Real-time auditory feedback may reduce abnormal movements in patients with chronic stroke.

PURPOSE: The current pilot study assesses the use of real-time auditory feedback to help reduce abnormal movements during an active reaching task in patients with chronic stroke. MATERIALS AND METHODS: 20 patients with chronic stroke completed the study with full datasets (age: M = 53 SD = 14; sex: male = 75%; time since stroke in months: M = 34, SD = 33). Patients undertook 100 repetitions of an active reaching task while listening to self-selected music which automatically muted when abnormal movement was detected, determined by thresholds set by clinical therapists. A within-subject design with two conditions (with auditory feedback vs. without auditory feedback) presented in a randomised counterbalanced order was used. The dependent variable was the duration of abnormal movement as a proportion of trial duration. RESULTS: A significant reduction in the duration of abnormal movement was observed when patients received auditory feedback, F(1,18) = 9.424, p = 0.007, with a large effect size (partial η2 = 0.344). CONCLUSIONS: Patients with chronic stroke can make use of real-time auditory feedback to increase the proportion of time they spend in optimal movement patterns. The approach provides a motivating framework that encourages high dose with a key focus on quality of movement. Trial Registration: ISRCTN12969079 https://www.isrctn.com/ISRCTN12969079 ISRTCN trial registration REF: ISRCTN12969079IMPLICATIONS FOR REHABILITATIONMovement quality during upper limb rehabilitation should be targeted as part of a well-balanced rehabilitation programme.Auditory feedback is a useful tool to help patients with chronic stroke reduce compensatory movements.

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.

In the physical to digital transition with friends - a story of performing inclusive research together no matter what life throws at you.

BACKGROUND: As part of 'The Hub' project at Wellcome Collection, a team of eight co-researchers with learning disabilities alongside academics created an online survey to challenge public understanding of learning disabilities. Using creative and arts-based methods, co-researchers remotely co-analysed the survey results amid Covid-19 lockdown challenges. Here, we explore our unexpected 'transition' journey from the physical 'Hub' to the digital space. METHODS: We organised 20 sessions at 'The Hub' and used audio/video/photo recordings to 'capture' key moments. With the lockdown, we ensured that every co-researcher had access to and support for digital technologies. Throughout 2020, we organized 28 Zoom meetings involving all co-researchers. In June, Lilly and Sue conducted Zoom interviews with the co-research team to reflect on our 'transition' journey. In this creative video-form submission accompanied by an accessible report, Lilly puts together a story of how we transitioned and felt throughout this process. FINDINGS: We identify that trust and the social bonds established at 'The Hub' are the key components of our transition to the digital environment. There is the tension between longing for in-person contact and trying to make the most out of the situation to maintain these relationships. At the heart of this is the motivation to 'change the world' and the strive for social justice. Having time and opportunity to improve, and co-researchers' steady growth in confidence, are equally important. CONCLUSIONS: The determination for maintaining friendships among co-researchers and the motivation to 'change the world' overcome Covid-19 related challenges in continuing co-research. SUMMARY: [Table: see text].

Network Bending: Expressive Manipulation of Generative Models in Multiple Domains.

This paper presents the network bending framework, a new approach for manipulating and interacting with deep generative models. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference. In addition, we present a novel algorithm for analysing the deep generative model and clustering features based on their spatial activation maps. This allows features to be grouped together based on spatial similarity in an unsupervised fashion. This results in the meaningful manipulation of sets of features that correspond to the generation of a broad array of semantically significant features of the generated results. We outline this framework, demonstrating our results on deep generative models for both image and audio domains. We show how it allows for the direct manipulation of semantically meaningful aspects of the generative process as well as allowing for a broad range of expressive outcomes.

"We have been magnified for years - Now you are under the microscope!": Co-researchers with Learning Disabilities Created an Online Survey to Challenge Public Understanding of Learning Disabilities.

Public attitudes towards learning disabilities (LDs) are generally reported as positive, inclusive and empathetic. However, these findings do not reflect the lived experiences of people with LDs. To shed light on this disparity, a team of co-researchers with LDs created the first online survey to challenge public understanding of LDs, asking questions in ways that are important to them and represent how they see themselves. Here, we describe and evaluate the process of creating an accessible survey platform and an online survey in a research team consisting of academic and non-academic professionals with and without LDs or autism. Through this inclusive research process, the co-designed survey met the expectations of the co-researchers and was well-received by the initial survey respondents. We reflect on the co-researchers' perspectives following the study completion, and consider the difficulties and advantages we encountered deploying such approaches and their potential implications on future survey data analysis.

Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology.

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable.

The effect of exercise-induced arousal on chosen tempi for familiar melodies.

Many previous studies have shown that arousal affects time perception, suggesting a direct influence of arousal on the speed of the pacemaker of the internal clock. However, it is unknown whether arousal influences the mental representation of tempo (speed) for highly familiar and complex stimuli, such as well-known melodies, that have long-term representations in memory. Previous research suggests that mental representations of the tempo of familiar melodies are stable over time; the aim of the present study was to investigate whether these representations can be systematically altered via an increase in physiological arousal. Participants adjusted the tempo of 14 familiar melodies in real time until they found a tempo that matched their internal representation of the appropriate tempo for that piece. The task was carried out before and after a physiologically arousing (exercise) or nonarousing (anagrams) manipulation. Participants completed this task both while hearing the melodies aloud and while imagining them. Chosen tempi increased significantly following exercise-induced arousal, regardless of whether a melody was heard aloud or imagined. These findings suggest that a change in internal clock speed affects temporal judgments even for highly familiar and complex stimuli such as music.

Perception and action de-coupling in congenital amusia: sensitivity to task demands.

Theories that purport the existence of a distinct auditory action stream have received support from the finding that individuals with congenital amusia, a disorder of pitch perception, are able to reproduce the direction of a pitch change that they are unable to identify (Loui, Guenther, Mathys, & Schlaug, 2008). Although this finding has proved influential in theorizing about the existence of an auditory action-stream, aspects of the original study warrant further investigation. The present report attempts to replicate the original study's findings across a sizeable cohort of individuals with amusia (n=14), obtaining action (production) and perception thresholds for pitch direction. In contrast to the original study, we find evidence of a double dissociation: while a minority of amusics had lower (better) thresholds for production compared to perception of pitch, more than half showed the reverse pattern. To explore the impact of task demands, perception thresholds were also measured using a two alternative, criterion-free, forced choice task that avoided labeling demands. Controls' thresholds were task-invariant while amusics' thresholds were significantly task-dependent. We argue that the direction and extent of a perception/production dissociation in this population reflects individual differences in the mapping of pitch representations to labels ("up"; "down") and to the vocal apparatus, as opposed to anything intrinsically yoked to perception or action per se.