dataesr/scientific-paragraphs-categorization huggingface dataset:
Mixed modelling For all outcomes (cognitive performance, HRQoL and sleep) we considered the data from all participants who completed at least three assessments on three different days. We analysed the association between each of the three markers of RF-EMF exposure on three endpoints using a mixed modelling approach, adjusting for age and sex, and with random intercepts for each individual, thereby taking into account the systematic differences between individuals in cognitive performance, HRQoL and sleep habits. We chose a linear approach considering there is no established dose-response relationship, between the exposure and any of the evaluated health outcomes, and as such no indication for threshold effects, plateaus, or other non-linear behavior that would justify choosing a more complicated (and likely less interpretable) model.
We considered that there might be a substitution effect (where participants can only engage in a single exposure related activity at any one time (cordless phone calls, mobile phone calls, or screen time). In addition, we therefore analysed the associations with all phone calls, and of the sum of all three exposure markers. All data compilation, management and analyses were done in R Version 4. 0. 3 (R Core Team, 2013). Mixed models were fitted using the R lme4 package with random intercepts for each study participant for all three outcomes.
Each looming sequence was composed of 5 repetitions of the looming stimulus, consisting of a black disk expanding to a grey background from 1 cm to 25 cm in 250 ms, then staying at that size for 500 ms (750bms in total for one looming disk, 3750bms in total for the sequence, called 5L). ITI was randomized between 30 s to 180 s and the looming sequence was displayed when the position of the animal was detected inside the zone covered by the screen. The videos of the different sessions were processed with DeepLabCut. DeepLabCut was used to extract, frame by frame, the x and y positions of several points, notably the nose and center of the mouse. These positions were processed using Matlab to automatize data processing and the scoring of behavioral responses. These positions were then smoothed over 5 consecutive points, and used to calculate the instantaneous speed of the mouse (using the center speed) and the instantaneous speed of its nose.
Statistical analyses. GraphPad Prism v7. 0 software was used for statistical analyses. Data are shown as the means ± SEM. Multiple comparisons were performed by two-way or three-way ANOVA repeated measures followed by a post-hoc analysis. * p < 0. 05, ** p < 0. 01 and *** p < 0. 001. Values are indicated in the Supplemental Table  2.
Funding and Disclosure This work was supported by  Inserm ,  Fondation pour la Recherche Médicale  ( EQU202203014705 ), by the  French National Research Agency  ( DISCOMMODE ,  ANR-21-CE37-0013 ;  FrontoFat ,  ANR-20-CE14-0020 , EV) and ( DOPAFEAR ,  ANR-16-CE16-0006 , EV and CH). This work was supported by the  French National Research Agency  ( ANR-10-EQPX-08 OPTOPATH ) and the  Fondation pour la Recherche Médicale  ( FRM -Equipes   FRM 2017 ) (CH). Laia Castell  was supported by the pre- doctoral Labex EpiGenMed  ( «Investissements d'avenir » ANR-10-LABX-12-01 ). Laura Cutando  was supported by the post -doctoral Labex EpiGenMed fellowship  ( «Investissements d'avenir » ANR-10-LABX-12-01 ), EP was a recipient of  Marie Curie Intra-European Fellowship   IEF327648  and is currently a recipient of a  Ramon y Cajal  contract ( RYC2020-029596-I ).
M ET HOD Participants Twenty-one healthy full-term infants (born between 39 and 42 weeks of pregnancy) were followed from the age of 4 to 8 months (10 male, 11 female, age range at 4 months: 119 to 130 days, M = 124 days; SD = 4 days). Infants were recruited from a list of local families who had expressed interest in participating in studies on infant development. Data were collected between September 2017 and July 2018 in Greater Paris, an ethnically diverse location. Participant data based on race or ethnicity were not collected in compliance with French law. Families were middle to upper class. Mothers' highest level of education was high school (N = 1), bachelor's degree (N = 9), and graduate degree (N = 11). Parents gave their informed consent before participating. Infants were assigned to the buzzer stimulation or the control condition as they became available until a count of at least 10 infants per condition was reached (11 buzzer stimulation, 5 male, 6 female, age range: 121-130 days, M = 123 days, SD = 4 days; 10 control, 5 male, 5 female, age range: 119-128 days, M = 124 days, SD = 4 days).
