dataesr/scientific-paragraphs-categorization huggingface dataset:
Apparatus and calibration Oculomotor data for the left and right eye were measured dynamically (200 Hz), using a head-fixed (i. e. , head mounted) infrared video eye tracker (Chronos Vision, Berlin); a chin rest was used to stabilize the children's head. The Chronos eye tracking system records digital image sequences and evaluates offline eye position variations with a reported resolution of less than 0. 1 degrees. Before each reading block, a standard saccadic paradigm was used to elicit visually guided saccades: a target (two segments of 0. 9 × 0. 7 degrees aligned with offsets of 0. 1 degrees vertically and 0. 7 degrees horizontally) jumped between five positions on the screen (at the center and at ±8 degrees horizontally and vertically). The subject was asked to follow accurately the center of the target (at the offset space).
Data analysis Calibration and analysis methods were similar to those used in prior studies  (Vernet and Kapoula, 2009; Jainta and Kapoula, 2011). Briefly a linear function was used to convert eye position signals into degrees. From the separate signals of the two eyes we calculated the conjugate eye movements [(left eye + right eye) / 2; i. e. , the version signal] and the disconjugate eye movements [left eye -right eye; i. e. , the vergence signal]. The onset or offset of horizontal saccades were defined as the time, when the eye velocity of the conjugate signal exceeded or dropped below, respectively 10% of the maximum velocity. We extracted several parameters from the eye movement signals. For each detected saccade, we calculated its amplitude as difference in the version signal between the ending of the saccade (E) and its beginning (B), (markers E and B, in figure  1.
Time analysis In order to account for eventual effects of repetition, we used a linear mixed-effects model [lmer from package lme4  (Pinheiro and Bates, 2000; Venables and Smith, 2001)  in R (http://www. r-project. org)]. Generally linear mixed-effects models are based on maximum likelihood methods, and commonly used in many areas  (Baayen et al. , 2008). The statistical package R provides reliable algorithms for mixed effect parameter estimations, as well as tools for their evaluation  (West et al. , 2007) : the p-values and confidence intervals were estimated by using posterior distributions for the model parameters obtained by Markov Chain Monte Carlo sampling, including typically a sample size of 10 000 (see  Baayen et al. , 2008). In our study separate estimations were made for each of the following variables: saccade disconjugacy, disconjugate post-saccadic drift and fixation duration.
The skin was cut, the temporal muscle retracted, and a craniotomy performed above the auditory cortex and the dura mater was opened. Anatomic landmarks, in particular the suprasylvian sulcus (SSS), the anterior and posterior ectosylvian sulcus (AES and PES) were used to guide the injections of tracers in the different areas of interest. Because of the variability in the cerebral cat sulci of the cat  [28] , the success in injecting correctly the dye in A1 or DZ was verified by analyzing the pattern of retrogradely labeled cells in the thalamic nuclei with respect to the published data  [29]. During surgery, the brain was photographed for documentation of the injections positions. After completion of injections an artificial dura was positioned over the exposed cortex, the bone of the skull was put back and then covered by dental acrylic.
Data analysis Sections were analyzed using light or fluorescent microscopy with a Leica microscope (DMR) equipped with a CCD camera. Each fluorescent dye presents a unique fluorochrome that under a specific excitation wavelength results in a specific emission wavelength that can be visualized with a specific filter. The characteristics of observation for each dye are: FB emission: 420nm, filter A Leica; DY emission: 390 nm, filter D Leica; FR emission: 580nm, filter L5 Leica; FE emission: 517 nm, filter N3 Leica. Labeled neurons were counted based on the identification of a nucleus. For each cortical area the exact position of labeled neurons was computed on individual sections observed at regular intervals using Mercator software (Explora Nova). Sampling frequencies of analyzed sections were adjusted to the size of the areas containing labeled neurons (defined as the projection zone  [37] ).
