dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative Analysis of Spontaneous FEEs in a Non-social Situation Spontaneous facial expressions in people with ASD have been evaluated by designing various experimental conditions that are considered to provoke emotion, such as watching videos with emotional content  (46) , free play in children  (47) , using auditory  (48)  or olfactory  (49)  stimuli, or during social interaction  (50). Studies using the FACS method, EMG or subjective human measures reported that subjects with ASD expressed less frequent and shorter FEEs  (47, 51, 52). The study conducted by  Trevisan et al. (38)  aimed to analyze automatically the quantity of spontaneous facial expressions in children with ASD using the FACS-based FACET software. In response to videos with emotional content without social interaction, the FEEs of 17 children with high functioning autism (HFA) were compared to 17 controls matched in gender and intellectual functioning.
Alexithymia may therefore be partly responsible for deficits in both FER  (54)  and FEEs in ASD. This independency of spontaneous facial expression production from ASD traits was partly replicated in a second study with a larger sample of participants conducted by Bangerter et al. (25)  using the same FACET software. This study focused on the expression of joy (action units 6 and 12) in response to humorous videos in a sample of adults and children with ASD. One hundred and twenty-four subjects with ASD with an intellectual quotient (IQ) > 60 and 41 subjects without ASD were included. The authors identified an "overreactive" ASD subgroup that statistically presented more facial expressions of joy than the control group (p < 0. 01, r = 0. 31) and an "hypo-reactive" subgroup that statistically presented fewer facial expressions of joy than the control group (p < 0.
The amount of basic FEEs of 18 Japanese adult males with HFA were compared to a group of 17 non-ASD adults matched for age, gender, parental socioeconomic level, and intellectual functioning. Their spontaneous facial expressions were recorded with the FACS-based Face Reader software during a semistructured social interaction situation extracted from the ADOS employed specifically in this study. More facial expressions of neutrality (d = 1. 02, p = 0. 005) and less facial expressions of joy (d = -0. 78, p = 0. 038) were reported in the ASD group compared to the control group. There was also less variability in the quantity of these two expressions during the transition periods between tasks in the ASD group (p = 0. 03). This study therefore suggested a reduced facial expressiveness in social interactions, reproducing the results previously reported during a non-social situation.
However, the emotional inner states and clinical characteristics of individuals were not accounted for in this study. Beside these results, spontaneous expressions during an interaction between children with ASD and a robot were also evaluated by Landowska et al. (33)  with the same Face Reader software. Interactions with a robot are supposed to be less complex and alarming than human interactions, and therefore more easily accepted by children with ASD  (58). The basic facial expressions of 11 children with ASD and 8 controls were monitored to assess the amount of facial expressions recognized as valid by the software according to FACS during an interaction with the robot. The results indicated that the children with ASD expressed significantly less sadness (p = 0. 002) and disgust (p = 0.
According to our literature review, one pilot study, conducted by Samad et al. (37) , used non-invasive 3D facial imaging technology to measure automatic facial mimicry in response to static Avatar faces presented on a computer screen. These authors proposed a new computer vision and data mining approach that was developed from curve-based geometric features of 3D facial data in order to discern the changes in facial muscle activations during mimicry. The program captured spatial information about the face that enabled detecting and quantifying subtle changes in facial expression based on the activation of different facial muscles. Eight participants with ASD and 8 controls aged 7 to 20 were included. In the group with ASD compared to the control group, a significant increase (p = 0. 049) in the activation of the right levator anguli oris (lip elevator) muscle and a decreasing tendency (p = 0.
This was due in part to a lack of the capability to measure dynamic facial expressions throughout the course of an interaction. On the other hand, automated facial analysis via new technologies provides a rapid, efficient and objective method for measuring in natural contexts the facial expressions and their coordination produced by participant partners during such interactions. In 2020, Zampella et al. used computer-assisted automatic detection of FEEs to analyze more specifically the coordination of facial emotions during reciprocal social interaction  (40). Twenty children with HFA and 16 matched controls participated in spontaneous conversations with their mothers and with nonfamiliar adults. Time-synchronized videos were analyzed, and the emotion of joy was recorded by the Open Face software for each of the interlocutors as well as the coordination of the expression of joy with that of the adult.
