Detecting meaning in RSVP at 13 ms per picture

Unveiling the neural dynamics of conscious perception in rapid object recognition

Our brain excels at recognizing objects, even when they flash by in a rapid sequence. However, the neural processes determining whether a target image in a rapid sequence can be recognized or not remains elusive. We used electroencephalography (EEG) to investigate the temporal dynamics of brain processes that shape perceptual outcomes in these challenging viewing conditions. Using naturalistic images and advanced multivariate pattern analysis (MVPA) techniques, we probed the brain dynamics governing conscious object recognition. Our results show that although initially similar, the processes for when an object can or cannot be recognized diverge around 180 ms post-appearance, coinciding with feedback neural processes. Decoding analyses indicate that gist perception (partial conscious perception) can occur at ∼120 ms through feedforward mechanisms. In contrast, object identification (full conscious perception of the image) is resolved at ∼190 ms after target onset, suggesting involvement of recurrent processing. These findings underscore the importance of recurrent neural connections in object recognition and awareness in rapid visual presentations.

What does decoding from the PFC reveal about consciousness?

Disputes between rival theories of consciousness have often centered on whether perceptual contents can be decoded from the prefrontal cortex (PFC). Failures to decode from the PFC are taken to challenge 'cognitive' theories of consciousness such as the global workspace theory and higher-order monitoring theories, and decoding successes have been taken to confirm these theories. However, PFC decoding shows both too much and too little. Too much because cognitive theories of consciousness do not need PFC rerepresentation of perceptual contents since pointers to perceptual representations suffice. Too little because there is evidence that PFC decoding of perceptual content reflects postperceptual cognitive representation, such as thoughts that have those perceptual contents rather than conscious percepts.

Task-irrelevant semantic relationship between objects and scene influence attentional allocation

Recent behavioral evidence suggests that the semantic relationships between isolated objects can influence attentional allocation, with highly semantically related objects showing an increase in processing efficiency. This semantic influence is present even when it is task-irrelevant (i.e., when semantic information is not central to the task). However, given that objects exist within larger contexts, i.e., scenes, it is critical to understand whether the semantic relationship between a scene and its objects continuously influence attention. Here, we investigated the influence of task-irrelevant scene semantic properties on attentional allocation and the degree to which semantic relationships between scenes and objects interact. Results suggest that task-irrelevant associations between scenes and objects continuously influence attention and that this influence is directly predicted by the perceived strength of semantic associations.

Contrast sensitivity is resilient to induced fast periodic defocus oscillations

This study investigates the potential effects of periodic defocus oscillations on contrast sensitivity. Sinusoidal fluctuations at 5, 15, and 25 Hz, with defocus peak-to-valley values ranging from 0.15 to 3 D, were induced by means of a focus-tunable lens after calibrating its dynamic behavior. Monocular contrast sensitivity was measured on five young emmetropic subjects. The experimental data shows that contrast sensitivity loss due to defocus fluctuations is low for a wide range of frequencies and amplitudes. Only for the more severe case studied (25 Hz, ± 1.5 D) contrast threshold showed a clear increase in most subjects. Qualitative comparison of the empirical data with a simulation of modulation loss due to time integration of defocused retinal point spread functions, suggests a short integration time by the eye for defocus blur, around or even below a hundredth of a second.

Information Provision Regarding Health-Related Direct-to-Consumer Genetic Testing for Dutch Consumers: An in-Depth Content Analysis of Sellers' Websites

Background: Previous studies have suggested that information offered by sellers of health-related direct-to-consumer genetic tests (DTC-GTs) is often incomplete, unbalanced, or too difficult to understand. The extent to which this is the case for sellers accessible to Dutch consumers has not previously been studied. Methods and Goals: The present study aimed to assess the completeness, balance, readability, and findability of informational content on a selection of websites from several health-related DTC-GT sellers accessible to Dutch consumers. An in-depth content analysis was performed based on a recently published checklist outlining key items for policy guidance regarding DTC-GT services. Results: The information provided by sellers did not equally cover all aspects relevant to health-related DTC-GT service provision. The provided information was slightly unbalanced, with benefits of health-related DTC-GT usage being overemphasized compared to its risks and limitations. The readability of the provided information was low, on average requiring college education for proper understanding. A findability analysis showed that information concerning all themes is overall relatively evenly distributed across analyzed sellers' websites. Conclusions: Information provision by assessed health-related DTC-GT sellers is suboptimal regarding completeness, balance, and readability. To better empower potential consumers to make an informed decision regarding health-related DTC-GT usage, we advocate industry-wide enhancement of information provision.

Rapid color categorization revealed by frequency-tagging-based EEG

There has been much debate on whether color categories affect how we perceive color. Recent theories have put emphasis on the role of top-down influence on color perception that the original continuous color space in the visual cortex may be transformed into categorical encoding due to top-down modulation. To test the influence of color categories on color perception, we adopted an RSVP paradigm, where color stimuli were presented at a fast speed of 100 ms per stimulus and were forward and backward masked by the preceding and following stimuli. Moreover, no explicit color naming or categorization was required. In theory, backward masking with such a short interval in a passive viewing task should constrain top-down influence from higher-level brain areas. To measure any potentially subtle differences in brain response elicited by different color categories, we embedded a sensitive frequency-tagging-based EEG paradigm within the RSVP stimuli stream where the oddball color stimuli were encoded with a different frequency from the base color stimuli. We showed that EEG responses to cross-category oddball colors at the frequency where the oddball stimuli were presented was significantly larger than the responses to within-category oddball colors. Our study suggested that the visual cortex can automatically and implicitly encode color categories when color stimuli are presented rapidly.

