dataesr/scientific-paragraphs-categorization huggingface dataset:
A psychometric function was fitted to each participant's data to obtain estimates of the underlying judgement noise during congruent and incongruent discriminations. The proportion of 'same orientation' responses was calculated for each degree of orientation difference. The data was then fitted with a model constructed from the differences of two cumulative Gaussian probability distributions  33, 34. This model was built for fitting data from simultaneity judgement tasks, in which participants judge whether two stimuli were simultaneously presented across a range of temporal delays. However, the model also applies to our task, in which participants judged whether two stimuli were tilted by the same angle across a range of orientation differences. Across all of the stimulus orientation differences the window of judged similarity is defined as the difference between a lower and upper decision boundary.
Behavioural data analysis. Separate ANOVAs with grasp congruency (congruent/incongruent) as a within-subject factor were used to analyse sensitivity values (d'), estimated of judgement noise and reaction times of the grating discrimination. For reaction time data, 3. 65% (SD = 0. 03) of trials were rejected for either having reaction times that fell outside ± 2. 5 SD of the mean or because they corresponded to incorrect grasping. EEG recording, pre-processing and analysis. EEG was recorded using a BioSemi ActiveTwo amplifier from 64 Ag-AgCl electrodes placed according to the extended 10-20 system and sampled at 1024 Hz; EEG signals were later down-sampled offline to 512 Hz. The electrodes were referenced to the average of electrodes placed on the left and right earlobes. The vertical and horizontal EOGs were recorded in bipolar fashion in order to monitor vertical (i.
e. blinks) and horizontal (i. e. saccades) eye movements, respectively. Offline pre-processing and analysis of the EEG data was conducted using a combination of EEGLAB version 13. 4. 4b  38 and FieldTrip toolboxes  39. For the analysis of the grating stimulus evoked potentials, continuous EEG data were divided into 700 ms epochs locked to the onset of the stimulus including a 100 ms pre-stimulus baseline. Epochs including voltages exceeding + and/or -100μV, well as epochs with discrimination or grasping errors, were automatically rejected prior to analysis. Eye-blink artefacts were corrected for using Independent Component Analysis. The mean amplitudes of ERP components within pre-defined time windows were extracted for analysis. The mean amplitude between 70 and 110 ms post probe onset was extracted as the P1 mean amplitude  40.
The mean of amplitudes between 130-170 ms post probe onset was extracted as the N1 mean amplitude  40. Mean amplitudes were extracted from electrode sites PO7 and PO8, which elicited the largest ERPs as observed in scalp maps of averages over all conditions. The difference between the mean P1 and N1 values was computed to obtain a mean peak-to-peak amplitude measure of the N1 component. For the ERP analysis, the mean peak-to-peak amplitudes of the N1 component were analysed in a 2 × 2 repeated measures ANOVA with factors of grasp congruency (congruent/incongruent) and electrode hemisphere (PO7/PO8). For the time-frequency analysis of beta power (13-30 Hz) following the grating stimuli, continuous EEG data were divided into 1000 ms epochs including a 300 ms pre-stimulus baseline. Time frequency representations of individual trials were then calculated using Morlet wavelet analysis with a wavelet width that linearly increased from 3 to 8 with the frequency range.
The following steps were taken to identify significant clusters: 1) dependent samples t-statistics comparing grasp congruent and incongruent data were gathered for each of the samples in the multidimensional data structure; 2) t-statistics above a p-value threshold (p < 0. 05) were then gathered; 3) Neighbouring data points exceeding the threshold were found; 4) The t-statistics were summed to calculate the cluster level statistic; 5) The maximum cluster statistic under its permutation distribution (shuffled data), derived from the test statistics obtained from the dependent samples t-tests based on 1000 random permutations, was evaluated. The cluster-level significance threshold was set at the two-tailed level of 0. 025. Electrodes had an average of 6. 6 neighbouring electrodes. Finally, dependent-samples t-tests were run on beta power values at cluster electrodes/time points comparing congruent and incongruent conditions.
