dataesr/scientific-paragraphs-categorization huggingface dataset:
TRAINING APPARATUS Equivalent conditioning apparatus and procedures were used between labs as previously described (USA: Coulbourn equipment, described in  Sevelinges et al. , 2008;  France: Emka and Coulbourn equipment, described in  Hegoburu et al. , 2011). In the USA, a standard Coulbourn shock chamber was used. In France, a custom built Plexiglas conditioning chamber for fear conditioning was used and equipped with features allowing fine-grain freezing analysis (4 camera views, B/W CMOS PINHOLE camera, Velleman, Belgium, and an homemade acquisition software using Matrox Imaging Library and acquisition card, Matrox video, UK) and a plethysmograph (diameter 30 cm, Emka Technologies, France) for measuring respiratory frequency (see  Hegoburu et al. , 2011  for further description of the plethysmograph).
ODOR FEAR CONDITIONING PROCEDURE CS-US parameters were standardized between labs. The American lab used the Coulbourn FreezeFrame software for stimuli delivery control and video recording. The French lab used a custom build program for stimuli delivery and recording. Conditioning took place in a sound attenuation chamber with deodorized air constantly flowing through the cage (2 L/min). The odor CS was a 30s peppermint odor (McCormick Pure Peppermint; 2 L/min; 1:10 peppermint vapor to air) and was controlled with a solenoid valve that diverted the airflow to the peppermint air stream, thus minimizing pressure change. The 1-s mild electric shock was delivered through a grid floor. In the US, for the youngest pups, the shock was delivered through an electrode to one hind limb. At both sites, adult rats were handled for about 4 days and placed into the conditioning chamber for context habituation.
STRIATUM 2-DG AUTORADIOGRAPHY In the 14 C 2-DG autoradiography, the animals (adults: n = 17, juveniles: n = 18) were injected with 2-DG (20 μCi/100 g) 5 min prior to conditioning (Paired, Unpaired and Odor). Brains were removed immediately after the 45 min of conditioning, frozen in 2-methylbutane (-45 • C) and stored at -70 • C. Brains were then cut into 20 μm coronal sections (following equilibration to -20 • C) and placed in exposure cassettes for 4 days with standards ( 14 C methylmethacrylate standard 10 × 0. 02 mCi; American Radiolabeled Chemicals, Inc. ), scanned (Epson) and analyzed using NIH Image J Software for quantitative optical densitometry. The dorsal striatum was divided into anterior (Bregma +1. 9 mm,  Paxinos and Watson, 1986)  and posterior dorsal striatum (Bregma +0. 1).
ASSESSMENT OF TEMPORAL PATTERNS OF FREEZING AND RESPIRATION DURING ACQUISITION To assess interval timing during development we used olfactory fear conditioning in PN12-15 infant (n = 30) and adult rats (n = 25), ages representative of before and after functional striatal development. Three experimental groups of animals per age were trained using either 30 s (Paired 30 s group), or 20 s (Paired 20 s group) Odor-Shock interval duration, or Odor alone presentation (Odor group). In each experimental group, the time course of respiration and behavior was monitored throughout the acquisition session. Offline, the respiratory signal was analyzed and momentary respiratory frequency was determined. The animal's freezing behavior was automatically detected using a LabView homemade software that had been validated by comparison to hand scoring by an experimenter blind to the rats' condition.
DISCUSSION The present study investigated the ontogeny of time durations encoding in odor fear conditioning. We first assessed whether the striatum, which is a brain area implicated in timing, was differentially activated in odor fear conditioning throughout development. This is the first study to assess striatum activity across development within this paradigm. The 2-DG metabolic mapping study revealed that, while dorsal striatum was activated during odor fear acquisition in adults, its activity remained unchanged in juveniles. These data are in agreement with data on striatum development, which indicates the juvenile striatum is still maturing  (Chronister et al. , 1976; Tepper and Trent, 1993; Tepper et al. , 1998). To investigate interval timing abilities before and after the striatum functional maturation, we performed finegrain analysis of behavioral and physiological responses shown to be valuable for assessment of timing in adults during the early stages of odor fear learning.
