Publications

Temporal processing is fundamental to visual perception, yet little is known about how it functions under compromised visual field conditions or whether emotional stimuli, as reported in the literature, can modulate it. This study investigated temporal resolution using a two-flash fusion paradigm with a static, semi-transparent overlay that degraded the right visual hemifield of opacity 0.60 and examined the potential modulatory effects of emotional faces. In Experiment 1, participants were asked to report if they perceived one or two flashes presented at either −6° (normal vision) or +6° (beneath a scotoma) across eight interstimulus intervals, ranging from 10 to 80 ms with a step size of 10 ms. Results showed significantly impaired temporal discrimination in the degraded vision condition, with elevated thresholds 52.29 ms vs. 34.78 ms and reduced accuracy, particularly at intermediate ISIs 30–60 ms. In Experiment 2, we introduced emotional faces before flash presentation to determine whether emotional content would differentially affect temporal processing. Our findings indicate that neither normal nor scotoma-impaired temporal processing was modulated by the specific emotional content (angry, happy, or neutral) of the facial primes.

Blink detection is a critical component of eye-tracking research, particularly in pupillometry, where data loss due to blinks can obscure meaningful insights. Existing methods often rely on fixed thresholds or device-specific noise profiles, which may lead to inaccuracies in detecting blink onsets and offsets, especially in heterogeneous datasets. This study introduces a novel blink detection model that dynamically adapts to varying pupil size distributions, ensuring robustness across different experimental conditions. The proposed method integrates dynamic thresholding, which adjusts based on the mean pupil size of valid samples, Gaussian smoothing, which reduces noise while preserving signal integrity, and adaptive boundary refinement, which refines blink onsets and offsets based on a trends in the smoothed data. Unlike traditional approaches that merge closely spaced blinks, this model treats each blink as an independent event, preserving temporal resolution, which is essential for cognitive and perceptual studies. The model is computationally efficient and adaptable to a wide range of sampling rates, from low-frequency (e.g., 250 Hz) to high-frequency (e.g., 2000 Hz) data, ensuring consistent blink detection across different eye-tracking setups, making it suitable for both real-time and offline eye-tracking applications. Experimental evaluations demonstrate its ability to accurately detect blinks across diverse datasets. By offering a more reliable and generalizable solution, this model advances blink detection methodologies and enhances the quality of eye-tracking data analysis across research domains.

This paper presents a novel, physiologically inspired framework for reconstructing missing pupil data during blink-induced interruptions. Pupillometry has become integral to studies of cognition, emotion, and neural function, yet the frequent data gaps caused by blinks can compromise the accuracy of inferences drawn from pupil measurements. This approach addresses these challenges by introducing an exponential recovery model that dynamically adjusts its time constant in proportion to blink duration, thus capturing the non-linear dynamics of pupil responses more effectively than traditional interpolation methods. A localised noise estimation procedure further enhances realism by incorporating both signal-dependent and baseline noise derived from statistical properties of the data surrounding each blink. Additionally, a Savitzky–Golay filter is selectively applied to the reconstructed intervals, preserving key physiological features while mitigating high-frequency artefacts. To facilitate the implementation of our framework, we introduce PRPIP, a Python package that implements our methodology. This work demonstrates the effectiveness of this method using empirical pupil data from visual tasks, illustrating that the reconstructed signals maintain physiological fidelity across a range of blink durations. These findings underscore the potential of combining physiological principles with data-driven noise modelling to generate robust, continuous pupil traces. By offering a more accurate reconstruction of pupil size, this framework stands to advance pupillometric research in fields ranging from cognitive psychology to clinical neuroscience.