Depending on usual caregiver practice and age, infants were either seated supine in an infant seat during the whole session or placed supine on a flat surface. The sessions were videotaped, with a camera placed behind and above the infant, at about 1,5 m from the infants' head, to have a complete view of the body and to avoid obstruction by the adult's head. Some sessions (N = 10, 0. 03% of all sessions) could not be fully recorded due to technical errors or limited space in the infants' home, therefore response durations and looking data could not be coded in these instances. Reaching success (yes/no) was noted manually by the experimenter in all the trials.
Data coding We coded the data by watching video recordings of the 84 test sessions, comprising 614 trials (672 trials were held in total; however, 58 could not be fully recorded due to technical errors or limited space in the infants' home). For each trial, we coded the buzzer location and whether the infant contacted the buzzer. Reaching success was coded "yes" if the infant contacted the buzzer with the hand or fingers. If the infant contacted the buzzer, we coded the response duration (the number of seconds elapsed between buzzer placement and contact). Looking was coded "yes" if in a given trial the infant looked at the buzzer (this was coded for all trials, independently of reaching success). The co-occurrence of success and looking within the same trial was coded. Because of the difficulty of extracting eye position from the videos, and also because looking and touching sequences were often intermingled in a complex way, it was not possible to code precisely whether look and touch were coordinated.
Data analyses Confirmatory analyses were conducted using generalized linear mixed-effects models (GLMMs) in the lme4 package  (Bates et al. , 2015)  in R (R Core Team, 2020). For binary dependent variables (reaching success, looking), a binomial distribution with a logit link function was used. The bobyqa optimizer with a set maximum of 200,000 iterations was used. For all analyses, participant ID was included as a random effect on the intercept to account for multiple trials completed by each participant. Follow-up tests were conducted using the emmeans package for R (Russell et al. , 2021) using Tukey paired comparisons. We did not analyze three-way interactions due to statistical power limitations; however, we did not have hypotheses about three-way interactions.
Manipulation checks We first tested the validity of fear induction (i. e. , presentation of fearful/neutral facial expressions before each trial). We collected rating data for fearful and neutral facial expressions of "how fearful do you feel when seeing this face" using a Likert scale from 0 (no experience of fear at all) to 8 (strong experience of fear). Paired-sample t test showed a significant stronger fearful experience for fearful versus neutral expressions (p = 0. 001; the mean rating for fearful expressions was 3. 52; see S5 Text and S3 Fig) , suggesting that participants indeed experienced slight-to-moderate fear during the experiment. To examine whether participants were able to learn the reward structure of our task, in line with Piray and colleagues (2019)  [36] , we plotted participants' performance after reversal.
Participants were asked to learn by trial and error to maximize their payoff. Although participants were explicitly informed that reward structure would change throughout the task, they needed to infer the moment on which reversal occurred and the speed at which reversal changed  [45]. At the beginning of each trial, a cue (a fearful or neutral face) was first shown on the screen center with duration of 1 s (Fig  1A ). To eliminate potential gender effects, facial expressions with the same gender with participants were used as cues. For example, the female face with the fearful or neutral expression was shown on the cue for the female participants. We selected 4 female faces and 4 male faces from the Taiwanese Facial Expression Image Database (TFEID)  [74] , with 2 fearful and neutral expressions for each gender.
Statistical analysis for learning rates Each parameter was represented by the mean of the posterior distribution of the parameters. We conducted LMMs on learning rate with subject as a random factor and with cue (fear/ neut) and volatility (infreq/freq) as within-subject factors for exp1 and exp2, respectively. Pearson correlations of fear-biased adaptation to volatility learning in terms of learning rate were implemented with total BVAQ scores, the cognitive dimension, and the affective dimension of BVAQ scores across 61 participants. Statistical analyses were conducted using SPSS 17. 0 (IBM) and R (4. 1. 0). We set the significance level at p = 0. 05.
