dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedure The experiment took place online. Participants were recruited via email and directed to a web address. Though no control was established over listening environment, participants were instructed to carry out the experiment in a quiet room using either headphones or speakers attached to the computer. Furthermore, recent research exists that shows data from online experiments is comparable to data from lab-based experiments  [54, 55]. Each participant was pseudo-randomly assigned to one of ten rating scales taken from previous literature examining social traits in face, voice and person perception  [12, 28, 29, 31, 32] : Aggressiveness, Attractiveness, Competence, Confidence, Dominance, Femininity, Likeability, Masculinity, Trustworthiness and Warmth. Each participant rated only one trait, as opposed to numerous traits e.
Acoustical measures Acoustical measures were extracted from the 64 voice stimuli using PRAAT software (V4. 2. 07; default settings unless stipulated; http://www. praat. org) [58]. 8 measures were selected, in order to constrain multiple comparisons, that reflected differing parts of voice production and perception  [59, 60] , across the duration of each sound: 1) mean fundamental frequency/pitch (f0) (range: min 75 Hz; max: 600 Hz); 2) changing f0 (maxf0 minus minf0) as an index of intonation  [61] ; 3) glide, measured as f0-end minus f0start; 4) formant dispersion, representing filtration of the sound by the vocal tract and related to vocal tract size (measured as the ratio between consecutive formant means, from F1 to F4  [62]  using the Burg linear predictive coding algorithm installed in PRAAT  [63]  maximum formant frequency was set to 5.
In addition, trials including the two basic figures in either the original or mirrored version were aggregated. One mean RT of a single participant was missing due to the selection of the data and was thus replaced by the mean of the group in the corresponding condition. The mean accuracy was calculated as mean proportion of correct reactions to the aggregated trials. x y 6 An ANOVA with repeated measures on the within-subjects factors angle and condition revealed main effects of angle, (1. 97, 57. 14) = 98. 5 1 , 2 =. 28 2 , condition, (1. 39, 40. 33) = 127, ² =. 37, and an interaction between angle and condition, (3. 97, 115. 14) = 11. 6, ² =. 05, s <. 001 (see Figure  2 ). For the angles, a linear trend was highly significant, (1, 29) = 153. 68, <. 001, G ² =. 98 3 , implying a proportional increase of reaction times with larger rotation angles typical for mental rotation.
Statistical analyses For each simulation, we obtained a number of steps ( = number of days) that depended on three independent variables: the Nutrition/Sociality ratio, the percentage of social relations transfer and the number of individuals (N = 10 or N = 20 individuals). We used a generalised linear model (GzLM) for all the data in order to determine the influence of the independent variables on the dependent variable. The GzLM is used to study data with nonnormal distribution and followed in this study the Poisson law (used when variables are discrete). Data obtained from 900 simulations were used for this test. Once this was done, we examined the relation between the percentage of social relations transfer and the mean number of days before the observation of irreversible fission. A survival curve was used to establish the type of relation between these two variables (linear, logarithmic or exponential), then we carried out a Kruskal-Wallis test followed by Dunn's multiple comparison test to study the relation between the Nutrition/Sociality ratio and the number of days before irreversible fission.
Electrophysiology All electrophysiological experiments were performed on h-TREK-1/HEK cells seeded at a density of 20,000 cells/35 mm dish after 2-6 days of culture. All electrophysiological recordings were performed in whole cell configuration of the patch clamp technique except for I KS measures which were obtained by using the patch clamp perforated configuration (amphotericin B 0. 9 mg/mL in the pipette medium) (Moha ou  Maati et al. 2011b). Each current was evaluated by using a RK 400 patch clamp amplifier (Axon Instrument, USA), low-pass filtered at 3 kHz, and digitized at 10 kHz using a 12-bit analog-to-digital converter digidata (1322 series, Axon Instrument, USA). All current amplitudes are expressed in current densities. Results are expressed as mean±standard error of the mean (SEM).
