Dynamic body feedback is used in dance movement therapy (DMT), with the aim to facilitate emotional expression and a change of emotional state through movement and dance for individuals with psychosocial or psychiatric complaints. It has been demonstrated that moving in a specific way can evoke and regulate related emotions. The current study aimed to investigate the effects of executing a unique set of kinetic movement elements on an individual mover’s experience of happiness. A specific sequence consisting of movement elements that recent studies have related to the feeling of happiness was created and used in a series of conditions. To achieve a more realistic reflection of DMT practice, the study incorporated the interpersonal dimension between the dance movement therapist (DMTh) and the client, and the impact of this interbodily feedback on the emotional state of the client. This quantitative study was conducted in a within-subject design. Five male and 20 female participants (mean age = 20.72) participated in three conditions: a solo executed movement sequence, a movement sequence executed with a DMTh who attuned and mirrored the movements, and a solo executed movement sequence not associated with feelings of happiness. Participants were only informed about the movements and not the feelings that may be provoked by these movements. The effects on individuals were measured using the Positive and Negative Affect Schedule and visual analog scales. Results showed that a specific movement sequence based on movement elements associated with happiness executed with a DMTh can significantly enhance the corresponding affective state. An additional finding of this study indicated that facilitating expressed emotion through movement elements that are not associated with happiness can enhance feelings such as empowerment, pride, and determination, which are experienced as part of positive affect. The results show the impact of specific fullbody movement elements on the emotional state and the support outcome of DMT on emotion regulation.
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Bij mensen met een stoornis in het gebruik van alcohol kan de gezichtsemotieherkenning verminderd zijn. In twee studies werd een relatie hiervan met terugval gevonden. Niet bekend is of deze relatie kan worden verklaard door de hypothese dat gezichtsemotieherkenning samenhangt met minder sociale steun, wat gerelateerd is aan terugval. Het doel van deze studie was nogmaals te onderzoeken in hoeverre gezichtsemotieherkenning een voorspeller is voor terugval en daarnaast of gezichtsemotieherkenning een negatieve invloed heeft op terugval doordat minder sociale steun wordt ervaren. In deze studie werd een gezichtsemotieherkenningstaak na twee tot vijf weken abstinentie afgenomen bij 66 patiënten die vanwege een stoornis in gebruik van alcohol in behandeling waren. Na zes maanden werd vastgelegd of er sprake was van terugval. In deze studie werden geen aanwijzingen gevonden dat gezichtsemotieherkenning van invloed is op terugval. Tevens werd geen relatie gevonden tussen gezichtsemotieherkenning en ervaren sociale steun. Geconcludeerd wordt dat verder onderzoek op dit gebied gewenst is.
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In daily interaction with horses, humans primarily rely on facial expression as a non-verbal equine cue for emotional information. Difficulties in correctly recognizing these signals might arise due to the species-specificity of facial cues, possibly leading to diminished equine welfare and health. This study aimed to explore human visual search patterns when assessing equine facial expressions indicative of various pain levels, utilizing eye-tracking technology. Hundred and eight individuals (N = 108), classified into three groups (affinity with horses (N = 60), pet owners with no affinity with horses (N = 32), and individuals with no affinity with animals (N = 16)) participated in the study; with their eye movements recorded using eye tracking glasses they evaluated four photos of horses with different levels of pain. Error score, calculated by comparing participant scores to Gold Standard Visual Analogue Score levels and fixation metrics (number of fixations and duration of fixations) were analysed across the four photos, participant group and Areas of Interest (AOIs): eyes, ears, nostrils, and mouth. Statistical analysis utilized linear mixed models. Highlighting the critical role of the eyes as key indicators of pain, findings showed that the eyes played a significant role in assessing equine emotional states, as all groups focused on them for a longer time and more frequently compared to other facial features. Also, participants showed a consistent pattern in how they looked at a horse's face, first focusing on the eyes, then the ears, and finally the nose/mouth region, indicating a horse-specific pattern. Moderate pain was assessed with similar accuracy across all groups, indicating that these signals are broadly recognizable. Nevertheless, non-equestrians faced challenges with recognizing the absence of pain, possibly highlighting the role of experience in interpreting subtle equine expressions. The study's limitations, such as variability in assessment conditions may have impacted findings. Future work could further investigate why humans follow this visual search pattern and whether they recognize the significance of a horse's ears. Additionally, emphasis should be placed on developing targeted training interventions to improve equine pain recognition, possibly benefiting equine welfare and health.