The FEET (Facial Emotion Expression Training) software used aimed at analyzing facial information in 3D using the Kinect system and automated facial emotion recognition was conducted in real-time to provide immediate corrective feedback. Three different types of stimuli were used, involving 3 "levels": videos of "cartoon faces" (level 1), videos of "human faces" (level 2) and finally, scenes without face, but eliciting emotion in participants (level 3). For each type of stimulus, the participant was instructed to imitate "With your face, show what I am feeling, " to produce the expected emotion. The authors hypothesized that, depending on the different type of stimulus, children with ASD produce less accurate FEEs compared to their TD peers. An overall difference in the production of FEEs was found between both groups with and without ASD (p = 0.
The FACET automatic facial expression analysis software was used to measure the quality of basic FEEs in 144 participants with ASD compared with 41 non-ASD participants. The activation of the two most representative Action Units (AU) for each emotion was recorded. Statistical analyses found significant differences (p < 0. 05) between groups in the use of AU 12 (which mainly involves the expression of joy), AU 5 (mainly involved in fear and surprise), and AU 9 (involved in the expression of disgust). It should be noted, however, that the software's treatment of such a small number of action units could not accurately reflect either the accuracy of the FEEs or the quality itself, although potential atypicality or abnormality in voluntary activation of global facial muscle in ASD could be revealed.
Quality of Voluntary FEEs in ASD: What Is Atypical? Traditional observer-based studies have failed to identify specific differences in autistic FEEs from the norm and the characteristics of FEEs responsible of the "atypicality" perceived by observers. However, through the use of new technology for motion capture with facial markers, Metallinou et al. (35)  investigated more precisely the atypicality of autistic FEEs. Specifically, this study focused on the global synchronization of facial movements  (35)  through a facial imitation task with dynamic human faces in 21 children with HFA and 16 non-ASD children. The stimuli used were human faces expressing basic emotions taken from the Mind Reading Corpus, a video database for facial expression recognition training. The activation of a facial region, that can be measured by the intensity of movement that a given region undergoes during a facial expression, was analyzed and compared between the two groups.
using motion capture analysis with the same stimuli (video clips of the six basic emotions) in a facial imitation task  (31, 32). The 2015 study included 24 children with HFA and 21 controls  (31). Facial Motion Capture software analyzed the whole face and eight facial sub-regions. As expected, the ASD group exhibited less efficient imitations than the control group (p = 0. 024), especially for sadness, fear and disgust. The results also indicated differences (p < 0. 001) in dynamic facial motions between the two groups, arising primarily from movements of the eyes. In addition, the symmetry between the right and left regions tended to be less, although this difference was not statistically significant (p = 0. 055). Participants with ASD also produced less variability across facial regions in terms of strength of activation, leading to the conclusion that ASD subjects appear to have a less complex underlying mechanism generating facial expressions than their TD peers.
Finally, sound design tools aimed at covering the semantic gap problem are being explored, especially to empower non-experts to access internal representations of sound and to span vast sound design spaces  [8]. A lexicon of 35 verbal descriptors of the salient morphological characteristics of sound (i. e. , basic psychoacoustic features, timbre, and temporal descriptions) was validated and conceptualised in a pack of cards as well in a software interface to facilitate the communication between sound designers and non-experts  [5]. Co-Explorer is a software tool that exploits reinforcement learning algorithms to enable creative human(s)machine partnerships in the exploration of high-dimensional, parametric sound spaces  [34]. These works provide evidence of a fertile and active ground of inquiry, yet they mostly embrace the perspective of the sound designer only, and are positioned on the periphery of research in design cognition and creativity.