Acknowledgements This work was supported by a doctoral studentship awarded to X. J. by the  Economic and Social Research Council (ESRC) ,  UK. The experiments reported here also feature in the doctoral thesis of X. J. (http://research. gold. ac. uk/26169/PSY_thesis_JobXE_2019. pdf). The authors would like to thank  Richard Smith  for building the response devices used in the experiments. The authors would also like to thank  Elena Gherri  and  Kielan Yarrow  for their helpful comments. Also, thanks to  Kielan Yarrow  for sharing his advice and scripts for the psychometric function.
The next section will detail the available methods of assessing the DFS. The complexity of assessing the DFS should point out the need to rely on different methodological solutions that must be integrated into a unique system. We will then propose a multimodal dataset acquisition requiring a close collaboration between the fields of engineering and behavioral neurophysiology thus leading to the redefinition of usual theoretical models. The whole of the preceding analysis will also have to take into account the singular characteristics of the drivers but also the external driving conditions. We will conclude by highlighting the contributions of our study to better understanding the relationships between vehicles automation and drivers functional state. We will also underline its limitations by acknowledging the path that remains to be done before we can propose complete autonomous driving solutions.
, 2016). Data acquisition and processing are provided by the seeing machines which "continuously measure operator eye and eyelid behavior to determine the onset of fatigue and micro sleeps and deliver real-time detection and alerts"  (Fernández et al. , 2016, p. 25 of 44). Recordings of EMG activity have a high potential to bring information about the DFS. The alteration of muscles function may be associated with impairment in driving abilities and fatigue. Surprisingly, there are little scientific contributions from this field. Fu and Wang (2014)  showed that the peak factor and the maximum of the cross-relation curve, two indices from surface EMG of the biceps femoris, were related to drivers' fatigue. EMG recorded from the neck and the back muscles are likely to provide information about sleepiness and driver fatigue.
AN OBVIOUS REQUIREMENT: A MULTIMODAL DATASET ACQUISITION Beside the methods used in laboratories, the challenge is to propose pragmatic, integrated systems, including a set of behavioral and physiological indices, simultaneously recorded in real time, both from the driver and the environment. This involves selecting indicators for their reliability and complementarity. Maglione et al. (2014)  simultaneously recorded high resolution EEG data associated with heart and eye blinks rates. Then, fusion of data provided a robust method in studying complex human activities, involving several functions  (Noori and Mikaeili, 2016). Rigas et al. (2011 Rigas et al. ( , 2012) )  described a set of physiological signals (ECG, EDA, and respiration) associated with driving history from the GPS and the vehicle's controller area network-bus (CAN) data.
METHOD Participants Since the avatar used here was female, only female participants were included in the experiment. As reported in previous studies, some individuals do not experience kinaesthetic illusions in the mirror paradigm  (Chancel et al. , 2016; 2017; Guerraz et al. , 2012; Metral et al. , 2015)  or in its virtual reality adaptation  (Giroux et al. , 2018). Hence, we screened the participants in a preliminary test: the virtual kinaesthetic illusion was evaluated in six trials during which displacement of both the avatar's (realistic) forearms in an illuminated virtual environment was combined with passive displacement of the participant's left forearm only (see  Giroux et al. , 2018). For each experiment we ran a power analysis with α = 0. 05 and 1-β=0. 80 for within-subject comparison (RStudio software, RStudio Team, 2016).
Material The HMD (Oculus Rift; Oculus VR, Irvine, CA, US) immersed the participant in a virtual room. The position and the orientation of the head was tracked online, and reported in the virtual world with perspective correction. The HMD supra-aural 3D spatial audio headphones masked outer sound interference with a continuous white noise. The virtual environment and actions were programmed with Unity software (Unity Technologies, San Francisco, CA, USA). Hardware comprised an MSI Geforce GTX 980 Gaming 4G graphics card (Micro-Star International, Taipei, Taiwan) and an Intel Core i7-4790K processor (Intel Corporation, Santa Clara, CA, USA). The participant was immersed in a virtual environment in which she could see the avatar (a female sitting as the participant, with her elbows positioned on a virtual table) from a first-person perspective.