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Bij mensen met een stoornis in het gebruik van alcohol kan de gezichtsemotieherkenning verminderd zijn. In twee studies werd een relatie hiervan met terugval gevonden. Niet bekend is of deze relatie kan worden verklaard door de hypothese dat gezichtsemotieherkenning samenhangt met minder sociale steun, wat gerelateerd is aan terugval. Het doel van deze studie was nogmaals te onderzoeken in hoeverre gezichtsemotieherkenning een voorspeller is voor terugval en daarnaast of gezichtsemotieherkenning een negatieve invloed heeft op terugval doordat minder sociale steun wordt ervaren. In deze studie werd een gezichtsemotieherkenningstaak na twee tot vijf weken abstinentie afgenomen bij 66 patiënten die vanwege een stoornis in gebruik van alcohol in behandeling waren. Na zes maanden werd vastgelegd of er sprake was van terugval. In deze studie werden geen aanwijzingen gevonden dat gezichtsemotieherkenning van invloed is op terugval. Tevens werd geen relatie gevonden tussen gezichtsemotieherkenning en ervaren sociale steun. Geconcludeerd wordt dat verder onderzoek op dit gebied gewenst is.
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De opkomst van Chat GPT laat zien hoe AI ingrijpt in ons dagelijks leven en het onderwijs. Maar AI is meer dan Chat GPT: van zoekmachines tot de gezichtsherkenning in je telefoon: data en algoritmes veranderen de levens van onze studenten en hun toekomstige werkveld. Wat betekent dit voor de opleidingen in het HBO waar voor wij werken? Voor de inspiratie-sessie De maatschappelijke impact van AI tijdens het HU Onderwijsfestival 2023 hebben wij onze collega’s uitgenodigd om samen met ons mee te denken over de recente AI-ontwikkelingen. We keken niet alleen naar de technologie, maar juist ook naar de maatschappelijke impact en wat de kansen en bedreigingen van AI zijn voor een open, rechtvaardige en duurzame samenleving. Het gesprek voerde we met onze collega’s (zowel docenten als medewerkers van de diensten) aan de hand van drie casussen met. De verzamelde resultaten en inzichten van deze gesprekken zijn samengebracht op een speciaal ontwikkelde poster voor de workshop (zie figuur 1). We hebben deze inzichten gebundeld en hieronder zijn ze te lezen.
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More people voted in 2024 than any other year in human history, while often relying on the internet for political information. This combination resulted in critical challenges for democracy. To address these concerns, we designed an exhibition that applied interactive experiences to help visitors understand the impact of digitization on democracy. This late-breaking work addresses the research questions: 1) What do participants, exposed to playful interventions, think about these topics? and 2) How do people estimate their skills and knowledge about countering misinformation? We collected data in 5 countries through showcases held within weeks of relevant 2024 elections. During visits, participants completed a survey detailing their experiences and emotional responses. Participants expressed high levels of self-confidence regarding the detection of misinformation and spotting AI-generated content. This paper contributes to addressing digital literacy needs by fostering engaging interactions with AI and politically relevant issues surrounding campaigning and misinformation.
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In the book, 40 experts speak, who explain in clear language what AI is, and what questions, challenges and opportunities the technology brings.
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Emergency care (from ambulance to emergency room) is focused on somatic care: fixing the body. When a patient with mental dysregulation who experiences ‘disproportionate feelings like fear, anger, sadness or confusion, possibly with associated behaviours’ (Van de Glind et al. 2023) does not get appropriate attention, this can result in the disruption of treatment and even psychological trauma upon trauma. To improve the emergency care process, the authors of this paper - health researchers and design researchers engaged in a project based on the experience-based co-design (EBCD) approach (Donetto et al. 2015; Bate and Robert 2007). EBCD is a method used to design better experiences in healthcare settings, in cooperation with (former) patients and healthcare professionals. The process of EBCD involves partnerships between stakeholders and the discovery and sensemaking of experiences through specialized methods to gain an understanding of the interface between user and service, to design new experiences (Bate and Robert 2007, 31). There is, however, an interesting challenge in bringing patients and care professionals together. In emergency care, patients depend greatly on their healthcare providers. The patients in this study had existing mental vulnerabilities and may have been traumatized by previous visits. We needed to enable these stakeholders to be equal partners with ownership and power, one of the characteristics of co-design in EBCD (Donetto et al. 2015). In this paper, we describe how we adapted and applied the EBCD method, with a focus on creating equal partnerships. We also reflect on the extent of our success and the diBiculties we encountered in attaining this objective.
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When an adult claims he cannot sleep without his teddy bear, people tend to react surprised. Language interpretation is, thus, influenced by social context, such as who the speaker is. The present study reveals inter-individual differences in brain reactivity to social aspects of language. Whereas women showed brain reactivity when stereotype-based inferences about a speaker conflicted with the content of the message, men did not. This sex difference in social information processing can be explained by a specific cognitive trait, one's ability to empathize. Individuals who empathize to a greater degree revealed larger N400 effects (as well as a larger increase in γ-band power) to socially relevant information. These results indicate that individuals with high-empathizing skills are able to rapidly integrate information about the speaker with the content of the message, as they make use of voice-based inferences about the speaker to process language in a top-down manner. Alternatively, individuals with lower empathizing skills did not use information about social stereotypes in implicit sentence comprehension, but rather took a more bottom-up approach to the processing of these social pragmatic sentences.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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