dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis and scoring of dream content Word count. Audio reports were transcribed from audio to written language by the company TranscribeMe! (http://transcribeme. com/) and were subsequently checked by the experimenters before word count. Characters and settings. Crowds counted as one person if individuals were not explicitly specified, following the Hall & Van de Castle rating system  [32]. Among the existing characters, close family members and close friends were counted by RV and BC using the names mentioned a priori in the initial questionnaire. Similarly, among the known places, RV and BC counted the significant ones, defined as those mentioned in the initial questionnaire by the participants. RV and BC conducted scorings independently and, in case of disagreement between scores, reached a consensus after discussion.
g. la souris -the mouse vs elle sourit -she smiles). Participants were at chance, with 53% noun answers in the noun prosody condition and 50% in the verb prosody condition (β= -0. 26; z = -0. 43; p = 0. 66). The details of this control experiment can be found on the OSF database: https://osf. io/744pq/?view_only=c50cd5300feb4832ad58d3566dd041ee. noise obtained by superimposing the end of all filler sentences. Thus, there was no lexical disambiguating information following the ambiguous word 3. There were 16 test sentences, 8 in the verb prosody condition and 8 in the noun prosody condition. Each participant was presented with only one member of each pair. Two counterbalanced lists of stimuli were used, each list containing four sentences in the noun prosody condition and four sentences in the verb prosody condition, plus four filler sentences (two of them having an unambiguous noun as a target and the other two having an unambiguous verb as a target).
Data processing and analysis Toddlers' eye-gaze towards the images was recorded by an Eyelink-1000 while they listened to the test sentences, with a time-sample collected every 2ms. Before statistical analysis, the data was down-sampled by a factor of 10, by averaging the data from 10 adjacent samples, so that the final sampling rate was one sample every 20ms. Thirty-nine trials out of 320 were removed from the statistical analysis (17 in the noun condition and 22 in the verb condition), because more than 25% of the data frames were missing between the onset of the ambiguous word and the end of the audio stimuli. allows us to test for the effect of Condition without inflating the rate of Type I error. It proceeds in two phases. First, for each time point, a paired two-tailed t-test testing for the effect of Condition (noun prosody vs.
Acknowledgements This research was supported by a PhD fellowship from the  École normale supérieure to Alex de Carvalho , and by a postdoctoral fellowship from the  Fyssen Foundation  to  Isabelle Dautriche. It was also supported by grants from the  Région IIe-de-France ,  Fondation de France ,  LabEx IEC  ( ANR-10-LABX-0087 ),  IdEx PSL  ( ANR-10-IDEX-0001-02 ), as well as the  ANR ' Apprentissages ' ( ANR-13-APPR-0012 ). The authors thank  Anne-Caroline Fiévet ,  Luce Legros  and  Mélanie Rouelle  for their assistance with recruitment of participants and data collection as well as all the parents and babies who participated in this study.
Introduction The use of well-defined stimulus material is an important requirement in experimental research, allowing for replicability and comparison with other studies. For this reason, researchers interested in the perception of emotions often use datasets of stimuli previously validated with affective norms. An increasing number of such datasets exist for both facial expressions (e. g. , the Karolinska Directed Emotional Faces -70 individuals, each displaying seven facial expressions, photographed from five different angles,  Goeleven et al. , 2008) , vocal expression (e. g. , the Montreal Affective Voices -ten actors, each recording nine non-verbal affect bursts,  Belin et al. , 2008)  and musical extracts (e. g. , The Montreal Musical Bursts -80 short musical improvisations conveying happiness, sadness, or fear,  Paquette et al.
, 2013). However, using datasets of static stimuli, regardless of how well controlled, comes with a number of generic limitations. First, such datasets leave researchers only little control over the para-emotional parameters of the expressions (e. g. , which specific person is expressing the emotion or what verbal content accompanies the expression), while some research questions may require more control over the stimulus material: for instance, to investigate social biases, one may want a certain emotion to be expressed by members of two different social groups with the exact same acoustic cues (see e. g. ,  Neuberg 1989). Second, actor-recorded stimuli do not allow for fine control over the intensity with which emotions are expressed: e. g. , some actors may be more emotionally expressive than others, or perhaps more expressive when it comes to happiness than sadness (see e.
g. , specific speakers) remains limited to the stimuli that are included in the database. A final generic limitation of pre-recorded datasets is that they necessarily only consist of third-person stimuli. However, in many experimental contexts, one may desire to control the emotional expression of the participants themselves, and not that of unknown actors. For example, social psychology researchers may want to study participants' interactive behavior while controlling whether they sound positive or negative. It remains difficult to create such situations without demand effect, e. g. , not asking or otherwise leading participants to "act" happy or sad. Rather than a data set of controlled emotional stimuli, it would therefore be useful to have a data set of controlled emotional transformations, that can be applied to arbitrary stimulus material while still preserving well-defined properties of recognizability, intensity and naturalness.
