dataesr/scientific-paragraphs-categorization huggingface dataset:
83. Subjective career success. This was measured with three items from Gattiker and Larwood (1986) (e. g. , "I am pleased with the promotions I have received so far," "I am drawing a high income compared to my peers"). Cronbach  was. 65. Concerns for the marginal alpha were alleviated by testing the adequacy of the measurement model (below). The discriminant and convergent validity of the mentoring receipt and subjective career success measures were supported by a Confirmatory Factor Analysis (CFA) using the EQS 6. 1 Structural Equations Program  (Bentler, 2004)  and employing the maximum likelihood robust method that corrects for non-normality in the data. To assess model fit across all our measurement (CFA) and structural models, we employed the chi-square test along with two widely used goodness-of-fit criteria: the comparative fit index (CFI) and the incremental fit index (IFI).
Measurement equivalence. Because data were collected from three different countries, the assumption that the measures assessed equivalent constructs across national settings (i. e. , measurement equivalence, Mullen, 1995) was tested. CFAs were performed across all possible country pairs using a one-factor (employability) and a two-factor (mentoring receipt and subjective career success) measurement model based on the different raters (i. e. , employees vs. corresponding line managers), as well as a three-factor model that included all latent constructs across raters. Factor loadings were constrained to be equal across each pair and error variances were left free to be estimated  (Vandenberg & Lance, 2000). In all cases, the one-and two-factor models demonstrated good fit (CFI range:. 945 to.
Results Descriptive statistics and inter-correlations are presented in Table  2. Cursory inspection of the correlation coefficients suggested that mentoring receipt had sizable significant associations with both job performance (r =. 25, p <. 001) and employability (r =. 30, p <. 001). Structural Equation Modeling (SEM) tested the hypotheses. Analyses were performed using the EQS 6. 1 program with the maximum likelihood robust method, and the same fit indexes described in the CFA were used to assess model fit. Following  Anderson and Gerbing (1988) , the fit of the measurement model (i. e. , factor loadings) was assessed first, which was followed by testing the structural model (i. e. , hypothesized path coefficients). To preserve statistical power, only those controls that demonstrated significant relationships were included in the final structural model (see  Wu, Tsui, & Kinicki, 2010).
Target Sounds Employed for the Time Estimation Task The auditory stimuli employed for the time estimation task were generated with the software PRATT (LF-ARX model). This is an algorithm for the sound analysis synthesis able to produce different voice components which has often been employed to explore the acoustic components associated to different emotions. Critically, the software allows generating pure sounds with the desired frequency and intensity. For the purposes of the present study, we selected a pure sound, which has the advantage of not being associated to any personal experience, as it does not exist in nature. The same sound was generated in seven different durations all shorter than 2 seconds (0. 4, 0. 6, 0. 8, 1, 1. 2, 1. 4,  and 1. 6 s). Indeed, we know that for sounds longer than 2 s we can use other strategies, such as counting, to increase the time estimation accuracy  (Burle and Casini, 2001).
Data Analysis In order to obtained an unbiased measure of time estimation, for each participant and for each stimulus we calculated the time warp as the difference between the time estimation provided in Phase 2 and the time estimation provided in Phase 1. Time warp was then submitted to a repeatedmeasure ANOVA with presentation order (from the 1st to the 7th presented stimulus) as a within-subject variable and condition (odor vs. video) as a between-subjects factor. The ANOVA linear contrast was also reported. SC was measured by means of the amplitude of the maximum peak shown in correspondence of the stimulus presentation. Also, SC was submitted to a repeated-measures ANOVA with presentation order (from the 1st to the 7th presented stimulus) as a within-subject variable and condition (odor vs.
Data Analysis In order to obtained an unbiased measure of time estimation, for each participant and for each stimulus we calculated the time warp as the difference between the time estimation provided in Phase 2 and the time estimation provided in Phase 1. Time warp in the two groups (''random priming stimuli'' and ''random target sounds'') was then submitted to repeatedmeasures ANOVAs with presentation order (from the 1st to the 7th presented stimulus) as a within-subject factor and condition (odor vs. video) as a between-subjects factor. The ANOVA linear contrast was also reported.
