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240 results for “social interactions”
Benchmark EEG data set for trust assessment for interactions with social robots
<p>The data collection consisted of a game interaction with a small humanoid EZ-robot. The robot explains a word to the participant either through movements depicting the concept or by verbal description. Depending on their performance, participants could "earn" or loose candy as remuneration for their participation.</p> <p>The dataset comprises EEG (Electroencephalography) recordings from 21 participants, gathered using Emotiv headsets. Each participant's EEG data includes timestamps and measurements from 14 sensors placed across different regions of the scalp. The sensor labels in the header are as follows: EEG.AF3, EEG.F7, EEG.F3, EEG.FC5, EEG.T7, EEG.P7, EEG.O1, EEG.O2, EEG.P8, EEG.T8, EEG.FC6, EEG.F4, EEG.F8, EEG.AF4, and Time.</p> <p>The EEG data provides insights into the electrical activity of the brain, offering a window into cognitive processes and emotional responses during various activities or stimuli in the form of microvolt and with a frame rate of 128 Hz. The whole data set consists of 3651124 data points for each sensor, i.e. 173863 on average for each participant (min. 128505, max. 249631). </p> <p>Files are named after participant numbers starting with ID01. The data has to be pre-processed making use of the information given in the details.xlsx file that contains annotations corresponding to the EEG recordings. These annotations denote the timing of different phases related to trust across the participants' interactions. Each phase is delineated by a start time and an end time, representing distinct stages of the trust-building process. All the other data (timestamps) which are outside the start and end of each phase should be considered as breaks, e.g. filling out the questionnaires. The last element is the trust score for the given phase, which is calculated on the answers in an MDMT questionnaire.</p> <p>The following phases have been annotated:</p> <ol> <li>Trust Building: This phase involves friendly initial interactions for establishing trust between participants and the robot.</li> <li>Situational Awareness: This phase continues to build up trust by showing situation awareness of the robot, e.g. by complimenting on the participant's fashion choice.</li> <li>Transparency: Trust is maintained by increased openness and clarity in communicating about the robot's abilities.</li> <li>Trust Violation: Trust is compromised during this phase by deliberately misleading the participant and making it impossible to answer correctly. </li> <li>Trust Repair: The robot shows efforts to repair trust by apologizing for the behavior in the previous stage.</li> </ol> <p>If you work with the data, please cite one of the article given below.</p>
Data for: Intergenerational genotypic interactions drive collective behavioural cycles in a social insect
<p>Many social animals display collective activity cycles based on synchronous behavioural oscillations across group members. A classic example is the colony cycle of army ants, where thousands of individuals undergo stereotypical biphasic behavioural cycles of about one month. Cycle phases coincide with brood developmental stages, but the regulation of this cycle is otherwise poorly understood. Here, we probe the regulation of cycle duration through interactions between brood and workers in an experimentally amenable army ant relative, the clonal raider ant. We first establish that cycle length varies across clonal lineages using long-term monitoring data. We then investigate the putative sources and impacts of this variation in a cross-fostering experiment with four lineages combining developmental, morphological, and automated behavioural tracking analyses. We show that cycle length variation stems from variation in the duration of the larval developmental stage, and that this stage can be prolonged not only by the clonal lineage of brood (direct genetic effects), but also of the workers (indirect genetic effects). We find similar indirect effects of worker line on brood adult size and, conversely but more surprisingly, indirect genetic effects of the brood on worker behaviour (walking speed and time spent in the nest).</p>
The Social Group Interaction Dataset
<p>The Social Group Interaction Dataset is a dataset consisting of recordings of 6 groups of 5 people completing a series of tasks which provoke group formation. The sorts of interactions encouraged by task completion are as follows: discussion, collaborative task, robot interaction and debate. Recordings include Vicon and facial go-pro recordings. Big 5 personality results and basic demographic information are also included.</p>
Social Virtual-reality on Enhancing Social Interaction Skills in Children With Attention-deficit/Hyperactivity Disorder
ClinicalTrials.gov study NCT05778526. IPD Sharing: NO. Countries: 1. Publications: 2.
