dataesr/scientific-paragraphs-categorization huggingface dataset:
Learning in 6-to 7-year-old children is strongly influenced by three functions of attention: alertness, orienting, and executive control. These functions share a close relationship with body mobility, such as the posture adopted or a request to stay still during tasks. The aim of this study (ClinicalTrials. gov) was to analyze the influence of body posture (standing versus sitting) and the influence of these imposed postures compared to a free body mobility on attention functions in 6-to 7-year-old children. Twenty-one children (11 girls) with a mean age of 6. 7 ± 0. 6 years performed the Attention Network Test for Children in three-body mobility conditions: sitting still, standing still, and free to move. Three attentional scores were calculated which would separately reflect performance of alertness, orienting, and executive control.
The other contrasts enable us to compare attention scores between the sitting still condition and the standing still condition (A1, O1, and C1), and between the imposed postures (both sitting and standing still) and the free to move condition (A2, O2, and C2). Data are presented as mean (M) and standard deviation (SD). The significance threshold was set at 0. 05, and standard error (SE) is presented for each significant value. Effect sizes were analyzed by Cohen's d. Analyses were performed in STATISTICA ® software (TIBCO Software, Inc. , Tulsa, OK, United States).
ACKNOWLEDGMENTS We thank  Christophe Bolsius  ( Inspecteur de l'Education Nationale ) for allowing us to inform teachers and parents of children about this study, and we sincerely thank all children and their parents for their participation. We especially thank  Alexandre Hascoët  for his great help during the experiments and  Anne-Fleur André  for her participation in data management. We also thank  San Francisco Edit  for their revision of the manuscript.
mtDNA analyses The original genetic study  (Radtkey & Singer, 1995)  included 24 populations of E. editha in California, of which 14 had sample sizes of n=4 individuals or more and are included in the current analysis. Butterflies used in these analyses were sampled independently of the censuses used to assess diet breadths. We used 17 restriction endonucleases to identify 22 mtDNA haplotypes of E. editha, the distributions of which were recorded within and among 24 populations of the butterfly. This molecular technique is long outdated, but the data that it generated are not susceptible to bias for the questions we are asking, since we are not using them to aim at a target, such as a phylogeny. Instead, we use them as an index of genetic diversity, to ask whether an association existed between this index and diet breadth in the 1980s.
Changes of diet and diet breadth: long-term observations, 1980s-2010s Detailed accounts of natural selection and behavioral mechanisms underlying evolution of diet and preference have been published for two of our study sites, Schneider's Meadow and the Rabbit metapopulation, each account filling an entire paper  (Singer & Parmesan 2018 , 2019). This level of detail is beyond our current scope. Our dataset is heterogenous: censuses were more frequent in some populations than in others, and we did not visit every population in every year. Here we summarize decadal changes for the set of populations reported here in order to focus on our primary question of the relationships between diet breadth and extinction/colonization dynamics. Table  1  shows time-trends of diet breadth across decades in 15 populations for which we have long-term data on diet, 14 of which also provide data on mtDNA.
Genetic evidence: mtDNA and diet breadth Re-examination of the mtDNA dataset first published long ago  (Radtkey & Singer 1995)  reveals a relationship on which the original study did not comment: an inverse association between population-level diet breadth in the 1980s and mtDNA diversity (Figure  4 , Table  1 ); samples from populations using fewer host genera contained more mtDNA haplotypes. Figure  4. Numbers of mtDNA haplotypes found in the 14 study populations of E. editha plotted against the 1980's diet breadths shown in Figure  1  and Table  1. Because sample sizes were diverse, the association shown in Figure  4  might have stemmed from sampling more individuals from populations that happened to be monophagous than from those with broader diets. However, the opposite was the case: a regression of mtDNA sample sizes on diet breadth, using the data in Table  1 , gives a slope of +3.
ACKNOWLEDGEMENTS. Paul Ehrlich introduced MCS to E. editha in 1967. The  U. S. National Park Service  permitted work in Sequoia and  Kings Canyon  and  Yosemite National Parks , and the  U. S. Forest Service  permitted our work in Sequoia National Forest. Chris Thomas,  Helen Billington  and  David Ng , together with the authors, gathered data shown in Figure 1. Matt Forister ,  Maud Charlery ,  Lesley Lancaster ,  Jayme Lewthwaite  and  Chris Thomas  critiqued the MS. MCS was intermittently supported by the  National Science Foundation  between 1978 and 2004; Parmesan was funded by a  French Make Our Planet Great Again award  (project  CCISS , number  ANR-17-MPGA-0007 ).