Stimuli Stimuli were managed using the Labview software package (National Instruments Corporation, Austin, TX, USA). They were delivered to a high luminance LCD projector, back projected onto a frosted screen positioned at the back end of the MRI tunnel, and viewed by the participants through a mirror. Target stimuli for the two main conditions consisted of 12 s videos of human hands performing slow extension-flexion movements. In the Self Hand condition videos were of the participant's right hand. In the Other Hand condition videos were of someone else's right hand. The Other Hand video was chosen so that the hand was in the same position as the participant's but looked different. Participants were presented with all the videos we had with other people's hands that were in the exact same position (i.
For each run, the scanner was in the acquisition mode for 14 s before the experiment to achieve the steady-state transverse magnetization. Image analysis. Data from one participant had to be discarded due to technical problems with the MRI scanner. Analyses reported here were performed on data from the 14 remaining participants. Statistical parametric mapping software was used for image processing and analysis (SPM 08, Wellcome Department of Cognitive Neurology, London, UK). The functional images were interpolated in time to correct phase advance during volume acquisition and were spatially realigned to the first image of each run using trilinear interpolation. To allow multi-subject analysis, the anatomical references and the realigned functional images of all participants were transformed into a common standard space by using the MNI template.
The individual Real movement vs Baseline contrasts were viewed with the threshold of significance for active voxel set at p < 0. 001 uncorrected. The clusters over the regions of interest were identified by visual inspection and double checked using the Anatomy toolbox  [34] [35] [36]. The Ventral Premotor cortex could not be reliably identified in some participants using this active movement localizer, and was therefore not included in the ROI analysis. The coordinates for the peak activation for each cluster were noted and used to build ROIs using the Marsbar software  [38]  as 10 mm diameter spheres. The Marsbar sofware was also used to calculate the percent signal change in each individual ROI for the three conditions: Self Hand, Other Hand, and Real Movement. The measures were entered in a 3 x 2 factorial analysis, with factors ROI (3 levels: M1, PMd, SMA) and Identity (2 levels: Self, and Other).
Behavioral data During the training sessions performed before the fMRI session, participants' perception was assessed while they were watching videos of their own hand or of someone else's hand presented in a random order. They were asked to press a button at the onset of any illusory perception. For all 15 participants included in the experiment, watching the video of their own moving hand resulted in an illusory kinesthetic sensation that their static hand was actually moving throughout the video presentation whereas such illusion was never elicited during the video of other hands. At the end of the training sessions (from 2 to 8 sessions according to the participant), the mean latency of the elicited illusions was 1. 74 (± 0. 38 SD) seconds. Results for the questionnaire are summarized in Fig  2.
The video clips that were used as stimuli consisted in a sequential action of two motor elements (1) reach to grasp and (2) move to place. The video clips were cut exactly one frame after the actor finished placing the object. Movies were compressed with FFdshow codec (MJPEG) at 30 frames per second with a screen resolution of 640 × 480 pixels. 3D kinematics were analyzed with RTMocap toolbox  (Lewkowicz and Delevoye-Turrell, 2015). Positional data points were filtered using a dual fourth-order Butterworth low-pass filter (fc = 15 Hz; forward and backward) and tangential 3D instantaneous velocities were calculated. A threshold of 20 mm•s -1 was used to determine the onset of movement (reaction time, RT). All velocity trajectories were bell shaped and consisted in two "bells, " the first corresponding to the reach to grasp element, the second being the move to place element of the preparatory action.
The videos stimuli in the categorization task were displayed on a gray background on a laptop computer using the PsychToolbox for Matlab (Natick, MA, USA). Before each trial, a white fixation cross-appeared on the gray screen during a variable interval of 500-1000 ms. After each video presentation, as soon as the clip ended, a blank screen was shown during which participants were prompt to give their decision. They were instructed to categorize each movie clip as fast and as accurately as possible. The response keys were marked with tape placed directly on the azerty computer keyboard ("a" for social and "p" for personal). The response keys were counterbalanced across participants. No feedback was given during the experiment. Finally, the participants were required to complete the French version of the RME-test and the MIQ-R.