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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Data from: Status-dependent metabolic effects of social interactions in a group-living fish
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Evolution of conditional cooperation in collective-risk social dilemma with repeated group interactions
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Data from: Social robots as conversational catalysts: Enhancing long-term human-human dyadic interaction at home
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Data for: Intergenerational genotypic interactions drive collective behavioural cycles in a social insect
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Title Digitized Social Interactions: Reaching Beyond the Limits of the Naked Eye
<p>The complexity and non-linear dynamics of socio-motor phenomena underlying social interactions are often missed by observation methods that attempt to capture, describe, and rate the exchange in real time. Unknowingly to the rater, socio-motor behaviors of a dyad influence each other through implicit mirroring and shared cohesiveness that escape the naked eye. Implicit in their ratings nonetheless is the assumption that the other participant of the social dyad has an identical nervous system as that of the interlocutor, and that sensory-motor information is processed similarly by both agents’ brains. What happens when this is not the case? We here use the Autism Diagnostic Observation Schedule (ADOS) to formally study social dyadic interactions, at the macro- and micro-level of behaviors, by combining observation with digital data from wearables. We find that integrating subjective and objective data reveals fundamental new ways to improve standard clinical tools more generally into outcome measures of human behaviors and treatment effectiveness.</p> <p>This Supplementary Material contains the results from analyzing the gyroscope data in parallel with the results presented in the main paper from the linear acceleration data. Further the video (animation) explains the advantages of using a hybrid model of pencil and paper with digital biomarkers to advance clinical and basic research.</p>
Data from: Achromatic plumage brightness predicts stress resilience and social interactions in tree swallows (Tachycineta bicolor)
Theory suggests that signal honesty may be maintained by differential costs for high and low quality individuals. For signals that mediate social interactions, costs can arise from the way that a signal changes the subsequent social environment via receiver responses. These receiver-dependent costs may be linked with individual quality through variation in resilience to environmental and social stress. Here, we imposed stressful conditions on female tree swallows (Tachycineta bicolor) by attaching groups of feathers during incubation to decrease flight efficiency and maneuverability. We simultaneously monitored social interactions using an RFID network that allowed us to track the identity of every individual that visited each nest for the entire season. Prior to treatments, plumage coloration was correlated with baseline and stress-induced corticosterone. Relative to controls, experimentally challenged females were more likely to abandon their nest during incubation. Overall, females with brighter white breasts were less likely to abandon, but this pattern was only significant under stressful conditions. In addition to being more resilient, brighter females received more unique visitors at their nest box and tended to make more visits to other active nests. In contrast, dorsal coloration did not reliably predict abandonment or social interactions. Taken together, our results suggest that females differ in their resilience to stress and that these differences are signaled by plumage brightness, which is in turn correlated with the frequency of social interactions. While we do not document direct costs of social interaction, our results are consistent with models of signal honesty based on receiver-dependent costs.
Data from: Complex selection on a regulator of social cognition: evidence of balancing selection, regulatory interactions and population differentiation in the prairie vole Avpr1a locus
Adaptive variation in social behavior depends upon standing genetic variation, but we know little about how evolutionary forces shape genetic diversity relevant to brain and behavior. In prairie voles (Microtus ochrogaster), variants at the Avpr1a locus predict expression of the vasopressin 1a receptor in the retrosplenial cortex (RSC), a brain region that mediates spatial and contextual memory; cortical V1aR abundance in turn predicts diversity in space-use and sexual fidelity in the field. To examine the potential contributions of adaptive and neutral forces to variation at the Avpr1a locus, we explore sequence diversity at the Avpr1a locus and throughout the genome in two populations of wild prairie voles. First, we refine results demonstrating balancing selection at the locus by comparing the frequency spectrum of variants at the locus to a random sample of the genome. Next, we find that the four SNPs that predict high V1aR expression in the RSC are in stronger linkage disequilibrium than expected by chance despite high recombination among intervening variants, suggesting that epistatic selection maintains their association despite recombination. Analysis of population structure and a haplotype network for two populations revealed that this excessive LD was unlikely to be due to admixture alone. Furthermore, the two populations differed considerably in the region shown to be a regulator of V1aR expression despite the extremely low levels of genome-wide genetic differentiation. Together, our data suggest that complex selection on Avpr1a locus favors specific combinations of regulatory polymorphisms, maintains the resulting alleles at populations-specific frequencies, and may contribute to unique patterns of spatial cognition and sexual fidelity among populations.
