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Journal articleCaltabiano A, Burke T, Nesi J, et al., 2025,
Virtual reality delivered exposure for fear of needles: a small-scale pilot
, Frontiers in Psychiatry, Vol: 16, ISSN: 1664-0640Background: Fear of needles significantly impacts individual and public health by leading many adults to avoid necessary medical procedures, including vaccinations and blood tests. Virtual Reality Exposure-Based Therapy has shown promise as an effective and accessible intervention for anxiety disorders but remains under-explored. Objectives: This study aimed to evaluate the efficacy and acceptability of a single-session virtual reality intervention targeting fear of needles in adults. Methods: A total of 62 adults reporting needle fear were recruited into experimental (n = 32) and online comparison groups (n = 30). The experimental group completed one Virtual Reality Exposure-Based Therapy session, which comprised of two self-paced virtual reality exposures simulating medical needle procedures. Anxiety and affect were assessed at baseline, during, and immediately following virtual reality exposures, and at a one-month follow-up. Acceptability, usability, presence, plausibility, and virtual reality sickness were also measured. Results: The intervention successfully elicited anxiety during exposure. At one-month follow-up, a modest but statistically significant reduction in symptom severity was observed on one measure (Specific Phobia Questionnaire), though no significant change was noted in life interference or on another severity measure (Medical Fears Survey). Participants rated the intervention highly in terms of usability and acceptability, although some reported symptoms of virtual reality sickness (e.g., disorientation, motion sickness). Conclusions: Virtual Reality Exposure-Based Therapy appears to be an effective and highly acceptable intervention for reducing immediate anxiety related to needle exposure, demonstrating strong potential as a scalable, accessible alternative to traditional exposure therapy. However, further research is necessary to confirm these findings, optimize intervention protocols, and examine long-term effectiveness for fear of needles.
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Conference paperChan EYK, Darvishi V, Parker TD, et al., 2025,
Effects of sex and brain anatomy on brain strain vary across different impact severities
, IRCOBI 2025, Publisher: IRCOBI Coouncil, Pages: IRC-25-134-IRC-25-134, ISSN: 2235-3151 -
Journal articleWang P, Khinvasara Y, Creijghton GJ, et al., 2025,
Enhancing designer creativity through human-AI co-ideation: a co-creation framework for design ideation with custom GPT
, Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, Vol: 39, ISSN: 0890-0604The emergence of large language models (LLMs) provides an opportunity for AI to operate as a co-ideation partner during the creative processes. However, designers currently lack a comprehensive methodology for engaging in co-ideation with LLMs, and there is a limited framework that describes the process of co-ideation between a designer and ChatGPT. This research thus aimed to explore how LLMs can act as codesigners and influence creative ideation processes of industrial designers and whether the ideation performance of a designer could be improved by employing the proposed framework for co-ideation with custom GPT. A survey was first conducted to detect how LLMs influenced the creative ideation processes of industrial designers and to understand the problems that designers face when using ChatGPT to ideate. Then, a framework which based on mapping content to guide the co-ideation between humans and custom GPT (named as Co-Ideator) was promoted. Finally, a design case study followed by a survey and an interview was conducted to evaluate the ideation performance of the custom GPT and framework compared with traditional ideation methods. Also, the effect of custom GPT on co-ideation was compared with a non-artificial intelligence (AI)-used condition. The findings indicated that if users employed co-ideation with custom GPT, the novelty and quality of ideation outperformed by using traditional ideation.
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Journal articleXuyi H, Jian L, Picinali L, et al., 2025,
Head-related transfer function upsampling using an autoencoder-based generative adversarial network with evaluation framework
, Journal of the Audio Engineering Society, Vol: 73, Pages: 533-547, ISSN: 0004-7554Accurate Head-Related Transfer Functions (HRTFs) are essential for delivering realistic 3D audio experiences. However, obtaining personalised, high-resolution HRTFs for individual users is a time-consuming and costly process, typically requiring extensive acoustic measurements. To address this, spatial upsampling techniques have been developed to estimate high-resolution HRTFs from sparse, low-resolution acoustic measurements. This paper presents a novel approach leveraging the spherical harmonic (SH) domain and an Autoencoder Generative Adversarial Network (AE-GAN) to tackle the HRTF upsampling problem. Comprehensive evaluations are conducted using both perceptualmodels and objective spectral metrics to validate the accuracy and realism of the upsampled HRTFs. The results show that the proposed approach outperforms traditional barycentric interpolation in terms of log-spectral distortion (LSD), particularly in extreme sparsity scenarios involving fewer than 12 measurements. These results go some way to justifying that the proposed AE-GAN approach is able to create high-quality, high-resolution HRTFs from only a few acoustic measurements, helping pave the way for more accessible personalised spatial audio across a range of applications.