Mouse locomotor activity To determine whether analog 3 induced a change in locomotor activity, mice (n=8 per group) were injected with the saline solution or analog 3 (10 -5 M in 100 μL bolus, i. v. ) 30 min before starting the test session. Locomotor activity was monitored individually for 24 h using an infrared photobeam activity monitoring system (Imetronic, Pessac, France), which measured consecutive horizontal beam breaks. Testing was in transparent plastic cages (43×20×20 cm 3 ) with fresh bedding in a grid of 8 cm horizontal infrared beams. Locomotor activity was defined as breaking of consecutive photobeams. Movements were recorded and totalized for each 10-min time section. Six periods were pooled to obtain data for 1 h of time. Different movements were monitored: the coming-and-going between the back and the front of the cage, climbing, and other movements in the back or the front of the cage.
Statistics Data were expressed as mean±SEM. Statistical analysis of differences between groups was performed by using Mann-Whitney test. In all analyses, the level of significance was set at p<0. 05 (*), p<0. 01 (**), and p<0. 001 (***). In the learned helplessness test, latencies to escape were recorded for each of the 30 trials. The average value was calculated for each of the five trials; thus, six blocks of values were obtained in addition to the overall average escape latency. A Mann-Whitney test was carried out on both overall latencies and blocks of trials.
Acknowledgments We thank  Anthony Rech  for his expert technical assistance in the preparation of injectable formulations. We thank  Delphine Debayle  for his expert technical assistance in the mass spectroscopy analysis. We thank  Layla Djillani  for carefully reading the manuscript. This work is supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  Agence Nationale de la Recherche-Emergence  ( ANR-EMMA-2011-059  and  ANR-13-RPIB-0002 ). J. Veyssiere was supported by a  CIFRE  fellowship. Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
DATA ANALYSES Participants were matched according to their age, sex, and level of education for all analyses in this study. Behavioral data (demographics and navigation performances) were analyzed using SPSS (SPSS, 2008) software. Neuroimaging data was analyzed with Statistical Parametric Mapping software (SPM8, 2008) and the VBM8 toolbox (http://dbm. neuro. uni-jena. de/vbm/).
Andra Smith contributed at the conception of the experimental neuroimaging design, ensured accuracy, and integrity in the neuroimaging data and approved the final version of the paper. Alain Labelle recruited schizophrenia patients, revised the work critically for important intellectual content and approved the final version of the paper. Véronique D. Bohbot contributed substantially to the conception or design of the work and hypotheses; helped with the acquisition parameters of the neuroimaging data, trained the first author on the creation of the neuroimaging paradigm, revised the work critically for important intellectual content, approved the final version to be published and ensured accuracy and integrity of navigation and neuroimaging work and made sure the work was appropriately investigated.
ACKNOWLEDGMENTS This study was funded by the  Canada Foundation for Innovation , the  University of Ottawa Medical Research Fund , the  Ontario Ministry of Development and Trade , the  Royal Ottawa Health Care Foundation  and  CIHR  fund number  64381  to  Véronique D. Bohbot. Andrée-Anne Ledoux  is supported by the  Canadian Institutes of Health Research , and the  University of Ottawa. We thank  François Rousseau Ph. D. ,  Catherine Smith M. A. , and  Guylaine Veillette.
This study included 95 individuals captured between 2006 and 2008  [31]. Dates of birth are known for most individuals within a range of a few days, and for those individuals without associated records, we estimated ages based on physical growth and development  [39]. Our sample includes mandibular and maxillary M1-M2 tooth casts of mandrills (n = 37 M1; n = 32 M2) and baboons (n = 94 M1; n = 94 M2). In the mandrill population, animals were darted using blowpipes and briefly immobilized (,30 sec) with a mix of two anaesthetics (400 mg ketamine for 500 mg of xylazine). Animals were then woken using atipamezole (Antisedan ND, 0. 5 mg/ml). In Amboseli baboons, we used similar methods but animals were anesthetized with Telazol  [31]. Morphological and physiological data were collected from individuals prior to obtaining high quality tooth molds.
Tooth wear analysis Occlusal digital photographs were taken and analyzed using ImageJ  [41]  to obtain the percent of dentine exposure (PDE) for each upper and lower M1-M2 molars (Figure  2 ) following previously established standard procedures  [31]. An average PDE (upper and lower) was obtained for each tooth and studied animal. When missing or broken tooth were found, PDE was based on the available molar (see Table  S1 ; see ref. [31]  also for raw data in baboons). Results from linear regressions between PDE and age were complemented with an overall multivariate analysis of variance (MANOVA) to detect changes with age for each molar tooth in both species and analysis of covariance (ANCOVA) to test for the homogeneity of slopes between species (mandrills vs. baboons), sex (female vs. male) and origin (wild vs.