Funding was provided by the the  ANR Hearing Loss  ( ANR-06-Neuro-021-04 ) to LL and PB, la  Federation pour la Recherche sur le Cerveau  (to BP), the  BQR ATUPS program  of the UniversiteP. Sabatier (to LL), the recurrent funding of the  CNRS (BP)  and  Deutsche Forschungsgemeinschaft (Kr 3370  and cluster of excellence Hearing4all to AK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
However, host specificity in this study (Table  1 ) was evaluated at global level; that is, the data on host range of each analyzed Thaparocleidus species were retrieved from published studies  [28, [33] [34] [35] [36] [37]. Thus, parasite species were separated into two categories: specialist parasitizing a single host species and generalist parasitizing at least two different host species  [38]. The geographical distribution of all fish species collected for this study was limited to Indonesia except for Pangasius micronema, which is distributed widely in South East Asia. The localities of the collected fish are given in Table  1. 2. 2. DNA Extraction, Amplification, and Sequencing. DNA extraction was performed by the chelex method. Each monogenean specimen was disrupted in a 5% chelex solution with proteinase K (0.
(HG491495) from the schilbeid catfish Schilbe intermedius and Quadriancanthus sp. (HG491496) from the airbreathing clariid Heterobranchus bidorsalis. As mentioned above, Thaparocleidus is not monophyletic; that is, Thaparocleidus parasitizing Siluridae and Thaparocleidus parasitizing Pangasiidae comprise different clades. Therefore, Wu et al. [27]  proposed taxonomic revision of the species recently included in Thaparocleidus. Hence, T. siluri and T. vistulensis may be included as outgroup taxa. Subsequent phylogenetic analyses were performed using a concatenated dataset of partial 18S rDNA and ITS1 including only sequences of Thaparocleidus species parasitizing Pangasiidae. Intraspecific variability was explored using uncorrected p-distances (i. e. , calculating the proportions of different nucleotide sites).
Phylogenetic analyses using minimum evolution (ME), maximum parsimony (MP), and maximum likelihood (ML) were performed in PAUP * 4b10  [43]. The Bayesian analyses were conducted using MrBayes 3. 1  [44]. ModelTest  [45]  was used to select the optimal evolutionary model for each dataset, based on hierarchical likelihood ratio tests. The selected model was applied in the ME, ML, and BI tree reconstructions. ME analyses were performed using a heuristic search with a distance optimality criterion  [46]. The search for the best ML tree was done via a heuristic search using the tree bisection reconnection branch-swapping algorithm (TBR). MP analyses were performed using a heuristic search algorithm with a stepwise random addition sequence running on unweighted informative characters and TBR branch swapping.
The degree of "tree-likeness" in data under a parsimony model for character change was measured by consistency index (CI) and retention index (RI). Sequence alignment and trees were deposited to TreeBase, a database of phylogenetic knowledge, and are available at http://purl. org/phylo/treebase/phylows/study/TB2:S14752. Support values for internal nodes were estimated using a bootstrap resampling procedure with 1000 replicates  [47]. BI analyses were performed using four Monte Carlo Markov Chains (MCMC) running on a given number of generations (starting at 10,000 and increasing until standard deviation was below 0. 01), with trees being sampled every 100 generations. Log likelihoods of the saved trees were graphically inspected, and all trees before stationary were discarded as "burn-in. " Two replicates were conducted for the Bayesian MCMC runs.
Parasite Morphometry. A total of 9 specialist species and 6 generalist species sampled in this study and identified on the basis of morphology were measured (morphometric data for T. citreum were not available; see Table  1 ). Morphometric measurements were taken from 10 individuals from each parasite species. 20 variables describing the haptor were taken into account for statistical analyses. The variables used are represented in Figure  1. The Mahalanobis distances between species were calculated for haptor morphometrics. Phylogenetic distances expressed as patristic distances were calculated between species in PAUP * 4b10  [43]. The existence of a correlation between morphometric distances and phylogenetic distances was tested using a Mantel test. A significant correlation indicated phylogenetic inertia.
Phylogenetic Analyses Using Combined Data 18S rDNA and ITS1. The congruence of 18S and ITS1 data sets was tested using the partition homogeneity test implemented in PAUP * 4b10  [43]. No significant difference was found between 18S rDNA and ITS1 ( = 0. 084). Therefore, the next analyses were performed using the concatenated dataset, including 21 sequences of Thaparocleidus parasites of Pangasiidae. The phylogenetic trees were oriented using the results obtained from the 18S rDNA analyses. The sequence alignment was comprised of 666 unambiguously alignable positions (including 36 positions with gaps), of which 232 were variable and 172 were parsimony informative. The TVM+I+G model was selected by ModelTest including equal frequencies of nucleotide bases, with a proportion of invariable sites pi = 0.