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Journal articleBaxter W, Mandeno P, Aunger R, et al., 2025,
Setting-driven design: a context-driven approach to behavioural design
, DESIGN SCIENCE, Vol: 11, ISSN: 2053-4701 -
Journal articleMai L, Tian X, Zhao Y, 2025,
Air taxis will soon be in our skies - if batteries can be made safer
, NATURE, Vol: 645, Pages: 36-38, ISSN: 0028-0836 -
Journal articleDemirel P, Martinez-Ros E, Quatraro F, 2025,
Innovation for the green transition: challenges and future perspectives
, Eurasian Business Review, Vol: 15, Pages: 631-645, ISSN: 2147-4281This paper explores eco-innovations as the key enabler of green transition and the main response to the climate change risks that firms increasingly face in all sectors. Taking a systems perspective and focusing on the barriers to eco-innovation, we discuss the most promising economic levers that could be engaged to accelerate eco-innovations and the green transition. We emphasise the complex and crucial roles in green transition played by policy-making and firm governance practices, firms’ sustainability-oriented and digitalisation capabilities as well as the need for extensive collaborations within and across firms. We bridge the literatures around climate change risks, firm capabilities and environmental policy to present a holistic reflection of what eco-innovations need to encompass in order to serve the green transition.
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Journal articleWang B, Zhao X, Zuo H, et al., 2025,
From analogy to innovation: a creative conceptual design approach leveraging large language models
, Advanced Engineering Informatics, Vol: 67, ISSN: 1474-0346Integrating creative concepts into Product Design and Manufacturing Systems (PDMS) is important for product innovation. However, current PDMS lack cognitive capabilities, particularly in reasoning and synthesis, which are essential for conceptual design. As a result, designers face challenges in retrieving relevant analogies, establishing meaningful mappings, and integrating knowledge into new design concepts. This paper proposes a computational conceptual product design approach that integrates Large Language Models’ (LLMs) knowledge representation with an analogy-based structured retrieval mechanism, supporting designers to explore and recombine design patterns and functionalities in an intuitive manner. Benefiting from the zero-shot learning and prompting capabilities of LLMs, given a source domain, this approach allows reasoning target domain based on abstract correspondences in both morphological and semantic associations. A template for the combinational regulation of the reuse of analogical knowledge has also been formulated. By decomposing analogical knowledge into ontological distinction, inspirational feature recognition, and associative mapping explanation, a creative conceptual design stimulation path is formed. An interactive tool named ViMimic based on this approach has been developed through a case study with 18 participants. Evaluation results demonstrate that the approach improves creative performance, increasing the novelty and functionality of conceptual designs by 51% and 22% respectively according to expert evaluations. It also boosts the efficiency and diversity of analogy mapping by 30% based on objective measures, while enhancing creative experiences and reducing cognitive load as measured in the participants’ self-assessment.
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Journal articleThillaithevan D, Hewson R, Murphy R, et al., 2025,
A subspace method for 3D multiscale heat sink modelling and optimization
, Structural and Multidisciplinary Optimization: computer-aided optimal design of stressed solids and multidisciplinary systems, Vol: 68, ISSN: 1615-147XThe increasing computational demands of modern microprocessors require efficient thermal man-agement solutions. To address this design challenge, a novel 3D multiscale heat sink modelling andoptimization framework is presented. The approach combines a multiscale momentum model withan iterative temperature-flux projection scheme that accurately resolves heat transport across theentire macroscale domain without relying on traditional homogenization methods. Unlike conven-tional fixed-grid topology optimization approaches, the framework maintains solution accuracy nearsolid/fluid interfaces while achieving superior computational efficiency, reducing memory requirementsand computation time relative to equivalent explicit single scale simulations. Bayesian optimizationis utilised to demonstrate the framework’s practical utility by designing multiscale heat sinks withhundreds of unit cells subject to homogeneous, and more accurate in-homogeneous surface heat flux,achieving significantly larger heat transfer in both cases, while maintaining pressure drop constraints.This framework enables the practical optimization of complex 3D heat sink designs at multiscaleresolutions previously intractable with traditional explicit modelling approaches.