With a suitable voice transformation tool, it should be possible to change the emotional expression of speech after it is produced and, if computed fast enough, the transformations could even appear to occur in "real time". With such a tool, one would be able to modify vocal emotional expressions in live and more realistic settings and study not only the perception of emotions in third-party stimuli, but also the perception of self-produced emotions, opening up a vast amount of experimental questions and possibilities. In this article, we present DAVID 1  , a novel open-source software platform providing a set of programmable emotional transformations that can be applied to vocal signals. The software makes use of standard digital audio effects, such as pitch shift and spectral filtering, carefully implemented to allow both realistic and unprecedentedly fast emotional transformations at the infra-segmental level of speech ("Emotional transformations").
Software distribution DAVID is a software platform developed to apply audio effects to the voice both online and offline. The platform provides four types of audio effects, or building blocks, that can be combined in different configurations to create several emotions: happy, sad, and afraid (and more are possible). DAVID is implemented as an open-source patch in the Max environment (Cycling74), a programming software developed for music and multimedia. The DAVID software and accompanying documentation can be downloaded under the MIT license from http://cream. ircam. fr. Using DAVID first requires to install the Max environment, which is provided in free versions for Windows and Mac systems. DAVID comes with the parameter presets used in the validation studies described below, but users also have full control over the values of each audio effect to create their own transformations and store them as presets for further use.
Roundtrip latency depends on the system's hardware and software, and can be easily optimized to the range 2-5 ms  (Wang et al. , 2010). However, when some processing is applied, low latencies can degrade sound quality, because the high rate at which computer and audio card exchange data provides less samples than needed for some algorithms to achieve a correct result. In the Max environment, the exchange rate between the CPU and the sound card is controlled by means of the I/O vector size (which corresponds to the input and output t ), while the signal vector size determines the exchange rate within Max itself. Our recommended software set-up for using DAVID in a real-time context consists of a I/O vector size: 256 samples and a signal vector size: 128 samples. The algorithmic latency is the delay added to the system's round-trip latency and needed to run the audio transformations.
All of the transformation modules in DAVID are based on the same pitch shifting engine, the harmonizer described in "Pitch shift". The only latency is thus given by the maximum delay time in the harmonizer, which is set by default to 10 ms. This latency does not depend on the system's hardware and software. Effect #2 Δt Effect #3 Δt Output Δt Input Δt Effect #1 Δt Total Δt Fig. 2  Illustration of the delays involved in the realization of our real-time audio processing system. Beyond a baseline I/O latency (input and output t ), each atomic effect in the signal data flow (3 as illustrated here) imparts further delay, which depends on the effect's algorithmic complexity Our recommended hardware set-up therefore is a system which allows to run DAVID with a minimal round-trip latency, using the above vector sizes: Note that the maximum acceptable delay depends on the context of the study.
Recordings were rejected in case of pronunciation errors and if there were clear recording artifacts, such as tongue clicks, breathing noise, microphone impact or pops. This selection was done based on the raw files, before manipulation with DAVID. The selected recordings were finally included in three behavioral experiments to test the validity of the software tool, yielding 24 different speaker-sentence combinations per language. For each speaker and sentence, we selected the first four neutral recordings for further processing. If the quality was insufficient (rejection criteria were the same as stated above), we selected the next available recording. For the sentences spoken with an emotional tone, we selected only one recording. Three out of the four neutral recordings were processed with our tool to transform the voices into happy, sad, and afraid voices.
Data analysis We calculated the mean ratings of naturalness and intensity for the naturalness and intensity tasks. In addition, we computed mean accuracy scores for the emotion recognition task. To take possible response biases in the recognition task into account, we calculated the unbiased hit rates (H u ) and individual chance proportions (p c ) for each participant  (Wagner, 1993). Unbiased hit rates take a value between zero and one and take into account how often an emotion is identified correctly, as well as the total number of times that an emotion label is used. H u therefore comprises a measure of both the sensitivity and the specificity of each participant's performance. We then compared the arcsine transformed H u and p c by means of paired t tests (Holm-Bonferroni corrected). As a measure of effect size and for easier comparison with other studies conducted on different numbers of stimulus and response categories, we also report the proportion index (pi).
