A (2026) HAPTI-Child: a finger-tracking video and keypoint dataset of haptic picture exploration and identification in congenitally blind and visually impaired children., 2026
Understanding how tactile exploration unfolds in childhood blindness provides a unique opportunit... more Understanding how tactile exploration unfolds in childhood blindness provides a unique opportunity to study self-organization in the developing brain under conditions of absent vision. The HAPTI-Child dataset offers the first trial-resolved, publicly accessible collection of videos, finger-tracking keypoints, and derived kinematic measures capturing how congenitally totally blind (CTB) and severely visually impaired (SVI) children explore and identify raised-line drawings. Sixteen participants completed two runs of an open-response identification task while an overhead camera recorded both hands throughout naturalistic tactile exploration. Markerless tracking with DeepLabCut produced twelve labeled hand and finger keypoints per frame, yielding thousands of datapoints per trial. The dataset includes raw and segmented videos, pose estimation weights, time-aligned keypoint trajectories, per-frame likelihood metrics, and quality controlled visualizations such as fingertip trajectories and dwell-time heatmaps. We detail all acquisition, annotation, and preprocessing procedures and provide a technical validation of model accuracy, missingness, and kinematic plausibility. Illustrative analyses reveal stable exploration patterns across repeated exposures, stimulus-dependent strategy variation, and group-level differences in exploration duration. Because the data preserve the fine-grained temporal structure of real haptic search, they provide a developmentally grounded benchmark for evaluating unsupervised and weakly supervised models of sequence learning, modular coordination, and local-to-global strategy formation. Beyond computational applications, the dataset supports translational work in tactile training, sensory substitution, and bio-inspired robotics by offering realistic action trajectories recorded under naturalistic constraints. HAPTI Child establishes a reproducible, high-resolution foundation for studying how coherent exploratory strategies emerge without vision and invites deeper integration with behavioral, developmental, and computational approaches.
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Papers by Birgitta Dresp
behavioral regulation from healthy reward-seeking to pathological adaptation to stress in response to adversity. This narrative review offers a spotlight view of the transition from healthy reward function, under the control of dopamine, to the progressive deregulation of this function in interactions with other brain centers and circuits, producing what may be called an anti-reward brain state. How such deregulation is linked to specific health-relevant behaviors is then explained in relation to pandemic-related adversities and the stresses they engendered. The long lockdown periods where people in social isolation had to rely on drink, food, and digital rewards via the internet may be seen as the major triggers of changes in motivation and reward-seeking behavior worldwide. The pathological adaptation of dopamine-mediated reward circuitry in the brain is discussed. It is argued that, when pushed by fate and circumstance into a physiological brain state of anti-reward, human
behavior changes and mental health is affected, depending on individual vulnerabilities. A unified conceptual account that places dopamine function at the centre of the current global mental health context is proposed.
Keywords: reward; dopamine; brain; addiction; stress; anhedonia; compulsive behavior; COVID-19
pandemic; mental health
patterns or insomnia during nighttime. Recent studies have shown that the problem has increased in magnitude worldwide during the COVID-19 pandemic. The extent to which dysfunctional sleep is a consequence of altered motivation, memory function, mood, diet, and other lifestyle variables or results from excess of blue-light exposure when looking at digital device screens for long hours at day and night is one of many still unresolved questions. This article offers a narrative overview of some of the most recent literature on this topic. The analysis provided offers a conceptual basis for understanding digital addiction as one of the major reasons why people, and adolescents in particular, sleep less and less well in the digital age. It discusses definitions as well as mechanistic model accounts in context. Digital addiction is identified as functionally equivalent to all addictions, characterized by the compulsive, habitual, and uncontrolled use of digital devices and an excessively repeated engagement in a particular online behavior. Once the urge to be online has become uncontrollable, it is always accompanied by severe sleep loss, emotional distress, depression, and memory dysfunction. In extreme cases, it may lead to suicide. The syndrome has been linked to the known chronic effects of all drugs, producing disturbances in cellular and molecular mechanisms of the GABAergic and glutamatergic neurotransmitter systems. Dopamine and serotonin synaptic plasticity, essential for impulse control, memory, and sleep function, are measurably altered. The full spectrum of behavioral symptoms in digital addicts include eating disorders and withdrawal from outdoor and social life.
Evidence pointing towards dysfunctional melatonin and vitamin D metabolism in digital addicts should be taken into account for carving out perspectives for treatment. The conclusions offer a holistic account for digital addiction, where sleep deficit is one of the key factors.
activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They
are re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. Keywords: multisensory perception; brain representation; contextual modulation; adaptive resonance; biological learning; self-organization; matching rules; winner-take-all principleThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward.
Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.
data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at a given moment in time and reliably captures the differences between novice and expert performance in terms of gripforce
variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
These results are directly explained by the difference in computational complexity between the two major (magnocellular vs. parvocellular) visual pathways involved in filtering the contrast (luminance vs. luminance and color) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics
exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property.