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Journal articleCaltabiano A, Burke T, Nesi J, et al., 2025,
Age, not sex, predicts needle fear and life interference
, Psychiatry Research Communications, Vol: 5, ISSN: 2772-5987Fear of needles is common and can impair health-related behavior, yet remains underexamined in adult populations. This study assessed symptom severity and life interference associated with needle fear in a UK sample (N = 396). Findings showed 77 % of participants reported life interference due to needle fear despite almost no formal diagnosis. Younger adults had higher severity, with no significant sex differences. These results suggest a need for broad, low-barrier interventions to reduce needle fear and improve public health outcomes like vaccine uptake.
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Journal articleXu L, Zou Y, Tian H, et al., 2025,
Cognitive and affective reactions to virtual facial representations in cosmetic advertising: a comparison of idealized and naturalistic features
, Electronics, Vol: 14, ISSN: 2079-9292The rise of virtual models in the digital age presents a new frontier for cosmetic advertising. Nevertheless, the comparative effectiveness of “idealized” versus “naturalistic” facial features in these models remains a topic of debate and an area of development. This study examines the impact of “idealized” and “naturalistic” facial features in virtual models on consumers’ cognitive and affective responses. Using eye-tracking and a structural equation model, we analyzed visual attention patterns and the roles of affective resonance, trustworthiness, likability, and expertise perception. The results indicate that non-homogeneous or defective naturalistic features increase visual attention and purchase intention, with consumers focusing on imperfections such as freckles. In contrast, idealized facial features mainly draw attention to areas such as the eyes and nose. Mediation analysis reveals that likability and affective resonance are primary influences on purchase intention, while expertise perception and trustworthiness are secondary. This experiment suggests that consumers prioritize socio-emotional connections over professional authority when evaluating naturalistic designs. Our findings provide a framework for virtual model design, helping brands balance aesthetics with psychological optimization, and offer insights into the interplay between visual stimuli and human cognitive and emotional processes in decision-making.
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Journal articleSmith F, Sadek M, Wan E, et al., 2025,
Codesigning AI with end-users: an AI literacy toolkit for nontechnical audiences
, Interacting with Computers, Vol: 37, Pages: 444-456, ISSN: 0953-5438This study addresses the challenge of limited AI literacy among the general public hindering effective participation in AI codesign. We present a card-based AI literacy toolkit designed to inform nontechnical audiences about AI and stimulate idea generation. The toolkit incorporates 16 competencies from the AI Literacy conceptual framework and employs ‘What if?’ prompts to encourage questioning, mirroring designers’ approaches. Using a mixed methods approach, we assessed the impact of the toolkit. In a design task with nontechnical participants (N = 50), we observed a statistically significant improvement in critical feedback and breadth of AI-related questions after toolkit use. Further, a codesign workshop involving six participants, half without an AI background, revealed positive effects on collaboration between practitioners and end-users, fostering a shared vision and common ground. This research emphasizes the potential of AI literacy tools to enhance the involvement of nontechnical audiences in codesigning AI systems, contributing to more inclusive and informed participatory processes.
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Journal articleEspinoza F, Cook D, Da Re M, et al., 2025,
Designing AI-powered chatbots for dementia care in Peru: stakeholder engagement and field observations
, Interacting with Computers, Vol: 37, Pages: 430-443, ISSN: 0953-5438In Peru, dementia caregivers face burnout, depression, stress and financial strain. Addressing their needs involves tackling the intricacies of resourcing, caregiving and managing emotional burdens. Chatbots could serve as a viable support mechanism for these issues in regions with limited resources. We study the perceptions of dementia caregivers in Peru regarding a chatbot tailored to offer care navigation and emotional support. We divided the study into four phases: the initial stage encompassed engaging stakeholders to understand potential design challenges; the second stage focused on the design of ‘Ana’, a chatbot for dementia caregivers; the third stage assessed the chatbot through interviews and a caregiver satisfaction survey and the fourth stage gathered contextual insights through a field study for culturally-sensitive recommendations for ‘Ana’. The findings reveal that caregivers seek immediate access to information on handling behavioural symptoms and a platform for emotional release. Moreover, ‘Ana’ was tested in two configurations—one employed predefined conversation patterns, while the other harnessed generative AI for more dynamic responses. Participants preferred the generative AI alternative of ‘Ana’ as it was perceived to be more empathic and human-like. The participants valued the generative approach despite knowing the potential risk of receiving inaccurate information. Lastly, the field observations challenge the practicality of offering care navigation due to the lack of resources available in the health system and highlight that ‘Ana’ needs to be accessible for the varying educational and technological literacy levels present in Peru. However, we found that WhatsApp could be leveraged to overcome the accessibility challenges due to its pervasiveness in Peruvian society and that Community Health Workers could play a vital role in dementia care interventions.