The existence of this area was predicted in a study of a subject with a number-reading deficit  11  and later by the triple-code model for number processing  12. It was argued that this area was not detected using fMRI due to signal dropout in the inferior temporal cortex  10  , although activation at or near this site can in fact be seen, in retrospect, in a contrast of Arabic versus verbal numerals  13  and in large-scale databases of calculation fMRI  14. A topographical dissociation between letter and digit recognition is also congruent with the occasional sparing of numerals in pure alexic patients  [15] [16] [17]  , and with the distinct hemispheric specialization in early visual processing for each of these categories  18. The aforementioned studies assigned the apparent cortical preference to specialized visual subprocesses.
Bottom: task types, sample stimuli and the correct answer for each case according to the task type and stimulus. Identical stimuli were used in all tasks. Numeral, Letter and Colour identification tasks during fMRI blood-oxygen-level-dependent (BOLD) signal acquisition (Fig. 1b ). We specifically used physically identical stimuli during all tasks to reveal task-specific activations and avoid possible biases due to the nature of the symbols used. Given the BOLD signal dropout in the temporal areas of investigation, we developed a method for preprocessing the data that exclude voxels with large signal dropout at the single-subject level (see Methods). We then examined the preferential brain activation when performing each task in comparison with the others. Furthermore, we acquired resting-state fMRI and analysed the functional connectivity of the VWFA and VNFA in the same group of blind subjects and a group of sighted subjects.
Indeed, an inspection of the data shows that the VWFA was activated versus rest under all three tasks (Numeral, Colour and Letter). Having seen the distinct cortical areas for number symbols (VNFA) and letters (VWFA), we now discuss how they might have emerged. Number symbols and letters are, arguably, highly similar in their physical features (consider for instance the symbol for zero and the letter O in English). It is only a cultural convention that makes them different. In fact, some cultures used the exact same symbol as a number with an assigned quantity and as a letter with an assigned phoneme, for example, in Roman script V is used to represent both the letter V and the number 5. The emergence of an area with preference for the task of deciphering number symbols, which is distinct from the area with preference for the task of deciphering letters, and yet located at a systematically reproducible localization across subjects (whether blind or sighted), cannot be explained by the visual features of such symbols.
Subjects were provided with earplugs to further reduce the scanner noise. The subjects were presented with the auditory instructions, the stimuli and the ding sound inside the scanner before the experiment, with all insulations in place. Behavioural data analysis. Single-subject behavioural results are reported in Supplementary Table  2. Wilcoxon rank-sum test was used for assessing whether the performance levels were above the chance level. There were always three possible answers and the chance level was thus 1/3. One-way ANOVA was used to assess whether performance differed across tasks. MRI acquisition. The BOLD fMRI measurements were performed in a wholebody 3T GE Signa scanner (GE Medical Systems, USA). The gradient-echo EPI pulse sequence was used. For the slow event-related experiment, we acquired 27 slices of 4.
5 mm thickness and 0 mm gap. The data in-plane matrix size were 64 Â 64, field of view (FOV) 220 mm Â 220 mm, time to repetition (TR) ¼ 1. 5 s, flip angle ¼ 70°and TE ¼ 35 ms. In all, 458 and 459 whole-brain images were collected during the two experiment sequences, respectively. The first 10 images of each scan were excluded from the analysis because of non-steady-state magnetization. Three runs were discarded due to technical difficulties with the sound system. For the resting-state run, we acquired 46 slices of 3 mm thickness and 0 mm gap. The data in-plane matrix size were 64 Â 64, FOV 240 mm Â 240 mm, TR ¼ 3 s, flip angle ¼ 90°and time to echo (TE) ¼ 30 ms. In all, 200 whole-brain images were collected during resting-state run. The first two images of each scan were excluded from the analysis because of non-steady-state magnetization, and the last 18 images were excluded to conform to previously acquired data.
Anatomy. Three-dimensional T1-weighted anatomical volumes were collected. 3D-Spoiled Gradient Recalled (SPGR) echo sequence was used with typical parameters of FOV 23 cm (RL) Â 23 cm (VD) Â 17 cm (AP), TR ¼ 9 ms, TE ¼ 4 ms, flip angle ¼ 13°, data matrix: 160 Â 160 Â 144 zero-filled to 256 in all directions, that is, B1 mm isovoxel native data. Data analysis. Data analysis was performed using the Brain Voyager QX 2. 6 software package (Brain Innovation, Maastricht, the Netherlands) and NeuroElf toolbox version 0. 9c (http://neuroelf. net/) for MATLAB (MathWorks, Natick, MA, USA). All fMRI data went through the standard preprocessing procedures that consisted of slice scan time correction, 3D motion correction and high-pass filtering using the GLM approach with Fourier basis set (cutoff: two cycles per scan).