Specifically, we monitored the time course of the animal's respiratory rate and freezing behavior during conditioning at two developmental ages: one with striatum activity correlated with learning (adult) and the other for which the striatum has been shown to be immature in the literature (infant). The data showed that for both adult and infant rats, duration-related temporal patterns can be detected for freezing and/or respiration curves suggesting that infants are able to encode time durations as well and as quickly as adults while their striatum is not yet functional.
ACKNOWLEDGMENTS This work was supported by  CNRS-PICS program ,  Partner University Fund Emotion & Time, LIA CNRS-NYU LearnEmoTime, ANR-Memotime, ANR-TDE, COST-Action TIMELY ,  NIH-DC009910 ,  NIH-MH091451. This work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The authors gratefully acknowledge  Ounsa Ben-Hellal  for taking care of the animals.
Perceivers' ratings  [29]  were consistent with these objective differences: observers judged false smiles as significantly less amused and less genuine than true smiles. All smiles were displayed as movie clips (136861026 pixels, 25 frames/s) in E-Prime Version 2. 0 (Psychology Software Tools) and shown in random order. Procedure. Participants first provided written informed consent to take part in the study. They worked individually, seated in front of a 140 screen connected to a PC. As they viewed videos of true and false smiles, we recorded the EMG activity of participants' zygomaticus major, the main muscle involved in smiling. Videos were displayed on a black screen, separated by self-paced pauses (no less than 500 ms). Given that the technique of EMG requires multiple repetitions of the same stimulus  [30, 31] , each of the 12 sequences was presented three times, for a total of 72 trials presented in two randomized blocks (36 in the free mimicry and 36 in the blocked mimicry condition).
), and stored with a sampling rate of 1000 Hz. Data preprocessing. EMG recordings were preprocessed using LabChart 7 (ADInstruments, Inc. ). Recordings were filtered with a 10-Hz high-pass filter, a 400-Hz low-pass filter, and a 50-Hz notch filter, and segmented from 500 ms before to 2 seconds after the video onset, given that the most distinct facial reactions occur during the first second after stimulus onset  [33, 34]. In order to control for random facial movements prior to the stimulus onset, we excluded from further analysis trials on which the zscores of mean amplitude of the baseline (500 ms before the stimulus onset) were higher than 3 (on average 1 out of 72 trials per participant, never more than 3). The remaining data were then expressed as percentages of the baseline and averaged per condition in 20 time bins of 100 ms, in order to reflect how the EMG signal evolved after the onset of true and false smile videos.
Results Statistical analyses were performed using PASW Statistics 18 (SPSS, Inc. , Chicago, IL) and RStudio (version 0. 96. 331, RStudio, Inc. ). Overall effect of condition on EMG responses. Given that the mouthguard stretches the mouth and the cheeks, we did not expect it to completely inhibit facial movements but rather to induce irrelevant muscle activity that would interfere with participants' mimicry. To test this hypothesis, we examined how average responses of zygomaticus major in the first 2 seconds after the video onset varied as a function of smile type (true, false) and mimicry condition (free, blocked). Data screening and Shapiro-Wilk tests revealed that the dataset violated normality assumptions (see Table  1  for details). A Wilcoxon Signed-ranks test indicated that when participants could freely mimic the video stimuli, they imitated true smiles to a greater extent (M = 1.
336, SD = 1. 476) than false smiles (M = 1. 08, SD =. 27), Z = 22. 64, p =. 008, consistent with previous research  [16, 35]. This difference disappeared when participants were wearing a mouthguard (respectively, M = 1. 08, SD =. 16, M = 1. 06, SD =. 14), Z = 0. 12, p =. 908. Mapping EMG data on stimuli's facial activity. In order to assess the time course of participants' zygomaticus major activity in both conditions, we compared their EMG responses to the smile dynamics of the stimuli videos, extracted with the Computer Expression Recognition Toolbox -CERT  [36]. The Computer Expression Recognition Toolbox. CERT is a software tool for automatic facial expression recognition, trained to code 19 FACS action units as well as prototypic facial expressions, facial features, and head orientation.