STATISTICS Statistical analyses were performed on the duration of the illusory movement (time interval between the beginning and end of the kinaesthetic illusion, expressed in seconds from 0 to 8) and its speed (from -10 to 10). Since the normality assumption required to run frequentist parametric analyses was not systematically achieved under our experimental conditions (Shapiro-Wilk: p<. 05), the data were subjected to Bayesian statistics  (Kruschke 2010). Analysis of variance and pairwise comparisons t-tests were assessed with Bayesian equivalent tests. Statistical analysis was performed using JASP software (JASP Team 2018, https://jaspstats. org/)(See also  Wagenmakers et al. , 2018). Statistical evidence was reported using Bayes factors (BFs), BF10 for paired sample comparisons as well as for correlation analysis and BFincl for ANOVAs denoting the level of evidence of the alternate hypothesis (non-signed difference), and the inclusion of a specific parameter in a model (ANOVA) respectively.
Neuroimaging data acquisition Images were acquired in a Siemens 3T scanner. T1-weighted images were obtained using a FSPGR sequence (TR: 11. 6 ms, TE: 4. 8 ms, FA: 12, matrix size: 280 Â 280, 150 slices, slice thickness: 1. 00 mm). An EPI-T2* sequence allowed obtaining the functional volumes, each comprising forty 3. 4 mm thick slices (TR: 2500 ms, TE: 27 ms, FA: 90, matrix size: 64 Â 64, 40 slices, slice thickness: 3. 4 mm). SPM12 (Wellcome Department of Imaging Neuroscience, London, United Kingdom) and the Analysis of Functional Neuroimaging software  [34]  were employed for the functional MRI data analysis. Functional images were despiked (AFNI), corrected for motion-related artefacts (SPM), normalized to MNI standard space (SPM), smoothed with a 8 mm full-width-at-half-maximum Gaussian kernel (SPM) and detrended (AFNI).
Neuroimaging paradigm The complete neuroimaging session included two fMRI paradigms (Rating 1 and Feedback) followed by a behavioural testing session where participants completed Rating 2 and an emotions questionnaire (figure  1 ). The rapid event-related fMRI paradigm was designed using Matlab 2012a (The MathWorks, Inc. , Natick, Massachusetts, United States) with Psychtoolbox extensions. Before starting the fMRI paradigms, participants practised the task on a computer until fully comprehending the exercise and mastering control of the response buttons. Different grammatical formulations for each item were included in order to avoid habituation effects (see below). Rating 1 encompassed 80 trials split in two runs (electronic supplementary material, table  S1 ). From the list of sacred and non-sacred values elaborated for each participant, the six top sacred values ranked in order of importance by the participant were included, while six non-sacred values were selected randomly.
Controlling for confounding effects Because sacred and non-sacred values potentially differ along a number of dimensions unrelated to sacredness per se, we conducted a number of control analyses to rule out the possibility that the observed neural differences between processing sacred and non-sacred values could be explained by factors such as emotional intensity, familiarity and salience. We also used this survey to check sacred value persistence by retesting all values for sacredness. Six months after the fMRI study, participants rated the sacredness as well as the emotional intensity, familiarity and salience of each of the fMRI stimuli. As some of these participants had left the country by the time of the followup data collection, we were successfully able to reach 18, all of whom agreed to participate in the follow-up survey.
Activity correlated with these three confounds was binarized and used as mask to exclude activity correlated with emotionality, familiarity and salience from the non-sacred value greater than sacred value contrast in the original dataset (N ¼ 29). Brain regions included in these masks encompassed the insula, the anterior and middle cingulate cortex, the caudate and the precuneus (see masks in electronic supplementary material, figure  S8B ). Finally, we turned to the broader literature to search for regions implicated in emotional intensity, familiarity and salience that could account for the observed differences in activity between sacred and non-sacred value conditions. For this purpose, we used Neurosynth  [35] , a platform that calculates probability maps reflecting the likelihood that a term such as 'emotion' has been used in a study based on the activation in a given voxel by evaluating 11 406 different studies.
