The mean number of trials in each condition (standard deviation).

Summarized data in each Experiment

Data Modalities

• head location: pose_Tx, pose_Ty, pose_Tz
• head rotation: pose_Rx, pose_Ry, pose_Rz
• facial landmark 3d locations: X_0, ... X_67, Y_0,...Y_67, Z_0,...Z_67
• facial action units intensity (r) and presence (c): AU01_r, AU02_r,...AU25_r, AU26_r, AU45_r, AU01_c, AU02_c,...AU28_c, AU45_c

Contents

• • post-interaction-questionnaire/
    • post-interaction-questionnaire.pdf
    • post-interaction-questionnaire-responses.csv: participant-id,model-id,Q1,Q2,Q3,Q4,Q5,Q6,Q7,Q8,Q9,Q10,Q11,Q12,Q13,Q14,Q15,Q16,Q17
• sensory-data/
  • depth-data/
    • P[participant_id]-M[model_id]-[trial_number].mp4
  • user-face-data/
    • P[participant_id]-M[model_id]-[trial_number].csv: timestamp,confidence,success,pose_Tx,pose_Ty,pose_Tz,pose_Rx,pose_Ry,pose_Rz,X_0,X_1,X_2,X_3,X_4,X_5,X_6,X_7,X_8,X_9,X_10,X_11,X_12,X_13,X_14,X_15,X_16,X_17,X_18,X_19,X_20,X_21,X_22,X_23,X_24,X_25,X_26,X_27,X_28,X_29,X_30,X_31,X_32,X_33,X_34,X_35,X_36,X_37,X_38,X_39,X_40,X_41,X_42,X_43,X_44,X_45,X_46,X_47,X_48,X_49,X_50,X_51,X_52,X_53,X_54,X_55,X_56,X_57,X_58,X_59,X_60,X_61,X_62,X_63,X_64,X_65,X_66,X_67,Y_0,Y_1,Y_2,Y_3,Y_4,Y_5,Y_6,Y_7,Y_8,Y_9,Y_10,Y_11,Y_12,Y_13,Y_14,Y_15,Y_16,Y_17,Y_18,Y_19,Y_20,Y_21,Y_22,Y_23,Y_24,Y_25,Y_26,Y_27,Y_28,Y_29,Y_30,Y_31,Y_32,Y_33,Y_34,Y_35,Y_36,Y_37,Y_38,Y_39,Y_40,Y_41,Y_42,Y_43,Y_44,Y_45,Y_46,Y_47,Y_48,Y_49,Y_50,Y_51,Y_52,Y_53,Y_54,Y_55,Y_56,Y_57,Y_58,Y_59,Y_60,Y_61,Y_62,Y_63,Y_64,Y_65,Y_66,Y_67,Z_0,Z_1,Z_2,Z_3,Z_4,Z_5,Z_6,Z_7,Z_8,Z_9,Z_10,Z_11,Z_12,Z_13,Z_14,Z_15,Z_16,Z_17,Z_18,Z_19,Z_20,Z_21,Z_22,Z_23,Z_24,Z_25,Z_26,Z_27,Z_28,Z_29,Z_30,Z_31,Z_32,Z_33,Z_34,Z_35,Z_36,Z_37,Z_38,Z_39,Z_40,Z_41,Z_42,Z_43,Z_44,Z_45,Z_46,Z_47,Z_48,Z_49,Z_50,Z_51,Z_52,Z_53,Z_54,Z_55,Z_56,Z_57,Z_58,Z_59,Z_60,Z_61,Z_62,Z_63,Z_64,Z_65,Z_66,Z_67,AU01_r,AU02_r,AU04_r,AU05_r,AU06_r,AU07_r,AU09_r,AU10_r,AU12_r,AU14_r,AU15_r,AU17_r,AU20_r,AU23_r,AU25_r,AU26_r,AU45_r,AU01_c,AU02_c,AU04_c,AU05_c,AU06_c,AU07_c,AU09_c,AU10_c,AU12_c,AU14_c,AU15_c,AU17_c,AU20_c,AU23_c,AU25_c,AU26_c,AU28_c,AU45_c
  • user-pose-data/
    • P[participant_id]-M[model_id]-[trial_number]-[camera_id].csv: time,L0-x,L0-y,L0-z,L1-x,L1-y,L1-z,L2-x,L2-y,L2-z,L3-x,L3-y,L3-z,L4-x,L4-y,L4-z,L5-x,L5-y,L5-z,L6-x,L6-y,L6-z,L7-x,L7-y,L7-z,L8-x,L8-y,L8-z,L9-x,L9-y,L9-z,L10-x,L10-y,L10-z,L11-x,L11-y,L11-z,L12-x,L12-y,L12-z,L13-x,L13-y,L13-z,L14-x,L14-y,L14-z,L15-x,L15-y,L15-z,L16-x,L16-y,L16-z,L17-x,L17-y,L17-z,L18-x,L18-y,L18-z,L19-x,L19-y,L19-z,L20-x,L20-y,L20-z,L21-x,L21-y,L21-z,L22-x,L22-y,L22-z,L23-x,L23-y,L23-z,L24-x,L24-y,L24-z,L25-x,L25-y,L25-z,L26-x,L26-y,L26-z,L27-x,L27-y,L27-z,L28-x,L28-y,L28-z,L29-x,L29-y,L29-z,L30-x,L30-y,L30-z,L31-x,L31-y,L31-z,L32-x,L32-y,L32-z
  • robot-joint-data/
    • P[participant_id]-M[model_id]-[trial_number].csv: time, arm_1_joint, arm_2_joint, arm_3_joint, arm_4_joint, arm_5_joint, arm_6_joint, arm_7_joint
  • dialogue-transcript/
    • P[participant_id]-M[model_id]-[trial_number].csv: start,end,text
• interaction-event-labels/
  • P[participant_id]-M[model_id]-[trial_number].csv: event,start,end