, 2001, see Supplementary Material). Three examples of experimental stimuli are provided in Table  1. All critical verbs were in the present tense and in neutral 3rd person. Verbs always occurred in the same position of the sentence. The sentences were spoken by a French male adult. His voice was recorded using Adobe Soundbooth and the recordings were adjusted to generate similar trial lengths using the Audacity 1. 2. 6 software. Two pseudo-randomized sentences lists were generated from trials; these lists contained uniform distributions of the different sentence types. The two lists were alternated between participants. The mean word duration was 459 ms (SD = 97 ms).
Equipment and data acquisition Two distinct computers were used for data recording and stimulus presentation to ensure synchronization between audio files and grip-force measurements (estimated error <5 ms). The first computer read the play-list of the pseudo-randomized stimuli. The second computer received two triggers from the first computer, which indicated the beginning and the end of the play-list. This second computer also recorded the incoming force signals from the load cell at a high sampling rate of 1 KHz. To measure the activity of the hand muscles, a standalone 6-axis load cell of 68 g was used (ATI Industrial Automation, USA, see Figure  1 ). In the present study, force torques were negligible due to the absence of voluntary movement; thus, only the three main forces were recorded: Fx, Fy, and Fz as the longitudinal, radial and compression forces, respectively (Figure  1B ).
Data analysis Prior to the data analysis, each signal component was pretreated with the Brain Vision Analyzer 2. 0 software (Brain Vision Analyzer software, Brain Products GmbH, Munich, Germany). The data were filtered at 10 Hz with a fourth-order, zero-phase, low-pass Butterworth filter, and a notch filter (50 Hz) was applied in case that artifact caused by electrical power lines would have persisted. Finally, a baseline correction was performed on the mean amplitude of the interval from -400 to 0 ms prior to word onset. The baseline correction was implemented because of a possible global change in grip-force during the session (12 min), and because we are only interested in grip-force changes. Thus, we adjusted the post-stimulus values by the values present in the baseline period. A simple subtraction of the baseline values from all of the values in the epoch was performed.
Stimuli A total of 158 French sentences served as stimuli (see Supplementary Material). Ten were distractor-sentences containing a country name. The data from the trials using the distractorsentences were not included in the analysis. For this experiment, 37 pseudo-verbs were created obeying French's phonotactic constraints using the Lexique Toolbox of the data base Lexique 3  (New et al. , 2001). The soundness of the verb as a French verb was controlled (see Supplementary Material). Thirty-seven target non-action words were utilized. All non-action words were verbs denoting no action performed with the hand or arm (e. g. , decide, think), as confirmed by the stimuli validation process (see Supplementary Material). Thirty-seven target action words were included. All action words were verbs denoting actions performed with the hand or arm (e.
All actions encoded by sentences were highly prototypical as hand actions. Cloze probability was defined as how easy was to anticipate a hand action verb from the previous sentential context. Only the contexts that induce highly cloze probability of hand action verbs were considered as action contexts (see Supplementary Material). In summary, the present study exploited four conditions: Four examples of experimental stimuli are provided in Table  2. All critical verbs were in the present tense and in neutral 3rd person. Verbs always occurred in the same sentential position (see Table  2 ). The sentences were spoken by a French female adult. Her voice was recorded using Adobe Soundbooth and the recordings were adjusted to generate similar trial lengths using the Audacity 1. 2. 6 software. Three lists of 37 action contexts (A, B, and C) were created to avoid context repetition between the three action context conditions.
ACKNOWLEDGMENTS The authors gratefully acknowledge the contribution of Fr ed eric Moisan as the professional violinist experimenter, and  Quentin Vallet ,  Emmanuel Ponsot ,  Laurent Quartier , and  Jo€ el Frelat  for their support and technical assistance. The luthiers  Philippe Mit eran-Bigorgne, Fr ed eric Levi  and  Nicolas Perrin  are acknowledged for their kind loan of violins. We are grateful to  Vincent Hayward ,  Stephen McAdams , and  Charles Spence  for many helpful discussions and comments. This work was supported by the  CNRS  and  UPMC  ( UMR 7190 ). I. W. was a recipient of a Ph. D. fellowship from  UPMC-Ministry of Higher Education and Research.
