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The COVID-19 pandemic forced higher education institutions in Switzerland to move to distance learning, with certain limitations such as “Zoom fatigue” and a lack of interaction with peers and teachers. This has also impacted the development of interprofessional skills and key concepts such as professional acknowledgement, cooperation, and communication skills. This study was conducted using mixed methods, including performance assessment regarding examination notes, the 12-item General Health Questionnaire, and semi-structured interviews, to assess the impact of the pandemic on the performance of paramedic students and their psychological state. The results of the semi-structured interviews provided insight into the impact of the pandemic. The period of the COVID-19 pandemic appears to have had an impact on the psychological state of the paramedic students, most of whom were considered to be either at risk or in psychological distress. There may have been an effect on their theoretical knowledge performance, with pre-pandemic promotions performing better than pandemic promotions.

Attention helps us to select what is relevant from the enormous amounts of information taken up by our senses. However, it remains unclear just how early in sensory processing attentional selection can occur. Here, we investigated this question in healthy volunteers by assessing the effect of attentional load on the earliest component (C1) of the visual evoked potential (VEP). We mapped participants' C1 responses to task-irrelevant peripheral textures of different densities and then selected those textures eliciting maximal C1 amplitudes in the upper and lower visual field in each participant. In a second experimental session, these optimal C1 stimuli served as peripheral distracters while participants performed easy or difficult detection tasks at fixation. Our results show a reduction of C1 amplitudes under high attentional load selectively in the lower visual field. This asymmetric effect is opposite to previously reported results obtained without preselecting stimuli. We conclude that attentional selection during early visual processing is possible, but depends on the interaction between anatomic and functional anisotropies of the visual system. This underscores the importance of precisely delineating when, where, and how attentional filtering can operate on initial perceptual processing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The C1 event-related potential (ERP) captures the earliest stage of feedforward processing in the primary visual cortex (V1). An ongoing debate is whether top-down selective attention can modulate the C1. One side of the debate pointed out that null findings appear to outnumber positive findings; thus, selective attention does not seem to influence the C1. However, this suggestion is not based on a valid approach to summarizing evidence across studies. Therefore, we conducted a systematic review and meta-analysis investigating the effects of selective attention on the C1, involving 47 experiments and 794 subjects in total. Despite heterogeneity across studies, results suggested that attention has a moderate effect on the C1 (Cohen'smml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:msubmml:mid/mml:mimml:miz/mml:mi/mml:msub/mml:math = 0.33, p lt; .0001); that is, C1 amplitude is larger for visual stimuli that are attended than unattended. These results suggest that C1 is affected by top-down selective attention.

Visual perceptual learning refers to long-lasting performance improvements on a visual skill - an ability supported by plastic changes in early visual brain areas. Visual perceptual learning has been shown to be induced by training and to benefit from consolidation during sleep, presumably via the reactivation of learning-associated neuronal firing patterns. However, previous studies have almost exclusively relied on a single paradigm, the texture discrimination task, on which performance improvements may rely on higher-order rather than lower-level perceptual skills. In the present study, we tested whether sleep has beneficial effects on a visual disparity discrimination task. We confirm previous findings in showing that the ability to discriminate different disparities is unaffected by sleep during a 12-hr retention period after training. Importantly, we extend these results by providing evidence against an effect of sleep on the generalisation of improved disparity discrimination across the vertical meridian. By relying on a between-subject design, we further exclude carry-over effects as a possible confound present in previous findings. These data argue against sleep as an important factor in the consolidation of a low-level perceptual skill. This sets important constraints on models of the role of sleep and sleep-associated neural reactivation in the consolidation of non-declarative memories.

Cette revue de littérature porte sur l'addiction à Internet (AI) chez les étudiants entre 12 et 25 ans. Elle définit les facteurs d'influence et les répercussions que cette addiction peut avoir sur la santé mentale et l'entourage familial de l'adolescent. Ce thème est mis en lien avec la théorie de MCGill élaboré par Moyra Allen.

Study Objectives The brain appears to use internal models to successfully interact with its environment via active predictions of future events. Both internal models and the predictions derived from them are based on previous experience. However, it remains unclear how previously encoded information is maintained to support this function, especially in the visual domain. In the present study, we hypothesized that sleep consolidates newly encoded spatio-temporal regularities to improve predictions afterwards. Methods We tested this hypothesis using a novel sequence-learning paradigm that aimed to dissociate perceptual from motor learning. We recorded behavioral performance and high-density electroencephalography (EEG) in male human participants during initial training and during testing two days later, following an experimental night of sleep (n = 16, including high-density EEG recordings) or wakefulness (n = 17). Results Our results show sleep-dependent behavioral improvements correlated with sleep-spindle activity specifically over occipital cortices. Moreover, event-related potential (ERP) responses indicate a shift of attention away from predictable to unpredictable sequences after sleep, consistent with enhanced automaticity in the processing of predictable sequences. Conclusions These findings suggest a sleep-dependent improvement in the prediction of visual sequences, likely related to visual cortex reactivation during sleep spindles. Considering that controls in our experiments did not fully exclude oculomotor contributions, future studies will need to address the extent to which these effects depend on purely perceptual versus oculomotor sequence learning.