Limitations

Acknowledgements

This work was supported by UK Research and Innovation [EP/S024298/1].

This dataset contains the raw data for the three experiments reported in the manuscript titled "Implicit sense of agency in independent and joint actions". Each file contains the data obtained by a single participant, with rows corresponding to individual trials and columns corresponding to different pieces of information recorded for each trial. In each experiment, the data contains numerical estimates made by participants undergoing versions of an intentional binding task, either alone or jointly with another participant. These estimates reflect the perceived time between the participant's action (e.g., pressing a button) and a consequent auditory tone that was played after a variable delay. Experiment 1A includes data from 100 participants performing a task in which they were paired together and pressed a key on their own or at the same time as the other participant. Experiment 1B includes data from 16 participants performing a similar task in which they simply reported the time between two auditory tones. Experiment 2 includes data from 72 participants performing a task in which they were paired together and engaged in a joint task: one participant moved a mouse to move an on-screen cursor towards a stimulus, and the other participant clicked the same mouse when the cursor reached the stimulus.

No description was included in this Dataset collected from the OSF

Voluntary actions can be fractionated in different phenomena: from the emergence of intentions and the ensuing motor plans and actions, to the anticipation and monitoring of their outcomes, to the appreciation of their congruency with intentions and to the eventual emergence of a sense of agency. It follows that motor intention and the sense of agency should occur at different stages in the normal generation of willed actions. Both these processes have been associated with a fronto-parietal motor network, but no study has investigated to what extent the two experiences can be dissociated for the brain regions involved. To this end, we assessed the PET/fMRI literature on agency and intentionality using a meta-analytic technique based on a hierarchical clustering algorithm. Beside a shared brain network involving the meso-frontal and prefrontal regions, the middle insula and subcortical structures, we found that motor intention and the sense of agency are functionally underpinned by separable sets of brain regions: an “intentionality network,” involving the rostral area of the mesial frontal cortex (middle cingulum and pre-supplementary motor area), the anterior insula and the parietal lobules, and a “self-agency network,” which involves the posterior areas of the mesial frontal cortex (the SMA proper), the posterior insula, the occipital lobe and the cerebellum. We were then able to confirm this functional organization by a subsequent seed-based fMRI resting-state functional connectivity analysis, with seeds derived from the intentionality/sense of agency specific clusters of the medial wall of the frontal lobe. Our results suggest the existence of a rostro-caudal gradient within the mesial frontal cortex, with the more anterior regions linked to the concept of motor intentionality and the brain areas located more posteriorly associated with the direct monitoring between the action and its outcome. This suggestion is reinforced by the association between the sense of agency and the activation of the occipital lobes, to suggest a direct comparison between the movement and its external (e.g., visual) consequences. The shared network may be important for the integration of intentionality and agency in a coherent appreciation of self-generated actions.