PILOT SELECTION Pilot selection started by asking Dr. Mariane Bruno of the Pasteur University Hospital in Nice, France, to present to us some of her patients with the disabilities listed by the Cybathlon competition, who would be both motivated and physically able to sustain the competition and its constraints (a long training, plus traveling to the competition site). Three motor-impaired women entered in this selection process. The selection process itself consisted in a few sessions of the Graz BCI protocol  (Pfurtscheller and Neuper, 2001)  as implemented in OpenViBE  (Renard et al. , 2010). This protocol tests the ability of the subject to achieve left and right hand motor imagery. The data was collected at the hospital in two half-day sessions and the signals were further analyzed offline using time-frequency plots to check visually that there was some signal to discriminate between the tasks.
Investigation phase The investigation phase is fundamental to define the most suitable MI tasks for the subject. Indeed, the mental tasks must fulfill three criteria: the subject must be able to perform each task and be comfortable with it, the individual mental task must produce a recognizable brain pattern and it must not cause undesirable side effects, like spasms, discomfort or stress  (Schwarz et al. , 2016). We collected data over several sessions in one month, from the middle of June to the middle of July 2019. This phase took time because this experience was new both for the user, who had never used a BCI system before, and for our team. Indeed, it was the first time we worked with a disabled person, which obviously requires specific attention. Therefore, a preliminary phase was necessary to create collaborative relationship between the team and the user, to allow the user to become more familiar with the hardware and also to allow the team to understand how to effectively manage this type of experience, defining a suitable experimental protocol  (Lotte et al.
Training phase The objective of the training phase was to train the subject to perform the mental tasks selected in the investigation phase. In this phase, the pilot had to perform many MI tasks without any feedback, aiming both at improving her ability to manage the tasks and at creating the training set to calibrate the BCI classifier. The data were collected with the same hardware described in the previous investigation phase (see Figure  3 ). The sessions took place once a week from the middle of June to the end of August 2019 for a total of 8 sessions. At the beginning (sessions S04 and S05), the experimental protocol consisted in 5 runs with the combination of 4 commands, but the subject reported that it was hard because it required a lot of concentration. Therefore, from sessions S06 to S07, the protocol consisted in 4 runs (RH-NC, RH-MUS-NC, RH-LAN-NC and RH-MUS-LAN-NC) and we collected 10 trials per task and run.
Figure7shows an illustration of the closed-loop BCI game. Basically, the EEG signal is acquired from the 13 channels (the same as in the training phase) and is bandpass filtered. In parallel, the EMG signal is processed in order to detect possible hand movement artifacts. Epochs corresponding to EMG artifacts are removed. Then, each retained epoch is tested for eye blink artifacts. EMG and EEG artifact rejection is detailed in the following paragraph. Each processed epoch provides a feature vector which is classified by the LDA classifier trained using the training dataset. Finally, the classification outputs are mapped to the video-game commands. In particular NC task was applied to move the car along the straight portions of the race track, RH task to turn the car right, MUS task to turn the car left and LAN task to switch on the lights.
These included smoking, drinking alcohol or the body mass index (BMI). Self-reported sleep quality and quantity were also controlled. The Hospital and Anxiety Depression Scale (HADS  42  ) and the Mini Mental State Examination (MMSE  43  ) were administered to measure anxiety, depression and global cognitive functioning, respectively. Following the test norms, a cut-off score ≥ 11 was used as a clinical score for the anxiety and depression subscales of the HADS, and a cut-off of < 27 was used for the MMSE. 8 participants were excluded based on these criteria. Among the young participants, 5 had a high depression or anxiety score and 1 had a BMI of > 35. One older adult had an elevated anxiety score and one had a score < 27 on the MMSE. For 16 more participants, there was a technical issue affecting physiological data acquisition which prevented us from analyzing the data.