High target prevalence may reduce the spread of attention during search tasks

Target prevalence influences many cognitive processes during visual search, including target detection, search efficiency, and item processing. The present research investigated whether target prevalence may also impact the spread of attention during search. Relative to low-prevalence searches, high-prevalence searches typically yield higher fixation counts, particularly during target-absent trials. This may emerge because the attention spread around each fixation may be smaller for high than low prevalence searches. To test this, observers searched for targets within object arrays in Experiments 1 (free-viewing) and 2 (gaze-contingent viewing). In Experiment 3, observers searched for targets in a Rapid Serial Visual Presentation (RSVP) stream at the center of the display while simultaneously processing occasional peripheral objects. Experiment 1 used fixation patterns to estimate attentional spread, and revealed that attention was narrowed during high, relative to low, prevalence searches. This effect was weakened during gaze-contingent search (Experiment 2) but emerged again when eye movements were unnecessary in RSVP search (Experiment 3). These results suggest that, although task demands impact how attention is allocated across displays, attention may also narrow when searching for frequent targets.

Mapping the dynamics of visual feature coding: Insights into perception and integration

The basic computations performed in the human early visual cortex are the foundation for visual perception. While we know a lot about these computations, a key missing piece is how the coding of visual features relates to our perception of the environment. To investigate visual feature coding, interactions, and their relationship to human perception, we investigated neural responses and perceptual similarity judgements to a large set of visual stimuli that varied parametrically along four feature dimensions. We measured neural responses using electroencephalography (N = 16) to 256 grating stimuli that varied in orientation, spatial frequency, contrast, and colour. We then mapped the response profiles of the neural coding of each visual feature and their interactions, and related these to independently obtained behavioural judgements of stimulus similarity. The results confirmed fundamental principles of feature coding in the visual system, such that all four features were processed simultaneously but differed in their dynamics, and there was distinctive conjunction coding for different combinations of features in the neural responses. Importantly, modelling of the behaviour revealed that every stimulus feature contributed to perceptual judgements, despite the untargeted nature of the behavioural task. Further, the relationship between neural coding and behaviour was evident from initial processing stages, signifying that the fundamental features, not just their interactions, contribute to perception. This study highlights the importance of understanding how feature coding progresses through the visual hierarchy and the relationship between different stages of processing and perception.

Metacontrast masking is ineffective in the first 6 months of life

Metacontrast masking is one of the most widely studied types of visual masking, in which a visual stimulus is rendered invisible by a subsequent mask that does not spatially overlap with the target. Metacontrast has been used for many decades as a tool to study visual processing and conscious perception in adults. However, there are so far no infant studies on metacontrast and it remains unknown even whether it occurs in infants. The present study examined metacontrast masking in 3- to 8-month-old infants (N = 168) using a habituation paradigm. We found that metacontrast is ineffective for infants under 7 months and that younger infants can perceive a masked stimulus that older infants cannot. Our results suggest that metacontrast is distinct from other simple types of masking that occur in early infancy, and would be consistent with the idea that metacontrast results from the disruption of recurrent processing.

Do robots outperform humans in human-centered domains?

The incessant progress of robotic technology and rationalization of human manpower induces high expectations in society, but also resentment and even fear. In this paper, we present a quantitative normalized comparison of performance, to shine a light onto the pressing question, "How close is the current state of humanoid robotics to outperforming humans in their typical functions (e.g., locomotion, manipulation), and their underlying structures (e.g., actuators/muscles) in human-centered domains?" This is the most comprehensive comparison of the literature so far. Most state-of-the-art robotic structures required for visual, tactile, or vestibular perception outperform human structures at the cost of slightly higher mass and volume. Electromagnetic and fluidic actuation outperform human muscles w.r.t. speed, endurance, force density, and power density, excluding components for energy storage and conversion. Artificial joints and links can compete with the human skeleton. In contrast, the comparison of locomotion functions shows that robots are trailing behind in energy efficiency, operational time, and transportation costs. Robots are capable of obstacle negotiation, object manipulation, swimming, playing soccer, or vehicle operation. Despite the impressive advances of humanoid robots in the last two decades, current robots are not yet reaching the dexterity and versatility to cope with more complex manipulation and locomotion tasks (e.g., in confined spaces). We conclude that state-of-the-art humanoid robotics is far from matching the dexterity and versatility of human beings. Despite the outperforming technical structures, robot functions are inferior to human ones, even with tethered robots that could place heavy auxiliary components off-board. The persistent advances in robotics let us anticipate the diminishing of the gap.

The relevance of a right scale for sampling when studying high-resolution behavioral dynamics

Many species used in behavioral studies are small vertebrates with high metabolic rates and potentially enhanced temporal resolution of perception. Nevertheless, the selection of an appropriate scales to evaluate behavioral dynamics has received little attention. Herein, we studied the temporal organization of behaviors at fine-grain (i.e. sampling interval ≤1s) to gain insight into dynamics and to rethink how behavioral events are defined. We statistically explored high-resolution Japanese quail (Coturnix japonica) datasets encompassing 17 defined behaviors. We show that for the majority of these behaviors, events last predominately <300ms and can be shorter than 70ms. Insufficient sampling resolution, even in the order of 1s, of behaviors that involve spatial displacement (e.g. walking) yields distorted probability distributions of event durations and overestimation of event durations. Contrarily, behaviors without spatial displacement (e.g. vigilance) maintain non-Gaussian, power-law-type distributions indicative of long-term memory, independently of the sampling resolution evaluated. Since data probability distributions reflect underlying biological processes, our results highlight the importance of quantification of behavioral dynamics based on the temporal scale pertinent to the species, and data distribution. We propose a hierarchical model that links diverse types of behavioral definitions and distributions, and paves the way towards a statistical framework for defining behaviors.

Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

Humans can quickly recognize objects in a dynamically changing world. This ability is showcased by the fact that observers succeed at recognizing objects in rapidly changing image sequences, at up to 13 ms/image. To date, the mechanisms that govern dynamic object recognition remain poorly understood. Here, we developed deep learning models for dynamic recognition and compared different computational mechanisms, contrasting feedforward and recurrent, single-image and sequential processing as well as different forms of adaptation. We found that only models that integrate images sequentially via lateral recurrence mirrored human performance (N = 36) and were predictive of trial-by-trial responses across image durations (13-80 ms/image). Importantly, models with sequential lateral-recurrent integration also captured how human performance changes as a function of image presentation durations, with models processing images for a few time steps capturing human object recognition at shorter presentation durations and models processing images for more time steps capturing human object recognition at longer presentation durations. Furthermore, augmenting such a recurrent model with adaptation markedly improved dynamic recognition performance and accelerated its representational dynamics, thereby predicting human trial-by-trial responses using fewer processing resources. Together, these findings provide new insights into the mechanisms rendering object recognition so fast and effective in a dynamic visual world.

Mechanism of Activation of the Visual Receptor Rhodopsin

Rhodopsin is the photoreceptor in human rod cells responsible for dim-light vision. The visual receptors are part of the large superfamily of G protein-coupled receptors (GPCRs) that mediate signal transduction in response to diverse diffusible ligands. The high level of sequence conservation within the transmembrane helices of the visual receptors and the family A GPCRs has long been considered evidence for a common pathway for signal transduction. I review recent studies that reveal a comprehensive mechanism for how light absorption by the retinylidene chromophore drives rhodopsin activation and highlight those features of the mechanism that are conserved across the ligand-activated GPCRs.

Verbal working memory encodes phonological and semantic information differently

Working memory (WM) is often tested through immediate serial recall of word lists. Performance in such tasks is negatively influenced by phonological similarity: People more often get the order of words wrong when they are phonologically similar to each other (e.g., cat, fat, mat). This phonological-similarity effect shows that phonology plays an important role for the representation of serial order in these tasks. By contrast, semantic similarity usually does not impact performance negatively. To resolve and understand this discrepancy, we tested the effects of phonological and semantic similarity for the retention of positional information in WM. Across six experiments (all Ns = 60 young adults), we manipulated between-item semantic and phonological similarity in tasks requiring participants to form and maintain new item-context bindings in WM. Participants were asked to retrieve items from their context, or the contexts from their item. For both retrieval directions, phonological similarity impaired WM for item-context bindings across all experiments. Semantic similarity did not. These results demonstrate that WM encodes phonological and semantic information differently. We propose a WM model accounting for semantic-similarity effects in WM, in which semantic knowledge supports WM through activated long-term memory.

The temporal cost of deploying attention limits accurate target identification in rapid serial visual presentation

Lag-1 sparing is a common exception to the attentional blink, where a target presented directly after T1 can be identified and reported accurately. Prior work has proposed potential mechanisms for lag 1 sparing, including the boost and bounce model and the attentional gating model. Here, we apply a rapid serial visual presentation task to investigate the temporal limitations of lag 1 sparing by testing three distinct hypotheses. We found that endogenous engagement of attention to T2 requires between 50 and 100 ms. Critically, faster presentation rates yielded lower T2 performance, whereas decreased image duration did not impair T2 detection and report. These observations were reinforced by subsequent experiments controlling for short-term learning and capacity-dependent visual processing effects. Thus, lag-1 sparing was limited by the intrinsic dynamics of attentional boost engagement rather than by earlier perceptual bottlenecks such as insufficient exposure to images in the stimulus stream or visual processing capacity limitations. Taken together, these findings support the boost and bounce theory over earlier models that focus only on attentional gating or visual short-term memory storage, informing our understanding of how the human visual system deploys attention under challenging temporal constraints.

Electrically programmable solid-state metasurfaces via flash localised heating

In the last decades, metasurfaces have attracted much attention because of their extraordinary light-scattering properties. However, their inherently static geometry is an obstacle to many applications where dynamic tunability in their optical behaviour is required. Currently, there is a quest to enable dynamic tuning of metasurface properties, particularly with fast tuning rate, large modulation by small electrical signals, solid state and programmable across multiple pixels. Here, we demonstrate electrically tunable metasurfaces driven by thermo-optic effect and flash-heating in silicon. We show a 9-fold change in transmission by <5 V biasing voltage and the modulation rise-time of <625 µs. Our device consists of a silicon hole array metasurface encapsulated by transparent conducting oxide as a localised heater. It allows for video frame rate optical switching over multiple pixels that can be electrically programmed. Some of the advantages of the proposed tuning method compared with other methods are the possibility to apply it for modulation in the visible and near-infrared region, large modulation depth, working at transmission regime, exhibiting low optical loss, low input voltage requirement, and operating with higher than video-rate switching speed. The device is furthermore compatible with modern electronic display technologies and could be ideal for personal electronic devices such as flat displays, virtual reality holography and light detection and ranging, where fast, solid-state and transparent optical switches are required.