The interpretation of these results deserves caution. First, in our view, this task is not testing for people's maximum capacity to recognize the manipulation, but for typical performance. In our view, there is always a situation where DAVID will fail. For instance, when one can compare an original recording with several of its transformations, it would not be hard to notice that transformations are bound to be the outcome of the system when the original prosody is exactly reproduced. What our data shows is that, at least in some situations, some natural sentences will be judged as equally or less natural than the transformations produced by DAVID. In our experience, the acceptance of DAVIDtransformed speech as authentic cases of emotional speech is heavily dependent on context. For instance, in our recent study of vocal feedback where we instructed participants to read a text out loud while the effect was gradually increased without their knowing, only 14% of the participants reported detecting an artifact with their voice  (Aucouturier et al.
Intercultural applicability Intercultural differences in the perception of vocal emotions, and emotional expression in general, are widely documented (for a review see e. g. ,  Elfenbein and Ambady 2002; Scherer et al. , 2011). The present set of tasks, conducted in four languages, depart a little from standard paradigms in that they are neither a test of cross-cultural universality, because the stimuli used in the four participant groups are not the same (e. g. ,  Biehl et al. , 1997) , nor a test of intercultural differences (e. g. ,  Elfenbein and Ambady 2002) , because both speakers and decoders belong to the same cultural group. What these results address is the cross-cultural validity of the acoustic cues on which DAVID operates: participants in each cultural group listened to voices produced in their own language, albeit transformed with a unique algorithm applied identically to all languages.
Our results, like most previous studies, point at the coexistence of both universal cues of emotional expression and culturally learned display rules. On the one hand, the three emotional transformations were recognized above chance levels in all languages. On the other hand, language had an influence on performance in all of the three tasks. In the recognition task, Swedish participants scored lower than French and Japanese participants, irrespective of emotion. In the naturalness task, ratings for afraid were lower in the Swedish population than in the English. Finally, in the intensity task, happy was rated as less intense in Japan compared to all the other languages. Swedish intensity ratings of happy were also lower than French. The fact that the same transformations were decoded above chance in four languages shows that the emotional cues manipulated in DAVID are not purely cultural.
As a result, one should be conscious that certain parameter settings that are acceptable in continuous speech may have a more pronounced effect in unconventional speech production, for example during the production of sustained vowels. Moreover, 96 (six speakers × four languages × four sentences) sentence-speaker combinations have been used in this study, resulting in a total of 288 different audio files that were transformed by DAVID (96 × 3 different recordings of each sentence). As such, any attempt at generalizing the results described here should be made with caution. All sound samples used in this study were made available for download from https://archive. org/details/DAVIDAudioFiles, and the tool is freely available at http://cream. ircam. fr. Again, even within a single language, we encourage users to experiment with parameters beyond the values we suggested here, and to adapt them to the type of stimuli used in their work.
Studies of emotional prosody Traditional studies of emotional prosody typically rely on actor-produced impersonations of various emotions, a method plagued with problems of stereotypicality and parameter co-variation  (Jürgens et al. , 2015). Using DAVID, one can parametrically sample a large, generic space of emotional prosodic variations (e. g. , all vibrato frequency between 1 Hz and 10 Hz) or selectively manipulate the depth of various parameters (e. g. , pitch shift independently from RMS energy). This can be used e. g. , to generate more ecologically valid templates of emotional expression for reverse correlation techniques (as done for faces -  Mangini & Biederman, 2004) , to produce a continuous range of stimulus difficulties, e. g. , for iterative psychophysical procedures, or to produce cross-cultural stimuli that utilize exactly the same acoustic cues, with the same acoustical strength, rather than stimuli produced by native speakers of each language.
Emotion regulation Emotion regulation paradigms often involve using expressive language or facial gestures while recollecting personal events or writing about oneself (e. g. ,  Slatcher and Pennebaker 2006; Siegman and Boyle 1993). In such paradigms, it is often difficult to disentangle the role of emotional re-engagement from the effects of factual recollection and language production. Using DAVID, experimenters could ask participants to recollect or read out their expressive writing while manipulating the emotional tone of their voice in a congruent or incongruent direction, testing e. g. , if the impact of emotional memories is attenuated when heard with a non-congruent tone of voice. This approach of mood induction via one's own voice may be of particular interest in situations where self-referential processing is altered, such as with depressed patients  (Grimm et al.
List of the parameters used in the validation experiments. For the afraid transformation different values were used for male and female voices, due to strong differences of the audio effects depending on the gender of the speaker because they did not satisfy age (18-40 years) or language requirements (participants had to be a native speaker of the test language). Furthermore, responses of one female English volunteer were not recorded during the emotion recognition task (see below) due to technical problems. Volunteers were recruited through local databases and mailing lists in the respective countries and were financially reimbursed for their participation. The study was approved globally by the IRB of the French Institute of Health and Medical Research (INSERM), as well as locally by the departmental review boards of University College London, Lund University and Waseda University.
