| Project Supervisor | Elisabetta Versace |
| Institution & Department | Queen Mary University of London – School of Biological and Behavioural Sciences |
| Research Area | RA1: Global Health Innovation |
| Project Start Date | End of June 2026 onwards – flexible start date offered. |
| Project Duration | 3 months |
| Application Deadline | 4th June 2026 |
| Working Pattern | Full-time (5 days per week over 3 months) |
| Working Arrangements | In person |
| How to Apply | View Guidance Here |
Project Description
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Human touch is mostly conceived as a proximal sense, that requires physical contact with our surroundings. However, this view has been challenged by the discovery of remote touch. Remote touch is the ability to detect objects at a distance, for instance objects buried beneath sand, through subtle mechanical cues transmitted via granular or elastic materials such as sand or silicone. The initial discovery of remote touch in birds specialised to find food buried in the sand has raised the possibility that humans might possess a remote touch sensitivity. This project will investigate the recently discovered human capacity to perceive objects hidden beneath layers of sand and soft silicone materials, combining insight from psychology, cognitive science, statistics, materials engineering and physics.
Preliminary experiments conducted in the PI laboratory (Chen et al. 2025, ICDL) indicate that humans can detect the presence of buried objects without direct contact, relying on minute vibrations and resistance patterns that propagate through the medium. These preliminary findings showed the existence of a previously unrecognised form of human remote tactile perception that we will systematically investigate in this project for the first time.
Establishing and characterising the boundaries of remote touch in humans has multiple implications: expanding current models of somatosensory perception, providing new theoretical and technological models to train medical palpation as well as massage and physical therapy (important for diagnosis and treatment), inspire new technologies for sensory substitution, tactile feedback, human-machine interaction and medical applications. In this project, the research assistant will develop and validate experimental paradigms to quantify human sensitivity to remote touch under controlled conditions, enabling future investigation in medical settings.
Objectives
We have two main objectives:
1. To quantify the range and sensitivity of human remote touch across different materials (sand and silicone) and movement speeds (Working Package 1).
2. To determine whether humans can use remote touch to discriminate the shape of buried objects (Working Package 2).
Methods
All experiments will take place in a controlled laboratory environment, using purpose-built apparatuses. Participants will be compensated for their time. The study complies with institutional and national ethical standards.
Work Package 1: Remote horizontal detection. Our preliminary data indicate that, when moving a finger horizontally at 2 cm/s in sand with grain diameter <0.5, participants can detect the presence of a buried object approximately 4-6 cm away. This suggests that mechanical cues generated by hand movement may transmit through the sand and be modulated by the presence of an object. Expanding on a methodology that has revealed effective tactile coupling, these results will be complemented by a systematic test of sand grain sizes, silicone stiffness substrates, movement speeds.
Participants will be instructed to stop as soon as they perceive the presence of an object, before touching it. The distance between the finger and the buried object will be measured using magnetic sensors. Detection sensitivity will be quantified using signal detection theory (SDT) measures (d′ and criterion). Detection thresholds will be estimated using psychometric fitting. Effects of sand grain size, silicon stiffness, speed, and lateral distance will be analysed via logistic regression.
Work Package 1 Outcomes: We will provide the first psychometric functions describing remote touch in humans, and will reveal whether perception depends primarily on mechanical impedance (transmission properties of the material) or on kinematic coupling (movement speed and pressure).
Work Package 2: Remote shape discrimination. We will test whether humans can extract information about the shape of a hidden object through tactile exploration of the sand/silicone containing buried objects. A box filled with sand or silicone will contain a central holder with either no object or a 3D shape buried at the centre, under the surface of the sand.
Participants will be instructed on how to move the hands to explore the medium. After exploration, participants will select the shape they believe is hidden from among four visual or tactile models presented in front of them. Accuracy of detection and discrimination will be analysed using signal detection theory. Qualitative data on exploratory strategies will be collected through hand movement and video recordings and brief post-trial interviews, coded using established haptic strategy taxonomies.