Image acquisition and preprocessing MRI data were acquired with a Siemens Trio 3T scanner. Both the fMRI and high-resolution 3D structural brain data were obtained using a 64-channel phased-array head. The fMRI data were acquired by means of a gradient-echo echo-planar imaging sequence containing the following parameters: repetition time (TR) = 1,000 ms, echo time (TE) = 30 ms, 78 multiband slices, voxel size = 2 × 2 × 2 mm  [3] , flip angle = 35˚, field of view (FOV) = 192 mm × 192 mm, data matrix = 96 × 96, and 240 volumes scanned in 2,640 seconds. Additionally, the 3D structural brain images (1 mm [3] isotropic) were acquired for each participant using a T1-weighted 3D magnetization-prepared rapid gradient echo sequence with the following parameters: TR/ TE = 2,300 ms/2. 26 ms, flip angle = 8˚, data matrix = 232 × 256, FOV = 232 mm × 256 mm, BandWidth = 200 Hz/pixel, 192 image slices along the sagittal orientation, obtained in about 9 min.
Functional MRI data were preprocessed with DPABI  [39]  (http://rfmri. org/dpabi), a software package based on SPM12 (version no. 7219; https://www. fil. ion. ucl. ac. uk/spm/software/ spm12/). It comprised the following steps: (i) realignment; (ii) co-registering the T1-weighted image to the corresponding mean functional image; (iii) segmenting into gray matter, white matter, and cerebrospinal fluid by DARTEL; (iv) normalizing to the standard Montreal Neurological Institute space (MNI template, resampling voxel size 2 × 2 × 2 mm  [3] ); (v) smoothing with a Gaussian kernel of 6 mm full width at half maximum (FWHM).
Other examples include auditory spaces, The premise and conclusion of a similarity-based inference are typically generics (see, e. g. ,  Gärdenfors and Osta-Vélez, )  and so it would be wrong to state them using a universal quantifier. Which of these spaces we are to apply depends on the viewing conditions. CIELAB space is a more accurate representation of similarity judgments when these concern colored cloths or colored pieces of paper, while CIELUV space is preferable when the comparisons concern colors shown on screen. For details, see Fairchild ( ). Frontiers in Psychology frontiersin. org taste space, olfactory space, various shape spaces, action and event spaces, face space, and "moral" space. Similarity spaces are commonly constructed by applying some statistical dimension-reduction technique (such as multidimensional scaling or principal component analysis) to a large set of similarity judgments or similar data (such as confusion probabilities or correlation coefficients; see  Abdi and Williams, 2010; Borg and Groenen, 2010; Hout et al.
We were interested in the effect of replacing in the previous nonlinear models the predictor variable, which for any given participant consisted of distances measured in that participant's personal space, with distances as measured in the aggregate space. We found that model fit was indeed somewhat better for the distances based on the aggregate space, as the median R 2 value was now 0. 87 (MAD = 0. 05). Here, too, the parameters were statistically significant for virtually all participants, the median p values (as well as the corresponding MADs) being essentially 0. Finally, we also aggregated the data obtained in the inference strength task, averaging, for each question, the responses given by the participants that had been presented with that question. This For this, we used the MASS package for the statistical programming language R (R Core Team, ).
Participants A total of 142 neurotypical participants ranging from 10 to 80 years of age took part in the study. Because of unfortunate data loss in one or another experimental task, the analyses were performed on 130 participants for which data were complete in all experimental tasks. Participants were spread across six defined age groups including children of 10-13 years of age (N = 19, mean age = 11 ± 0. 8; 8 female participants) and adolescents of 15-18 years of age (N = 24, mean age = 16. 7 ± 1; 14 female participants), as well as groups of adults with an age range of 1 decade, including young adults of 20-30 years of age (N = 23, mean age = 24. 7 ± 3; 13 female participants), middle-aged adults of 40-50 years of age (N = 17, mean age = 45. 6 ± 3. 5; 10 female participants), senior adults of 58-68 years of age (N = 26, mean age = 64.