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Journal articleLucas A, Cunningham J, Harrison J, et al., 2025,
The Qualities of Convivial Tools and Their Relevance to Music Software Accessibility
, ACM TRANSACTIONS ON ACCESSIBLE COMPUTING, Vol: 18, ISSN: 1936-7228 -
Conference paperDong W, Bhattacharya D, Kobayashi A, et al., 2025,
Precise top-layer fabric segmentation for fabric destacking with edge- and shape-aware deep networks
, 2025 IEEE International Conference on Mechatronics and Automation (ICMA), Publisher: IEEE, Pages: 1343-1348Fabric destacking requires precise segmentation of the topmost fabric layer, a task complicated by subtle fabric boundaries and high visual similarity between fabric layers. Existing semantic and edge-based segmentation approaches often struggle with these complexities, limiting the performance of robotic manipulation for different tasks. In this work, a novel segmentation training architecture tailored for top-layer fabric segmentation in stacked fabrics is proposed. The method ex-tends the classical encoder-decoder framework by introducing two specialized branches-an edge-aware branch and a shape-aware branch-that are used to supervise the backbone network for better tuning. The edge-aware branch enhances boundary delineation, while the shape-aware branch guides the network to capture and align the overall fabric shape with reference masks derived from Computer Aided Design (CAD) models. Experiments on a real-world fabric dataset demonstrate that the training approach outperforms established baselines, verifying the effectiveness of the multi-branch design through both quan-titative results and ablation studies.
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Journal articleMartin V, Engel I, Picinali L, 2025,
Effects of geometrical acoustics model simplification on binaural reverberation
, IEEE Transactions on Audio, Speech and Language Processing, Vol: 33, Pages: 3480-3493, ISSN: 1558-7916Virtual reverberation rendering typically requires detailed information about room geometry and surface acoustic characteristics. While comprehensive modeling approaches can account for all aspects of an acoustic environment, they often incur high computational cost that may be perceptually unnecessary. Therefore, finding a trade-off between perceptual authenticity and model complexity becomes a relevant challenge. This study investigates such compromise through the use of geometrical acoustics to render Ambisonics-based binaural reverberation. The accuracy of the rendering is determined, among other factors, by its fidelity to the room’s geometry and to the acoustic properties of its materials. In particular, the model fidelity is varied by simplifying the room geometry and frequency resolution of the absorption coefficients. Several decimated models based on a single room were perceptually evaluated using a multi-stimulus comparison method. Additionally, these differences were numerically assessed through the comparison of acoustic parameters of the rendered reverberation. According to numerical and perceptual evaluations, lowering the frequency resolution of absorption coefficients can have a significant impact on the perception of reverberation, while a smaller impact was observed when decimating the geometry of the model.
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Journal articleVamvakeros A, Vamvakeros A, Papoutsellis E, et al., 2025,
nDTomo: a modular python toolkit for X-ray chemical imaging and tomography
, Digital Discovery, ISSN: 2635-098XnDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface. Prioritising transparency and ease of learning, nDTomo adopts a function-centric design that facilitates straightforward understanding and extension of core workflows, from phantom generation and pencil-beam tomography simulation to sinogram correction, tomographic reconstruction and peak fitting. While many scientific toolkits embrace object-oriented design for modularity and scalability, nDTomo instead emphasises pedagogical clarity, making it especially suitable for students and researchers entering the chemical imaging and tomography field. The suite also includes modern deep learning tools, such as a self-supervised neural network for peak analysis (PeakFitCNN) and a GPU-based direct least squares reconstruction (DLSR) approach for simultaneous tomographic reconstruction and parameter estimation. Rather than aiming to replace established tomography frameworks, nDTomo serves as an open, function-oriented environment for training, prototyping, and research in chemical imaging and tomography.
