dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , response variable). All statistics were performed in R version 3. 1. 3 (R Core Team 2015), with the significance threshold set at a ¼ 0. 05. All results are discussed using the estimated parameter values provided by the models. All networks were visualized using R package igraph  (i. e. , in total 2012: 12, 2014: 13, 2015 : 12 networks; package igraph v. 0. 701; Csardi and Nepusz 2006; Figure  2 ).
ACKNOWLEDGMENTS The present research was supported by a grant from the  German Research Council  to AA, DFG, Ab/45 10-2. We thank  Daniela Bernhard  and  Tamara Hagmaier-Göttle  for helpful suggestions to an earlier version of this paper. Julia Burger  (Study 6),  Jenny Hibbeler  ( Study 1c ),  Annette Kasprowicz  ( Study 4b ),  Lea Langer  ( Study 1c ), and  Saskia Rietzel  (Study 1b) helped in data collection.
Stimuli We manually selected 121 words from Lexique  (35) , a large database with lexical properties of French words. There were four word categories: brightness-conveying words (e. g. illuminé or 'illuminated'; N=33), darkness-conveying words (e. g. foncé or 'dark'; N=33), neutral words (e. g. renforcer or 're-inforce'; N=35), and animal names (e. g. lapin or 'rabbit'; N=20). During the experiments, words were shown as dark letters (8. 5 cd/m 2 ) against a gray background (17. 4 cd/m 2 ). Because we wanted to compare the pupillary responses to brightness-and darkness-conveying words, these two categories needed to be matched as accurately as possible. We focused on two main properties: lexical frequency, how often a word occurs in books (bright: M = 41 per million, SD = 147; dark: M = 39, SD = 119); and visual intensity (bright: M = 1.
Acknowledgements The research leading to these results has received funding from: the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n°  622738  awarded to Sebastiaan Mathôt;  Intra-European Fellowship  ( FP7-PEOPLE-2012-IEF ) of the  Marie Curie Actions (Grant   302807 ) awarded to  Kristof Strijkers.
This methodology allows to estimate the performance of a binary classifier as its threshold is varied, by representing the true positive rate (TPR) against the false positive rate (FPR,  Bamber, 1975). In this regard, for each subject a k-fold cross-validation (k = 10,  McLachlan et al. , 2005)  has been performed on the spectral features dataset related to the Calibration scenarios [PSD calculated by using frontal theta and parietal alpha bands, f j (t) ]. In particular, all the possible (k-1) subsamples have been used to train the asSWLDA classifier, and the related parameters ( w j i , b j ) have been applied on the remaining subsample to compute the Linear Discriminant Function [ y j (t), Formula 7]. All the y j (t) values related to the k iterations, have been stacked in the Y vector (Formula 8).
ACKNOWLEDGMENTS This work is co-financed by the  European Commission  by  Horizon2020 projects Sesar-01-2015 Automation in ATM, "Stress, " GA n. 699381 and Sesar-06-2015 High Performing Airport Operations , "MOTO, " GA n. 699379. The grant provided by the  Italian Minister of University and Education  under the PRIN 2012, GA n. 2012WAANZJ scheme to FB. is also gratefully acknowledged.
EEG Acquisition EEG recordings were acquired with the Biosemi ActiveTwo System (Biosemi B. V. , Amsterdam, The Netherlands) using active Ag-AgCl electrodes mounted on an elastic cap (Electro-Cap, Inc. ). The recordings were made in single-ended mode that amplify the difference between each electrode site and a common mode sensor electrode with referencing taking place offline. Impedance does not need to be lowered with this system due to the combination of preamplifiers at each electrode site, a driven right leg circuit, and high electrical isolation that provide good signal-to-noise ratio (see  Kappenman & Luck, 2010 , for more details). However, to ensure good signal quality, it was confirmed the offset potentials for each electrodes were under ± 40 mV (a recommendation from Biosemi), and visual inspection of the data was performed prior to and throughout the recording to ensure stability and quality of the EEG signal.