Stimulus-response congruency effects depend on quality of perceptual evidence: A diffusion model account

Individuals often need to make quick decisions based on incomplete or "noisy" information. This requires the coordination of attentional, perceptual, cognitive, and behavioral mechanisms. This poses a challenge for isolating the unique effects of each subprocess from behavioral data, which reflect the summation of all subprocesses combined. Sequential sampling models offer a more detailed examination of behavioral data, enabling us to separate decisional and non-decisional processes at play in a task. Participants were required to identify briefly presented shapes while perceptual (duration, size, location) and response features (location-congruent/-incongruent/-neutral) of the task were manipulated. The diffusion model (Ratcliff, 1978) was used to dissociate decisional and executive processes in the task. In Experiment 1, stimuli were presented for either 20 or 80 ms to the left or right of a central fixation while response keys were positioned horizontally. In Experiment 2, stimulus size was manipulated rather than duration. In Experiment 3, response keys were positioned vertically. Results showed a duration x response mapping interaction. Participants displayed stimulus-response (S-R) congruency biases only on short-duration trials. This effect was observed for both horizontal and vertical response key mappings. Stimulus size affected participant response speed, but did not elicit S-R congruency biases. The present findings show that when perceptual quality of evidence is poor, individuals rely more heavily on spatial-motor mechanisms when making speeded choice decisions. Furthermore, positioning response keys vertically is insufficient to eliminate S-R congruency effects. Diffusion model parameters are presented and implications of the model are discussed.

The temporal dynamics of selective attention are reflected by distractor intrusions

When observers have to identify an object embedded in a rapid serial visual presentation (RSVP) stream, they often erroneously report the identity of a distractor instead of the target (distractor intrusion). In two experiments, we examined whether these intrusion errors are associated with the speed of attentional engagement. Participants reported the identity of target digits indicated by shape selection cues. To manipulate the speed of engagement, targets appeared either within a single RSVP stream or unpredictably in one of two streams. Objects that followed the selection cue were reported more frequently when engagement was delayed (two streams), whereas the probability of reporting objects preceding the cue was higher when engagement was faster (single stream). These results show that distractor intrusions are closely linked to the allocation of selective attention in time, making the intrusion paradigm a useful tool for research into the temporal dynamics of attention. They also provide new evidence for the idea that attentional selectivity operates within brief periods of perceptual enhancement (attentional episodes), facilitating the processing of all objects within this period, regardless of their status as targets or distractors.

Using global feedback to induce learning of gist of abnormality in mammograms

Extraction of global structural regularities provides general 'gist' of our everyday visual environment as it does the gist of abnormality for medical experts reviewing medical images. We investigated whether naïve observers could learn this gist of medical abnormality. Fifteen participants completed nine adaptive training sessions viewing four categories of unilateral mammograms: normal, obvious-abnormal, subtle-abnormal, and global signals of abnormality (mammograms with no visible lesions but from breasts contralateral to or years prior to the development of cancer) and receiving only categorical feedback. Performance was tested pre-training, post-training, and after a week's retention on 200 mammograms viewed for 500 ms without feedback. Performance measured as d' was modulated by mammogram category, with the highest performance for mammograms with visible lesions. Post-training, twelve observed showed increased d' for all mammogram categories but a subset of nine, labelled learners also showed a positive correlation of d' across training. Critically, learners learned to detect abnormality in mammograms with only the global signals, but improvements were poorly retained. A state-of-the-art breast cancer classifier detected mammograms with lesions but struggled to detect cancer in mammograms with the global signal of abnormality. The gist of abnormality can be learned through perceptual/incidental learning in mammograms both with and without visible lesions, subject to individual differences. Poor retention suggests perceptual tuning to gist needs maintenance, converging with findings that radiologists' gist performance correlates with the number of cases reviewed per year, not years of experience. The human visual system can tune itself to complex global signals not easily captured by current deep neural networks.

Recognition of brief sounds in rapid serial auditory presentation

Two experiments were conducted to test the role of participant factors (i.e., musical sophistication, working memory capacity) and stimulus factors (i.e., sound duration, timbre) on auditory recognition using a rapid serial auditory presentation paradigm. Participants listened to a rapid stream of very brief sounds ranging from 30 to 150 milliseconds and were tested on their ability to distinguish the presence from the absence of a target sound selected from various sound sources placed amongst the distracters. Experiment 1a established that brief exposure to stimuli (60 to 150 milliseconds) does not necessarily correspond to impaired recognition. In Experiment 1b we found evidence that 30 milliseconds of exposure to the stimuli significantly impairs recognition of single auditory targets, but the recognition for voice and sine tone targets impaired the least, suggesting that the lower limit required for successful recognition could be lower than 30 milliseconds for voice and sine tone targets. Critically, the effect of sound duration on recognition completely disappeared when differences in musical sophistication were controlled for. Participants' working memory capacities did not seem to predict their recognition performances. Our behavioral results extend the studies oriented to understand the processing of brief timbres under temporal constraint by suggesting that the musical sophistication may play a larger role than previously thought. These results can also provide a working hypothesis for future research, namely, that underlying neural mechanisms for the processing of various sound sources may have different temporal constraints.

Dual-gate manipulation of a HfZrOx-based MoS2 field-effect transistor towards enhanced neural network applications

Artificial neural networks (ANNs) have strong learning and computing capabilities, and alleviate the problem of high power consumption of traditional von Neumann architectures, providing a solid basis for advanced image recognition, information processing, and low-power detection. Recently, a two-dimensional (2D) MoS₂ field-effect transistor (FET) integrating a Zr-doped HfO₂ (HZO) ferroelectric layer has shown potential for both logic and memory applications with low power consumption, which is promising for parallel processing of massive data. However, the long-term potentiation (LTP) characteristics of such devices are usually non-linear, which will affect the replacement of ANN weight values and degrade the ANN recognition rate. Here, we propose a dual-gate-controlled 2D MoS₂ FET employing HZO gate stack with a crested symmetric structure to reduce power consumption. Improved nonlinearity of the LTP properties has been achieved through the electrical control of the dual gates. A recognition rate reaching 100% is obtained after 60 training epochs, and is 7.89% higher than that obtained from single-gate devices. Our proposed device structure and experimental results provide an attractive pathway towards high-efficiency data processing and image classification in the advanced artificial intelligence field.

MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion

Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, better performance, and high reliability are required. Inspired by the parallel and large-scale information processing structures in the human brain, a shallow broad neural network model is proposed on a specially designed multi-order Descartes expansion operation. Such Descartes expansion acts as an efficient feature extraction method for the network, improve the separability of the original pattern by transforming the raw data pattern into a high-dimensional feature space, the multi-order Descartes expansion space. As a result, a single-layer perceptron network will be able to accomplish the classification task. The multi-order Descartes expansion neural network (MODENN) is thus created by combining the multi-order Descartes expansion operation and the single-layer perceptron together, and its capacity is proved equivalent to the traditional multi-layer perceptron and the deep neural networks. Three kinds of experiments were implemented, the results showed that the proposed MODENN model retains great potentiality in many aspects, including implementability, parallelizability, performance, robustness, and interpretability, indicating MODENN would be an excellent alternative to mainstream neural networks.

Foveated Stochastic Lightcuts

Foveated rendering provides an idea for accelerating rendering algorithms without sacrificing the perceived rendering quality in virtual reality applications. In this paper, we propose a foveated stochastic lightcuts method to render high-quality many-lights illumination effects in high perception-sensitive regions. First, we introduce a spatiotemporal-luminance based lightcuts generation method to generate lightcuts with different accuracy for different visual perception-sensitive regions. Then we propose a multi-resolution light samples selection method to select the light sample for each node in the lightcuts more efficiently. Our method supports full-dynamic scenes containing over 250k dynamic light sources and dynamic diffuse/specular/glossy objects. It provides frame rates up to 110fps for high-quality many-lights illumination effects in high perception-sensitive regions of the HVS in VR HMDs. Compared with the state-of-the-art stochastic lightcuts method using the same rendering time, our method achieves smaller mean squared errors in the fovea and periphery. We also conduct user studies to prove that the perceived quality of our method has a high visual similarity with the results of the ground truth rendered by using the stochastic lightcuts with 2048 light samples per pixel.

Screen-based digital learning methods in radiation oncology and medical education

The field of radiation oncology is rapidly advancing through technological and biomedical innovation backed by robust research evidence. In addition, cancer professionals are notoriously time-poor, meaning there is a need for high quality, accessible and tailored oncological education programs. Digital learning (DL) is well-placed to cater to these needs, as it provides teaching options that can be delivered flexibly and on-demand from anywhere in the world. The evidence for usage of these techniques in medical education has expanded rapidly in recent years. However, there remains many reservations in the oncological community to adopting and developing DL, largely due to a poor familiarity with the pedagogical evidence base. This article will review the application of the screen-based DL tools that are at educators' disposal. It will summarize best-practice in developing tailored, made-for-screen videos, gamification, and infographics. It also reviews data behind the following practical tips of 1) strategically combining text with graphics to decrease cognitive load, 2) engaging users through use of interactive elements in digital content, and 3) maximizing impact through thoughtful organization of animations/images. Overall, the digital space evolving is well placed to cater to the evolving educational needs of oncology learners. This review and its practical tips aim to inspire further development in this arena, production of high-yield educational products, use of engaging delivery methods and programs that are tailored to individual learning needs.

Low and high frequency intracranial neural signals match in the human associative cortex

In vivo intracranial recordings of neural activity offer a unique opportunity to understand human brain function. Intracranial electrophysiological (iEEG) activity related to sensory, cognitive or motor events manifests mostly in two types of signals: event-related local field potentials in lower frequency bands (<30 Hz, LF) and broadband activity in the higher end of the frequency spectrum (>30 Hz, High frequency, HF). While most current studies rely exclusively on HF, thought to be more focal and closely related to spiking activity, the relationship between HF and LF signals is unclear, especially in human associative cortex. Here, we provide a large-scale in-depth investigation of the spatial and functional relationship between these 2 signals based on intracranial recordings from 121 individual brains (8000 recording sites). We measure category-selective responses to complex ecologically salient visual stimuli - human faces - across a wide cortical territory in the ventral occipito-temporal cortex (VOTC), with a frequency-tagging method providing high signal-to-noise ratio (SNR) and the same objective quantification of signal and noise for the two frequency ranges. While LF face-selective activity has higher SNR across the VOTC, leading to a larger number of significant electrode contacts especially in the anterior temporal lobe, LF and HF display highly similar spatial, functional, and timing properties. Specifically, and contrary to a widespread assumption, our results point to nearly identical spatial distribution and local spatial extent of LF and HF activity at equal SNR. These observations go a long way towards clarifying the relationship between the two main iEEG signals and reestablish the informative value of LF iEEG to understand human brain function.

Capacity for movement is an organisational principle in object representations

The ability to perceive moving objects is crucial for threat identification and survival. Recent neuroimaging evidence has shown that goal-directed movement is an important element of object processing in the brain. However, prior work has primarily used moving stimuli that are also animate, making it difficult to disentangle the effect of movement from aliveness or animacy in representational categorisation. In the current study, we investigated the relationship between how the brain processes movement and aliveness by including stimuli that are alive but still (e.g., plants), and stimuli that are not alive but move (e.g., waves). We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Movement explained significant variance in the neural data over and above that of aliveness, showing that capacity for movement is an important dimension in the representation of visual objects in humans.