Material Virtual environment and scenario. The virtual environment was created with the Realax software. It was displayed at scale 1:1, and computed for the viewpoint of the participant (1. 15 m above the simulated ground). It represented a two-lane highway in a rural environment, with a simplified landscape which helped to reduce bottom-up attention. The road was straight and crossed a new road at right angle every kilometer. The crossroads were announced 150 m beforehand with a road sign (see Fig. 3 ). The road markings at the crossroads were consistent with the road signs. A driving simulator software architecture (ARCHISIM) was used to introduce traffic in this virtual environment via two computers. In order to improve the sense of immersion, some vehicles were simulated coming along the highway in the opposite direction.
The distance between the participants' heads and the screens (at 90 • ) was 1. 70 m. All five screens formed together an angle of 275 • (see Fig. 4 , right). The simulator included a gearbox, a wheel with force feedback, three pedals, a dashboard with speed and engine speed, and a model of a combustion engine. The eye tracking system was SMI (SensoMotoric Instruments) Eye Tracking Glasses (weight: 75 g). It included a scene camera (resolution: 1280×960 pixels, 24 fps) and two cameras capturing the driver's gaze with a tracking range of 80 • horizontal and 60 • vertical. This system had a gaze position accuracy of 0. 5 • (according to the manufacturer), and a sampling rate of 30 Hz. The data was processed using BeGaze (SMI software, version 3. 1. 77). The fixations were calculated with a dispersion based algorithm, whose parameters were: 80 ms for minimum fixation duration and 100 pixels (4.
Acknowledgments We wish to thank  Fabrice Vienne ,  Jacky Robouant ,  Stéphane Caro  and  Isabelle Aillerie  for their technical support in the driving simulation experiment;  Jean-Philippe Tarel  for sharing with us his expertise in machine learning (SVM);  Dana Cohen  for helping us to improve the manuscript;  Régis Lobjois ,  Otto Lappi  and the anonymous reviewer for their valuables suggestions.
Each stimulus remained on the screen for 3000 msec and was followed by an inter-trial interval of 500 msec, during which a fixation point appeared (Figure1B). For each category (i. e. , the campus, Italy and the clock) and orientation (i. e. , horizontal or vertical) we computed accuracy and response time (RT). The experiment was implemented in Matlab, using Cogent 2000. Each participant completed also a Vividness Task (VT;  [25] ) and the Verbalizer-Visualizer Questionnaire (VVQ;  [11] ). The VT tests the vividness of mental images by asking participants to imagine a common object. The VVQ investigates consistencies and preferences in processing visual versus verbal information and classifies individuals as either Visualizers (also called Imagers), who rely primarily on imagery when performing cognitive tasks, or Verbalizers, who rely primarily on verbalanalytical strategies.
Statistical analysis Statistical analysis was performed using SPSS. We performed two repeated measures ANOVAs to test for significant effects of category (i. e. , the campus, Italy and the clock) and orientation (horizontal and vertical) and a possible interaction effect between the two factors. Then we performed two factorial ANOVAs to test the effect of the vividness of mental images on accuracy and RTs across different categories and orientations. We calculated the median of the participants' scores on the VT and divided our sample into two groups: good (above median) and poor imagers (below median). The last two factorial ANOVAs were aimed at testing the effect of preferences in processing visual versus verbal information on accuracy and RTs across different categories and orientations.
, 2011). Our study attempted to classify visual strategies automatically. The classification data were used to explore how the effect of advertising banners on visual strategies varies according to the depth of processing required by the target task and how advertisements generate task-switching. From a theoretical standpoint, the present study potentially provides new perspectives on theories on online advertising and attention and the methodologies used to investigate online attention. From a practical standpoint, information on the effects of advertising banners could guide Web designers, developers and advertisers in their choice of banners distance and animation.
APPARATUS Eye movements were recorded using an infrared video eyetracking device (SMI RED500, SensoMotoric Instruments, Teltow: Germany) sampling pupil and corneal reflection at 500 Hz. The screen coordinates of the left eye were sampled. The system has a spatial tracking accuracy of approximately 0. 5 • of visual angle. The calibration was run on 9 points to optimize spatial tracking accuracy. Drift was corrected once during the experiment, after 12 trials. Data were recorded with Experiment Center software (SMI Teltow, Germany) and processed with BeGaze software. The participants were seated on a chair at a fixed distance of approximately 57 cm away from the monitor and the eye-tracker. A chin-rest was used to minimize head movements during the recording. Participants were given the opportunity to adjust the seat and chin-rest to the most comfortable position.