Sequence Consecutive use: NPD followed by qualitative data or vice versa. Typical for these studies is that NPD are collected first, followed by a qualitative phase, usually in the form of a semi-structured interview. A typical application is to study stress. Nowadays, the physiological effects of stress can be measured with small sensors that are, for instance, integrated into a wristwatch or they can be attached to the body with a patch, an application known as carewear. It is then possible to monitor signs of stress over a relatively long time (for instance, a few hours). The result is usually a graph displaying when stress peaked or when recovery took place, for instance. In a consecutive qualitative interview, the researcher can then probe in-depth into what evoked this stress and why certain situations are stressful.
We also have biomedical studies of neurophysiological changes in the severe stages of dementia, but we do not know what these patients experience (or even if they are able to experience anything at all). An example of a tMM study could be to start in early dementia with both qualitative and NPD, until communication becomes impossible, and then to continue with NPD collection alone. Nowadays, machine learning is becoming a powerful tool that can be used to let the computer learn how subjective experiences (e. g. pain, happiness) are reflected in neurophysiological signatures. After this 'learning' phase, once communication has become impossible, the computer is still able to make 'statements' about subjective experiences based on neurological assessments  (Brown et al. , 2011). Concurrent designs: simultaneous use of qualitative data and NPD.
Materials and Methods Participants. Seventeen volunteers, including author G. E. (10 females; age range, 19 -40 years), participated in the experiment. All participants had normal or corrected-to-normal vision. Three participants were rejected from the analysis (leaving 14 participants), because we were unable to efficiently decode between the neural signals of faces and houses when the stimuli were presented in the peripheral visual field (for further details, see the Criteria section). Stimuli. Stimuli were presented with a 16 inch Sony Triton Monitor (resolution, 1024 ϫ 768; refresh rate, 85 Hz) at a distance of 50 cm from the participants. The stimuli were designed and presented using MatLab 2009a and the Psychophysics Toolbox extension  (Brainard, 1997). The stimuli were presented in separate saccade and fixation blocks.
One-sample t tests were performed in each condition to determine those with a significant difference from chance (50%). Multiple comparisons were based on Bonferroni-corrected p values from paired t tests performed at each time-point  (Shaffer, 1995). Behavioral analyses were also performed using repeated measures. Specific details of each analysis are presented below. Behavioral data analysis. Analysis of the saccade task and fixation task was performed in MatLab 2016a. For the saccade task, the mean correct responses and reaction times (RTs) were calculated across participants for each saccade condition. A correct response was defined as responding "same" in the same condition, "different" in the change condition, and "same" in the disappear condition where the participant might perceive the presaccadic stimulus as still present  (Wolf et al.
, 1980) , but a priori would have no reason to report the opposite stimulus. A fixed-effect one-way ANOVA was performed on the percentage correct responses and mean reaction times across participants and conditions. Responses were performed in a designated response period 300 ms after each trial was concluded. A designated response time window was used to reduce motor response noise during the postsaccadic time period. This constraint ruled out their use for reaction time analyses. For the fixation task, subjects performed a one-back task, and the mean correct responses were calculated across participants. A t test was conducted for the group analysis. Eye-tracker data acquisition and analysis. The timing of the stimulus sequence on saccade trials was locked to the detection of the saccade initiation.
We used an Eyelink 1000 Plus eye-tracking system (SR Research) to record participants' eye movements throughout the runs. Calibration was conducted at the beginning of each run and again during the runs if the Eyelink 1000 Plus lost the ability to track the pupil. Participants' saccade onsets and offsets were extracted and combined with the EEG triggers for analysis of the EEG signal. We found a consistent delay of 4. 28 ms between saccade onset and the EEG triggers for saccade onset embedded in the EEG signal. EEG acquisition and preprocessing. EEG and EOG were recording using a BioSemi system, with 64 active electrodes and 3 ocular electrodes at 1024 Hz. The continuous EEG data were preprocessed off-line using MatLab and the EEGLAB toolbox  (Delorme and Makeig, 2004). First, the continuous data were notch filtered ϳ50 Hz to remove electrical artifacts, then they were bandpass filtered between 0.
1 and 80 Hz, and finally downsampled to 256 Hz. Saccade trials were analyzed in the following two epochs: the presaccade epoch was locked to the stimulus onset and covered 200 ms before stimulus onset until 300 ms after stimulus onset; the postsaccadic epoch was time locked to the saccade offset and covered 200 ms before saccade offset until 300 ms after saccade offset. Fixation trials were analyzed around stimulation onset, with 200 ms prior to onset and 300 ms post-onset. The data collected 200 -100 ms before stimulus onset or saccade offset for each trial were used to baseline the remaining data in the trial. Individual electrodes with artifacts were interpolated by the mean of the adjacent electrodes, and manual rejection of epochs with artifacts was performed (average rejected epochs of saccade trials, 87.