Soil composition analysis Regarding the sediment analyses, we collected soil samples in both Amboseli (n = 9) and the Lekedi Park (n = 7) locations, in the area where the primate populations live year round. The samples were pulverized and the mineral composition was determined using X Ray Diffraction analysis (XRD) (Bragg-Brentano PANalytical X'Pert PRO MPD alpha1), following the standard Powder Diffraction File of the ICDD-JCPDS (International Centre for Diffraction Data -Joint Committee of Powder Diffraction Standards). All the analyses were done at the XRD Table  1. Quadratic (Q) and linear (L) regressions for predicted percent of dentine exposure (PDE) with age for M1 and M2. Unit of the Centres Cientı ´fics i Tecnologics at the University of Barcelona. Statistical differences in mineral composition (t-test) were performed in PAST  [42].
Acknowledgments Thanks to Sylvere Mboumba,  Romain Cassaigne ,  Benoit Quintard ,  Stephanie Bourgeois ,  Alix Ortega  and the field assistants of the 'Mandrillus Project' for particular assistance during the darting seasons. Regarding the research developed in Amboseli baboon's population, we acknowledge  Jeanne Altmann ,  Susan C. Alberts ,  Beth Archie  and  Jenny Tung , directors of the  Amboseli Baboon Research Project. We also thank the  Office of the President of the Republic of Kenya  and the  Kenya Wildlife Service  for permission to work in Amboseli over the years. We also thank the  Institute of Primate Research  for local sponsorship in Kenya, the  Wardens and staff of Amboseli National Park , and the pastoralist communities of Amboseli and Longido for continuous cooperation and assistance.
Particular thanks go to  Raphael Mututua ,  Serah Sayialel , and  Kinyua Warutere  for their contributions to all of the baboon's data presented in this paper, to  Mercy Akinyi  for particular assistance with the darting, and to  Lacey Maryott  and  Katherine Fenn  for their assistance with the Amboseli baboon database (BABASE). We thank  Xavier Alcobe  ´from the  XRD Unit of the Centres Cientı ´fics i Tecnologics at the Universitat de Barcelona , and we are also grateful to  Nuria Garriga (Universitat de Barcelona ) and two anonymous reviewers who provided helpful comments on the manuscript. Finally we thank  Kate McGrath  (The  George Washington University ) for the English corrections. This is 'Mandrillus Project' publication number: 1.
Methods Ethics Statement. These experiments have been approved by the Pitie-Salpetriere ethical committee. All the 65 subjects gave their written informed consents, and were paid 10 Euros to participate in the experiment. All investigations were conducted according to the principles expressed in the Declaration of Helsinki. Participants. Twenty-six subjects were included in the 'Reminder' group (17 women; age M = 25. 3 years old, SD = 5. 6; 92% right-handed), and 25 in the 'No reminder' group (16 women, age M = 26. 4, SD = 5. 2; 96% right-handed). They reported normal, or corrected-to-normal, visual acuity. Data from three participants were excluded (2 from the 'Reminder' group, and 1 from the 'No reminder'). One dataset was not saved due to technical failure. The remaining 2 excluded participants essentially used the highest rating values within the 8-values scale in rating 1 (rating 1 median = 8), with a ceiling effect biasing the possible rating changes only to decreased values.
Results We first replicated results reported by Izuma et al (  [16]  -see Figure  2a  and 2b ). Indeed, within the 'Reminder' group which is a direct adaptation of Izuma et al. 's procedure to our vacation destinations dataset, preference for destinations which were rejected in the Self-Difficult condition significantly decreased compared with rejected destinations in the Self-Easy condition (t(23) = 9. 67, p,10 28 ) or those rejected in the Computer condition (t(23) = 4. 14, p,0. 001). These comparisons were used before Chen and Risen's criticism to assess the existence of preference change, but they do not allow controlling for the statistical artefact highlighted by Chen and Risen  [18]. Importantly, in this group, we also observed a significant difference between the critical condition and the proper control condition: the spread was significantly larger for the Self-difficult/RCR sequence than for the PostEx-Choice/RRC sequence (t(23) = 5.