8 cm long, that is, they subtended 1. 2-1. 8 • of visual angle. SMI Experiment Center software allows the user to specify the triggers which advance the task from one trial to another; Areas of Interest (AOIs) can be used as triggers. We defined the target words as AOI triggers to ensure that participants always located the targets and completed the task. We defined a 1000 ms threshold for the time clock of the trigger AOIs, i. e. , the participants had to fixate the target words for 1000 ms to access the next trial.
Holmqvist et al. (2003)  applied a similar method to identify scanning and reading fixations recorded when readers covered newspapers and net papers. They analyzed fixation data above 100 ms through a custommade reading filter. Reading fixations were filtered if they were (1) before, between or after two successive forward saccades and (2) before and after return sweeps. Correction and backward saccades were not recognized. The fixations that were not filtered were labeled as scanning. Contrary to  Holmqvist et al. (2003)  filter, our classifier algorithm classify fixations that occurs after backward saccades.
DATA ANALYSES Analyses of variance for repeated measures (rm ANOVA) were conducted on 5 dependent variables: fixation duration, number of fixations, first-fixation duration, gaze duration and saccade amplitude, with a fixed significance threshold of p < 0. 05. Firstfixation duration was defined as the mean duration of the first 5 fixations. The objective was to investigate where subjects fixated when the webpage was first displayed and how the durations of these early fixations differed from those of the rest of the fixations. Gaze duration was the sum of fixation durations for an AOI (the text or the banner). The objective was to examine the total processing time for all the elements of the webpage. All analyses were corrected using Bonferroni post-hoc tests. A low cut-off of 100 ms and high cut-off of 500 ms were used for filtering fixations, these cut-offs corresponded to 2 SD above and below the average (i.
Advertisement and Areas of Interest (AOIs) In order to investigate the banner blindness effect, an ANOVA was carried out for all eye movement data from two AOIs: one defined on the banner and another one on the central text. We defined another factor Zone to investigate differences between the two AOIs. We weighted the fixation durations and the number of fixations according to the size of the AOIs. The results showed that the central text received significantly longer fixations than the banner, F (1, 23) = 176. 61, p < 0. 001; η 2 = 0. 88, α = 0. 05. It also attracted significantly more fixations, F (1, 23) = 324. 89, p < 0. 001; η 2 = 0. 93, α = 0. 05. The position of the banner also affected fixation duration, F (2, 46) = 3. 39, p < 0. 050; Word-search task Dynamic 3. 9 • (0. 8) 3. 9 • (1.
Post-test questionnaire and visual strategies The post-test questionnaire was used to investigate participants' subjective perception of shifts of attention toward the banners and how they thought they had been affected by the banners. The higher the score, the more attention grabbed and the more distraction felt. Of the 24 participants, 13 (54%) reported that they did not pay attention to the banner and were not affected by the banners (M = 1. 4; SD = 0. 46) (see Figure  5 ). ANOVAs were carried out for the eye movement data from the 13 participants who reported that they had not paid any attention to the banners, to investigate possible automatic and unconscious shifts of attention (see Table  2 ). These participants covered the Web pages with more fixations when they contained a dynamic banner, F (1, 12) = 7.
The Japanese quail is a widely used model organism for the study of embryonic development; however, anatomical resources are lacking. The Quail Anatomy Portal (QAP) provides 22 detailed three-dimensional (3D) models of quail embryos during development from embryonic day (E)1 to E15 generated using optical projection tomography. The 3D models provided can be virtually sectioned to investigate anatomy. Furthermore, using the 3D nature of the models, we have generated a tool to assist in the staging of quail samples. Volume renderings of each stage are provided and can be rotated to allow visualization from multiple angles allowing easy comparison of features both between stages in the database and between images or samples in the laboratory. The use of JavaScript, PHP and HTML ensure the database is accessible to users across different operating systems, including mobile devices, facilitating its use in the laboratory.