PARTICIPANTS The study was part of a broader research project which was approved by the Ethics Commission of the Medical Faculty of the University of Leipzig under the code 360-10-13122010. 94 right-handed individuals were recruited from the database of the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (Mean age = 29 years, Age range 19-38 years, 49 females). All of them were native German speakers, had normal or corrected-to-normal vision, no history of psychiatric or neurological conditions, and no history of substance abuse. Ten participants were subsequently excluded from the analysis due to extreme scores on the CRT, the WM, or the MEPS [scores were considered as extreme when higher than (Q3 + 1. 5 × IQR) or lower than (Q1 -1. 5 × IQR), with Q1 and Q3 the first and third quartiles, and IQR being the Interquartile Range].
EXPERIMENTAL SESSION The experimental session lasted 2 h and was divided into three blocks (block order counterbalanced across subjects). Participants were allowed to take short breaks between the blocks if desired. One block consisted of performing the cognitive tasks; another block consisted of performing the MEPS. A number of other tasks were measured during the third block and results have been previously published  (Smallwood et al. , 2013). All participants gave written consent before the beginning of the experiment and were remunerated at least 16 C for their participation (8 C per hour of participation plus an additional reward according to their performance during a temporal discounting task). E-prime 2 was used for stimulus presentation  (Schneider et al. , 2002). All statistical analyses were performed using R and results were plotted using the ggplot2 package  (Wickham, 2009; R Core Team, 2012).
Link between MEPS and CRT/WM performance In addition to the link between SGT and social problem solving, our data also allowed us to look at the relationship between the MEPS and cognitive task performance i. e. , whether efficient problem solvers were also good at performing the CRT and WM tasks. Pearson's correlations between MEPS measures and task performance are reported in Table  2. We computed a measure of task performance, by averaging Error Rate and RT (previously z-scored) and reversing the score. This was calculated separately for the CRT and WM task. We computed a repeated-measures 2way ANOVA (Task: CRT, WM) predicting task performance with Efficiency and RM as covariates. This analysis revealed a trend for a triple interaction (Task × Efficiency × RM, F = 3. 52, p = 0. 06) but no significant main effects (Efficiency, F = 0.
g. , "Do certain individuals exhibit different patterns of thoughts in different contexts?"). Given the novelty of our data analysis strategy, it is worth explicitly considering its strengths and weaknesses. One practical question is whether our approach yields results that are either unreliable or inconsistent with what is known about SGT: this would provide straightforward evidence that this approach lacks utility. In terms of replicability, the PCA components we obtained in the current study have a striking resemblance to previously published SGT components obtained from an independent dataset  (Ruby et al. , 2013). This consistency across datasets suggests that the components obtained from an unconstrained trial-level PCA are reasonably replicable. Our work using the trial-by-trial PCA also found results consistent with previous findings in the SGT literature.
Material and Procedure Six stories containing an emotional utterance produced by one of two story characters were constructed. The stories were computerized using E-Prime software to combine drawings and sounds (see Figure  1 ). The first drawing provided the setting in which the two characters, i. e. Pilou the bunny and Edouard the duck would interact. When the drawing was displayed, an offscreen narrator's voice was heard describing the situational context. In all the stories, this situational context was neutral. The emotional valence of the situations was pre-tested by a sample of 28 adults, who had to judge the valence of 18 situational contexts on a 5-point scale ranging from 1 (totally negative) to 5 (totally positive). The judges read the description of each situation, which was the same as that which was then spoken by the off-screen narrator in the experiment.
Judgment Task The dependent variable was the child's response, which was scored "1" if correct ("Pilou's feeling happy" when the prosody was happy; "Pilou's feeling sad" when the prosody was sad) and "0" otherwise. Analyses of the participants' judgments of the speaker's emotional state were conducted using SPSS software (version 20. 0) and a logistic mixed model [see  48, 49] , with participants as the random intercept, and age, valence, and the age-by-valence interaction as fixed factors. The analyses were conducted on the correct responses. The final model included only significant effects. The valence effect and the age-by-valence interaction were not significant. The final model included only one fixed factor since age predicted correct responses, F(3, 476) = 16. 95, p <. 001. The odds of correct responses at age 9 were used as the baseline values because the so-called lexical and contextual biases decline around this age  [4, 23].
