Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
- URL: http://arxiv.org/abs/2603.18798v1
- Date: Thu, 19 Mar 2026 11:47:57 GMT
- Title: Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity
- Abstract summary: We use early ocular and cardiac signals to predict later performance in a game environment.<n>High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified.
- Score: 15.610977452709617
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: User performance is crucial in interactive systems, capturing how effectively users engage with task execution. Prospectively predicting performance enables the timely identification of users struggling with task demands. While ocular and cardiac signals are widely used to characterise performance-relevant visual behaviour and physiological activation, their potential for early prediction and for revealing the physiological mechanisms underlying performance differences remains underexplored. We conducted a within-subject experiment in a game environment with naturally unfolding complexity, using early ocular and cardiac signals to predict later performance and to examine physiological and self-reported group differences. Results show that the ocular-cardiac fusion model achieves a balanced accuracy of 0.86, and the ocular-only model shows comparable predictive power. High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified, with a more positive affective experience. These findings demonstrate the feasibility of cross-session prediction from early physiology, providing interpretable insights into performance variation and facilitating future proactive intervention.
Related papers
- EveLoad: Cognitive Workload Recognition from Event-Based Eye Movements [4.802081767877088]
Existing eye-movement-based cognitive workload recognition methods rely on frame-based eye trackers.<n>Event cameras provide microsecond-level temporal resolution, high dynamic range and low latency, making them suitable for capturing fine-grained ocular dynamics.<n>We introduce EveLoad, which is the first event-based eye-movement dataset with graded cognitive workload annotations.
arXiv Detail & Related papers (2026-06-23T21:06:40Z) - SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics [49.771521708359955]
SurgVista is a surgical world model that mitigates spatial interaction incoherence and temporal fidelity collapse.<n>It consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity.
arXiv Detail & Related papers (2026-06-18T07:47:28Z) - Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults [1.1744028458220426]
We present a longitudinal multimodal study of 66 older adults conducted in real-world conditions.<n>We investigate how the alignment between sensed signals and target variables affects predictive performance.
arXiv Detail & Related papers (2026-05-29T20:34:50Z) - Data-centric Design of Learning-based Surgical Gaze Perception Models in Multi-Task Simulation [16.689550165317765]
In robot-assisted minimally invasive surgery (RMIS), reduced haptic feedback and depth cues increase reliance on expert visual perception.<n>We introduce a paired active-passive, multi-task surgical gaze dataset collected on the da Vinci SimNow simulator across four drills.<n>We quantify skill- and modality-dependent differences in gaze organization and evaluate the substitutability of passive gaze for operative supervision.
arXiv Detail & Related papers (2026-02-09T22:52:59Z) - Adaptive Temporal Dynamics for Personalized Emotion Recognition: A Liquid Neural Network Approach [0.0]
This work presents to the best of our knowledge, the first comprehensive application of liquid neural networks for EEG-based emotion recognition.<n>The proposed framework combines convolutional feature extraction, liquid neural networks with learnable time constants, and attention-guided fusion.<n>Subject-dependent experiments conducted on the PhyMER dataset across seven emotional classes achieve an accuracy of 95.45%, surpassing previously reported results.
arXiv Detail & Related papers (2026-01-28T12:14:45Z) - Reproducible Physiological Features in Affective Computing: A Preliminary Analysis on Arousal Modeling [1.9400282838579548]
We identify physiological features from cardiovascular and electrodermal signals associated with arousal levels.<n>Our approach is particularly promising for applications in safety-critical environments requiring trustworthy and reliable white box models.
arXiv Detail & Related papers (2025-08-14T11:58:36Z) - Human Scanpath Prediction in Target-Present Visual Search with Semantic-Foveal Bayesian Attention [49.99728312519117]
SemBA-FAST is a top-down framework designed for predicting human visual attention in target-present visual search.<n>We evaluate SemBA-FAST on the COCO-Search18 benchmark dataset, comparing its performance against other scanpath prediction models.<n>These findings provide valuable insights into the capabilities of semantic-foveal probabilistic frameworks for human-like attention modelling.
arXiv Detail & Related papers (2025-07-24T15:19:23Z) - An Active Inference Model of Covert and Overt Visual Attention [0.0]
This paper introduces a model of covert and overt visual attention through the framework of active inference.<n>The model determines visual sensory precisions based on both current environmental beliefs and sensory input.
arXiv Detail & Related papers (2025-05-06T09:26:00Z) - Unobtrusive Monitoring of Physical Weakness: A Simulated Approach [22.856249489748617]
Aging and chronic conditions affect older adults' daily lives, making early detection of developing health issues crucial.
We employ a non-intrusive camera sensor to monitor individuals' daily sitting and relaxing activities for signs of weakness.
We simulate weakness in healthy subjects by having them perform physical exercise and observing the behavioral changes in their daily activities before and after workouts.
arXiv Detail & Related papers (2024-06-14T13:52:58Z) - Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
Engagement measurement finds application in healthcare, education, services.
Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors.
We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
arXiv Detail & Related papers (2024-04-05T20:39:16Z) - A Self-supervised Framework for Improved Data-Driven Monitoring of
Stress via Multi-modal Passive Sensing [7.084068935028644]
We propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response.
Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices.
We perform training experiments using a corpus of real-world data on perceived stress.
arXiv Detail & Related papers (2023-03-24T20:34:46Z) - A Neural Active Inference Model of Perceptual-Motor Learning [62.39667564455059]
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience.
In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans.
We present a novel formulation of the prior function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy.
arXiv Detail & Related papers (2022-11-16T20:00:38Z) - On the Robustness of Pretraining and Self-Supervision for a Deep
Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
We compare the impact of different training procedures for diabetic retinopathy grading.
We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions.
Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
arXiv Detail & Related papers (2021-06-25T08:32:45Z) - Continuous Emotion Recognition via Deep Convolutional Autoencoder and
Support Vector Regressor [70.2226417364135]
It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy.
Deep neural networks have been used with great success in recognizing emotions.
We present a new model for continuous emotion recognition based on facial expression recognition.
arXiv Detail & Related papers (2020-01-31T17:47:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.