dataesr/scientific-paragraphs-categorization huggingface dataset:
The core prediction that, in synchrony, the socioeconomic level of people should impact the preference for fictional stories set in imaginary worlds, through a mediating effect of environmental curiosity, should be further tested with other datasets or experimental tests. Future research should also further study the causal impact of ecological conditions on the production of speculative fictional stories in diachrony, at the macro-level of societies. The association between sex and the reported preference for imaginary worlds. (E) Levels of systemizing mediate the effect of sex on the preference for imaginary worlds with an indirect effect of -0. 17 (p < 0. 001), leaving a non-significant direct effect (in parentheses on the center path, next to the total effect). The proportion of effects mediated is 70%.
Data. Extraction of existing data from the Internet or previous studies (Studies 1 & 2). We use the Internet Movie Database (IMDb) to obtain metadata about 9424 movies, such as their genres, summary plots, and their keywords. In the Movie Personality Dataset (MPD), for each of the 846 movies, we have (1) the movie metadata, (2) the average personality traits, average age, and average sex of people who like it on Facebook, and (3) the presence (or not) of an imaginary world in it (see "Algorithmic methods", below). Nave et al. 81  built an important dataset that makes it possible to map the associations between movie characteristics and the characteristics common to people who like such movies. Note that because socio-demographic scores are aggregated, the sex variable associated with each movie becomes a continuous variable between 0 and 1 (as a percentage of males who liked it, where 1 would mean that all people who liked this movie on Facebook self-reported themselves as men).
Collection of original data through experimental designs (Study 3). The design and predictions for this study were pre-registered (https:// osf. io/ 8yj3v). All methods were carried out in accordance with relevant regulations and approved by the Conseil d' évaluation éthique pour les recherches en santé, CERES n°201,659. We recruited 350 participants from the online research participation platform Prolific (180 males, 165 females, 4 others, M age = 46, SD age = 19. 5). Participants confirmed their informed consent. We removed participants failing the attention check and participants failing to respond to the follow-up study, leaving a total sample of 230 participants (101 males, 127 females, 2 others, M age = 48, SD age = 16. 3, Range age : 19-82; we still run the analyses that were possible without the follow-up study with the entire sample size, after removal of participants who failed the attention check; see "Supplementary Materials").
Algorithmic methods. Random-forest algorithm (study 1 & 2). The first step is to detect the presence of imaginary worlds in movies. We start by manually coding 385 movies randomly selected in the IMDb dataset, as being set in an imaginary world or not. We base this decision on one main criterion: whether or not the IMDb movie summary mentions a location that does not exist in the real world. Then, we extend this categorization to 9424 movies with a classification algorithm based on a random-forest method  211  and trained on plot keywords (i. e. , user-generated keywords associated with movies which describe "any notable object, concept, style or action that takes place during a title"). This algorithm is successful in identifying movies set in imaginary worlds with an out-of-bag error rate of 9.
Then, we use the K-Means algorithm to cluster this space into 7 clusters (with the elbow method  218  determining the number of clusters that maximizes the explained variation). For every cluster, we compute the most 20 specific n-grams using the chi-squared statistics test, thus providing the words that most specifically describe the clusters. For these computations, we use the Python 'bunkatech' package (https:// github. com/ charl esded ampie rre/ Bunka Topics). Creation of an extended list of exploration-related terms. First, we manually create a list of 5 core terms directly related to exploration (i. e. , 'exploration' , 'explorer' , 'explorers' , 'explores' , 'exploring'). Then, we extend this list using again the algorithm Sbert Transformer, this time applied to the movie summaries.
Auditory stimuli and the experimental paradigm The stimulus consisted of two rhythmic patterns, named hereafter Duple/Triple Rhythm and Quadruple Rhythm. To create the rhythmic patterns, rock drum sounds composed of snare and bass were used. The stimuli were created using the open-source software Audacity 2. 2. 2 program and exported as WAV files. Duple/Triple Rhythm consisted of a six-beat rhythmic pattern based on Phillips-Silver and  Trainor (2005)  and  Cirelli et al. (2016)  that lasted 2 s (Fig. 1A ) and was repeated 19 times for 38-s-long trials. Quadruple Rhythm consisted of a 12-beat rhythmic pattern based on  Nozaradan et al. (2011 Nozaradan et al. ( , 2012a)) , that lasted 3. 996 s (Fig. 1B ), and was repeated nine times for 36-s long trials. Each beat in both rhythmic patterns had a 333 ms inter-onset interval (180 beats per minute), which translated into a beat frequency of 3 Hz.