1 + 3. 2; 16 female participants), and old adults >70 years of age (N = 21, mean age = 73. 3 ± 3. 1; 14 female participants). Participants were French native speakers, right-handed, and had normal or corrected-to-normal vision. None reported a significant history of language, psychiatric, or neurological impairments. The final dataset comprised 130 participants for whom all data were collected (see Results section). Years of education was recorded as additional demographic information, although it was not helpful for the younger groups of children and adolescents, as they were all in the same class. On average, the number of years of education was 7. 4 (±1. 2) in children, 11. 2 (±1. 2) in adolescents, 16. 7 (±2. 2) in young adults, 17. 3 (±4. 4) in middle-aged adults, 14. 2 (±3) in senior adults, and 13.
Stimuli For referential naming, we used a picture naming task. A total of 120 black and white drawings and their corresponding modal names were selected from two French databases  (Alario and Ferrand, 1999; Bonin et al. , 2003). The 120 words were all acquired before the age of 9 years to ensure that they were known by the youngest participants. The word age-of-acquisition range was 1. 19-3. 55 on a 5-point scale (1: learned between 0 and 3 years; 4: learned between 9 and 12 years; 5: learned after the age of 12). There were 40 monosyllabic, 60 bisyllabic, and 20 trisyllabic words. Word frequency ranged from 0. 13 to 227 occurrences per million words (mean = 17. 3) according to the French database Lexique  (New et al. , 2004). All picture words had high name agreement (mean = 92. 5%)  (Alario and Ferrand, 1999).
Stimuli For inferential naming, we designed a naming from definition task. A total of 108 target words were selected, and their corresponding definitions were constructed similarly to previous study  (Fargier and Laganaro, 2017) , i. e. , definitions had a similar structure across items and target words could not be anticipated before the last word of the definition was heard (e. g. , "Animal from which we obtain honey" bee, "Object that we use to shoot arrows" bow). Only imageable concrete words were used, and 24 items were also used in the picture naming. All target words corresponded to words learned before the age of 9 years, according to the previously mentioned databases, and the word age-ofacquisition range was 1. 27-3. 65 on the 5-point scale. There were 25 monosyllabic, 65 bisyllabic, 15 trisyllabic, and 3 quadrisyllabic words.
Lexical frequency ranged from 0. 06 to 605. 75 occurrences per million words (mean = 35. 4) according to the French database Lexique  (New et al. , 2004). The obtained name agreement for the definitions was on average 89%. Given that the average name agreement was 92. 5% for picture naming, the level of difficulty of trials used in the inferential naming task and the referential naming task was considered equivalent. Definitions were presented orally from a female French native speaker and delivered through speakers.
Picture naming Stimuli were presented on-screen: Each trial started with a centered fixation cross (500 ms) followed by the picture for 3,000 ms. Participants had to overtly produce the word corresponding to the picture as fast and as accurately as possible. The interval between the stimuli was set at 2,000 ms. Four warmup filler trials were used at the beginning of the experiment and after the break, which occurred halfway through the task. Two pseudo-random orders were used (one pseudo-random order and its reverse order), and the task was implemented with the software E-Prime (Version 2. 0)  (Schneider et al. , 2002).
Data pre-analyses Word productions in the picture naming and the naming from definition tasks were digitized and accuracy and production latencies (reaction times in milliseconds) were systematically checked offline with a speech analysis software (Check-Vocal 2. 2. 6,  Protopapas, 2007). In the picture naming task, the reaction time corresponded to the time separating the onset of the picture and the onset of the speech wave. In the naming from definition task, the reaction time corresponded to the time separating the offset of the last word of the definition and the onset of the speech wave. We used the phonological uniqueness point of the last word as the reference point when it differed from the offset of the last word of the definition (<5 items). For these two tasks, outliers and unexpected answers (additional articles, hesitation marks, and errors) were excluded from the RT analyses.
Data analyses Similar analyses were performed on accuracy and production latencies for the picture naming task on the one hand and the naming from definition task on the other hand. Two main analyses were performed. The first set of analyses consisted of characterizing the lifespan pattern of picture naming and naming from definition using multivariate adaptative regression splines (package Earth,  Milborrow et al. , 2013). This approach produces a regression that is allowed to bend at certain knots that mark a change in the behavior of the function. The knots obtained across the lifespan, i. e. , spanning the different age groups, could then be compared between picture naming and naming from definition. The second analysis consisted of multiple regression models with several cognitive predictors to test for the critical effects of these measures on performance across the lifespan.
