Periodic Reporting for period 1 - TICKLISHUMAN (The neuroscience of human tickle perception)
Reporting period: 2023-01-01 to 2025-06-30
Gargalesis, or tickle, is one of the most trivial yet enigmatic human behaviours. We do not know how a touch becomes ticklish or why we respond to other people’s tickles but not our own. No theory satisfactorily explains why touch on some body areas feels more ticklish than on others, or why some people are highly sensitive while others remain unresponsive. Gargalesis is likely the earliest trigger for laughter in life, but it is unclear whether we laugh because we enjoy it. Socrates, Aristotle, Bacon, Galileo, Descartes, and Darwin theorized about tickling, but after two millennia of intense philosophical interest, experimentation remains scarce. The EU-funded TICKLISHUMAN project seeks to answer these questions through a multidisciplinary approach, integrating haptic technology, somatosensory psychophysics, physiological, electromyographic and audiovisual recordings, neuroimaging, brain stimulation, and advanced statistical modeling.The insights gained will not only satisfy scientific and popular curiosity but could have far-reaching implications for developmental, sensorimotor, social, affective, and evolutionary neuroscience. Clinically, the findings may contribute to a growing repertoire of biomarkers for mental disorders and neurodevelopmental conditions.
Work is progressing across all four research lines of the project. One manuscript is currently under review, and two manuscripts are in preparation.
WP1 (PROTOCOL)
The primary goal of TICKLISHUMAN is to characterise the perceptual and neural principles underlying tickle sensations on the foot sole.
To achieve this, we developed EKTOR, a fully automated haptic device that enables precise and controlled stimulation of participants' foot soles under experimental conditions. Using EKTOR, we are currently designing a stimulation protocol to elicit ticklish sensations in the laboratory. Specifically, we are testing which features of strokes on the foot sole contribute to a ticklish sensation. We take a data-driven approach, collecting multiple data streams—including physiological and behavioral data—to identify the best descriptors of human ticklishness. To facilitate this, we have successfully integrated EKTOR with physiological (skin conductance responses, respiration, heart rate, electromyography), electrophysiological (electroencephalography, EEG), and behavioral (video and audio recordings) measures. This integration paves the way for a fully automated characterisation of human ticklish sensations.
As a next step, we plan to conduct similar experiments while participants undergo functional magnetic resonance imaging (fMRI) to investigate the neural processes that differentiate ticklish from non-ticklish touches. To achieve this, we are currently developing an MRI-compatible version of EKTOR.
WP2 (MAPS)
The second aim of TICKLISHUMAN is to map the spatial distribution of tickle sensations across the body.
We have conducted a large-scale investigation, and we make use of machine learning algorithms to identify spatial features of ticklish sensations in the human body. Preliminary analyses suggest that ticklish sensations follow a somatotopic pattern distinct from other somatic sensations.
WP3 (SELF)
The third aim of TICKLISHUMAN is to identify the brain mechanisms that suppress self-generated tickling on the foot sole.
To investigate this, we adapted EKTOR to enable self-stimulation, allowing us to compare responses to self-generated and externally generated touches. We are currently piloting this paradigm.
WP4 (INDIVIDUAL)
The fourth aim of TICKLISHUMAN is to model key predictors of individual differences in ticklishness on the foot sole.
We have already collected demographic, personality, and physiological data and are currently analysing these datasets.
WP1 (PROTOCOL)
The primary goal of TICKLISHUMAN is to characterise the perceptual and neural principles underlying tickle sensations on the foot sole.
To achieve this, we developed EKTOR, a fully automated haptic device that enables precise and controlled stimulation of participants' foot soles under experimental conditions. Using EKTOR, we are currently designing a stimulation protocol to elicit ticklish sensations in the laboratory. Specifically, we are testing which features of strokes on the foot sole contribute to a ticklish sensation. We take a data-driven approach, collecting multiple data streams—including physiological and behavioral data—to identify the best descriptors of human ticklishness. To facilitate this, we have successfully integrated EKTOR with physiological (skin conductance responses, respiration, heart rate, electromyography), electrophysiological (electroencephalography, EEG), and behavioral (video and audio recordings) measures. This integration paves the way for a fully automated characterisation of human ticklish sensations.
As a next step, we plan to conduct similar experiments while participants undergo functional magnetic resonance imaging (fMRI) to investigate the neural processes that differentiate ticklish from non-ticklish touches. To achieve this, we are currently developing an MRI-compatible version of EKTOR.
WP2 (MAPS)
The second aim of TICKLISHUMAN is to map the spatial distribution of tickle sensations across the body.
We have conducted a large-scale investigation, and we make use of machine learning algorithms to identify spatial features of ticklish sensations in the human body. Preliminary analyses suggest that ticklish sensations follow a somatotopic pattern distinct from other somatic sensations.
WP3 (SELF)
The third aim of TICKLISHUMAN is to identify the brain mechanisms that suppress self-generated tickling on the foot sole.
To investigate this, we adapted EKTOR to enable self-stimulation, allowing us to compare responses to self-generated and externally generated touches. We are currently piloting this paradigm.
WP4 (INDIVIDUAL)
The fourth aim of TICKLISHUMAN is to model key predictors of individual differences in ticklishness on the foot sole.
We have already collected demographic, personality, and physiological data and are currently analysing these datasets.