As shown in Figure  1 , C and D, the envelopes of both Duple/Triple and Quadruple Rhythms consisted of distinct frequencies ranging from the frequency corresponding to the period of the entire rhythm to the frequencies corresponding to the periods of the grouped (meter) as well as beat levels. Each experimental session started with 20 min of silence, during which the spontaneous neural activity of the neonate was recorded as a baseline condition for future analysis (data not shown). Next, the trials corresponding to each rhythm were presented to sleeping neonates in separate blocks; four blocks (two for Duple/Triple Rhythm and two for Quadruple Rhythm) were ordered randomly for each participant via loudspeakers placed at the infant's feet. Each block consisted of 25 trials of the rhythm for that block.
This made the total number of trials corresponding to each rhythm equal to 50. Block durations of Duple/Triple Rhythm and Quadruple Rhythm were 950 s and 900 s, respectively. The blocks were separated by 30 s of silence. The stimuli were delivered through a speaker at 65 dB SPL, which was located at the feet of the neonates, using Psychtoolbox for MATLAB  (Kleiner et al. , 2007). The total duration of the experiment was 63 min. The recordings were stopped if the infants woke up, started to cry, or moved. EEG acquisition and preprocessing EEG signals were collected using a 124-channel HydroCel Geodesic Sensor Net with an Electrical Geodesic NetAmps 200 amplifier passing a digitized signal to Electrical Geodesics Net Station software (version 5). The EEG was digitized at a 1000 Hz sampling rate, with a Cz vertex electrode as reference.
The recorded signals were analyzed with MATLAB software (MathWorks) using FieldTrip  (Oostenveld et al. , 2011) , EEGLAB  (Delorme and Makeig, 2004) , and custom MATLAB functions and codes. We applied a two-pass 0. 5-45 Hz finite impulse response filter (order, 3 cycles of the low-frequency cutoff) and a 50 Hz notch filter by EEGLAB toolbox to remove low-and high-frequency artifacts and also the line noise from the EEG signals. The data were then downsampled to 512 Hz. We removed the electrodes belonging to the outer ring because of their low signal-to-noise ratio (98 electrodes remaining; Fig. 2 ). The EEG signals were visually inspected, and bad channels and large artifacts and the beginning of acquisition were removed from the data. Next, each channel in each trial was marked and removed from further processing if the average absolute value over the trial exceeded 30 mV.
Statistical analysis Statistical analyses were performed with MATLAB (MathWorks), using the FieldTrip  (Oostenveld et al. 2011)  and CircStat  (Berens, 2009)  toolboxes as well as custom MATLAB functions. Testing neural responses to beat and meter frequencies. For the spectral analysis of EEG, the spectra were averaged across all scalp electrodes (Fig. 2 ) for each participant to avoid any electrode selection bias  (Nozaradan et al. , 2011 (Nozaradan et al. , , 2015;; Cirelli et al. , 2016; Nozaradan et al. , 2016). Paired-samples t tests, corrected for multiple comparisons using Bonferroni correction, were used to determine whether the obtained amplitudes measured at the beat and meter frequencies were significantly different from the average noise floor (mean amplitude of frequencies unrelated to the beat and meter).
5 mol/L sucrose solution. This provisory setup was maintained in the dark to deprive the bees of visual stimulations. Virtual-reality setup. After the 3-h period, the bees were moved to the VR setup  (Lafon et al. , 2021). The VR relied on a custom software developed using the Unity engine  (version 2020. 3. 4f1) , which updates the position of the bee within the VR every 0. 017 s. The open-source code is available at https://github. com/G-Lafon/BeeVR. In addition, it includes information about troubleshooting and about the customization of the 3D models used in the VR setup. The VR apparatus consisted of a spherical Styrofoam ball (diameter: 50 mm, weight 1. 07 g), floating on an air cushion. The ball was positioned within a 3D-printed, hollow, cylindrical support (cylinder: 50 mm high, 59 mm diameter).