The structure of the database guaranteed the participants' anonymity. We provided the participants with this information before entering the study. Permission to carry out fieldwork. No licences were required to conduct fieldwork. All fieldwork was approved by the IRB as stated in the Ethics section. All researchers who interacted with participants received certified training in human subjects research protections. Data accessibility. Our data are deposited in the Dryad Digital Repository: https://doi. org/10. 5061/dryad. c0k33vf [48]. Authors' contributions. The project director was S. A. The field studies were conceived by S. A. , and they were designed.
The current study The study presented here addresses yet another kind of horizontal décalage, that between conversationally and monologically-produced narratives. In particular, it will consider children's understanding and expression of the causes of the story events and of the mental states of the characters motivating their behavior, in a conversation focused on the explanation of the events, and their expression in a monological narrative produced before and after such a conversation. To this effect, we will analyze data gathered in a study where a short conversational intervention was implemented in between a first and a second narrative, produced monologically by the same children about the same wordless pictured story. We the children say during the SCI and their previous and subsequent monologically-produced narratives will provide new and hitherto unreported results to finely determine whether conversationally-supported narration indeed leads to the expression of a higher level of evaluative/inferential content than is the case for monologically-produced narration.
, 2009; Veneziano, 2016; Veneziano et al. , forthcoming). As described in the data analysis section above, children's responses to the four causal questions asked during the SCI were categorized as appropriate to the story of a misunderstanding (MRE), as providing explanations of the events but not contributing to the story of a misunderstanding (OE), or as no explanatory response (NoE). The same categorization was applied to the corresponding events mentioned in the monologically-produced narratives. During the SCI, 31% of the children (25 out of 80) were able to explain all four events with MREs. However, out of the 25, only 52% of them (13 children) told a monological narrative (SMN) in which all four events were explained in a misunderstanding relevant way. The other twelve children (48%), for at least one of the events, provided either another explanation (OE) or no explanation at all (NoE).
In oter words, children's participation in the SCI reduces the horizontal décalage between conversationally and monologically produced narratives. The comparison between the SCI and the FMN will provide further data on this point. Comparison between explanations produced during the SCI and those expressed previously in the first monological narrative (FMN) The décalage between the SCI and the FMN should be more clear-cut than that observed between the SCI and the SMN above. Indeed, as discussed above, earlier studies have shown that children produce evaluative/inferential components earlier in a conversational than in a monological setting; moreover, all the studies conducted with the SCI procedure have shown a significant increase in these elements between the narrative produced before (FMN) and that produced after (SMN) the SCI, a result again confirmed in the present study (see the first section of the results above).
49 Digital images were captured at 10× magnification using an Olympus (BX50) and analyzed by image analysis software (Icy version 1. 7. 3. 0;icy. bioimageanalysis. org). At all stages, the experimenter was blind to the experimental groups. Slices showing clear and reliable Fos staining were pooled from animal performing either D1 or D2. Three sections from each animal were examined bilaterally, and the number of positive nuclei/mm 2 was averaged (8-12 animals/group; 3 slices per animal) and were expressed in mean counts ± SEM in relative variations as compared to naive controls (food deprived animal staying in animal room that were excluded from the acquisition phase).
Information Sources and Search Strategy Existing papers were identified by searching the academic databases PubMed and PsycINFO, from March to May 2018, published from January 1990 to February 2018. Both authors drew up a list of agreed English keywords for the systematic search: Gambling (MeSH term) OR "Gambling disorder" OR "problem gambling" OR "Pathological gambling" AND "Aged (MeSH term)" OR Aged 80+ (MeSH term) "elders" OR "older adults" in the title, abstract or keywords. Inclusion and exclusion criteria are presented in Table  1.