EEG was recorded from 61 scalp sites (10-20 system positioning, see Figure  2 ), a vertical eye channel for detecting blinks, a horizontal eye channel to monitor for saccades, and two additional electrodes affixed to the mastoid bone. The EEG was recorded with a low-pass hardware filter at 104 Hz and then digitized at 512 Hz with 24 bits of resolution. Signal processing and analysis were performed in MATLAB using EEGLAB toolbox  (Delorme & Makeig, 2004)  and ERPLAB toolbox  (Lopez-Calderon & Luck, 2014). All channels were referenced offline to an average of the mastoids. Offline filtering was applied (band-pass 0. 1-30 Hz). Artifact detection was performed using the steplike artifact function available through the ERPLAB toolbox and confirmed with visual inspection. Trials with blinks, eye movements, and muscle artifact were rejected prior to averaging.
Data Analysis ERP averages were formed by time-locking to the onset of the target word and averaging across primed trials for each condition and across unprimed trials for each condition from 100 ms prior to target onset until 700 ms after using a -100 to 0 baseline. Mean amplitude measurements were taken between 150-350 ms (N250 epoch) and 350-550 ms (N400 epoch). Six regions were computed by collapsing across the following electrodes (Figure  2 ): left frontocentral (LFC) region-F7, F5, F3, FT7, FC5, FC3; midline frontocentral (MFC) region-F1, Fz, F2, FC1, FCz, FC2; right frontocentral (RFC) region-F8, F6, F4, FT8, FC6, FC4; left centroparietal (LCP) region-T7, C5, C3, TP7, CP5, CP3; midline centroparietal (MCP) region-C1, Cz, C2, CP1, CPz, CP2; right centroparietal (RCP) region -T8, C6, C4, TP8, CP6, CP4.
Acknowledgments This research was funded by the  Ellison Medical Foundation. MDE was funded by  F32HD061180 , PJH was funded by  HD25889 , and  Jonathan Grainger  was funded by  ERC (European Research Council ) advanced grant  230313. We thank the families who took part in this study. Marianna Eddy  is now at  US Army Natick Soldier Research, Development, and Engineering Center  and the  Center for Applied Brain and Cognitive Sciences at Tufts University.
Braille reading tests The subjects' braille reading speed was tested 6 times-at the beginning of the course (baseline), and once per month, starting in the fifth month (Fig  1A ). Blindfolded participants were asked to read as many letters as possible aloud in 60 seconds time. Next, the procedure was repeated for words. Words were 3-6 letters long, and their frequency was higher than 1 per million, according to the SUBTLEX-PL database  [32]. Stimuli were printed on a single braille sheet. In each testing session, a new set of words was used, and the order of letters was shuffled. The distribution of words' frequencies was similar across all versions of the test. Because we expected word length to have a significant impact on reading speed  [33] , the word lists were ordered such that on each version of the test, words of specific length appeared in exactly the same order (e.
Data analysis All analyses were performed using SPSS 22 (SPSS Inc. , Chicago, USA). To test for a general increase in braille reading speed, a repeated-measures ANOVA was performed on the number of words read in each testing session. Two subjects were excluded from the analysis, due to missing data from either the third or the fifth testing session. Twenty-seven subjects attended all six testing sessions and were included in the ANOVA. Subsequently, paired t-tests were used to compare word and letter reading speeds in the baseline session with the word and letter reading speeds in each of the following testing sessions. Comparisons including the third session and the fifth session were performed on 28 and 27 subjects, respectively, due to missing data. In the case of other testing sessions, all subjects were included.
It is possible that tactile acuity becomes critical at higher braille reading speeds. Alternatively, this relationship might have been obscured by other factors, for example differences in subjects' motivation and persistence. Unfortunately, tactile acuity data was collected only at the end of the braille course, which makes impossible to test for a change in this measure, potentially induced by braille learning. Nevertheless, data collected at the end of the course shows that even subjects with low tactile acuity were able to read whole braille words. Thus, the spatial resolution of the sighted's sensorimotor system is not a limiting factor for learning whole-word braille reading, at least at this learning stage. Learning braille in sighted adults and blind children: a comparison After 9 months of training, our sighted subjects reached mean braille reading speed of 6 wordsper-minute.
Acknowledgments We thank  Adam Ryba  and  Weronika Dębowska  for connecting us to the braille communities,  Institute for the Blind and Partially Sighted Children (Krakow) ,  Academy of Special Education (Warsaw)  and  Laboratory of Brain Imaging (Warsaw)  for help in course preparation,  Karolina Dukała  for administrative assistance, and three anonymous reviewers for their input in helping to improve the manuscript.