Body size and weight measuring Body size was assessed by measuring the distance between the 2 wing joints  (Cane, 1987; Ravi et al. , 2020)  using the Toupview software (ToupTek Photonics, Zhejiang, China). After size measurements, insects were placed in an oven at 70 °C for 4 h in order to evaporate all water from their bodies. Dry weight  (Sage, 1982)  was measured with a precision scale (OHAUS, Nänikon, Switzerland).
In Experiment 3 (differential conditioning of colors), the first choice in each trial and in the retention test was categorized as choice of the CS+, of the CS-or no choice (NC). Thus, a bee choosing the CS+ was recorded as (1, 0, 0) for choice of the CS+, CS-, and NC, respectively. A bee choosing the CS-was recorded as (0, 1, 0) while a bee not making a choice was recorded as (0, 0, 1). Data were bootstrapped to plot the proportion of bees in each category with their corresponding 95% confidence intervals. Learning was analyzed using generalized mixed linear models (GLMM) with a binomial error structure-logit-link function (glmer function of R package lme4)  (Bates et al. , 2015). The in-dependent variables (fixed factors) were the trial number (Trial, 1 to 6), the choice category (Choice) and the color of the CS+ (Color, Blue or Green).
Individual Identity was included as a random factor to account for the repeated-measure design. Post hoc ANOVAs were performed on those models to assess the impact of each factor. During the retention tests of Experiment 2, in addition to the color choice of each bee, we recorded the time spent fixating each color (CS+ vs. CS-). Fixation times were compared using a Wilcoxon signed-rank test. During acquisition trials, we also recorded the total Distance Walked during a trial, and the Walking Speed of each bee. In addition, we recorded the Latency to Make a Choice, starting from the beginning of a trial to the moment in which a choice (either for the CS+ or the CS-) was made. NC data were excluded from the latency analysis. The analysis of these continuous variables was done using a linear mixed model (lmer function of R package lme4), in which Individual Identity was a random factor and Trial Number (Trial, 1 to 6) was a fixed factor.
Experiment 2: Choosing colors with spontaneous equal attraction in the VR setup We then recorded the spontaneous choice of bumble bees presented with 2 shades of green and 2 shades of blue in the VR setup: Green 1 versus Blue 1 and Green 2 versus Blue 2. The irradiance difference between green and blue was 10 times higher for Blue in the first pair and 26 times higher in the second pair. Each bee was presented a single time with each color pair following a random sequence that varied from bee to bee. As this sequence had no significant effect on the choice of bees (z = 1. 094, P = 0. 27), we pooled the data according to the color pair. Fig. 3  shows the first choice made by each bee presented with both color pairs. Bees exhibited a significant preference for Green 1 over Blue 1 (Fig. 3 , left; Green 1 : 28, Blue 1 : 11, χ 2 = 7.
Acknowledgments We thank 2 anonymous reviewers for encouraging comments on our manuscript and  Naïs Judan ,  Eva Blot , and  Karolina Pecharova  for assistance with some of the experiments. We also thank  Lars Chittka  for kindly providing the spectral sensitivity curves of bumble bee photoreceptors shown in Suppl. Fig. S1. This work was supported by an  ERC   Advanced Grant  ("Cognibrains") to MG, who also thanks the  Institut Universitaire de France (IUF) , the  CNRS  and the  University Paul Sabatier  for support. M. Paoli ,  B. Paffhausen  and  G. Lafon  were also supported by the  ERC Advanced Grant  ("Cognibrains").
Results Our aim was to examine to what extent people can accurately ascribe confidence to their performance in an AM ordering task. There are two ways to address this question-Either more objectively, considering how participants respond to differences in the difficulty task (in this case comparing intra and inter period pairings)-And more subjectively, examining how confidence maps onto the accuracy of the first order judgement. Our study was carried out online on the platform Pavlovia  33. Analyses were conducted using R (version 4. 1. 1)  34. Scripts, auxiliary results, and data can be found on OSF: https:// osf. io/ wzt2p/? view_ only= 54405 0b3f6 9948d 089ed 40593 2299e 3f. Our main hypothesis was preregistered and tested with an alpha threshold of 0. 05 (i. e. the difference of confidence between pairs of memories associated with consistent judgement of order across blocks, and pairs of memories associated with contradictory judgements of order across blocks).