Limitations This study is one of the few that investigates language processing across the lifespan, spanning childhood to late adulthood, but some limitations are worth mentioning. Although we collected data from more than 140 participants, which represent a high number considering that they also engaged in neural recordings, the sample size may seem small considering the range in age. Moreover, although we introduced several cognitive assessments, additional measurements could have been helpful to better characterize aging differences and disentangle cognitive processes across the tasks. For example, because naming from definition was delivered from auditory input and picture naming from visual input, tasks that measure visuo-perceptual processes and auditory processes could have been useful to determine the role of visuo-perceptual abilities in decremental performance in picture naming in older individuals.
Study design Participants were drawn from three existing cohorts located in the UK, France and Germany: IMAGEN, STRATIFY and ESTRA. IMAGEN was a longitudinal population cohort, whereas STRATIFY and ESTRA were case-control cohorts. To be eligible for inclusion, participants needed to respond to our invitation and provide informed consent through an online form sent via email. Data collection was conducted through online questionnaires, with the initial round taking place during the first national lockdown in the UK and Europe (April-May 2020). Subsequent follow-up surveys were administered when the first lockdown was released (July 2020) and when the second lockdown was imposed (November 2020). The design and reporting of our study were in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.
Survey and assessments The COVID-19 survey We adapted the CoRonavIruS Health Impact Survey (CRISIS v0. 1 http://www. crisissurvey. org)  23  to examine changes to individuals' mental health and behaviours induced by the pandemic. The survey encompassed data collection at various time points, specifically: pre-pandemic (3 months prior, pre-LD1), during the first lockdown (LD1), after the first lockdown (after-LD1) and during the second lockdown (LD2). This questionnaire assessed a range of data domains including COVID-19-related health status and life changes, daily behaviours and emotions, and worries due to the COVID-19 crisis (see Supplementary Information available at https://doi. org/10. 1192/bjo. 2023. 601 for details).
Statistical analyses Data were analysed in SPSS version 27 using mixed-effects analysis of variance (ANOVA), with within-subject effect (time) adjusted by country and between-subjects effects (cohort and sex) adjusted by country and age. For each analysis, participants were included if they had no missing data for any variable needed. Separate analyses were conducted, as detailed in the Supplementary Methods, on the whole sample or on each cohort separately. Statistical significance was set at P < 0. 05.
Time effects in each group were estimated by comparing data collected before the pandemic with data collected at other time points (*P < 0. 05, **P < 0. 01, ***P < 0. 001) and by comparing data collected during the first lockdown with data collected afterwards ( + P < 0. 05, ++ P < 0. 01, +++ P < 0. 001). Differing impact of the COVID-19 pandemic on youth mental health at home with their families, the less likely they were to gain access to these substances.
Histology At the termination of the experiment, rats were euthanized by an overdose of sodium pentobarbital (150 mg/kg, i. p. ). Their brains were removed, sectioned at 40 µm thickness and examined with light microscopy for cannula penetration. After histological verification, only animals that had both cannulae into the BLA (including anterior and posterior areas), ranging from -2. 52 to -3. 24  (Paxinos and Watson, 2007)  were included in the data analysis. After histological analysis, five rats of the Vehicle group and seven rats of the Anisomycin group were discarded because of incorrect placements of the cannula tip. The remaining rats of Vehicle (n = 16) and Anisomycin group (n = 15) were included in the behavioral analysis.
Behavioral analyses Response rates were analyzed during the 18 s tone in different phases: last 2 sessions of Training (Experiments 1, 2, 3, and 4); last 2 sessions of Retraining (Experiments 3 and 4), the Error session (Experiments 1, 2, 3, and 4) and the ROE test session (Experiments 1, 3 and 4). The average of lever presses from each rat was calculated dividing the total number of lever presses for each second performed in one session, by the number of trials (20 trials in 100% reinforcement condition, or 10 trials R and 10 trials N in 50% reinforcement condition). The data of the subjects were grouped to obtain the lever presses average for each 2 s during the 18 s tone (2, 4, 6, 8, 10, 12, 14, 16, and 18 s). Data were analyzed by two-way analyses of variance (ANOVA) with repeated measures using Jasp (0.