will be used.