Research into the developing sense of agency has traditionally focused on sensitivity to sensorimotor contingencies, but whether this implies the presence of a causal action-effect model has recently been called into question. Here, we investigated whether 3- to 4.5-month-old infants build causal action-effect models by focusing on behavioral and neural measures of violation of expectation. Infants had time to explore the causal link between their movements and audiovisual effects before the action-effect contingency was discontinued. We tested their ability to predict the consequences of their movements and recorded neural (EEG) and movement measures. If infants built a causal action-effect model, we expected to observe their violation of expectation in the form of a mismatch negativity (MMN) in the EEG and an extinction burst in their movement behavior after discontinuing the action-effect contingency. Our findings show that the group of infants who showed an MMN upon cessation of the contingent effect demonstrated a more pronounced limb-specific behavioral extinction burst, indicating a causal action-effect model, compared to the group of infants who did not show an MMN. These findings reveal that, in contrast to previous claims, the sense of agency is only beginning to emerge at this age.

Baltimore City has a rich history of community organizing and civic engagement. On any given night, somewhere in the City a neighborhood group is meeting, discussing community concerns, planning for their collective future and advocating for action. BNIA-JFI currently tracks nine neighborhood and community indicators to measure the extent to which the City��_��s neighborhoods are active, organized, and empowered. These include: the number of neighborhood associations and block groups; the number of Community Development Corporations (CDCs); the number of umbrella organizations; the number of park stewardship groups; the number ofcommunity gardens; Healthy Neighborhood Initiative locations; the number of historic properties; the percentage of the population registered to vote; and the percentage of persons voting.

Whether and how different reinforcers, motives, rewards and needs combine to influence motivation is not understood. In the current study, we utilize an action selection framework to test the combined influence of two different motivators: action-contingent control over the environment and action-contingent monetary-reward. The findings show that the combined influence of these motivators is non-additive. Specifically, when an action leads to negligible action-contingent monetary rewards and leads to control over the environment not only does the monetary reward fail to motivate action selection but it also leads to the loss of the motivating effect that control has on action selection. The findings shed light on the boundary conditions of motivation from control and on the surprising complexity of how human motivation affects action. They may also suggest new ways to understanding the motivational processes behind pathological behaviors (e.g., impulsivity and addictions).

Introduction This corpus contains sense-tagged word occurrences for 121 nouns and 70 verbs which are among the most frequently occurring and ambiguous words in English. These occurrences are provided in about 192,800 sentences taken from the Brown corpus and the Wall Street Journal and have been hand tagged by students at the Linguistics Program of the National University of Singapore. WordNet 1.5 sense definitions of these nouns and verbs were used to identify a word sense for each occurrence of each word. Data In addition to providing the word occurrences in their full sentential context, the corpus includes complete listings of the WordNet 1.5 sense definitions used in the tagging. The following example illustrates the format of a sentence with a sense tag for the word "action," followed by the corresponding WordNet1.5 sense definition: ca01.db #020 `` These >> actions 8 proceeding, legal proceeding, judicial proceeding, proceedings -- (the institution of a legal action) => due process, due process of law -- (the administration of justice according to established rules and principles) => group action -- (action taken by a group of people) => act, human action, human activity -- (something that people do or cause to happen) (In the actual corpus, all tagged occurrences of a given noun or verb are stored together in one file, with each full sentence on one line; all noun and verb word sense definitions are stored together in two separate files.) This sense tagged corpus was provided by Hwee Tou Ng of the Defence Science Organisation (DSO) of Singapore. It was first reported in the following paper at ACL-96: "Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach," by Hwee Tou Ng and Hian Beng Lee, in Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pages 40-47, Santa Cruz, California, USA, June 1996. ( http://xxx.lanl.gov/abs/cmp-lg/9606032 ) Updates There are no updates at this time.

