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.

Lying is a common act in daily life and may have various degrees of falsehood. Deception detection has always been a fascinating area of research in which many studies have been conducted using subjects’ facial, verbal or bodily cues to spot potential deceit. However, none of the studies have investigated the physiological responses of observers in response to misleading statements with various degrees of falsehood. In this paper, we investigated this problem by first conducting designed experiments to collect participants’ physiological signals while they were watching stimulus videos with various falsehood levels. Then, the data was analysed using machine learning or deep learning models. Various challenges including relatively small amounts of training data and imbalanced classes have been addressed by implementing data augmentation. The results show that deep learning models, such as ResNet and VAE-LSTM, can predict the degree of falsehood with an F1-measure up to 0.83 from observers’ reactions when compared to the stimuli ground truth. This was attained when the model was trained with the most useful physiological signal in this study, Electrodermal Activity (EDA). This result indicates that observers’ physiological signals can be used as an indicator to determine the degree of falsehood for misleading statements. In the future, this system may be applied to provide an objective evaluation for deception detection.

We live in a world surrounded with `fake news' and manipulated information, so a system assisting people with knowing what information to trust would be beneficial. Our research investigates situations where the presenters themselves have doubts about the information they are delivering, and we detect this via advanced affective computing techniques. To this end we examine the physiological foundations for observer recognition of the doubt effect: the subjective belief or disbelief of a presenter in some information he or she is presenting. Firstly, we construct stimulus videos that display presenters delivering information about which we manipulate their degree of doubt. We then show these stimuli to observers, and record four of their physiological signals. We find that a generalised neural network trained with physiological features is more accurate in differentiating the presenters' doubt/manipulated belief when compared with the same observers' own conscious judgments. The affective recognition performance improves when we analyse the physiological signals using multi-task learning techniques to train personalised and group personalised neural networks. The ability to recognise this doubt effect derives from observers' fundamental emotional reactions to the viewed stimuli, reflected in their physiological responses, and learnt by our neural networks. We believe this system using observer physiological signals collected in real life could reveal accurate and hidden audience distrust, which could in turn lead to enhanced truthfulness in future public- presented statements.

The relationship between music and human physiological signals has been a topic of interest among researchers for many years. Understanding this relationship can not only lead to more enhanced music therapy methods, but it may also help in finding a cure to mental disorders and epileptic seizures that are triggered by certain music. In this paper, we investigate the effects of 3 different genres of music in participants' Electrodermal Activity (EDA). Signals were recorded from 24 participants while they listened to 12 music stimuli. Various feature selection methods were applied to a number of features which were extracted from the signals. A simple neural network using Genetic Algorithm (GA) feature selection can reach as high as 96.8% accuracy in classifying 3 different music genres. Classification based on participants' subjective rating of emotion reaches 98.3% accuracy with the Statistical Dependency (SD) / Minimal Redundancy Maximum Relevance (MRMR) feature selection technique. This shows that human emotion has a strong correlation with different types of music. In the future this system can be used to distinguish music based on their positive of negative effect on human mental health.