Items To justify the use of the predictability metric rather than the more conventional consistency metric, we first verified that predictability reflects a psychologically valid construct. We created two models from the full dataset that is analysed in Experiment 1 (see below). The models were nearly identical to those described in the Results section below. The independent variables were predictability (coded as a binary contrast) or consistency (centred ratio of friends to enemies), centred log frequency, and the two-way interaction. Note that we centred all continuous independent variables (by subtracting each value from the mean) and contrast-coded dichotomous conditions (as 0. 5 and -0. 5) because LME provides parameter estimates as deviations from the point closest to zero rather than deviations from the mean.
For the experiment, we used only monosyllabic words, because traditionally, measures of regularity and consistency, which form the basis of the predictability construct, have been defined for monosyllabic words only (but see  Chateau & Jared, 2003; Kearns et al. , 2014; Yap & Balota, 2009 , for an extension of the consistency measure to multisyllabic words). We extracted all monosyllabic words from the British Lexicon Project (BLP;  Keuleers, Lacey, Rastle, & Brysbaert, 2012). We retained all words with log frequencies between 0 and 2, because analyses of largescale lexical databases have shown that the frequency effect is most robust for this log frequency range  (Balota et al. , 2007; Brysbaert et al. , 2011; Ferrand et al. , 2010; Keuleers, Diependaele, & Brysbaert, 2010). All words had a lexical decision accuracy > 80%, suggesting that the words should be familiar to the majority of undergraduate students.
In separate analyses, each centred potential covariate was used as the dependent variable; centred frequency, contrast-coded predictability, and their interaction were used as predictor variables. The outcomes of this set of analyses are shown in Table  1. The individual items and their full descriptives, as well as the raw data and the R script used for the current study, can be found here: osf. io/hm8fw. Note that orthographic neighbourhood and Phonological Levenshtein Distance (the number of phoneme substitutions, deletions, or additions which are required to reach the nearest 20 neighbours; see  Yarkoni, Balota, & Yap, 2008 ) co-vary with frequency, and the ratio of letters to phonemes differs across predictable and unpredictable words. However, the critical comparison in the current experiment is the interaction between predictability and frequency, and none of the covariates show a stronger manipulation for the predictable than unpredictable condition, all p > 0.
Results and discussion The reading aloud responses were scored offline with the software CheckVocal  (Protopapas, 2007) , as correct, incorrect, or no response. Response latencies were readjusted using CheckVocal, based on the onset of the sound waves, in the case of premature of late voice key triggers. This removes potential biases associated with first phonemes. The data were further analysed using the software R, both with Linear Mixed Effect (LME) models  (Baayen, Davidson, & Bates, 2008)  and with Bayes Factors  (Morey & Rouder, 2014; Rouder, Speckman, Sun, Morey, & Iverson, 2009). LMEs allow us to obtain an estimate of the slope (which serve as descriptives given the use of a continuous measure of frequency). We provide the results of t-tests, and p-values, when appropriate, to provide a point of reference for those unfamiliar with Bayesian analyses.
2% of the data) and > 1200 ms (0. 1% of the data). This yielded an approximately normal distribution of inverse RTs. When we artificially dichotomise frequency into high (log frequency > 1) and low (log frequency < 1), the averages RTs of the trimmed dataset are 494. 2 (SD = 97. 8) and 494. 1 (SD = 97. 2), respectively, for the predictable words, and 503. 6 (SD = 97. 5) and 528. 3 (SD = 115. 1), respectively, for the unpredictable words. The LME showed a significant main effect of predictability, β = -0. 08, t = -5. 3, p < 0. 0001, reflecting shorter RTs for predictable ("forge") than unpredictable ("ghost") words, and a main effect of frequency, β = -0. 05, t = -3. 5, p = 0. 0006, indicating shorter RTs for words of higher frequencies. The interaction was also significant, β = 0. 07, t = 2.