Description of the areas of action defined in the regional spatial planning programme. The regional spatial planning programme specifies and concretises these in the Upper Austria. ROG 1994 formulated goals, addresses current challenges, formulates measures for the whole country as well as functional areas of action and thus forms the strategic framework for spatial planning and spatial development in Upper Austria. Areas of action are subspaces that have similar action requirements in the sense of future spatial development. Specific objectives have been set for the areas of action. Description of the areas of action defined in the regional spatial planning programme. The regional spatial planning programme specifies and concretises these in the Upper Austria. ROG 1994 formulated goals, addresses current challenges, formulates measures for the whole country as well as functional areas of action and thus forms the strategic framework for spatial planning and spatial development in Upper Austria. Areas of action are subspaces that have similar action requirements in the sense of future spatial development. Specific objectives have been set for the areas of action.

Sense of agency i.e., the feeling of being in control over one's own action has been studied extensively over the past years. Temporal binding as the implicit measure of agency has been employed to understand how sense of control is formulated. The effect occurs when the actor perceives the time interval between a voluntary action and its outcome to be significantly less than the actual time it takes for the effect to occur since the onset of the respective action. That said, no studies have taken into account whether attenuating one’s feeling of control by introducing disruptions or disturbances would influence their judgement of temporal interval between a later and independent action and its effect. The current project will, therefore, focus on understanding the effects that having the ability to control motor movements being disrupted would have on the consequential perceived sense of agency.

Attenuating one’s feeling of control by introducing disruptions or disturbances is predicted to later influence not only their judgement of temporal interval between action-effect of the same voluntary event but also later execution of a different action. This could shed light on the relationship between an individual’s general sense of control and their perception of time intervals between events (Wen & Haggard, 2020). Chronic disruptions or disturbances that attenuate one’s feeling of control might later affect their perception of time intervals, which can be moderated by variables like self-efficacy or self-esteem (Christensen et al., 2019). Despite how temporal binding might not be diagnosis of the sense of agency, the paradigm has been shown to establish a strong connection between the voluntary execution of action as well as the observed effect, and the compression of the action-effect temporal interval (Haggard & Chambon, 2012). This research could shed light on how the attenuation of feeling in control affects the compression of time at a motor-cognitive level. Supposing that the hypothesis is supported; this further means that having one’s ability to control actions and external factors in the environment constantly disrupted could lead to skewed temporal perception of one’s own action.

We are also interested in investigating if the interaction between individual differences, such as self-efficacy, and having the ability to control motor movements disrupted would also influence one's perceived sense of agency. It is expected that individuals who score higher on self-efficacy etc., which reflects their resilience in challenging situations, are more likely to exhibit the temporal binding effect despite having experienced a persistent deprivation of motor control.

Percentage of persons aged 15 years and over by strength of sense of belonging to Canada, by gender, for Canada, regions and provinces.

In 2023, more than half of Polish employees stated that employers provided health and safety training to increase their sense of security at work.

The Actor-Action Dataset (A2D) by Xu et al. [29] serves as the largest video dataset for the general actor and action segmentation task. It contains 3,782 videos from YouTube with pixel-level labeled actors and their actions. The dataset includes eight different actions, while a total of seven actor classes are considered to perform those actions. We follow [29], who split the dataset into 3,036 training videos and 746 testing videos. As we are interested in pixel-level actor and action segmentation from sentences, we augment the videos in A2D with natural language descriptions about what each actor is doing in the videos. Following the guidelines set forth in [12], we ask our annotators for a discriminative referring expression of each actor instance if multiple objects are considered in a video. The annotation process resulted in a total of 6,656 sentences, including 811 different nouns, 225 verbs and 189 adjectives. Our sentences enrich the actor and action pairs from the A2D dataset with finer granularities. For example, the actor adult in A2D may be annotated with man, woman, person and player in our sentences, while action rolling may also refer to flipping, sliding, moving and running when describing different actors in different scenarios. Our sentences contain on average more words than the ReferIt dataset 12, even when we leave out prepositions, articles and linking verbs (4.5 vs 3.6). This makes sense as our sentences contain a variety of verbs while existing referring expression datasets mostly ignore verbs.