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Journal articlePerera S, Bornassi S, Ghajari M, et al., 2025,
Joints with angle dependent damping can help to reduce impact forces in robots
, Scientific Reports, Vol: 15, ISSN: 2045-2322This paper investigates how a new angle-dependent damper design can help a robot to reduce collision forces. We designed a fluid-viscous angle-dependent damper by smoothly changing the clearance between the stationary and moving parts. Analytical and numerical simulation-based predictions were experimentally tested. Analytical modelling shows that angle-dependent damping has a 48%reduction in peak forces when compared to constant damping. Numerical simulations show that variable-gap dampers can change damping by 134× during a 10× gap change. The experimental findings confirm the analytical predictions by reducing collision force by up to 10%. These findings suggest that the angle-dependent variable damping solution could be used for robots that experience collisions such as industrial robotic manipulators, legged robots, perching robots, or robots that catch moving objects.
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Journal articleHogg A, Barumerli R, Daugintis R, et al., 2025,
Listener acoustic personalisation challenge - LAP24: head-related transfer function upsampling
, IEEE Open Journal of Signal Processing, Vol: 6, Pages: 926-941, ISSN: 2644-1322Head-related transfer functions (HRTFs) often play a crucial role in spatial hearing, immersive audio applications for virtual reality (VR) and augmented reality (AR), and help in improving hearing assistive devices. The Listener Acoustic Personalisation (LAP) challenge 2024 aimed at advancing research in spatial audio personalisation, with a focus on head-related transfer functions (HRTFs). The challenge was split into two tasks: Task 1 was on HRTF harmonisation, and Task 2 dealt with spatial HRTF upsampling. This paper presents the results and reports the findings related to Task 2 of the LAP challenge. The submissions to Task 2 employed both algorithmic and machine learning-based approaches, which were evaluated on three key spatial audio objective metrics, including the log-spectral distortion (LSD), interaural time difference (ITD), and the interaural level difference (ILD). The results highlighted the strengths and limitations of various upsampling techniques, with learning-based methods demonstrating superior performance at lower sparsity levels. In terms of the LSD, seven of the submissions achieved an impressive performance of less than 5 dB when upsampling from only three measurement points. The results also highlighted that most submissions were often not able to outperform a generic HRTF created by averaging the HRTFs in the training dataset. One of the main contributions of this paper is that it showcases the limitations of objective metrics when it comes to evaluating HRTF upsampling. Therefore, this paper argues that a more holistic approach is needed going forward, which should include the integration of multiple perceptually relevant measures, as this is the only way to ensure a well-rounded assessment of HRTF upsampling quality.
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Journal articleWatanabe T, Li J, Nakagami G, et al., 2025,
Special issue on nursing robotics (Part I)
, ADVANCED ROBOTICS, Vol: 39, Pages: 935-935, ISSN: 0169-1864 -
Conference paperYin Y, Wang B, Zuo H, et al., 2025,
Abilities of design professors to distinguish design assignments generated by students and AI
, Publisher: Cambridge University Press, Pages: 319-328, ISSN: 2732-527XThis study aims to detect the ability of professors to distinguish design assignments generated by students with and without using AI. Ten students were recruited to undertake a conceptual design task twice, one with and one without the help of AI. 105 higher-education associate, assistant and full professors from industrial and product design programmes were recruited to assess the generated designs using a 7-point Likert Scale with nine indexes. The results indicate that assessors have moderate ability to distinguish between design assignments of students using AI and those where students did not use AI. Three cues to suggest the risk of the design assignment is made with AI instead of students who did not use AI were identified. By considering the three cues, lecturers distinguish design assignments generated by students with or without AI.