Method Participants. An a priori power analysis was conducted for a unilateral one-sample t-test with an alpha threshold of 0. 05, a power of 0. 80 and a medium Cohen's d of 0. 30 using the pwr R package (version 1. 3-0  48  ). This estimated a required sample size of 71 participants. In order to get this number of participants after the application of our exclusion criteria, we increased this estimation by 15% thus we aimed to recruit 82 participants. Eighty-three participants (M age = 25, SD age = 8. 6, 72% of females, 61% of French speakers) volunteered for this experiment. Participants who failed at least one attentional check, participants who did not write appropriate memories (e. g. a participant wrote rien, ("nothing"), for the majority of their memories) and those without data (e.
g. because of technical issues) were excluded. Finally, data from 51 participants were analysed (M age = 24. 2, SD age = 6. 9, 70% of females, 78% of French speakers). Participants were recruited through social media. Participants had to be over 18 years old, they had to be (or have been) university student (or equivalent) and they had to carry out the experiment on a computer. The experiment was available both in French and English. On average, the experiment lasted approximately 20 min. Participation was on a voluntary basis and no compensation was offered. This study was not approved by a named institutional and / or licensing committee. In France, researchers do not seek ethics approval when research involving humans are not concerned by the Law No. 2012-300 of March 5, 2012 of the Public Health Code.
Materials and design. This experiment was implemented on Psychopy (version 2021. 2. 3  33  ) with Python language (version 3. 6. 6  49  ) and was exported online on the website Pavlovia (https:// pavlo via. org/ 33 ) by using Javascript language (version ECMAScript 2021  50  ). Each participant generated 12 memories at the beginning of the experiment. Half of the memories were memories from their high school, and the other half of the memories were memories from the time they were at university (or equivalent). Each memory had to be summarised with three or four words. It had to be related to a unique event, and not to a repeated one, and to an event that did not last longer than 1 day. Four memories from each period were randomly selected. After that, participants' memories were grouped into pairs.
Introduction 4DHumanOutfit is a new dataset of 4D human motion sequences, sampled densely in space and time, with 20 actors, dressed in 7 outfits each, and per-forming 11 motions exhibiting large displacements in each outfit. We designed 4DHumanOutfit to enable the combined analysis of shape, outfit and motions with humans. This results in a dataset shaped as a cube of data containing 4D motion sequences with three different factors that vary along the axes identity, outfit, and motion. Fig. 1  illustrates the morphology and clothing axes of this cube. Analyzing and modeling the dynamics of human garments during motion and across actors is a wellstudied problem in computer vision and computer graphics, with the goals of understanding human motion from partial data and generating realistic digital human animations.
This has applications in video understanding, including action and fashion recognition; telepresence, including virtual change rooms and fashion transfers; and entertainment, including animation content generation. Many existing works study this problem from a data-driven perspective, where the goal is to learn motion dynamics from example data. To facilitate these studies, three main types of datasets of humans in motion have been introduced. The first type of dataset contains 2D videos of dressed humans in motion e. g. [61] , which allows to capture the appearance of rich clothing dynamics observed in real garments. More recently, large-scale 4D datasets of minimally clothed 3D human bodies in motion have been published, either captured using acquisition platforms e. g. [45]  or computed by fitting models to sparse motion capture data e.
g. [38] , which allow to learn detailed 3D body shape deformations over time. To enhance such datasets with garments, recent works use physical simulation to drape clothing on this 4D data e. g. [51] , enabling therefore to model clothing dynamics for synthetically generated garments. 4DHumanOutfit contributes 4D data sampled densely in space and time of human bodies captured in different outfits and different motions. This combines the advantages of existing 2D datasets of capturing dynamic behaviour of layered clothing, including complex dynamics caused by seams and friction, with the advantages of existing 4D datasets of containing 3D shape information, including fine-scale geometric details. The data we present has been captured in a multiview acquisition platform that acquires 68 synchro-nized RGB streams at 50 frames per second, which are subsequently used to reconstruct densely sampled 3D geometric models with texture information per frame.