Study Selection and Data Collection Process At the first stage, 867 articles were identified with the key words Gambling (MeSH term) OR "Gambling disorder" OR "problem gambling" OR "Pathological gambling" AND "Aged (MeSH term)" OR Aged 80+ (MeSH term) "elders" OR "older adults". At the second stage, duplicated papers were excluded. The selection of papers for the systematic review was based on the inclusion and exclusion criteria previously described. Following the search strategy presented in the flow diagram in Figure  1 , the inspection of article titles and abstracts concluded with the inclusion of a total of 51 papers. We included studies or literature reviews in the English or French languages, which concerned older people (over 50 years of age) specifically, in clinical settings, and those which concerned gamblers (with or without problems).
Study Selection and Characteristics In this review, the first group of 867 papers was identified by searching for the keyword in the scientific database. As described in the flow diagram (Figure  1 ), 670 papers were excluded because they were replicated records or because the topic was not GD in elderly people. One hundred thirty-eight were assessed for eligibility, 87 full texts were excluded because they did not describe clinically elderly persons with GD. A total of 51 studies met the inclusion criteria. The publication dates ranged from 1990 to 2018 and contained clinical samples of elderly persons with GD. Selected articles are listed in Table  2.
Forest edges that border savanna are dynamic features of tropical landscapes. Although the role of fire in determining edge dynamics has been relatively well explored, the role of mega-herbivores, specifically elephants, has not received as much attention. We investigated the role of forest elephants in shaping forest edges of the forestsavanna mosaic in LopeNational Park, Gabon. Using forty camera traps, we collected 1. 2 million images between May 2016 and June 2017. These images were classified by over 10,000 volunteers through an online citizen science platform. These data were combined with a 33-year phenology dataset on elephant-favoured fruiting tree species, and field measurements of elephant browsing preferences and damage. Our results showed a strong relationship between forest elephant density at the forest edge and fruit availability.
org, available at h ttps://www. zooniverse. org/projects/anabellecardoso/elephant-expedition, Figure  S3 ), through which more than 10,000 interested members of the public (citizen scientists) classified our images. Each image was classified based on what animal species was in the image, and how many forest elephants were in the image. Images containing animals were each classified by ten volunteers; thus, each image was associated with up to ten potentially conflicting classifications. To assign each image, a single aggregated classification we applied a plurality algorithm (Swanson and others 2015, script available at https://github. com/zooniverse/Data-diggin g/tree/master/example_scripts/R_code/survey-task s/generalized). We assigned the animal species in the image as the one with the highest number of ''votes'', and the number of elephants in the image as the median count of elephants.
Considering that (1) elephants are readily distinguishable from other animals in the study site, (2) images with animals were classified by ten volunteers, (3) the high trigger frequency resulted in multiple images usually being captured during the same elephant visit, we have a high level of confidence in the data obtained from the citizen science classifications  (Kosmala and others 2016). To obtain forest elephant density estimates for each site, we applied a random encounter model (REM) (Rowcliffe and others 2008; Cusack and others 2015) using the formula: D ¼ y t : p v:r 2 þ h ð Þ Ã mean group size: Here D is the density of elephants (elephants km -2 ). y t is the trapping rate, or total number of independent capture events (y) divided by the total camera effort (t, h). Forest elephants move in cohesive groups and the camera trap can be triggered up to once per second.
To avoid counting the same elephant twice we looked for independent elephant group capture events. Independent elephant group capture events were peaks in elephant counts separated from other peaks by at least 1800 s before and after the peak (find_peaks function, available at https://github. com/stas-g/fin dPeaks/blob/master/find_peaks. R). Elephant density was then obtained by multiplying the density of independent group capture events by the mean size of the elephant group  (Rowcliffe and others 2008). Mean group size was the sum of peaks in elephant count of all independent group capture events divided by the number of independent group capture events. This is a conservative estimate of group size, as the maximum count of elephants in any one image is the minimum group size. Total camera effort is the number of hours for which the camera was functional.