Follow-up analyses To potentially strengthen the case for the interaction, we retrieved all triallevel data for our items from the English Lexicon Project reading aloud database  (Balota et al. , 2007). Note that we included data from the ELP and not the BLP because the ELP has both lexical decision and reading aloud data, while the BLP only has lexical decision. As both the ELP and our experiment employed a standardised reading-aloud procedure, we can increase the amount of evidence by collapsing the two datasets. The ELP contains trial-level reading aloud data for 375 of the original 376 words. These include 10342 valid and correct trials, with an average of 27. 6 participants per word. We combined the data from our experiment with data of the ELP. The trimming procedure of this bigger item set was identical to that of the original data, as was the model, except that previous RT was not included, as it was unavailable in the ELP database.
Items We retrieved words and their corresponding information from the Lexique 2 database  (New, Pallier, Brysbaert, & Ferrand, 2004 ) and the French Lexicon Project  (Ferrand et al. , 2010). For frequency, we relied on subtitle counts  (Brysbaert et al. , 2011; Brysbaert & New, 2009; New, Brysbaert, Veronis, & Pallier, 2007). We again removed words with log frequencies of < 0 or > 2. To classify words as complex or simple, we used the ratio of letters to phonemes in each word: the presence of multiletter correspondences means that multiple letters correspond to a single phoneme, thus a complex word has a letter-to-phoneme ratio > 1. Simple words were those with a letter-to-phoneme ratio of one (e. g. , "garnir"), and words with a ratio of greater than one were considered complex (e. g. , "gâteau").
In the database, this procedure classified 280 words (8. 9%) as "simple", and 2852 (91. 1%) as "complex". We selected 384 words, half with complex correspondences ("gâteau") and half with simple correspondences ("garnir"). The words were chosen to vary in frequency, where half the items had frequency counts lower than 1, and the other half higher than 1. In addition, the items were chosen such that they did not differ, across conditions, on average grapheme consistency  (Lété, Sprenger-Charolles, & Colé, 2004) , suggesting that there were no differences in the degree of unpredictability. Overall, the French orthography has a high degree of predictability once complex rules are taken into account  (Schmalz et al. , 2015; Ziegler, Perry & Coltheart, 2003). However, there are some words with ambiguous pronunciations (e.
g. , "femme", where the second letter is pronounced as /a/ rather than the default /ε/). While, to our knowledge, there is no quantification method of regularity that can be applied to polysyllabic words in French, the Manulex database contains average grapheme consistency ratings  (Lété et al. , 2004). We use these as a measure of unpredictability, as words with unpredictable pronunciations necessarily have graphemes that can be pronounced in multiple ways. All items had a lexical decision accuracy, according to the FLP, of > 80%. The descriptive statistics are listed in Table  2. For the full item set with individual word characteristics, as well the raw data and R scripts, see here: osf. io/hm8fw. Again, in the results section, we will follow up with covariate analyses to ensure that the results cannot be explained by the variables that differ as a function of the manipulation.
Results and discussion The data were scored with CheckVocal as correct, incorrect, or no response, and the RTs were adjusted when the voice-key had been triggered prematurely or late (again, adjusting for potential biases associated with first phonemes). As for the English analyses, we used inverse RTs as the dependent variables, continuous centralised frequency and binary contrast-coded complexity (-0. 5 = simple) as independent variables, and previous RT. Participants and items were included as random factors, and the effect of frequency was allowed to vary across participants. As the items were matched on the number of letters (as is common in studies on multi-letter rules; see  Rastle & Coltheart, 1998; Rey et al. , 1998) , the simple ("garnir") condition had, by definition, more phonemes than the complex ("gâteau") condition.
Acknowledgements We thank  Melvin Yap  and  David Balota  for making the trial-level ELP data available to us, and  Sachiko Kinoshita  for valuable discussions. Correspondence concerning this manuscript should be addressed to  X. Schmalz , email: xenia. schmalz@gmail. com,phone:  +39 320 639 2523 , fax:  +39 049 827 6547 ;  Dipartimento di Psicologia dello Sviluppo e della Socializzazione, Università degli Studi di Padova, Via Venezia 15, 35131 Padova, Italy.