Methods Participants. Thirty nine subjects (22 women; age M = 22. 9 years old, SD = 3. 1; 95% right-handed) participated in this study (see Ethics Statement above). All reported normal or corrected-tonormal visual acuity. The data from 3 participants were excluded from the analysis. These subjects were removed because their responses were shifted toward scale's extremities ('Rating 1' median = 8 for two of them and 1 for the other). Stimuli. Stimuli were the same images of vacation destinations as in the experiment 1. In the n-back task, stimuli were capital letters (size 18) presented at fixation for 1 second. Procedure. Procedure was similar to the one used in the first experiment with four major differences (see Figure  3a ). First, subjects' responses were not limited in time. Second, items were coupled by sorting them according to their first rating resulting in 60 pairs of similarly rated items.
Indeed, as opposed to a classical ANOVA, such a model does not require prior averaging of the spread for each subject and thus offer the possibility to handle the heteroskedasticity related to the unbalanced number of items in each condition  [28]. Significance of the fixed effects was assessed using the Kenward-Roger approximation for degrees of freedom of the denominator, with the 'lmerTest' package in R. A main effect of memory was found The interaction between the sequence (RCR -Self-difficult/RRC -PostEx-Choice) is significant (* p, 0. 05, ANOVA) and the critical comparison between RCR -Self-difficult and RRC -PostEx-Choice conditions is significant in both groups (* p,0. 05, *** p,0. 001, paired t test, two tailed). (d) Effect of memory on spreading of alternatives. The difference between the spread in the RCR -Self-difficult and the RRC -PostEx-Choice was significant only in pairs for which the choice was remembered (*** p,0.
Acknowledgements Sanabria obtained ethical approval for her research from the  University of Cambridge  and the  Comite de Etica em Pesquisa of the Instituto de Saude Coletiva, UFBA. Her work was supported by the  British Economic and Social Research Council  under Grant  PTA 03020033301669. Edmonds obtained ethical approval from  Princeton University  and received funding from  Princeton , the  Social Science Research Council , and the  Woodrow Wilson Foundation. The authors would like to thank  Eileen Moyer  and  Vinh-Kim Nguyen  who organized the  Beyond Biosocialities in Medical Anthropology conference at the University of Amsterdam in January 2013  for which this paper was developed, as well as  Marian Burchardt ,  Carlo Caduff  and the conference.
Image preprocessing UPenn Natural Image Database A database of images was collected in the Okavango Delta, a savannah habitat of Botswana  (Tkačik et al. , 2011). Panoramic, eye-level shots were taken with a Nikon D70 camera during the dry season in midday illumination. Trichromatic images were then converted to equivalent log-luminance images. From this database, we selected a set of 924 images with minimal amounts of sky (see following paragraph).
Image selection Natural images were taken from two different databases: the UPenn Natural Image Database (shown Figures  1  and 3 ) and the van Hateren Natural Image Dataset (shown in 'Materials and methods', Comparison with van Hateren Database). Images from the UPenn Natural Image Database were selected by hand to ensure that they contained no man-made objects. We required that images contained minimal (less that one-third of the total image area) amounts of sky, as the contribution of sky to the overall power spectrum of natural images is well-documented  (Torralba and Oliva, 2003)  and is not the focus of the present study. Images from the van Hateren Natural Image Dataset were chosen subject to the additional constraint that scenery which was clearly the result of human landscaping (e.
g. trees all in a line) be excluded. The analyses presented here were performed using the logarithms of the pixel intensities, a standard procedure in the study of natural images  (Ruderman and Bialek, 1994). However, the results were unchanged if absolute pixel intensities were used instead. For more details about the construction of the images from these sources, see  (Tkačik et al. , 2011)  (UPenn dataset) and http://www. kyb. tuebingen. mpg. de/?id=227 (van Hateren dataset).