BOLD intensity thresholding was performed to ensure that the voxels analysed are not influenced by signal dropout and do not contain attenuated signal, which could lead to false-positive results (detailed below). The data were then grouped using a GLM using a predictor for each task (Numeral, Colour and Letter; Fig. 1b ) in a hierarchical random-effects analysis  57. Serial correlations were corrected using a voxel-wise AR(2) model  58. In order to determine the whole-brain preferential activation for the Numeral identification and the Letter identification tasks, we used the Task-versus-others contrast in conjunction with the Task-versus-rest contrast. We refer to these conjunctions as the Task name contrast, for example, Numeral task contrast, throughout the text. We used the 'Minimum Statistic compared to the Conjunction Null' method  59  (MS/CN) therefore, refuting the null hypothesis for the conjunction infers a logical AND, which means that both comparisons are satisfied.
We then used the Numeral-versus-others contrast in conjunction (MS/CN  59  ) with the Numeral-versus-rest contrast for each of the three parts of the protocol, for example, 'Numeral instruction'-versus-'other instructions'. This enabled disentangling between the activation induced by each of the three parts of the events. For the resting-state analysis, ventricle and white-matter average time courses were calculated per subject and regressed out of the spontaneous BOLD data using MATLAB, followed by a bandpass filter of 0. 01-0. 1 Hz (frequencies in which spontaneous BOLD fluctuations occur  54, 62  ). Single subject data were spatially smoothed with a 6-mm 3D Gaussian in order to reduce intersubject anatomical variability. BOLD intensity thresholding was performed to ensure that the voxels analysed were not affected by signal dropout and did not contain attenuated signal, which could lead to false-positive results (detailed below).
For each of the original seeds, we defined its symmetrical seed by changing the sign of its x coordinate. We therefore performed the analysis on the Numeral seed and the Sym. Letter seed pair situated in the right hemisphere and on the Sym. Numeral seed and the Letter seed pair situated in the left hemisphere. Result representation. Average BOLD signal percent change plots are computed using the event-related averaging feature in BrainVoyager by point-by-point averaging of time course segments belonging to the same condition. The graphs generated reflect the average time courses for each condition across voxels belonging to a single ROI across subjects. A 3D surface reconstruction was performed on one set of anatomical images in Talairach space using the Brain Voyager QX 2. 6 software. The images went through inhomogeneity correction followed by the automatic white-matter greymatter segmentation functions embedded in BrainVoyager.
Acknowledgements This work was supported by a  European Research Council  grant (grant number  310809  to AA); The  James S. McDonnell   Foundation scholar award  (grant number  220020284  to AA); The  Edmond and Lily Safra Center for Brain Sciences (ELSC)   Vision center grant ; the  Maratier family and The Charitable Gatsby Foundation. We wish to thank  E. Striem-Amit  for acquiring the sighted resting-state fMRI data set,  D. Tauber  for coordinating the training programme and  L. Cohen  for the valuable input on the manuscript.
This study and the consent procedure for the children was approved by both the inspector of the academy of the French National Education Ministry and the Clermont-Ferrand Sud-Est VI Statutory Ethics Committee (CPP, Sud-Est 6, France) according to the articles of law L. 1121-1-1 and R 1121-3. The students received course credit in exchange for their participation. Material. The children were tested individually in a quiet room in their school. A computer controlled the experimental events and recorded data using E-prime (Psychology Software tools Inc. ). Responses were made on the D and K keys of the computer keyboard, with the sticker "Short" or "Long" being placed on the relevant keys. Response contingencies were counterbalanced across participants. Three stimuli were used, all composed of the same small rectangle and triangle.
