Unveiling Technorelief: Enhancing Neurodiverse Collaboration with Media Capabilities

• URL: http://arxiv.org/abs/2310.00949v1
• Date: Mon, 2 Oct 2023 07:41:48 GMT
• Title: Unveiling Technorelief: Enhancing Neurodiverse Collaboration with Media Capabilities
• Authors: Maylis Saigot
• Abstract summary: The implications of collaboration on the cognitive, socio-affective experiences of autistic workers are poorly understood. We ask how digital technologies alleviate autistic workers' experiences of their collaborative work environment. The resulting "technorelief" enables autistic workers to tune into their perceptions and regain control of their collaborative experiences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the workforce settles into flexible work arrangements, researchers have focused on the collaborative and psychological consequences of the shift. While nearly a fifth of the world's population is estimated to be neurodivergent, the implications of remote collaboration on the cognitive, sensory, and socio-affective experiences of autistic workers are poorly understood. Prior literature suggests that information and communication technologies (ICTs) introduce major psychological stressors. Theoretically, these stressors ought to be exceptionally straining considering autistic traits $\unicode{x2013}$ yet, studies describe a strong attraction to ICTs. We thus ask: how do digital technologies alleviate autistic workers' experiences of their collaborative work environment? Thirty-three interviews were conducted to address this question. Findings suggest that digital media present capabilities that filter input from the environment, turning it into a virtual stage that lets workers "time out". The resulting "technorelief" enables autistic workers to tune into their perceptions and regain control of their collaborative experiences.

Related papers

• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• Collaborative Design for Job-Seekers with Autism: A Conceptual Framework for Future Research [7.75987826648167]
  Recent empirical findings have started to show how facilitating collaboration between people with autism and their social surroundings through new design can improve their chances of employment. This work aims to provide actionable guidelines and conceptual frameworks that future researchers and practitioners can apply to improve collaborative design for job-seekers with autism.
  arXiv  Detail & Related papers  (2024-05-09T20:05:43Z)
• EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets [0.0]
  This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioural cues.
  arXiv  Detail & Related papers  (2024-03-28T10:31:09Z)
• Love, Joy, and Autism Robots: A Metareview and Provocatype [0.0]
  We conduct a review of autism robot reviews and find the dominant research direction is Autistic people's second to lowest (24 of 25) research priority. We draw on the principles and findings of the latest Autism research, Feminist HRI, and Robotics to imagine a role reversal.
  arXiv  Detail & Related papers  (2024-03-08T07:01:12Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration [116.09561564489799]
  Solo Performance Prompting transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist is an intelligent agent that collaboratively combines multiple minds' strengths and knowledge to enhance problem-solving in complex tasks. Our in-depth analysis shows that assigning multiple fine-grained personas in LLMs improves problem-solving abilities compared to using a single or fixed number of personas.
  arXiv  Detail & Related papers  (2023-07-11T14:45:19Z)
• Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology [0.0]
  Current technologies are an exciting opportunity to improve behavioral characterization. New capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometers, open avenues of novel questioning. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge.
  arXiv  Detail & Related papers  (2023-05-24T17:45:10Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• Can Workers Meaningfully Consent to Workplace Wellbeing Technologies? [65.15780777033109]
  This paper unpacks the challenges workers face when consenting to workplace wellbeing technologies. We show how workers are vulnerable to "meaningless" consent as they may be subject to power dynamics that minimize their ability to withhold consent. To meaningfully consent, participants wanted changes to the technology and to the policies and practices surrounding the technology.
  arXiv  Detail & Related papers  (2023-03-13T16:15:07Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• A Robotic Positive Psychology Coach to Improve College Students' Wellbeing [16.70932067272569]
  We investigate the use of a social robot coach to deliver positive psychology interventions to college students living in on-campus dormitories. We found a statistically significant improvement in participants' psychological wellbeing, mood, and readiness to change behavior for improved wellbeing after they completed the study. Students' personality traits were found to have a significant association with intervention efficacy.
  arXiv  Detail & Related papers  (2020-09-08T15:51:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.