In our study, we take a step back and equally consider (sound) designers and stakeholders to make sense of designing with sound. We exploit the critical care as starting context to ground our reasoning, understand how far an approach informed by listening can propagate in design projects, and ultimately grasp the inner meaning of a design process driven by sound. To do so, we use a topic modelling approach to explore the textual data in the interviews, by using Leximancer 1  , software for automatic content analysis and text analytics visualisation. In the next section we briefly discuss the rationale behind the choice of using this software environment, and then we report the study and our findings.
FARAWAY, SO CLOSE, LOOKING AT THE UNEXPECTED Computer-aided Qualitative Data Analysis Software (CAQDAS) have become increasingly popular in social sciences and design  [38]. They are used to support the researcher in the understanding of complex phenomena, typically by providing semi-automated coding of video, transcripts, and document data, and by assisting their interpretation by means of interactive visual analytics. Whereas the discussion of existing CAQDAS environments is beyond the scope of this paper, we point out that the choice of the tool is not exempt from ontological and epistemological considerations. Computer-assisted environments for qualitative data analysis can be broadly split in two main categories, that is tools that emphasise and require the researcher's intervention in handling the data (e.
Q1: -Which role does sound play in your working environment? -How does sound affect your daily routine and tasks? Q2: -Have you ever been involved in designing with others? How? -How did you manage to express and share your ideas on sound to them? Q3: -If you had a magic wand that could fix two things in your experience, which would you choose? on average. The transcripts data underwent a clean up, including full anonymisation, the editing of transcription errors, basic text processing (e. g. , stopwords removal), and were fed to Leximancer. In the concept coding settings, we removed the data relative to the interviewer's dialogue. A few caveats must be acknowledged. First, the population is limited in number and, secondly, the category of engineers is clearly unbalanced. Thirdly, the interviews were held in English, yet only two participants were native speakers.
We highlighted a number of emergent topics, in particular we stress the observations regarding the use of the word "sound" as a conceptual placeholder, as a systemic manifestation of something to use, wherein listening occurs as an active behaviour. We aim at expanding the database of interviews and refine the text mining analysis in order to achieve a clearer understanding. At the same time, we consider timely the introduction of the sound matter in design cognition studies. Two research directions are suggested in the interviews, that is 1) externalising the tacit knowledge of sound experts and making it possibly available and shareable in some kinds of sound sensitising forms, and 2) developing a design-centered understanding of the processing mechanisms of non-verbal sounds and their conceptual representations.
DISCUSSION When humans intend to carry out a joint action, such as to move a large table, they do this by first establishing a joint commitment with their partners and, once the action is completed, by terminating their commitment. Joint actions, in other words, are encased by joint commitment, a process that starts before and terminates after the actual action. We tested the hypothesis that joint commitment as process is evolutionarily old and also present in great apes. To do so, we investigated two main joint actions commonly carried out by great apes, social grooming and play, by collecting and analyzing a large number of initiations and terminations. Our data showed that bonobos and chimpanzees consistently exchanged mutual gaze and communicative signals prior to engaging in these joint actions.
, 2007 ; but see  Jaeggi et al. , 2010; Tan et al. , 2017). Less despotic societies allow for more negotiation between partners, potentially fostering the emergence of more complex communication systems  (Freeberg et al. , 2012). The fact that bonobos responded more to social bond strength also suggests that bonobos, more so than chimpanzees, may take into account interactional histories and emotional bonds with certain individuals-a possible precursor for the development of dyad-specific conventions  (Tomasello, 2016). Patterns of evolutionary change are determined both by phylogenetic inertia and species-specific local adaptations. While the close phylogenetic proximity between bonobos, chimpanzees, and humans predicts similarities in joint commitment processes, our data suggest that differences in the socio-ecology of bonobos and chimpanzees have led to differences in the prevalence of joint commitments.