Detecting social interactions via mobile sensing - Dataset
<p>The goal of Markus' master thesis was to establish whether real-life social interactions could be detected using smartphone internal sensors.</p> <p>The present dataset is a labelled set of sensor data. It contains sensing data of six persons, who each used the sensing application for three days in a row. During these days, the test persons in addition created labels for their real-life sensing data.</p> <p>Participants in the first group in addition worked in the same office, and used the sensing at the same time. Their social interactions therefore overlap.</p>
Social interactions generate complex selection patterns in virtual worlds
<p>Understanding the influence of social interactions on individual fitness is key to improving our predictions of phenotypic evolution. However, we often overlook the different components of selection regimes arising from interactions among organisms, including social, correlational, and indirect selection. This is due to the challenging sampling efforts required in natural populations to measure phenotypes expressed during interactions and individual fitness. Furthermore, behaviours are crucial in mediating social interactions, yet few studies have explicitly quantified these selection components on behavioural traits. In this study, we capitalize on an online multiplayer videogame as a source of extensive data recording direct social interactions among prey, where prey collaborate to escape a predator in realistic ecological settings. We estimate natural and social selection and their contribution to total selection on behavioural traits mediating competition, cooperation, and predator-prey interactions. Behaviours of other prey in a group impact an individual's survival, and thus are under social selection. Depending on whether selection pressures on behaviours are synergistic or conflicting, social interactions enhance or mitigate the strength of natural selection, although natural selection remains the main driving force. Indirect selection through correlations among traits also contributed to the total selection. Thus, failing to account for the effects of social interactions and indirect selection would lead to a misestimation of the total selection acting on traits. Dissecting the contribution of each component to the total selection differential allowed us to investigate the causal mechanisms relating behaviour to fitness and quantify the importance of the behaviours of conspecifics as agents of selection. Our study emphasizes that social interactions generate complex selective regimes even in a relatively simple ecological environment.</p>
Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets
<p>Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to <br> <br> Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. <em>Front. Phys.</em> 9:670317. doi: 10.3389/fphy.2021.670317</p> <p> </p> <p>exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C).</p> <p>exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C).</p> <p>exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C).</p> <p>exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment).</p> <p>exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.</p>
Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop
<p>Supplemental code and dataset for the <a href="https://doi.org/10.1145/3411764.3445729">ACM CHI 2021 paper on "Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop"</a>. In this research paper we <a href="https://hubs.mozilla.com/">instrumented Mozilla Hubs Cloud</a> to record where participants were during the event. From there, we measured proxemic and plotted the activity along with some semi-structured interviews.</p> <p>Updates to the Hubs logger can be found in the <a href="https://github.com/ayman/hubs-research-acm-chi-2021/">repository</a>.</p>
Closed-loop microstimulations of the orbitofrontal cortex during real-life gaze interaction enhance dynamic social attention
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Survey on social characteristics of human-chatbot interaction
<p>These datasets contain the citations downloaded from Google Scholar, using the following search string:</p> <p><em>(chatbots + chatterbots + conversational agents + conversational interfaces + conversational systems + conversation systems + dialogue systems + digital assistants + intelligent assistants + conversational user interfaces + conversational UI) -</em><em>ECA -multimodal -robots -eye-gaze -gesture -</em><em>speech-based -speech-recognition -voice-based</em></p> <p>Extra options selections are: title only, exclude patents</p> <p>The search was performed between February and September 2018. The file "All.csv" contains all the 1046 papers selected. The other files contains the studies excluded per round. The file "Included.csv" is the primary studies analyzed in the survey, authors version available at: https://arxiv.org/abs/1904.02743</p>
Interaction-Based Behavioral Analysis in Twitter Social Network
<p>Literature studies usually use data sets consisting of data collected from many different metrics and user counts collected over different time periods. The data set used in this article was formed using completely up-to-date data obtained as a result of metrics measured in terms of scope and efficiency, sufficient and effective user counts, and filtering processes. To classify users correctly and make the classification performance high—in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets and average likes—other parameters such as diameter, density, reciprocity, centralization and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms.</p>
FIGURE 8 in Amateur paleontological societies and fossil clubs, interactions with professional paleontologists, and social paleontology in the United States
FIGURE 8. Map of amateur paleontological organizations, Facebook likes, and Twitter followers.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.