is a self-reported 20item questionnaire assessing anxiety. The questionnaire focuses on the current state of anxiety, asking how participants feel "right now, " in response to items that measure subjective feelings of apprehension, tension, nervousness, worry, and activation/arousal of the autonomic nervous system. Physiological stress. Stress response was assessed using the EMPATICA E4 Wristband (Empatica E4, Italy), which is a medical-grade wearable device that permits the acquisition of physiological data such as electrodermal activity, temperature and heart rate. The reliability and accuracy of EMPATICA E4 for research on HR and HRV have been previously demonstrated through comparisons with electrocardiography (ECG) measurements  45. Heart rate was recorded in order to extract the inter-beat intervals (IBI).
IBI were then used to obtain HRV outcomes using the Kubios HRV software (v. 3. 3. 1). A visual check was performed to identify the presence of artefacts or occasional ectopic beats and a very low threshold filter was applied when judged necessary. The two main HRV outcomes were the High Frequency band (HF) and the Root Mean Squared of Successive Differences (RMSSD) as they reflect parasympathetic activity through parasympathetic mediated changes in HRV  46. The HF spectrum is a frequency domain measure (range from 0. 15 to 0. 4 Hz), called the respiratory band, that reflects parasympathetic or vagal activity on variations affecting the respiratory circle  47. The HF power was computed in normal units as a percentage of the normalized total power which ranges from 0. 04 to 0. 4 Hz (total power minus the very low frequency)  48.
The physiological data were recorded for the whole session in order to accustom participants to wearing the wristband. By placing tags (or triggers) on the recording, the pre-test and post test periods were timed to last between three and 5 min (the recommended range for assessing short-term HRV  53  ) and took place, respectively, immediately before and after DSB, with the participants comfortably seated and not engaged in any cognitive demanding task. Deep and slow breathing. The breathing exercise was guided by a video displayed on a computer screen on which a drop of water moved up and down in circles. When the drop went up, the participant had to inhale and when the drop went down the participant had to exhale. At first, the inhalation and exhalation duration were equal (4 seconds) and then, little by little, exhalation became longer than inhalation (4 seconds in and 6 seconds out).
Statistical analyses. All the statistical analyses were performed using RStudio software  61. For all statistical analyses, the employed statistical significance threshold was set at p < 0. 05. Pre-DSB and post-DSB anxiety scores were obtained from the SAI questionnaire. Since these variables were very left-skewed, the data did not meet the normality assumption. Therefore, Wilcoxon tests (non-parametric tests) were conducted to determine whether the reduction in self-reported anxiety occurred after DSB across all participants and within each age group. RMSSD and HF power were obtained from the HRV analyses performed with Kubios on the data from the Empatica E4 Wristband. A visual examination confirmed the normality of the distribution. A mixed analysis of variance (ANOVA) using a 2x (Group: young vs older participants) by 2x (Time : pre DSB vs post DSB) design was therefore conducted, with Group as between-subject variable and Time as within-subject variable.
Acknowledgements The authors are supported by the  European Union  under the  European Regional Development Fund (FEDER)  in the context of a pluridisciplinary project " Aging, Chronic Illness and Cognitive Stimulation ". The authors would like to thank  Pierre Chausse  and  Laurie Mondillon  for designing the breathing task used in the present study as well as  Stéphanie Picq  and  Anne Groenendaal  for their assistance with the experimental trials.
Our native language influences the way we perceive speech sounds, affecting our ability to discriminate non-native sounds. We compare two ideas about the influence of the native language on speech perception: the Perceptual Assimilation Model, which appeals to a mental classification of sounds into native phoneme categories, versus the idea that rich, fine-grained phonetic representations tuned to the statistics of the native language, are sufficient. We operationalize this idea using representations from two state-of-the-art speech models, a Dirichlet process Gaussian mixture model and the more recent wav2vec 2. 0 model. We present a new, open dataset of French-and English-speaking participants' speech perception behaviour for 61 vowel sounds from six languages. We show that phoneme assimilation is a better predictor than fine-grained phonetic modelling, both for the discrimination behaviour as a whole, and for predicting differences in discriminability associated with differences in native language background.
