“Space Plague”: an investigation into immersive theatre and narrative transportation effects in informal pandemic science education

Stories are fundamental to human history, culture and development. Immersive theatre has created a landscape where participants have agency within stories, and within this landscape the concept of narrative transportation provides a framework where change within stories creates change in real life. “Space Plague” is a co-designed, fully immersive theatrical experience for young people and families about a fictional pandemic. It was developed using community-based participatory action research (CBPAR) employing a novel model for engaging underserved and under-represented audiences, “SCENE”. Results confirmed that indications of narrative transportation effects were achieved, demonstrating enhanced learning and understanding alongside changing attitudes and indicated positive change when negotiating the COVID-19 crisis

Communities and narratives in neglected spaces: voices from SMASHfestUK

Many people are under-served by existing informal science learning (ISL) provisions and under-represented in STEAM (Science, Technology, Engineering, Arts, Mathematics/Medicine) study choices and careers. This paper reflects upon SMASHfestUK which was established, as both a STEAM festival and research platform, to explore methods and approaches for lowering the barriers to engagement with ISL in marginalised communities. To do this SMASHfestUK located its events in the heart of communities and worked with those communities to create those events. This paper tells their story through the voices of participating communities

SCENE: A novel model for engaging underserved and under-represented audiences in informal science learning activities

Inequitable access to science, technology, engineering, arts and mathematics (STEAM) has been explored by multiple studies which have shown that some publics are underserved by existing informal educational and cultural provision, and under-represented in related study choices and careers. Informal science learning (ISL) and public engagement with research activities (such as science festivals) tend to attract audiences which are largely white, middle class and already engaged with STEM (science, technology, engineering and mathematics). This article describes the development of an engagement approach and model through a story-based festival (SMASHfestUK) which was specifically designed to attract new and diverse audiences, including Black and mixed-heritage families, and families living with socio-economic disadvantage. The festival was delivered on five annual occasions, each co-designed with a wide selection of stakeholders, including audiences, researchers, performers, institutions and organizations, and considered as an iterative prototype. Key messages • Engagement with STEM can be tailored to under-represented audiences by co-designing a festival format and content that resonates with those audiences. • Enquiry-based learning with participants immersed in a meaningful story provides an opportunity for participants to experience STEM with agency. • Success was built on the SCENE model (STEAM, community, enquiry, narrative, entertainment), co-designed hyperlocally, leading with free entertainment and an overarching narrativ

Learning a face space for experiments on human identity

Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed "police sketches" in a small number of trials.Comment: 10 figures. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018). *JWS and JCP contributed equally to this submissio

SCENE: A novel model for engaging underserved and under-represented audiences in informal science learning activities

Inequitable access to science, technology, engineering, arts and mathematics (STEAM) has been explored by multiple studies which have shown that some publics are underserved by existing informal educational and cultural provision, and under-represented in related study choices and careers. Informal science learning (ISL) and public engagement with research activities (such as science festivals) tend to attract audiences which are largely white, middle class and already engaged with STEM (science, technology, engineering and mathematics). This article describes the development of an engagement approach and model through a story-based festival (SMASHfestUK) which was specifically designed to attract new and diverse audiences, including Black and mixed-heritage families, and families living with socio-economic disadvantage. The festival was delivered on five annual occasions, each co-designed with a wide selection of stakeholders, including audiences, researchers, performers, institutions and organizations, and considered as an iterative prototype

Asymptotic solutions of glass temperature profiles during steady optical fibre drawing

In this paper we derive realistic simplified models for the high-speed drawing of glass optical fibres via the downdraw method, that capture the fluid dynamics and heat transport in the fibre via conduction, convection and radiative heating. We exploit the small aspect ratio of the fibre and the relative orders of magnitude of the dimensionless parameters that characterize the heat transfer to reduce the problem to one- or two-dimensional systems via asymptotic analysis. The resulting equations may be readily solved numerically and in many cases admit exact analytic solutions. The systematic asymptotic breakdown presented is used to elucidate the relative importance of furnace temperature profile, convection, surface radiation and conduction in each portion of the furnace and the role of each in controlling the glass temperature.\ud \ud The models derived predict many of the qualitative features observed in the real industrial process, such as the glass temperature profile within the furnace and the sharp transition in fibre thickness. The models thus offer a desirable route to quick scenario testing, providing valuable practical information into the dependencies of the solution on the parameters and the dominant heat-transport mechanism

Capturing human category representations by sampling in deep feature spaces

Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult. The challenge is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires a workable representation of these stimuli. Deep neural networks have recently been successful in solving a range of computer vision tasks and provide a way to compactly represent image features. Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators. We provide qualitative and quantitative results as a proof-of-concept for the method's feasibility. Samples drawn from human distributions rival those from state-of-the-art generative models in quality and outperform alternative methods for estimating the structure of human categories.Comment: 6 pages, 5 figures, 1 table. Accepted as a paper to the 40th Annual Meeting of the Cognitive Science Society (CogSci 2018

Deciding to Remember:Memory Maintenance as a Markov Decision Process

Working memory is a limited-capacity form of human mem-ory that actively holds information in mind. Which memoriesought to be maintained? We approach this question by showingan equivalence between active maintenance in working mem-ory and a Markov decision process in which, at each moment,a cognitive control mechanism selects a memory as the targetof maintenance. The challenge of remembering is then findinga maintenance policy well-suited to the task at hand. We com-pute the optimal policy under various conditions and defineplausible cognitive mechanisms that can approximate these op-timal policies. Framing the problem of maintenance in thisway makes it possible to capture in a single model many of theessential behavioral phenomena of memory maintenance, in-cluding directed forgetting and self-directed remembering. Fi-nally, we consider the case of imperfect metamemory — wherethe current state of memory is only partially observable — andshow that the fidelity of metamemory determines the effective-ness of maintenance

Fitting Together the HI Absorption and Emission in the SGPS

In this paper we study 21-cm absorption spectra and the corresponding emission spectra toward bright continuum sources in the test region (326deg< l < 333 deg) of the Southern Galactic Plane Survey. This survey combines the high resolution of the Australia Telescope Compact Array with the full brightness temperature information of the Parkes single dish telescope. In particular, we focus on the abundance and temperature of the cool atomic clouds in the inner galaxy. The resulting mean opacity of the HI, , is measured as a function of Galactic radius; it increases going in from the solar circle, to a peak in the molecular ring of about four times its local value. This suggests that the cool phase is more abundant there, and colder, than it is locally. The distribution of cool phase temperatures is derived in three different ways. The naive, ``spin temperature'' technique overestimates the cloud temperatures, as expected. Using two alternative approaches we get good agreement on a histogram of the cloud temperatures, T(cool), corrected for blending with warm phase gas. The median temperature is about 65 K, but there is a long tail reaching down to temperatures below 20 K. Clouds with temperatures below 40 K are common, though not as common as warmer clouds (40 to 100 K). Using these results we discuss two related quantities, the peak brightness temperature seen in emission surveys, and the incidence of clouds seen in HI self-absorption. Both phenomena match what would be expected based on our measurements of and T(cool).Comment: 50 pages, 20 figure