Wildlife Photos on Social Media: A Quantitative Content Analysis of Conservation Organisations’ Instagram Images

Simple Summary

Although images are more effective than words at communicating important conservation ideas, different aspects of these images have been demonstrated to have positive and negative effects on viewers’ views towards wildlife and towards the organisation that posted the image. The most prevalent and engaging characteristics of wildlife photographs posted to Instagram in 2020 and 2021 were assessed using a quantitative content analysis, with Australian organisations as a case study. The findings show that conservation organisations can confidently share and post photographs that promote positive attitudes towards wildlife and the conservation organisation, and that Instagram posts can feature and promote a wide range of currently underrepresented species.

Abstract

Wildlife populations are vanishing at alarmingly high rates. This issue is being addressed by organisations around the world and when utilizing social media sites like Instagram, images are potentially more powerful than words at conveying crucial conservation messages and garnering public support. However, different elements of these images have been shown to potentially have either positive or negative effects on viewers’ attitudes and behaviours towards wildlife and towards the organisation posting the image. This study used a quantitative content analysis to assess the most common and engaging elements of wildlife images posted to Instagram in 2020 and 2021, using Australian conservation organisations as a case study. A total of 670 wildlife images from the Instagram accounts of 160 conservation organisation Instagram accounts were coded and analysed. Results highlight that the most common image elements used included natural backgrounds, mammals and birds, and no human presence. In addition, it was found that the taxon of the animal featured in a post and the presence of humans did not impact engagement levels. Our findings highlight the potential for Instagram posts to feature and promote a wide range of currently underrepresented species, and for conservation organisations to be able to confidently share and post images that promote positive perceptions of both the animal and the conservation organisation.

Rapid neural categorization of facelike objects predicts the perceptual awareness of a face (face pareidolia)

The human brain rapidly and automatically categorizes faces vs. other visual objects. However, whether face-selective neural activity predicts the subjective experience of a face - perceptual awareness - is debated. To clarify this issue, here we use face pareidolia, i.e., the illusory perception of a face, as a proxy to relate the neural categorization of a variety of facelike objects to conscious face perception. In Experiment 1, scalp electroencephalogram (EEG) is recorded while pictures of human faces or facelike objects - in different stimulation sequences - are interleaved every second (i.e., at 1 Hz) in a rapid 6-Hz train of natural images of nonface objects. Participants do not perform any explicit face categorization task during stimulation, and report whether they perceived illusory faces post-stimulation. A robust categorization response to facelike objects is identified at 1 Hz and harmonics in the EEG frequency spectrum with a facelike occipito-temporal topography. Across all individuals, the facelike categorization response is of about 20% of the response to human faces, but more strongly right-lateralized. Critically, its amplitude is much larger in participants who report having perceived illusory faces. In Experiment 2, facelike or matched nonface objects from the same categories appear at 1 Hz in sequences of nonface objects presented at variable stimulation rates (60 Hz to 12 Hz) and participants explicitly report after each sequence whether they perceived illusory faces. The facelike categorization response already emerges at the shortest stimulus duration (i.e., 17 ms at 60 Hz) and predicts the behavioral report of conscious perception. Strikingly, neural facelike-selectivity emerges exclusively when participants report illusory faces. Collectively, these experiments characterize a neural signature of face pareidolia in the context of rapid categorization, supporting the view that face-selective brain activity reliably predicts the subjective experience of a face from a single glance at a variety of stimuli.

Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials

The refresh rate is one of the important parameters of visual presentation devices, and assessing the effect of the refresh rate of a device on motion perception has always been an important direction in the field of visual research. This study examined the effect of the refresh rate of a device on the motion perception response at different stimulation frequencies and provided an objective visual electrophysiological assessment method for the correct selection of display parameters in a visual perception experiment. In this study, a flicker-free steady-state motion visual stimulation with continuous scanning frequency and different forms (sinusoidal or triangular) was presented on a low-latency LCD monitor at different refresh rates. Seventeen participants were asked to observe the visual stimulation without head movement or eye movement, and the effect of the refresh rate was assessed by analyzing the changes in the intensity of their visual evoked potentials. The results demonstrated that an increased refresh rate significantly improved the intensity of motion visual evoked potentials at stimulation frequency ranges of 7-28 Hz, and there was a significant interaction between the refresh rate and motion frequency. Furthermore, the increased refresh rate also had the potential to enhance the ability to perceive similar motion. Therefore, we recommended using a refresh rate of at least 120 Hz in motion visual perception experiments to ensure a better stimulation effect. If the motion frequency or velocity is high, a refresh rate of≥240 Hz is also recommended.

The diachronic account of attentional selectivity

Many models of attention assume that attentional selection takes place at a specific moment in time that demarcates the critical transition from pre-attentive to attentive processing of sensory input. We argue that this intuitively appealing standard account of attentional selectivity is not only inaccurate, but has led to substantial conceptual confusion. As an alternative, we offer a 'diachronic' framework that describes attentional selectivity as a process that unfolds over time. Key to this view is the concept of attentional episodes, brief periods of intense attentional amplification of sensory representations that regulate access to working memory and response-related processes. We describe how attentional episodes are linked to earlier attentional mechanisms and to recurrent processing at the neural level. We review studies that establish the existence of attentional episodes, delineate the factors that determine if and when they are triggered, and discuss the costs associated with processing multiple events within a single episode. Finally, we argue that this framework offers new solutions to old problems in attention research that have never been resolved. It can provide a unified and conceptually coherent account of the network of cognitive and neural processes that produce the goal-directed selectivity in perceptual processing that is commonly referred to as 'attention'.