This study investigated the influence of predictive cues on temporal integration and segregation processes, examining how prior knowledge affects perceptual accuracy under varying temporal constraints. Across three experiments, participants completed a spatial detection task in which cues were absent (Experiment 1), introduced with stable probabilistic validity (Experiment 2), or reversed and supplemented with additional predictive information (Experiment 3). The results demonstrated that predictive cues significantly improved accuracy, and Experiment 2 yielded higher performance compared to the uncued condition in Experiment 1. However, in Experiment 3, the reversal of the cue and additional predictive elements initially impaired the accuracy, suggesting that prior cue associations influenced early performance. Over time, the participants adapted to the new cue structure, leading to a significant learning effect. Importantly, the interaction between cueing and interstimulus interval (ISI) was not significant, indicating that while cues generally improved accuracy, their effectiveness was not strongly modulated by temporal resolution. Furthermore, adding additional cues in Experiment 3 did not further enhance performance and led to increased response variability. These findings provide insight into the role of predictive coding in perceptual decision-making, suggesting that while cues aid performance, their benefit depends on stability and cognitive load rather than temporal precision alone.

Single-case experimental designs (SCEDs) offer a rigorous framework for evaluating intervention effects at the individual level; however, current analytical approaches face methodological limitations. Traditional methods, which rely on visual analysis and nonparametric indices, fail to adequately account for the serial dependence and phase-specific variability inherent in intensive longitudinal data. This paper introduces a Bayesian first-order autoregressive (AR(1)) model that simultaneously estimates phase-specific means, autocorrelation parameters, and innovation variance within a unified framework. The model incorporates weakly informative priors calibrated to the scale of the observed data to ensure stable parameter estimation. We demonstrate the model's application using an empirical dataset from an ABAB single-case design, showing how it provides a full posterior distribution for treatment effects with robust uncertainty quantification. Our approach provides researchers with a principled framework for analyzing complex single-case data structures, thereby addressing a critical gap in SCED methodology for clinical, educational, and behavioral research.

Individuals adapt to central vision loss with significant variability, a phenomenon insufficiently explained by current theories emphasizing spatial compensatory strategies. We hypothesized that individual differences in the temporal precision of oculomotor control are crucial for successful adaptation. Using a gaze-contingent artificial scotoma paradigm during conjunction visual search using machine learning, we examined oculomotor adaptation mechanisms. Our analysis revealed that while the architecture of visual search remains intact under simulated central vision loss, individuals diverge into two distinct adaptation phenotypes. 'Resilient' adapters where consistently maintained efficient fixation durations across all scotoma conditions. In contrast, 'Vulnerable' adapters defaulted to prolonged and inefficient fixations, representing a temporal inefficiency, and showed minimal ability to optimize fixation timing with practice. These findings demonstrate that successful adaptation relies more critically on achieving temporal efficiency in visual sampling than on spatial reorganization alone. This temporal precision framework challenges existing models of visual adaptation. It identifies trainable oculomotor control mechanisms, offering new targets for rehabilitation strategies to enhance functional outcomes for individuals with central vision loss.

Amblyopia is a neurodevelopmental disorder characterized by both spatial and temporal visual processing deficits, yet therapeutic approaches have predominantly focused on spatial vision. This study investigated whether enhancing temporal resolution through perceptual learning could induce improvements in spatial visual acuity. A replicated ABAB single-case experimental design was implemented with four adults with anisometropic and/or strabismic amblyopia. During the intervention (B) phases, participants engaged in a two-flash fusion training task designed to improve millisecond-scale temporal discrimination, while visual acuity (logMAR) was measured systematically across all baseline (A) and intervention phases. Visual analysis and comprehensive statistical modeling revealed that all participants demonstrated systematic and clinically meaningful improvements in visual acuity that were functionally related to the temporal training. These gains occurred exclusively during intervention phases, with robust non-overlap effects confirmed by Tau-U analysis and significant improving trends identified through piecewise linear regression. The improvements in acuity were sustained at a follow-up assessment conducted after the final training phase. These findings provide evidence that temporal perceptual learning is a viable therapeutic pathway for driving durable recovery of spatial vision in adult amblyopia, suggesting that the neural substrates for temporal and spatial processing are functionally interconnected.