To further examine our bisection results, we measured a BP and a WR for each experimental condition, by fitting a pseudo-logistic function to individual data (8 years, R 2 =. 87; 10 years, R 2 =. 93; adults, R 2 =. 95, all p <. 05). The flat bisection curves for the 5-year-olds were obviously excluded from these analyses. The fit was not significant for one 10-year-old whose results were also excluded from the subsequent statistical analyses. The ANOVA on the BP showed a significant main effect of digit, F(1, 56) = 10. 74, η2 =. 52, p =. 002, such that BP was lower for the number 9 than for the number 1 (Fig  4 ), which is consistent with a lengthening effect of large numbers. Surprisingly enough, the effect of digit also reached significance for the WR, F(1, 56) = 4. 47, η2 =. 07, p =. 04, suggesting a tendency for participants to be more variable in their time judgments for the high than low digit.
Acknowledgments The authors would like to thank  Mylène Bertrand ,  Gaëlle Allegre  and  Laetitia Bartomeuf  who collected data. We thank all the teachers and the directors from the  nursery schools and primary schools in Gerzat (Simone Godart school-Ms Guyot-Moreau and Ms Regnaud ) and in Clermont-Ferrand ( Jules Ferry school-Ms. Gadeau  and  Mr. Mantelet ;  Chanteranne school-Ms Sarrazin ).
Materials and Methods Longitudinal data came from a French population based birth cohort study (EDEN; N = 1311 mother-child pairs followed from the pregnancy onwards). Inattention-hyperactivity symptoms were assessed with the Strengths and Difficulties Questionnaire when participating children were 3 years of age. Potential risk factors were classified in four domains (fetal exposures and child somatic characteristics, child temperament, child neurodevelopmental status, psychosocial environment) and four periods (before pregnancy, prenatal/birth, infancy, toddlerhood). Their role as potential moderator or mediator was tested with path analysis to determine the developmental sequence.
Acknowledgments We acknowledge the members of the  EDEN Mother-Child Cohort Study Group  ( M. A. Charles ,  M. de Agostini ,  A. Forhan ,  B. Heude ,  P. Ducimetière ,  J. Botton ,  B de Lauzon-Guillain  ( UMR 1018 );  M. Kaminski ,  M. J. Saurel-Cubizolles ,  P. Dargent-Molina ,  X. Fritel ,  N. Lelong ,  L. Marchand ,  C. Nabet ,  A. Germa  ( INSERM U953 );  I. Annesi-Maesano  ( INSERM U707 );  R. Slama ,  J. Lepeule  ( INSERM U823 );  V. Goua ,  G. Magnin ,  R. Hankard  ( Poitiers University Hospital );  O. Thiebaugeorges ,  M. Schweitzer ,  B. Foliguet  ( Nancy University Hospital )) for the design and coordination of the cohort. We thank the  Mental Health Intervention Research Center (MHIRC) ,  NIMH  grant  MH030915  for providing us the Schematic software.
Method Participants Forty right-handed Spanish native speakers, (17 female; M = 23 years, SD = 0. 86), students at Universitat Pompeu Fabra (UPF), took part in the study for financial compensation. Participants were recruited from the database of the Centre for Brain and Cognition of UPF and from advertisements displayed on the Centre's Neuroscience Laboratory website (http://cbclab. upf. edu/?q=es/node/25). They all had normal hearing and no record of neurological diseases. Ethical approval was issued by UPF and informed written consent was obtained from all volunteers. Along with the written consent form, participants were also given to complete a detailed language experience questionnaire prior to the experiment to make sure that they fitted the profile of Spanish native speakers who were born and raised in monolingual Spanish families, had little knowledge of any other language, and used Spanish in their daily interactions.
All stimuli were recorded digitally into 16-bit stereo at 44,100 Hz and their noise removal and normalization were performed using the audio editor software Audacity 2. 0. 2. The manipulation of accent was between participants, because the present study used single words as experimental stimuli which allow less information of foreign-accented speech to unfold and hold on by its listeners. Hence, a within-participant design with single words might jeopardize our aim to obtain clear effects of native-biased or foreign-biased accented speech. We thus used four native speakers of standard Spanish (two males and two females; mean age 26 years) for the listeners of the native accent group, and four speakers of different nationalities (two males and two females: French, Dutch, Hungarian, and German; mean age 1 Ratings of Valence and Arousal are scored on a 9-point Likert scale: For valence 1 = negative, 5 = neutral, 9 = positive; for arousal: 1 = low arousal, 9 = high arousal.