Data collection Data were collected using continuous focal behavior sampling  (Altmann, 1974) , maintaining a balanced record of focal samples across individuals. All focal samples (15-30 min, depending on the site and its visibility conditions) were recorded using two Panasonic HC-V770 camcorders with two external Sennheiser unidirectional microphones (MKE 400). Overall, we collected 1298. 55 hr of observation (600. 25 hr for bonobos, 698. 30 hr for chimpanzees, see Table  S4 ), with on average 30. 00 hr per bonobo at San Diego Zoo, 20. 64 hr (SD = 0. 52 hr) per bonobo at La Vallee des Singes, 37. 01 hr (SD = 0. 45 hr) per chimpanzee at La Vallee des Singes, 30. 00 hr per chimpanzee at Basel Zoo, and 18. 08 hr (SD = 0. 42 hr) per chimpanzee at La Reserve Africaine de Sigean. To collect affiliation data for the later computation of social bonds, we recorded social behaviors during focal samples on an iPad 6.
To this end, we created individual coding sheets using FileMaker Pro 15 (v. 15. 0. 3. 305). We recorded data on proximity, socially directed approaches, and social interactions (grooming and play). Proximity data were recorded during scan samples, every 5 min of the focal sampling. During each scan, we recorded the identity of the nearest neighbors (at arm's length distance) to the focal individual and the identity of the other present group members. The direction and outcome of socially directed approaches were recorded continuously, determining if a focal individual approached another group member or was approached by one; we recorded only positive (resulting in a social interaction or contact-sitting) or neutral (resulting in proximate sitting) approaches. In addition, we continuously recorded the duration and directionality (who initiated the social interaction) of social grooming and play between a focal individual and a partner.
To collect data for the later computation of rank differences, we recorded the outcome of agonistic interactions ad libitum (any time, in or outside of focal samples), noting the IDs of involved individuals and assigned winner and loser, or recorded whether the interaction ended without a clear winner and loser, i. e. tie). To code the entry and exit phases of social play, grooming and mixed interactions (i. e. , in which partners switched between grooming and/or play), we used the ELAN software package, v. 5. 0  (Wittenburg et al. , 2006). We analyzed 1,242 interactions that included 1,242 beginnings, and 1,240 endings. Some of these beginnings (N = 27) and endings (N = 45) were of insufficient visibility and could therefore not be further analyzed, leaving a final dataset of N = 1,215 visible interaction beginnings and N = 1,195 visible interaction endings (Table  1 , results).
Initiators were those individuals who approached and recruited a partner in the entry phase, and those who first initiated leaving movements or the first who stopped the activity-typical movements in exit phases. We assessed inter-observer agreement for a combination of both the presence and duration of entry and exit phases using the EasyDIAg software package  (Holle and Rein, 2015). The software allows to assess the agreement of raters in ELAN by detecting whether the raters agreed on the annotation value and duration. To reach agreement, the annotation values had to be identical and the duration had to overlap by at least 60%. The reliability agreement is thus based on a combined measure of annotation value and duration. The test revealed good agreement between RH and EG for bonobos (N = 38 interactions with entries and exits, Cohen's k = 0.
Social relations As an inverse proxy for social distance, we computed the social affinity or social bond strength between partners, the Dyadic Composite Sociality Index (DSI)  (Silk et al. , 2013)  using the socialindices package in R (https://github. com/gobbios/socialindices). The DSI is defined as Equation  1 , where d comprises the numbers (or duration) of behaviors chosen to select the index, f ixy denotes the rate (or duration) of behavior i for the dyad xy, and f i constitutes the mean rate (or mean duration) of that behavior i considering all dyads of the sample  (Silk et al. , 2013). DSI xy = P d i = 1 f ixy f i d (Equation1) DSI values range from 0/N; larger values indicate stronger social bonds, and lower values indicate weaker social bonds. Our computation included the measures of grooming and play duration between two partners, their proximity rates (i.