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Journal articleSadek M, Calvo RA, Mougenot C, 2025,
The value-sensitive conversational agent co-design framework
, International Journal of Human-Computer Interaction, Vol: 41, Pages: 9533-9564, ISSN: 1044-7318Conversational agents (CAs) are rapidly advancing across industry and academia and it is crucial to consider the values embedded within these systems. Value-sensitive design practices have benefited AI-based systems, but have not yet been widely applied to CAs. This paper introduces the Value-Sensitive Conversational Agent (VSCA) Framework. The framework uses collaborative design (co-design) activities to guide CA creators and CA users to collaboratively create three key artefacts that elicit CA users’ values and are technically useful for CA creators to drive implementation forward, resulting in value embodied CA prototypes. The paper presents the practical framework and toolkit, followed by a mixed-method evaluation through design workshops, semi-structured interviews, and a comparative survey. Results show that the framework and toolkit increase CA creators’ value-sensitivity, empower CA users, enhance collaboration, and produce value-embodied prototypes. Based on this work, we offer 14 guidelines to practically support value sensitivity in CAs.
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Journal articleZhou H, Li H, Zhao Y, et al., 2025,
Image-based Artificial Intelligence-driven modelling for blank shape optimisation in sheet metal forming
, Materials and Design, Vol: 256, ISSN: 0264-1275Design for manufacturing is essential to fully exploit the potential of emerging materials and processing technologies. However, traditional trial-and-error optimisation often exhibits inferior performance in manufacturability-driven problems, particularly when handling complex shapes. Surrogate modelling and optimisation have been widely investigated for efficiently predicting simulation results and enhancing manufacturability. Nevertheless, existing methods are mostly constrained by fixed shape parameterisation schemes, limiting their flexibility and effectiveness. To overcome this limitation, this research develops a non-parametric optimisation framework, validated on a sheet metal forming case study, specifically the blank shape optimisation of a hot-stamped B-pillar. The framework integrates an auto-decoder, serving as a differentiable blank shape generator, a convolutional neural network (CNN)-powered surrogate model for manufacturability evaluation, and an Adam optimiser for automated shape optimisation. The surrogate model predicts thickness distributions from the signed distance fields (SDFs) of blank shapes, which are generated by the auto-decoder from latent vectors; based on the predictions, the optimiser iteratively updates the latent vectors to acquire a blank shape with optimised manufacturability. The proposed framework demonstrates superior performance in terms of the accuracy of thickness distribution prediction, the fidelity of blank shape generation, and the efficiency of blank shape optimisation.
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Journal articleRamanathan V, Head P, Eltahir E, et al., 2025,
Climate Design: Holistic Solution for Climate Resilience
, LANDSCAPE ARCHITECTURE FRONTIERS, Vol: 13, Pages: 87-97, ISSN: 2096-336X -
Journal articleRen X, Zhao Y, 2025,
Hydrogen therapy for ischemic injuries
, NATURE CHEMICAL ENGINEERING, Vol: 2, Pages: 467-469 -
Journal articleMarggraf-Turley N, Shiell MM, Pontoppidan NH, et al., 2025,
Electroencephalographic decoding of sound location: comparing free-field to headphone-based non-individual head-related transfer functions
, Journal of the Acoustical Society of America, Vol: 158, Pages: 859-870, ISSN: 0001-4966Sound source localization relies on spatial cues, such as interaural time differences, interaural level differences, and monaural spectral cues. Individually measured head-related transfer functions (HRTFs) facilitate precise spatial hearing but are impractical to measure, necessitating non-individual HRTFs, which may compromise localization accuracy and externalization. To further investigate this phenomenon, the neurophysiological differences between free-field and non-individual HRTF listening are explored by decoding sound locations from EEG-derived event-related potentials. Twenty-two participants localized stimuli under both conditions with EEG responses recorded and logistic regression classifiers trained to distinguish sound source locations. Lower cortical response amplitudes were observed for KEMAR compared to free-field, especially in front-central and occipital-parietal regions. ANOVA identified significant main effects of auralization condition and location on decoding accuracy (DA), which was higher in free-field and interaural-cue-dominated locations. DA negatively correlated with front-back confusion rates, linking neural DA to perceptual confusion. These findings demonstrate that headphone-based non-individual HRTFs elicit lower amplitude cortical responses to static, azimuthally varying locations than free-field conditions. The correlation between EEG-based DA and front-back confusion underscores neurophysiological markers' potential for assessing spatial auditory discrimination.