The uploaded datasets cover moderation and moderate-mediation studies on the role of message framing in obesity prevention.

Citizen Sensing, a correlative of Citizen Science, employs low-cost sensors to evidence local environmental issues and empowers citizens to use the data they collect. Whilst motivations for participation can vary, communities affected by pollution frequently have changemaking as their goal. Social innovation is closely aligned with citizen sensing, however the process of co-creating practices and solutions with citizens who wish to shape their world can be highly complex to design. Therefore, our research articulates an action-orientated framework which emerges from a 2-year pan European project by which follow-on communities may replicate sensing initiatives more easily. The authors examine five studies and explore the cross-cutting principles, phases, stakeholders, methods, and challenges which form this framework. The authors argue that whilst data collection and data awareness are crucial to the citizen sensing process, there are precursory and subsequent stages which are necessary to equip citizens to address complex environmental challenges and take action on them. Therefore, this paper focuses on the stages and methods which are distinctive to citizen sensing. It concludes with recommendations for future practice for citizen sensing and citizen science.

Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on this, the current learning method is improved. A new model that combines attention mechanisms and long and short-term RNN is proposed, which can generate performance actions based on music beat sequences. In addition, image description generative models with attention mechanisms are adopted technically. Combined with the RNN abstract structure that does not consider recursion, the abstract network structure of RNN-Long Short-Term Memory (LSTM) is optimized. Through music beat recognition and dance movement extraction technology, data resources are allocated and adjusted in the edge server architecture. The metric for experimental results and evaluation is the model loss function value. The superiority of the proposed model is mainly reflected in the high accuracy and low consumption rate of dance movement recognition. The experimental results show that the result of the loss function of the model is at least 0.00026, and the video effect is the best when the number of layers of the LSTM module in the model is 3, the node value is 256, and the Lookback value is 15. The new model can generate harmonious and prosperous performance action sequences based on ensuring the stability of performance action generation compared with the other three models of cross-domain sequence analysis. The new model has an excellent performance in combining music and performance actions. This paper has practical reference value for promoting the application of edge computing technology in intelligent auxiliary systems for music performance.

This is the first study to test the hypothesis that individuals' sense of commitment in joint activities and relationships may be influenced by personality traits characteristic of borderline personality disorder (BPD). This study consisted of 3 online experiments implemented via Amazon Mechanical Turk. Participants were presented with videos (Experiment 1) or vignettes (Experiments 2, 3) describing situations in which everyday commitments were violated. Participants then reported their perceptions, interpretations, and affective and behavioral responses to those situations. Participants' BPD traits (BPDt) were assessed using the short form of the Five-Factor Borderline Inventory on the basis of which they were divided into two groups: High and Low BPDt. The results revealed that participants with High BPD traits were less optimistic about others acting in accordance with an implicit sense of commitment (Experiment 1), although there was no difference between groups when the commitment was explicitly stated (Experiment 3). Participants in the High BPDt group also reported heightened emotional responses (Experiments 1–3) and less adaptive behavioral responses (Experiments 1, 3) to perceived or anticipated violations of commitment. Our findings suggest that high levels of BPD traits may give rise to a difficulty in adapting one's social expectations and behavior in light of interpersonal commitments and in a manner that is calibrated to the social norms in the community. Future research should investigate to what extent a disturbed sense of commitment may contribute to the difficulties in interpersonal functioning experienced by many individuals with a clinical diagnosis of BPD.

In this study, we conducted a behavioural experiment using literal, idiomatic, conventional and novel metaphorical action sentences. Participants viewed an action video, immediately after a sentence containing a verb that did (matching modality) or did not (mismatching modality) match the observed action. All the sentences were presented both in the matching modality and the mismatching modality. Participants had to indicate whether the sentence made sense or not by pressing a designated response key. We recorded participants' reaction times and accuracy. We found no significant differences between the matching and mismatching modality in the idiomatic condition. Instead, we found a facilitation effect for the literal and the metaphorical conventional condition in the matching modality compared to the mismatching modality and an interference effect for the metaphorical novel condition in the matching modality compared to the mismatching modality. We interpret these findings in light of the Embodied Cognition approach to language.