Visual perceptual learning refers to long-lasting performance improvements on a visual skill, an ability presumably supported by plastic changes in early visual brain areas. Visual perceptual learning has been shown to be induced by training and to benefit from consolidation during sleep, presumably via the reactivation of learning-associated neuronal firing patterns. However, previous studies have almost exclusively relied on a single paradigm, the texture discrimination task, on which performance improvements may not rely on strictly perceptual skills. Here, we tested whether sleep has beneficial effects on a visual disparity discrimination task. We confirm previous findings in showing that the ability to discriminate different disparities is unaffected by sleep during a 12-hour retention period after training. Importantly, we extend these results by providing evidence against an effect of sleep on the generalization of improved disparity discrimination across the vertical meridian. We further rule out carry-over effects as a possible confound in our earlier within-subject study by relying on a between-subject design. The combined data from both studies argue against sleep as an important factor in the consolidation of a strictly perceptual skill. This sets important constraints on models of the role of sleep and sleep-associated neural reactivation in the consolidation of non-declarative memories.

Sleep supports the consolidation of recently encoded declarative and procedural memories. An important component of this effect is the repeated reactivation of neuronal ensemble activity elicited during memory encoding. For perceptual learning, however, sleep benefits have only been reported for specific tasks and it is not clear whether sleep targets low-level perceptual, higher-order temporal or attentional aspects of performance. Here, we employed a coarse binocular disparity discrimination task, known to rely on low-level stereoscopic vision. We show that human subjects improve over training and retain the same performance level across a 12-h retention period. Improvements do not generalize to other parts of the visual field and are unaffected by whether the retention period contains sleep or not. These results are compatible with the notion that behavioural improvements in binocular disparity discrimination do not additionally benefit from sleep when compared with the same time spent awake. We hypothesize that this might generalize to other strictly low-level perceptual tasks. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future'.

Performance (mean ± 95 % boot-strapped confidence interval) after splitting training trials into three blocks of equal length (Training 1-3). Data are shown for the parameters Slope (a) and Threshold (b) of the psychometric function. In none of the training blocks and for neither parameter did sleep and wake conditions differ significantly (all Holm-corrected p = 1.0). Bars in insets show the difference between conditions in gain (change over retention period; (RetrievalSleep – Training 3Sleep) – (RetrievalWake – Training 3Wake)) for each subject. Positive values thus denote higher gain on that parameter in the sleep condition, negative values higher gain in the wake condition. For the parameter Slope, only 16 bars are shown because one of the values from one subject was dropped during outlier rejection. Differential gains over the retention period were analysed directly using a Wake/Sleep × Phase ANOVA including only the last Training block and Retrieval.

Better understanding of Multi Domain Battle (MDB) challenges in complex military environments may start by gaining a basic scientific appreciation of the level of generalization and scalability offered by Machine Learning (ML) solutions designed, trained and optimized to achieve a single, specific task, continuously daytime and nighttime. We examine the generalization and scalability promises of a modern deep ML solution, applied to a unique spatial-spectral dataset that consists of blackbody calibrated, longwave infrared spectra of a fixed target site containing three painted metal surrogate tanks deployed in a field of mixed vegetation. Data was collected at roughly six minute intervals, nearly continuously, for over a year. This includes collection in many atmospheric conditions (rain, snow, sleet, fog, etc.) throughout the year. This paper focuses on data collected by a Telops Hyper-Cam from a 65 meter observation tower located at slant range of roughly 550 meters, from the targets. The dataset is very complex. There are no obvious spectral signatures from the target surfaces. The complexity is due in part to the natural variations of the various types of vegetation, cloud presence, and the changing solar loading conditions over time. This is precisely the environment MDB applications must function in. We detail some of the many training sets extracted to train different deep learning stacked auto encoder networks. We present performance results with receiver operator characteristic curves, confusion matrices, metric-vs-time plots, and classification maps. We show performance of ML models trained with data from various time windows, including over complete diurnal cycles and their performance processing data from different days and environmental conditions.