, 2016; Dunbar et al. , 2017). We generate fine-grained statisticallydriven representations of the experimental stimuli using a Dirichlet process Gaussian mixture model  (Chen et al. , 2015) , the model proposed by  Schatz et al. (2021)  as an account of early phonetic learning. We also test wav2vec 2. 0  (Baevski et al. , 2020) , a more recent speech representation model. We compare these as predictors on a novel, open speech perception dataset containing French and English participants' behavioural results for 61 different vowels in Brazilian Portuguese, German, Turkish, Estonian, French and English. Our goal is to assess which model predicts participants' discriminability behaviour the best for the two groups, and, which one is able to predict the difference of behaviour between the two groups, presenting a native language effect.
Discrimination dataset To compare the predictions of PAM and finegrained acoustic models, we create an experimental dataset that measures French-and Englishspeaking listeners' ability to discriminate native and non-native vowels. Participants judged stimuli constructed from twelve Brazilian Portuguese vowels ([ ĩ], [i], [e], [E], [ẽ], [ũ], [u], [5], [õ], [o], [a], [O]), seven standard German vowels ([e], [i], [E], [a], [o], [O], [a:]), eight Bavarian-accented (Mu- nich) German vowels ([i:], [I], [Y], [U], [ø:], [y:], [u:],[a:]), five Turkish vowels ([i], [y], [u], [W], [oe]), six Estonian vowels ([i:], [ø:], [y:], [7:], [ae:], [a:]), thirteen French vowels ([i], [E], [e], [oe], [a], [Õ], [y], [ø], [o], [u], [O], [Ẽ], [Ã] ), and ten English vowels ([i], [I], [E], [eI], [u], [U], [2], [oU], [ae], [A] ).
Stimuli Native speakers of English, French, Brazilian Portuguese, Turkish, Estonian and Bavarianaccented German recorded consonant-vowelconsonant (CVC) stimuli in carrier sentences. We also used recordings of six speakers taken from the OLLO database  (Wesker et al. , 2005) , in standard German. Together, we use these CVC stimuli as the basis for items in both a discrimination and an assimilation experiment, described below. Some phones from different languages shared the same IPA symbol (for example, Turkish and French [i]). We treat these as distinct phones for the purposes of our analyses. All the stimuli were resampled to 16000 Hz, and we equalised their volume.
Discussion and summary We have presented a dataset of non-native speech perception combining a native phoneme assimilation task with a discrimination task, appropriate for exploring questions about the role of native language categories in speech perception. The data is open, and contains a large number of vowels, presented to French-and English-speaking listeners. Our analysis shows that French and English speakers' patterns of assimilation to native vowel phonemes are good predictors of discriminability by naive listeners, consistent with the Perceptual Assimilation Model. The prediction, however, is not perfect. We then did a comparable analysis using rich, fine-grained phonetic models obtained by applying machine learning to speech, which do not directly use coarse-grained phoneme categories.
Acknowledgements Before doing the experiment, participants first had to listen to reference words for each of their native vowels, in order to be able to recognise them in the stimuli we gave them afterwards. Then, they trained for the task, first judging each of the vowels from their native language (ten for English participants, thirteen for French), following the process described above, then one non-native example. After this training, the experiment started. They performed the assimilation task on 63 to 75 stimuli. This number differed because of counterbalancing. Three breaks were included in the experiment, during which participants could rest. During the breaks, they also had to listen to the reference words for each vowel again, uttered by another speaker. There were five catch trials during the test in order to test participants' attention, and filter them out, see Section B.
This research was supported by the École Doctorale Frontières du Vivant (FdV) -Programme Bet-tencourt, and by grants ANR-17-CE28-0009 (GE-OMPHON), ANR-11-IDFI-023 (IIFR), ANR-11-IDEX-0005 (USPC), ANR-10-LABX-0083 (EFL), ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute. This work was performed using HPC resources from GENCI-IDRIS (Grant 20XX-AD011012415).
Selection of items For the first step, 2 neuropediatricians who had experimented with the management of patients with polyhandicap for more than 15 years, suggested candidate severity criteria on the basis of an international literature review. The search was performed within the past 20 years (1984 to 2014; 1 st included patient in the study: 1/3/2015) using the Medline database. Medical subject headings (MeSH) reflecting the actual key search concept were determined by the following significant terms: "Polyhandicap" or "Profound Multiple Disability(ies)" or "Profound Intellectual and Multiple Disability(ies)" or "cerebral palsy". The first search yielded a total of 436 citations, and a second manual selection was performed based on the titles and the abstracts of the citations, excluded all citations not focusing on the health status.
