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Mutual gaze and movement synchrony boost observers’ enjoyment and perception of togetherness when watching dance duets
Get PDFAs social beings, we are adept at coordinating our body movements and gaze with others. Often, when coordinating with another person, we orient ourselves to face them, as mutual gaze provides valuable cues pertaining to attention and intentions. Moreover, movement synchrony and mutual gaze are associated with prosocial outcomes, yet the perceptual consequences of these forms of coordination remain poorly understood. Across two experiments, we assessed how movement synchrony and gaze direction influence observers’ perceptions of dyads. Observers’ behavioural responses indicated that dyads are perceived as more socially connected and are more enjoyable to watch when moving synchronously and facing each other. Neuroimaging results showed modulation of the Action Observation and Theory of Mind networks by movement synchrony and mutual gaze, with more robust brain activity when evaluating togetherness (i.e., active and intentional collaboration) than aesthetic value (i.e., enjoyment). A fuller understanding of the consequences of movement synchrony and mutual gaze from the observer’s viewpoint holds important implications for social perception, in terms of how observers intuit social relationships within dyads, and the aesthetic value derived from watching individuals moving in these ways
Non‑pharmacological Interventions for Problematic Substance Use: a Rapid Overview of Cochrane Systematic Reviews
Get PDFA Rapid review of Cochrane Systematic Reviews to identify the non-pharmacological interventions in substance use treatment services and their effectiveness levels where reported. Cochrane systematic reviews were matched to the inclusion criteria and data extracted. A total of 667 studies and 532041 participants are included. The non-pharmacological interventions found can be grouped into three categories: information dissemination, non-specialized face to face interactions, and qualified therapeutic interventions. The measured intervention effectiveness ranged from poor to moderate. The most often reported interventions were cognitive behavioural therapy, motivational interviewing, mindfulness, and contingency management. A wide range of non-pharmacological interventions are being used to treat problematic substance use despite the lack of supportive effectiveness evidence. Missing non-pharmacological interventions include creative arts interventions and lived experience recovery organisations, both of which are gaining momentum in the treatment of substance use
Enhanced Solar Potential Analysis: Separating Terraced House Rooftops Using Convolutional Neural Networks
Get PDFSolar power, a clean and renewable energy source, plays a pivotal role in achieving sustainable development goals by offering affordable, reliable, modern energy solutions and mitigating energy-related emissions and pollutants. Current studies predominantly focus on solar potential analysis derived from machine learning-based rooftop area segmentation. However, these studies reveal an overestimation of usable area for solar output calculations in terraced houses, due to failing to distinguish individual households within terraced structures. This research delineates state-of-the-art Machine Learning and computer vision techniques applied on remote-sensing images obtained via the Google API. The dataset, manually annotated and augmented to include 5000 training images and 1000 validation images, is focused on the UK, particularly terraced house areas. The stand-alone Convolutional Neural Network used to segment terraced-structure rooftop areas reaches an intersection over union of 69.11%. The model uniquely addresses the segmentation of contiguous terraced houses in the UK, which is pivotal for the solar installation assessments in the UK’s residential landscape
Seeds for Reclaiming Art in Education
Get PDFThe concern of this paper is to provide a number of ‘seeds’ for a reclaiming of art in education by placing emphasis upon art's pedagogy or art's education. The notion of reclaiming does not infer a return to a utopian past or to a halcyon future, but it invokes a reaffirmation of the adventure of events of art practice that can take us beyond ourselves towards new creative assemblages and possibilities for becoming-with. Such reclaiming requires a culture of trust, care and response-ability. In relation to art's pedagogy the paper calls for opening up what is formally recognised as ‘practice’ in art education to a sensing towards what might be obscured by such recognition and in doing so reshape our ideas and modes of practice
On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper
Get PDFThe ability to coactivate (or “superpose”) multiple conceptual representations is a fundamental function that we constantly rely upon; this is crucial in complex cognitive tasks requiring multi-item working memory, such as mental arithmetic, abstract reasoning, and language comprehension. As such, an artificial system aspiring to implement any of these aspects of general intelligence should be able to support this operation. I argue here that standard, feed-forward deep neural networks (DNNs) are unable to implement this function, whereas an alternative, fully brain-constrained class of neural architectures spontaneously exhibits it. On the basis of novel simulations, this proof-of-concept article shows that deep, brain-like networks trained with biologically realistic Hebbian learning mechanisms display the spontaneous emergence of internal circuits (cell assemblies) having features that make them natural candidates for supporting superposition. Building on previous computational modelling results, I also argue that, and offer an explanation as to why, in contrast, modern DNNs trained with gradient descent are generally unable to co-activate their internal representations. While deep brain-constrained neural architectures spontaneously develop the ability to support superposition as a result of (1) neurophysiologically accurate learning and (2) cortically realistic between-area connections, backpropagation-trained DNNs appear to be unsuited to implement this basic cognitive operation, arguably necessary for abstract thinking and general intelligence. The implications of this observation are briefly discussed in the larger context of existing and future artificial intelligence systems and neuro-realistic computational models
The shape of the change: Cumulative and incremental changes in daily mood during mobile-app-supported mindfulness training
Get PDFUnderstanding of the exact trajectories of mood improvements during mindfulness practice helps to optimize mindfulness-based interventions. The Mindfulness-to-Meaning model expects mood improvements to be linear, incremental, and cumulative. Our findings align with this expectation. We used multilevel growth curve models to analyze daily changes in positive mood reported by 190 Polish participants during 42 days of a mobile-app-supported, mindfulness-based intervention. The daily positive mood increased among 83.68% of participants. Participants who started the training reported worse mood improved more and faster than participants with better mood at the baseline. Dispositional mindfulness and narcissism – individual difference variables associated with high vs. low emotion regulation ability, respectively – were not associated with mood improvement trajectories. A small group of participants (16.32%) showed a steady decline in positive mood during the intervention. The results underscore the importance of a more comprehensive understanding of individual variability in benefiting from mindfulness-based interventions
A Magnetometer-based Method for In-situ Syncing of Wearable Inertial Measurement Units
Get PDFThis paper presents a novel method to synchronize multiple wireless inertial measurement unit sensors (IMU) using their onboard magnetometers. The basic method uses an external electromagnetic pulse to create a known event measured by the magnetometer of multiple IMUs and in turn uses this to synchronize the devices. An initial evaluation using four commercial IMUs reveals a maximum error of 40 ms per hour as limited by a 25 Hz sample rate. Building on this we introduce a novel method to improve synchronization beyond the limitations imposed by the sample rate and evaluate this in a further study using 8 IMUs. We show that a sequence of electromagnetic pulses, in total lasting <3-s, can reduce the maximum synchronization error to 8 ms (for 25 Hz sample rate, and accounting for the transient response time of the magnetic field generator). An advantage of this method is that it can be applied to several devices, either simultaneously or individually, without the need to remove them from the context in which they are being used. This makes the approach particularly suited to synchronizing multi-person on-body sensors while they are being worn