e. , how frequently they were within arm's reach distance from one another) and their approach rates (i. e. , focal having approached the partner or the reverse). Because the bonobos of San Diego Zoo were not always let out in a group on the same day, we additionally controlled for observation time and co-residency of dyads for this group. As a proxy for power differences between social partners, we computed rank differences with Elo-ratings for individuals of each site using the Elo-Rating package in R  (Neumann and Kulik, 2014). Elo-ratings are based on the outcomes of agonistic interactions between partners and depend on the sequence in which interaction take place over time. At the start of the rating procedure, each individual is assigned the same arbitrary value (e. g. , 1,000), and the rating of the two interacting individuals will be updated according to the outcome of their conflict; winners gain points, losers lose points, with the amount depending on the expected probability that the higher-rated individual wins.
Quantification and statistical analysis To test our predictions, we fitted Bayesian generalized mixed models (bGLMMs) and Bayesian linear mixed effect models (bLMMs) using the Stan computational framework (http://mc-stan. org/), accessed through the brms package (Bu ¨rkner, 2017) in R v. 3. 6. 1 (R Core Team, 2013). Each model included four chains relying on Markov chain Monte Carlo estimation (MCMC), with 10,000 iterations per chain, of which we specified 2,000 iterations as warm-up to ensure sampling calibration. The model diagnostics revealed that the posterior distributions reflect the distribution of the original response values appropriately, R-hat statistics were <1. 05, the number of effective samples was >100, and the MCMC chains had no divergent transitions (Table  S2 ; Figure  S2-S6 ).
For all models, we used the default priors of the brms package (v. 2. 9. 0), which were weakly informative with a student's t-distribution of 3 degrees of freedom and a scale parameter of 10 (see Table  S2 ). For inference, we calculated 95% credible intervals (CrI) from the posterior distributions and checked whether 0 was comprised in this interval. In all models, we controlled for age differences and sex differences by including age difference and whether the dyad was same-sex as covariates. To analyze whether entry and exit phases are affected by social bond strength and rank difference, we first fitted bGLMMs for the presence of entry phases (fitting a Bernoulli distribution with binary dependent variable, model 1) and exit phases (binary outcome, model 3). Additionally, we fitted bLMMs for the duration of entry phases (fitting a lognormal distribution, model 2) and exit phases (model 4).
e. , same or different sex), species, and activity type (i. e. , play, mixed, grooming). Additionally, we fitted crossed random intercepts for exit phase dyad ID. For the purpose of assessing the mutual responsiveness typical of joint commitment in humans, we were particularly interested in assessing the likelihood of mutual exits (i. e. , mutual gazes or unilateral gaze and touch) versus non-mutual exits (including all other gaze classes) in apes. Further details on the datasets used for these analysis can be found in the Data S1. Additional information about datasets used for Bayesian mixed models 1-5 Due to the incapability of determining a clear phase initiator for mutually initiated exit phases, we could not integrate N = 66 observations of mutually initiated exit phases in any of our models.
For the analysis of exit phase presence (model 3), we considered the total of N = 1,195 interaction endings, but excluded N = 66 data points of mutually terminated activities, as we were unable to determine a clear ''terminator'' and hence a direction of rank difference between partners. This yielded a final dataset of N = 1,129 exits in model 3. Next, we fitted bLMMs for the log duration (measured in seconds) of entry phases (model 2), and exit phases (model 4). For the analysis of entry phase duration (model 2), we considered only cases where entries were present; thus, from the total of N = 1,215 interaction beginnings, we excluded N = 294 data points where entries were absent (no duration measurable), yielding a final dataset of N = 921 entries in model 2. For the analysis of exit phase duration (model 4), we considered only cases where exits were present; thus, from the total of N = 1,195 interaction endings, we excluded N = 142 data points where exits were absent (no duration measurable), and further N = 51 remaining data points of mutually terminated activities, yielding a final dataset of N = 1,002 exits in model 4.