The perception/cognition distincton: Challenging the representational account

A central goal for cognitive science and philosophy of mind is to distinguish between perception and cognition. The representational approach has emerged as a prominent candidate to draw such a distinction. The idea is that perception and cognition differ in the content and the format in which the information is represented -just as perceptual representations are nonconceptual in content and iconic in format, cognitive representations are conceptual in content and discursive in format. This paper argues against this view. I argue that both perception and cognition can use conceptual and nonconceptual contents and be vehiculated in iconic and discursive formats. If correct, the representational strategy to distinguish perception from cognition fails.

Meaning and expected surfaces combine to guide attention during visual search in scenes

How do spatial constraints and meaningful scene regions interact to control overt attention during visual search for objects in real-world scenes? To answer this question, we combined novel surface maps of the likely locations of target objects with maps of the spatial distribution of scene semantic content. The surface maps captured likely target surfaces as continuous probabilities. Meaning was represented by meaning maps highlighting the distribution of semantic content in local scene regions. Attention was indexed by eye movements during the search for target objects that varied in the likelihood they would appear on specific surfaces. The interaction between surface maps and meaning maps was analyzed to test whether fixations were directed to meaningful scene regions on target-related surfaces. Overall, meaningful scene regions were more likely to be fixated if they appeared on target-related surfaces than if they appeared on target-unrelated surfaces. These findings suggest that the visual system prioritizes meaningful scene regions on target-related surfaces during visual search in scenes.

Understanding Collections of Related Datasets Using Dependent MMD Coresets

Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean discrepancy (MMD) coreset can provide interpretable summaries of a single dataset, but are not easily compared across datasets. In this paper, we introduce dependent MMD coresets, a data summarization method for collections of datasets that facilitates comparison of distributions. We show that dependent MMD coresets are useful for understanding multiple related datasets and understanding model generalization between such datasets.

Fifth Generation Cellular Networks and Neurosurgery: A Narrative Review

Connectivity is a driving force for productivity across a wide variety of sectors in the 21st century, with health care being no exception. Fifth generation cellular technology (5G) is frequently alluded to in the mainstream media but understanding of the technology and its potential impact is not widespread in clinical communities. It promises unprecedented improvement in speed, bandwidth, reliability, and latency, all of which have significant implications for the way we use wireless data. 5G can be subdivided into 3 parallel technological architectures: extended mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and massive machine type communication (mMTC). These domains each present different and exciting prospects for the future of health care. This narrative review aims to elucidate the nature of 5G, its context within the development of telecommunications, and describe some of the notable opportunities it presents to the neurosurgical community. In many cases the requisite hardware has already been developed, but use has been limited by the requirements of a fast, reliable, and omnipresent network connection. Examples include telesurgical robots, remote supervision of procedures, integrated smart operating rooms, and clinician telepresence. The events of 2020 and the COVID-19 pandemic have brought the world's attention to digital transformation. The mechanics of 5G connectivity creates the capacity for these changes to be applied practically. An understanding of this technology is essential to appreciate the development and opportunities which will be part of our professional future.

Short-Latency Covert Saccades - The Explanation for Good Dynamic Visual Performance After Unilateral Vestibular Loss?

Background: Functional head impulse test (fHIT) tests the ability of the vestibulo-ocular reflex (VOR) to allow visual perception during head movements. Our previous study showed that active head movements to the side with a vestibular lesion generated a dynamic visual performance that were as good as during movements to the intact side. Objective: To examine the differences in eye position during the head impulse test when performed with active and passive head movements, in order to better understand the role of the different saccade properties in improving visual performance. Method: We recruited 8 subjects with complete unilateral vestibular loss (4 men and 4 women, mean age 47 years) and tested them with video Head Impulse Test (vHIT) and Functional Head Impulse Test (fHIT) during passive and active movements while looking at a target. We assessed the mean absolute position error of the eye during different time frames of the head movement, the peak latency and the peak velocity of the first saccade, as well as the visual performance during the head movement. Results: Active head impulses to the lesioned side generated dynamic visual performances that were as good as when testing the intact side. Active head impulses resulted in smaller position errors during the visual perception task (p = 0.006) compared to passive head-impulses and the position error during the visual perception time frame correlated with shorter latencies of the first saccade (p < 0.001). Conclusion: Actively generated head impulses toward the side with a complete vestibular loss resulted in a position error within or close to the margin necessary to obtain visual perception for a brief period of time in patients with chronic unilateral vestibular loss. This seems to be attributed to the appearance of short-latency covert saccades, which position the eyes in a more favorable position during head movements.

Task-Irrelevant Semantic Properties of Objects Impinge on Sensory Representations within the Early Visual Cortex

Objects can be described in terms of low-level (e.g., boundaries) and high-level properties (e.g., object semantics). While recent behavioral findings suggest that the influence of semantic relatedness between objects on attentional allocation can be independent of task-relevance, the underlying neural substrate of semantic influences on attention remains ill-defined. Here, we employ behavioral and functional magnetic resonance imaging measures to uncover the mechanism by which semantic information increases visual processing efficiency. We demonstrate that the strength of the semantic relatedness signal decoded from the left inferior frontal gyrus: 1) influences attention, producing behavioral semantic benefits; 2) biases spatial attention maps in the intraparietal sulcus, subsequently modulating early visual cortex activity; and 3) directly predicts the magnitude of behavioral semantic benefit. Altogether, these results identify a specific mechanism driving task-independent semantic influences on attention.

Can Liquid Lenses Increase Depth of Field in Head Mounted Video See-Through Devices?