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Conference paperLintunen EM, Ady NM, Deterding S, et al., 2025,
Towards a formal theory of the need for competence via computational intrinsic motivation
, CogSci 2025, Pages: 2175-2183Computational modelling offers a powerful tool for formalising psychological theories, making them more transparent, testable, and applicable in digital contexts. Yet, the question often remains: how should one computationally model a theory? We provide a demonstration of how formalisms taken from artificial intelligence can offer a fertile starting point. Specifically, we focus on the "need for competence", postulated as a key basic psychological need within Self-Determination Theory (SDT)—arguably the most influential framework for intrinsic motivation (IM) in psychology. Recent research has identified multiple distinct facets of competence in key SDT texts: effectance, skill use, task performance, and capacity growth. We draw on the computational IM literature in reinforcement learning to suggest that different existing formalisms may be appropriate for modelling these different facets. Using these formalisms, we reveal underlying preconditions that SDT fails to make explicit, demonstrating how computational models can improve our understanding of IM. More generally, our work can support a cycle of theory development by inspiring new computational models, which can then be tested empirically to refine the theory. Thus, we provide a foundation for advancing competence-related theory in SDT and motivational psychology more broadly.
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Journal articleShen K, Yao X, Song H, et al., 2025,
All-solid-state batteries stabilized with electro-mechano-mediated phosphorus anodes
, ENERGY & ENVIRONMENTAL SCIENCE, Vol: 18, Pages: 7568-7578, ISSN: 1754-5692 -
Journal articleDaugintis R, Barumerli R, Geronazzo M, et al., 2025,
Listener acoustic personalisation challenge – LAP24: head-related transfer function dataset harmonization
, IEEE Open Journal of Signal Processing, Vol: 6, Pages: 950-964, ISSN: 2644-1322Big data analysis and collation for data-driven head-related transfer function (HRTF) personalization methods are often hindered by systematic differences between HRTF datasets. To address this issue, we designed Task 1 of the inaugural listener acoustic personalisation (LAP) challenge. Researchers were invited to propose strategies for harmonizing HRTFs from a collection of eight different datasets so that dataset-specific artifacts were mitigated while preserving the perceptually relevant attributes of the original HRTFs. Defining the two-sided task required a deeper assessment of the acoustic and perceptual HRTF descriptions to find an evaluation framework that encompassed the two domains. Consequently, a two-stage evaluation was devised to assess the submissions. First, an auditory sound localization model was used to test the perceptual validity of the harmonized HRTFs by estimating the difference in sound localization performance between the original and the harmonized versions. Then, a machine learning classifier was employed to differentiate harmonized HRTF datasets, and its accuracy was used to rank submissions. Three submissions were received, and one was declared a winner according to the evaluation criteria. Further analysis of the submissions revealed some limitations of the evaluation system, prompting a comprehensive review of the task’s inherent complexities. This paper serves as a systematic account of the challenge and relevant considerations, intended to guide future advancements in the field of HRTF personalization research.
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Journal articleTan R, Baker C, Xiancheng Y, et al., 2025,
Superior linear and comparable rotational protection of an air-filled helmet versus foam helmets
, Scientific Reports, Vol: 15, ISSN: 2045-2322Air-filled chambers offer a promising approach for designing lightweight and portable bicycle helmets, yet their effectiveness in real-world cycling accidents, particularly under oblique impacts, remains unexplored. Here, for the first time, we evaluated the brain injury mitigation performance of a commercially available air-filled helmet, Ventete aH-1, under oblique impacts, and compared it with three conventional cycle helmets, ranking high, middle and low in a recent study of 30 cycle helmets. Helmets were fitted to a new headform with more biofidelic physical properties than other existing headforms, allowing for more accurate measurements of linear and rotational motion during impacts. The helmeted headform was subjected to impacts to the front, front-side, side and rear against a 45° anvil at 6.5 m/s. The risk of linear and rotational injuries was calculated using risk functions based on PLA (peak linear acceleration) and BrIC (brain injury criterion) and exposure weighting. The PLA and linear risk were lower for the air-filled helmet than the EPS helmets in all impact locations. The air-filled helmet showed a 44% reduction in overall linear brain injury risk compared to the best-performing EPS helmet, attributed to its nearly twice as long impact duration. The air-filled helmet’s rotational performance compared to the EPS helmets was dependent on the impact location, with its overall rotational risk being slightly better than the EPS helmet ranked middle. Our study shows that air-filled chambers have the potential to provide superior protection compared with EPS liner helmets under oblique impacts. We hope our results will inspire new helmet designs which adopt air-filled chambers to improve brain injury protection and address portability concerns that limit helmet adoption.
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