For this dataset, we provide the RGB videos with masked background, and the reconstructed motion sequences in 4D in different spatial resolutions. To demonstrate the potential of our dataset, we perform a baseline evaluation along each of the three axis independently. To this end, we introduce three tasks together with evaluation protocols. For the identity axis, we aim to predict the body shape of an identity given a 4D motion sequence of an actor wearing an arbitrary outfit. As reference solution for evaluating this task, we provide sequences captured in minimal clothing with body shapes resulting from fitting a standard parametric human body model  [35]  to the data. For the outfit axis, we aim to retrieve the outfit in a standardized pose from a given 4D motion sequence of an actor wearing an arbitrary outfit.
As a reference solution to evaluate this task, we provide static scans of the outfit acquired on a mannequin. For the motion axis, we aim to retarget motion between actors from a 4D motion sequence showing the source motion to a static 3D scan of the target actor. When applied to data within the datacube, reference solutions are available as every actor was acquired performing each of the motions in each of the outfits. These tasks demonstrate that each axis of the datacube provides unique information that can be exploited in a large variety of practical applications. The main contributions of this work are: The introduction of 4DHumanOutfit, a datacube of dynamic 4D human motion of 20 actors in 7 outfits each, performing 11 motions in each outfit, i. e. 1540 sequences in total. A large subset of 18 actors in 6 outfits and 10 motions is released for research purposes.
2D fashion datasets Many works focus on the creation of datasets of 2D images, as these are relatively easy to acquire. This includes early works, such as the Fashionista dataset  [63]  or works targeting to describe clothing with semantic attributes  [15] , as well as more recent datasets, such as FashionPedia  [27] , among many others made available for research purposes. A recent survey  [16]  provides an exhaustive list of published datasets until 2020, with a comprehensive classification of the tasks that can be addressed with 2D data. These include landmark detection, clothing parsing, retrieval, and attribute recognition. In addition, these datasets have allowed to tackle the task of virtual try on, where one can create high quality, compelling images  [34, 22]  or videos  [18]  of how a person would look like wearing a given clothing in a target pose.
Synthetic datasets Capturing data with cameras or 3D scanners usually requires tremendous human and material efforts. In order to circumvent this issue, generated synthetic datasets of clothed humans can be considered. In this category works have proposed different datasets, with one or multiple characters in different settings, by leveraging 3D editing tools, such as MakeHuman  [3] , Mixamo  [4] , or human body models, such as SMPL  [35]. For example, several datasets such as SURREAL  [57] , MHOF  [47]  or LTSH  [24] , place 3D models of humans on background images. These datasets have been designed to address the task of 2D or 3D pose estimation from a single image. Other datsets, such as AGORA  [43] , have increased the challenge by including images of multiple dressed persons with plausible interactions with the environment.
As all these images are static and lack 3D realism, they do not capture and model the complexity of real cloths' dynamics. To include dynamics in the data, most works leverage physics simulators, which can account for the type of clothing through physical parameters. Since the early work of Guan et al. [21] , several methods have considered different clothing parameters, which allow creating plausible variations in the wrinkle patterns present on cloths. Synthetic datasets, with modest sizes such as BCNet  [28] , or larger scale datasets, such as 3DPeople  [46]  and Cloth3D  [8, 37]  provide 3D models of clothed humans. The last two explicitly explore the three dimensions of identity, motion, and clothing. While the explored range of subjects, poses, and cloth variations is impressive, the realism of these data are limited by the accuracy of the simulator used to create the data.
The proposed 4DHumanOutfit dataset takes an alternative approach by capturing reality in a multi-view studio. Interesting recent works have even modeled synthetic clothing at the sewing pattern level, allowing to automatically adjust the garment size to a personalized shape  [31]. In our 4DHumanOutfit dataset we also release scans of the clothes on mannequins, which could allow to work on the clothes at the sewing pattern level. All these works providing synthetic datasets have highly contributed to the community to advance the algorithmic approaches. With our work we argue that the acquisition of actual humans performing dynamic motion in varied clothing is necessary to validate the applicability of existing approaches to real data.