4% overall), these were categorized along with the "other explanations" in order to simplify the interpretation to the results. Analyses of the participants' explanations were conducted using SPSS software (version 20. 0) and a multinomial logistic mixed model with participants as the random intercept, and age, response (correct or not), and the age-by-response interaction as fixed factors. The "prosody-based explanations" were chosen as the reference category and 9 years as the baseline age. The response effect and the age-by-response interaction were nonsignificant. The final model included only age, which predicted the type of explanation given by the participants, F(6, 472) = 14. 22, p <. 001. An examination of Figure  2  shows that the odds for giving context-based explanations compared to prosody-based explanations were lower in the 13-year-olds and adults than in the 9-year-olds (respectively, OR = 0.
Their head was restrained and equipped with MRI-compatible headphones customized for monkeys (MR Confon). A radial receiveonly surface coil (10 cm diameter) was positioned above the head. Eye position was monitored at 120 Hz during scanning using a pupil-corneal reflection tracking system (Iscan). Monkeys were rewarded with liquid dispensed by a computer-controlled reward delivery system (Crist) thanks to a plastic tube coming to their mouth. The task, all the behavioral parameters, and the sensory stimulations were monitored by two computers running with Matlab and Presentation. Visual stimulations were projected onto the screen with a Canon XEED SX60 projector. Auditory stimulations were dispensed with an MR Confon. Tactile stimulations were delivered through Teflon tubing and 6 articulated plastic arms connected to distant air pressure electro-valves.
5 years). Scrambled stimulations were obtained by cutting the movement sounds in 100 ms or 300 ms segments and randomly mixing them. Static stimulations consisted of auditory stimuli evoking a stable stimulus in space (selected from the same 3D holographic sounds database and evaluated by the same group of subjects as the coherent movement auditory sounds). In M2, we also used pure-tones auditory stimulations (generated in Goldwave, at 300, 500, and 800 Hz). Coherent movement stimulations consisted of two kinds of movement: (1) 2. 0 s long sounds simulating far away to near movements (linear increase in signal amplitude ϩ Doppler effect) or the opposite (linear decrease in sound amplitude); and (2) sound moving between the left and the right ear (3. 5 s stimuli, corresponding to two back-and-forth cycles, thanks to opposite amplitude variations between the two binaural sounds and a 1 Hz stimulus frequency binaural offset).
Analysis The analyzed runs were selected based on the quality of the monkeys' fixation (Ͼ85% within the tolerance window): a total of 23 (25) runs were selected for visual stimulations in M1 (M2), 20 (32) for tactile stimulations, 26 (32) for complex auditory stimulations (and 34 for pure-tones auditory stimulations in M2) and 20 (24) for the retinotopy localizer. Time series were analyzed using SPM8 (Wellcome Department of Cognitive Neurology, London, United Kingdom). For spatial preprocessing, functional volumes were first realigned and rigidly coregistered with the anatomy of each individual monkey (T1-weighted MPRAGE 3D 0. 6ϫ0. 6ϫ0. 6 mm or 0. 5ϫ0. 5ϫ0. 5 mm voxel acquired at 1. 5T) in stereotactic space. The JIP program  (Mandeville et al. , 2011)  was used to perform a nonrigid coregistration (warping) of a mean functional image onto the individual anatomies.
To evaluate the robustness of the VAT conjunction analysis, we performed test-retest analyses (Fig. 6B ). ODD and EVEN groups of runs were selected independently from the visual, tactile, and auditory runs. PSC were calculated for the relevant contrasts (coherent movement vs scrambled and coherent movement vs static for the visual modality, center of the face vs fixation for the tactile modality, and coherent movement vs static for the auditory modality), for each of the ROIs identified by the VAT conjunction analysis (2 ROIs per hemisphere in monkey M1 and 1 ROI per hemisphere in monkey M2), on all the runs, on the ODD test-retest dataset and on the EVEN test-retest dataset. All PSC changes are significant at p Ͻ 0. 05 or at p Ͻ 0. 001, except for the auditory contrasts calculated in the left ROI of monkey M2, for which PSC reaches significance for all the runs together, are close to significance for the EVEN test-retest dataset ( p ϭ 0.