It has been demonstrated that the stimulus's features, including size, brightness, numerosity, and loudness, can affect the perception of subjective and explicit time. But, in a daily life situation, actual events presumably involve an implicit processing of time rather than an explicit processing, with some studies suggesting that the presentation of emotional stimuli before the target stimulus influences implicit timing. The present study aims to test the implicit component of temporal processing that the symbolic meaning of speed might influence. We used a time foreperiod task in which participants were first presented with a warning signal recalling the meaning of fast or slow speed, followed by the target. Our study shows significant main effects of the presented image cue and foreperiod effect. We observed faster reaction time when the target was preceded by a faster image compared to a slow image and in dependence of the factor of weight. Based on this, we conclude the symbolic meaning of speed can affect implicit timing by altering how the brain interprets temporal data.

Background: The Attention Control Scale (ATTC) is a widely used self-report measure of attentional control capacities. However, research questions whether it accurately substitutes for objective attention control tasks. This study investigated ATTC's correlation with the Attention Network Test (ANT) across alerting, orienting, and executive control networks. We also used the Inverse Efficiency Score (IES) as an additional factor to check ATTC using ANT. Methods: We administered 143 participants who completed the ATTC questionnaire and ANT behavioral test assessing network efficiencies. Results: The results showed non-significant ATTC-ANT correlations across all networks. In an additional analysis, while the ATTC demonstrated factorial validity, subjective control was disconnected from actual attention regulation efficiency. A small male advantage emerged for executive control. Conclusions: Dissociations likely stem from attention complexity and method variances rather than overlap. The findings do not support the ATTC as a stand-alone proxy for performance-based measurement. Multifaceted assessments are essential for comprehensively capturing attentional control.

While stress can impair cognitive functions, including attention and inhibition, it is not always deleterious; e.g., stress could also increase alertness, motivation, and concentration. Limited research has examined these effects in clinical populations experiencing high perceived stress. The current study investigated the impact of perceived stress levels, using the Perceived Stress Scale (PSS), on attention and inhibition in 91 participants categorized into high, moderate, and low perceived stress groups. Participants completed online versions of the Stroop and Go/No Go tasks to assess attention and inhibition abilities. Results revealed that participants with higher perceived stress exhibited prolonged reaction times and reduced correct responses relative to those reporting lower stress across both tasks. ANOVA identified significantly poorer performance on the Stroop and Go/No Go tasks among individuals in the high versus moderate and low-stress groups. Introducing auditory or visual interference further disrupted cognitive performance for the high-stress group. These findings indicate that raised perceived stress negatively impacts attention and inhibition. The study provides evidence that chronic stress may impede cognitive abilities and suggests further research should examine stress effects in clinical cohorts. This investigation makes a meaningful contribution by demonstrating the deleterious impacts of high perceived stress on cognition in a clinical sample, addressing a gap in the literature.

This comprehensive multi-cultural online survey examined the psychological impacts of the COVID-19 pandemic on perceived stress levels, emotion regulation strategies, and overall quality of life across diverse international populations. The research investigated how individuals from different cultural backgrounds experienced and coped with the unprecedented challenges posed by the global health crisis. Through systematic data collection and analysis, the study revealed significant patterns in stress responses and adaptive coping mechanisms that varied across cultural contexts. The findings contribute to our understanding of cross-cultural differences in psychological resilience and highlight the importance of culturally-sensitive approaches to mental health support during global crises.

This research was conducted on students that studying in high schools in Sanandaj city, Kurdistan Province, Iran. In total, 743 students were selected from all of the interested students, and then participants completed a sociodemographic questionnaire, the Internet Addiction Test (IAT), Negative Activities test (DASS-21), Emotion Regulation test (ER), and Farsi Toronto Alexithymia test (FTAS-20). In total, 287 students marked as moderate and severe addicted users. Among all 287 students, 243 students agreed to attend the rest of the tests (Main group). Also, 137 students are selected as control group. The study shows that IA score is associated with NA, ER, and alexithymia scores. Students with high scores in IA showed higher scores on the other test. Although we could not find any correlation between some subscales, still the research indicates that most addicted users are involved with the other psychological impairments. Clinical results were discussed.