Data collection The final set of items was included in the data collection of the French national Polyhandicap study. The items were completed by the healthcare workers in charge of the patient. The following data were collected to describe the sample and assess the external validity of the tool: 1) Sociodemographic data: age, age categories (child, adult), gender. 2) Aetiology status: classified as unknown or known, progressive or non-progressive. 3) Health status: -Impairments: i) severe motor impairments: tetraparesis, paraparesis, and hemiparesis; ii) other neurologic impairments: movement disorders, severe dystonia, global hypotonia, extrapyramidal syndrome; and iii) behavioral disorders (including withdrawn behavior  [13] , intermittent screaming and crying, agitation, self-aggressivity or heteroaggressivity, stereotypies and/or merycism).
Acknowledgements The authors are grateful to  Claire Morando  for her logistical support. The French Polyhandicap Group includes the following individuals:  Tanguy Leroy ;  Souhali Haddadou ;  Cécile Freihuber ;  Sofiane Amalou ;  Julie Bonheur ;  Stéphanie Valence ;  Marie-Christine Nougues ;  Laurent Luciani ;  Jean-Pierre Nouet ;  Catherine Coiffier ;  Philippe Sellier ;  P Julien ;  JC Grasset ;  S Delvert ;  M Gaulard ;  A Belorgey ;  Sophie Mathieu  ;  Mustafa Ardati  ; Kammache I ;  Delphine Héron  ;  Arnaud Isapof  ;  Alexandra Afenjar ;  K Maincent ;  Diana Rodriguez  ;  Diana Doummar ;  Marie-Laure Moutard ;  Daniel Willocq  ;  Julie Teulade ;  Stéphane Pietra  ;  Stéphane Lenormand ;  Etorre Laracca ;  Valérie Aynie ;  Elizabeth Grimont ;  Fatima Zahra Ouadghiri ;  David Drummont ;  Marie Vonthron ;  Anaïs Renault ;  Aline Sider ;  Arnaud Isapof ;  Bénédicte Héron ;  Isabelle Kemlin ;  Kim Maincent ;  Florence Renaldo ;  Denis Garel  ;  Mathilde Heulin ;  Coraline Grisel ;  Mélodie Aubart ;  Mélanie Cochez ;  Marion Keller ;  Aurélie Sellam ;  Claire Bastian ;  Nathalie Dorison.
Statistical analysis Behavioral data were analyzed with Linear Mixed Effects (LME) models for response times (RTs -log 10 transformed) and Generalized (logistic) Linear Mixed Effects (GLME) models for errors, with random effects for participants and items, using the lme4 library (version 3. 5. 1) in R software  (Bates et al. , 2015). These analyses tried to maximize randomness by including whenever possible random slopes in addition to random intercepts  (Barr et al. , 2013). We report b-values, standard errors (SEs) and t-values (for RTs) and z-values (for Error Rates), with tand z-values beyond |1. 96| deemed significant  (Baayen, 2008). A complementary Bayes factor analysis  (Wagenmakers, 2007)  was performed using the BayesFactor library in R  (Rouder, 2012; Morey and Rouder, 2011). Type of Change (transposition vs.
Behavioral data As RTs were not normally distributed, they were first converted using a logarithmic transformation. The CSE on RT was estimated using a linear mixed model comparing the log RTs between the four conditions and both groups. The fixed factors were the congruency of the preceding trial (Pre_Cong: Pre_congruent vs. Pre_non-congruent), the congruency of the current trial (Congruency: congruent vs. non-congruent) and the group (LLD and HC). Participant was added as a random intercept. Mixed models optimize statistical power as they use all available data, account for intrasubject repeated measures correlation and easily handle unbalanced designs  (Hox et al. , 2017). As recommended by  Nakagawa and Schielzeth (2013) ,we reported the conditional (cR2) and marginal R2 (mR2) using the {MuMIn} package  (Bartoń, 2013).