07) but not significant for the ODD test-retest dataset. In the following section, we address the fact that, in monkey M1, two multimodal regions are observed in the fundus of the IPS, whereas only one multimodal region was found in monkey M2. First, we tested the robustness of the spatial disjunction between these two VAT regions in monkey M1. To this end, we defined two cubic ROIs of 8 contiguous voxels lying in the fundus of the right and left IPS, respectively, just between the two multimodal regions of each hemisphere. Percentage signal changes remained far from significance for both ROIs, for all tested contrasts (tactile [center of the face vs fixation], visual [coherent movement vs scrambled], visual [coherent movement vs static], auditory [coherent movement vs static]), on all data as well as on a test-retest analysis considering first the ODD runs of each modality then the EVEN runs of each modality.
For each sensory modality and each contrast, PSC of the whole dataset (full colored bars) and even/odd subsets (135°/45°hatched colored bars) are presented. t tests were performed on the PSC for each condition (**p Ͻ 0. 001; *p Ͻ 0. 05). the reported physiological properties of area AIP  (Sakata and Taira, 1994; Murata et al. , 2000) , it appears that the IPS cytoarchitectonic organization of monkey M1 is not perfectly captured by its coregistration onto the F6 atlas. As a result, we think that this anterior ROI actually belongs to VIP. Corroborating this fact, this ROI never extends anteriorly to the anterior border of area LIP in either hemisphere (as defined by the eye movement activation anterior limit, p Ͻ 0. 05, FWE-corrected level). This is in agreement with most electrophysiological and anatomical studies that describe the anterior border of VIP as matching that of LIP  (Sakata and Taira, 1994; Luppino et al.
Acknowledgments We thank the  Hannan Jingang Laboratory Animal Corporation (China)  for hosting the observer (SC), animal care and general technical assistance,  Agnes Aubert  and  Sophie Laye(Nutrineuro UMR INRA 1286, Bordeaux, France)  for lending us the Luminex instruments and helping us with the ACTH immunoassays,  Steve McGuire  ( Motac Neuroscience Ltd, Manchester, UK ) for performing the HPLC analyses, and  Evelyne Doudnikoff  ( IMN UMR 5293, Bordeaux, France ) for performing the ACTH and cortisol immunoassays. We thank  Pr Crossman  ( University of Manchester, Manchester, UK ) for proofreading our manuscript before submission.
This work was supported by  ANR-08-MNP-018  MCHPRIMAPARK  Agence Nationale de la Recherche grants (EB) ,  Biotheque Primate -Centre National de la Recherche Scientifique Life Sciences Department (EB) , and an unrestricted grant from  Motac Neuroscience Ltd. (UK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study received an unrestricted grant from  Motac Neuroscience Ltd. QL  and.
Methods Corpus analysis. One assumption underlying our hypothesis is that the sentential position of frequent functors provides a reliable cue to word order in the various OV languages our bilingual participants were exposed to. To test this assumption, we examined infant-directed corpora in Farsi and Turkish, as such measurements already exist in Japanese  6  , and infant-directed corpora are not available for Korean or Hindi/Punjabi. The corpora were taken from the CHILDES database  26  for Farsi  27  and Turkish  28. For both languages, we extracted the infant-directed adult utterances from the corpora. As one-word utterances are not informative about word order, we discarded these, and used only multiword utterances for our analysis. With this manipulation, we obtained corpora of 10,495 utterances in Turkish and 7,180 utterances in Farsi.
The measures for the vowels were normalized to the respective measures of their carrier sentences to correct for variation in speech rate, fundamental frequency and so on across speakers and languages. The normalized data were entered into t-tests (corrected for multiple comparison) to compare the relative acoustic properties (of the stressed vowels) of the noun and the functor (Fig. 2 ). Participants. Twenty-four (8 females) 7-month-old (mean age: 7. 5 months, range: 6. 5-9 months) bilingual infants participated in Experiment 1. They received at least 25% exposure to each language (mean: 51% English, 49% OV language) according to the Language Exposure Questionnaire  30. The number of infants exposed to each specific OV language is as follows: 12 Hindi/Punjabi, 7 Japanese, 3 Korean, 1 Turkish, 1 Farsi.