Work Package 2 Outcomes: The results will provide crucial insight into how complex structural information can be encoded through indirect mechanical coupling.
Action plan and timeline
This research builds directly on pilot work already conducted, where the apparatus has been prototyped and preliminary detection effects have been observed. Our collaborator Dr Joshua Brown (Imperial College) will support the creation of silicone objects. Our collaborator Dr Antonio Cataldo (Queen Mary University) will support the use of magnetic receptors. The sand equipment required is already available, detailed plans for the other experiments are in place. Ethics approval has already been granted, thus reducing risks of delays. Each work package is feasible within a 1-month timeframe, leaving time for practicing skills in data analysis, paper/report writing and presentation.
Internship Details
This internship will be conducted in a highly collaborative, interdisciplinary research environment. The student will be expected to work in person in the laboratory, engaging directly with the research team and with study participants during the experimental sessions. Active participation in team activities is central to the internship, including weekly lab meetings, interactions on the laboratory slack channel, regular discussion of ongoing work, and sharing progress and findings with collaborators across psychology, cognitive neuroscience, medical sciences, and robotics.
The working culture in the host emphasises high scientific standards (ethics, good research practice), open scientific exchange across disciplines, critical discussion, and shared responsibility for experimental quality and data interpretation. The student will be encouraged to contribute ideas, engage with methods and perspectives from other disciplines, and respond constructively to feedback. In person interaction with researchers, technical staff, and participants is essential for hands on experimental training and for developing strong interdisciplinary communication skills.
Internship Structure
This research builds directly on pilot work already conducted, where the apparatus has been prototyped and preliminary detection effects have been observed.
Our collaborator Dr Joshua Brown (Imperial College) will support the creation of silicone objects. Our collaborator Dr Antonio Cataldo (Queen Mary University) will support the use of magnetic receptors. The sand equipment required is already available, detailed plans for the other experiments are in place. Ethics approval has already been granted, thus reducing risks of delays.
Each work package is feasible within a 1-month timeframe, leaving time for practicing skills in data analysis, paper/report writing and presentation Find out more about the Work Packages in the Project Description above.
Anticipated Benefits for the Student
This internship will provide training at a doctoral level, strengthening the student’s ability to design, implement, and critically evaluate complex interdisciplinary research.
The student will develop advanced competence in good research practice, including experimental protocol development, preregistration, and the use of transparent and reproducible analytical workflows. They will gain substantial experience in sophisticated quantitative analysis using R and Python, including generalised linear mixed models, mixed effects logistic regression, signal detection theory, psychometric modelling, and advanced data visualisation, complemented by qualitative approaches such as sentiment analysis, thematic and lexical analysis.
The internship is embedded within a highly interdisciplinary research environment spanning psychology, cognitive neuroscience, robotics, and medical sciences. This will cultivate a core doctoral level skill: the ability to translate concepts, methods, and evidential standards across disciplinary boundaries. Technical training in 3D design and video editing will support experimental prototyping, stimulus development, and scientific dissemination.
The student will further develop high level scientific communication skills through journal style manuscript preparation, formal reporting, and academic presentations. Collectively, these competencies are directly relevant to research and innovation in psychology, engineering, and healthcare, with clear implications for medical technologies, human–machine interaction, and cognitively informed system design.
Skills, Experience and Knowledge Requirements
Essential requirements: The student should demonstrate intellectual curiosity, flexibility, and a strong interest in interdisciplinary and human-centred research.
They should have a genuine interest in questions of human perception and behaviour, particularly at the interface of psychology, engineering, robotics, and medical or health related sciences.
Basic competence in coding for data analysis using R or Python (or skills in other environments) is required, sufficient to engage with quantitative datasets and develop analysis skills during the internship.
Desirable requirements: Prior exposure to experimental research in psychology, cognitive neuroscience, human-machine interaction or a related discipline would be advantageous, as would familiarity with statistical reasoning or computational approaches to data analysis.
Experience working across disciplinary boundaries, or an openness to learning methods and conceptual frameworks from other fields is beneficial.