The total duration of the recording time was approximately 90 minutes. Behavioral data recording and pre-processing Reaction times (RTs) were not recorded from the voice-key triggering system given by the serial response box of E-Prime. Several studies have indeed shown that differences in microphone sensitivity can lead to important differences in the detection of the acoustic energy produced by the first and second phonemes  (Rastle & Davis, 2002; Duyck, Anseel, Szmalec, Mestdagh, Tavernier, & Hartsuiker, 2008; Rey, Courrieu, Madec, & Grainger, 2013). Therefore, letter naming was completely digitized at 22040 Hz on a single channel in order to check for response accuracies and to determine RTs offline. On each digitized trial, we first employed a semi-automatic procedure by applying an algorithm based on Teager-Kaiser operator, which detect sudden variations of energy in the acoustic signal  (Li, Zhou, & Aruin, 2007 ; see also  Riès, Legou, Burle, Alario, & Malfait, 2012).
EEG data recording The EEG was recorded from 64 Ag/AgCl Active-2 pre-amplified electrodes (BIOSEMI, Amsterdam; 10-20 system positions). The vertical electro-oculogram (EOG) was recorded by mean of one electrode (Ag/AgCl) just below the right eye. The horizontal EOG was recorded with two electrodes (Ag/AgCl) positioned over the two outer canthi. Analog signal was digitized at 1024 Hz. Electrode offsets were kept below ±25 µV. EEG pre-processing Offline, data were referenced on the average of the 64 scalp electrodes. Continuous signals were band-pass filtered by using a butterworth filter of order 4 between 1Hz and 100 Hz. Because the purpose of this experiment is to find onsets of differences between uppercase and lowercase letters, we relied on the property of causal filters (filters applied in only one direction, as opposed to non-causal filters applied in both forward and backward directions) to preserve onsets of differences  (Rousselet, 2012; VanRullen, 2011; Widmann & Schroger, 2012).
BSS-CCA method was applied on nonoverlapping consecutive windows of 1. 2 seconds (corresponding to the maximum length of an epoch) enabling the targeting of local EMG bursts related to articulation (we used the EEGLAB plug-in Automatic Artifact Removal implemented by Gómez-Herrero available at http://www. cs. tut. fi/~gomezher/projects/eeg/software. htm#aar). EMG related components were selected according to their power spectral density. Components were considered to be EMG activity if their average power in the EMG frequency band (approximated by 15-30 Hz) was at least 1/5 of the average power in the EEG frequency band (approximated by 0-15 Hz) (see  De Vos et al. , 2010). Finally, all other artifacted epochs were rejected after a trial-by-trial visual inspection.
Acknowledgements This study was funded by the  European Research Council  ( ERC Research Grant   230313 ) and carried out within the  Labex BLRI  ( ANR-11-LABX-0036 ). It has benefited from support from the  French government , managed by the  French National Agency for Research (ANR) , under the project:  Investments for the Future, A*MIDEX  ( ANR-11-IDEX-0001-02 ). S. K. R. was supported by a post-doctoral grant from the  Fyssen Foundation  and by the  National Institute on Deafness and Other Communication Disorders of the National Institutes of Health  under Award Number  F32DC013245. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
MARTIAL ARTS STUDIES 96 Spring 2016 three Data and Methods In this paper, I examine the aikido interaction following an empirical perspective inspired by ethnomethodology  [Garfinkel 1967; Clayman and Maynard 1995]. I describe the aikido movements as they are performed from the practitioners' point of view in naturally occurring interaction  [Heritage and Atkinson 1984] , relying on video recordings  [Sacks 1984: 26]  analyzed through sets of screenshots. 2 All the extracts examined in this paper are taken from a video corpus recorded in Japan in 2007. The extracts detail techniques performed by aikido master Tada Hiroshi (born 1929), a direct student of Morihei Ueshiba. In these extracts, Hiroshi is shown with a partner interacted in the same semiotic structure, the structure introduced previously as Aihan-mi Katate-dori Irimi Nage.
By contrast, the additive hypothesis would require no change in visual attention toward the non-realized outcome of the chosen lottery in the complete feedback condition compared with the partial feedback condition, and possibly no difference between the two forms of counterfactual information. When considering decision mechanisms,  Camille et al. (2004)  proposed a model of choice that incorporates anticipated regret and disappointment in addition to maximizing expected earnings. This approach has been successful in explaining choices in several other datasets  (Coricelli et al. , 2005; Larquet, Coricelli, Opolczynski, & Thibaut, 2010; Simioni et al. , 2012). Anticipating regret versus evaluating expected values (in order to maximize earnings) in choices implies several differences in terms of visual patterns of information acquisition.