Block averaging Images of size L 1 × L 2 are block-averaged by a factor of N, which involves averaging the intensities of pixels arranged into contiguous N × N squares. The resulting image is of size L 1 /N × L 2 /N. To the extent that natural images are scale invariant (a well-supported hypothesis  (Field, 1987; Ruderman, 1997; Stephens et al. , 2013) ), this procedure leaves the underlying statistics invariant. In our analyses, we block average images by at least a factor of two (thereby eliminating the Nyquist frequencies) in order to avoid sampling artifacts imposed by the camera matrix during image acquisition. In Figures  1  and 3 , we presented two values of N: N = 2, 4. In 'Materials and methods', Analysis variants for Penn Natural Image Database, we show that our results are consistent when N is extended to include N = 8, 12, 16, 20.
Fourier whitening We divide each block-averaged image into square R × R patches. In Figures  1  and 3 , we presented results using three values of R: R = 32, 48, 64. In 'Materials and methods', Analysis variants for Penn Natural Image Database, we show that our results are consistent when R is extended to include R = 80, 128. To remove global correlations in natural images, we whiten the set of image patches by flattening the Fourier power spectrum of the image patch ensemble. This procedure removes expected ensemble-average (and thus predictable) pairwise correlations, but non-zero pairwise correlations may still exist within individual patches; such correlations are the subject of this study. To carry out this procedure, the whitening filter is the inverse square-root of the ensemble-averaged Fourier power spectrum.
Removal of blurry images In any image database, there will be blurring due to camera motion and focus artifacts. Because we are interested in the statistics computed from in-focus image patches, we use a mixture of components (MOC) method to separate blurred from in-focus image patches. To perform this separation, we first examined the 9-dimensional distribution of natural image statistics (see Figure  1 -figure supplement 1A for the projection of the distribution onto the (α, β -) plane). When projected onto various coordinate planes, the structure of the distribution suggested that the distribution could be well-described by a weighted sum of two components. We explored this two-component description by running a standard maximum likelihood MOC inference that described each component by a Gaussian distribution.
Subjects Four normal subjects (2 male, 2 female), ages 23 to 54 participated. One subject (MC) was a very experienced observer (several thousand hours); the other three had modest viewing experience (10-100 hr) prior to the experiment. JD and DF were naive to the purposes of the experiment. All subjects had visual acuities (corrected if necessary) of 20/20 or better. For subjects MC and DT, data from all coordinate planes other than the ( ) \ , β α -plane were previously reported  (Victor and Conte, 2012; Victor et al. , 2013). For subjects JD and DF, data from the seven pairs of coordinates not containing α were previously reported  (Victor et al. , 2013).
Determination of sensitivity To summarize the psychophysical performance, we fit Weibull functions to the fraction correct (FC) for each subject and each kind of block (i. e. , each pair of coordinates). In the first step of the analysis of each dataset, maximum-likelihood fits were obtained separately for each of its eight rays r (the rays consisted of the positive and negative values for the two coordinates, and the four diagonal directions): ( ) ( ) ( ) -/ 1 3 FC = + 1-2 , 4 4 b r r x a x (0. 1) where x is the Euclidean distance from the coordinate vector to the origin, a r is the distance at which FC = 0. 625 (halfway between chance and perfect), and r b is a shape parameter, controlling the slope of the psychophysical curve. Since the shape parameter b r was usually in the range 2. 2-2.
7 for each pairwise coordinate plane, we then fit the entire dataset within each plane by a set of Weibull functions constrained to share a common exponent b, but allowing the parameter a r to vary across rays. For each on-axis ray, we averaged the value of 1/ r a obtained from all planes that included the ray (these were mutually consistent  (Victor et al. , 2013) ) to obtain a final value for the perceptual sensitivity.
Alignment of principal components As described in the previous subsection, we use principal component analysis for the multivariate comparison of natural image statistics and perceptual sensitivities. In addition to the standard approach of ordering components by percentage of variance explained within each dataset, followed by comparing components of corresponding rank, we use an additional tool: the symmetries in the definitions of the image statistic coordinates. As detailed below, we use these symmetries to group principal components into symmetry classes, and we then rank-order the components within each class. By matching components based on both symmetry and rank order of explained variance, we avoid ambiguities that would otherwise occur if only explained variance was considered. The four symmetry classes are defined as follows: 1.