Wearable Video See-Through (VST) devices for Augmented Reality (AR) and for obtaining a Magnified View are taking hold in the medical and surgical fields. However, these devices are not yet usable in daily clinical practice, due to focusing problems and a limited depth of field. This study investigates the use of liquid-lens optics to create an autofocus system for wearable VST visors. The autofocus system is based on a Time of Flight (TOF) distance sensor and an active autofocus control system. The integrated autofocus system in the wearable VST viewers showed good potential in terms of providing rapid focus at various distances and a magnified view.

Time-Sensitive Networking in IEEE 802.11be: On the Way to Low-Latency WiFi 7

A short time after the official launch of WiFi 6, IEEE 802.11 working groups along with the WiFi Alliance are already designing its successor in the wireless local area network (WLAN) ecosystem: WiFi 7. With the IEEE 802.11be amendment as one of its main constituent parts, future WiFi 7 aims to include time-sensitive networking (TSN) capabilities to support low latency and ultra-reliability in license-exempt spectrum bands, enabling many new Internet of Things scenarios. This article first introduces the key features of IEEE 802.11be, which are then used as the basis to discuss how TSN functionalities could be implemented in WiFi 7. Finally, the benefits and requirements of the most representative Internet of Things low-latency use cases for WiFi 7 are reviewed: multimedia, healthcare, industrial, and transport.

Blinded by our sight: Understanding the prominence of visual information in judgments of competence and performance

Visual information often quickly dominates people's judgments of others' competence and performance, including in the selection of leaders and decision makers. Reviewing recent research on static and dynamic visual cues, we discuss how people extrapolate judgments of competence and performance from visual information. We highlight how these two streams of research contribute to understanding performance perceptions and offer future avenues for research integrating the consideration of both static and dynamic visual cues.

The roles of gaze and head orientation in face categorization during rapid serial visual presentation

Little is known about how perceived gaze direction and head orientation may influence human categorization of visual stimuli as faces. To address this question, a sequence of unsegmented natural images, each containing a random face or a non-face object, was presented in rapid succession (stimulus duration: 91.7 ms per image) during which human observers were instructed to respond immediately to every face presentation. Faces differed in gaze and head orientation in 7 combinations - full-front views with perceived gaze (1) directed to the observer, (2) averted to the left, or (3) averted to the right, left ¾ side views with (4) direct gaze or (5) averted gaze, and right ¾ side views with (6) direct gaze or (7) averted gaze - were presented randomly throughout the sequence. We found highly accurate and rapid behavioural responses to all kinds of faces. Crucially, both perceived gaze direction and head orientation had comparable, non-interactive effects on response times, where direct gaze was responded faster than averted gaze by 48 ms and full-front view faster than ¾ side view also by 48 ms on average. Presentations of full-front faces with direct gaze led to an additive speed advantage of 96 ms to ¾ faces with averted gaze. The results reveal that the effects of perceived gaze direction and head orientation on the speed of face categorization probably depend on the degree of social relevance of the face to the viewer.

On the control of attentional processes in vision

The study of attentional processing in vision has a long and deep history. Recently, several papers have presented insightful perspectives into how the coordination of multiple attentional functions in the brain might occur. These begin with experimental observations and the authors propose structures, processes, and computations that might explain those observations. Here, we consider a perspective that past works have not, as a complementary approach to the experimentally-grounded ones. We approach the same problem as past authors but from the other end of the computational spectrum, from the problem nature, as Marr's Computational Level would prescribe. What problem must the brain solve when orchestrating attentional processes in order to successfully complete one of the myriad possible visuospatial tasks at which we as humans excel? The hope, of course, is for the approaches to eventually meet and thus form a complete theory, but this is likely not soon. We make the first steps towards this by addressing the necessity of attentional control, examining the breadth and computational difficulty of the visuospatial and attentional tasks seen in human behavior, and suggesting a sketch of how attentional control might arise in the brain. The key conclusions of this paper are that an executive controller is necessary for human attentional function in vision, and that there is a 'first principles' computational approach to its understanding that is complementary to the previous approaches that focus on modelling or learning from experimental observations directly.

Visual behavior modelling for robotic theory of mind

Behavior modeling is an essential cognitive ability that underlies many aspects of human and animal social behavior (Watson in Psychol Rev 20:158, 1913), and an ability we would like to endow robots. Most studies of machine behavior modelling, however, rely on symbolic or selected parametric sensory inputs and built-in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition.

Intelligent action guidance and the use of mixed representational formats

My topic is the intelligent guidance of action. In this paper I offer an empirically grounded case for four ideas: that [a] cognitive processes of practical reasoning play a key role in the intelligent guidance of action, [b] these processes could not do so without significant enabling work done by both perception and the motor system, [c] the work done by perceptual and motor systems can be characterized as the generation of information (often conceptually structured information) specialized for action guidance, which in turn suggests that [d] the cognitive processes of practical reasoning that play a key role in the guidance of intelligent action are not the abstract, syllogistic ones philosophers often treat as the paradigm of practical reasoning. Rather, these cognitive processes are constrained by, and work well with, the specialized concepts outputted by perception and the feedback outputted by sensorimotor processes.

Social Media Message Designs to Educate Adolescents About E-Cigarettes

PURPOSE

E-cigarette use is increasing among adolescents, despite potential harms. Social media messages are a promising way to educate youth about e-cigarettes, yet little is known about what message topics and formats will have beneficial impacts for message reception, reach, e-cigarette knowledge, and beliefs about harms.

METHODS

A national convenience sample of adolescents (n = 928, aged 15-18 years) in high school was recruited for an online experiment. In October 2019, participants were randomized to view one of three social media formats (visual based, quiz, and text only) or a no-message control. Participants in format conditions viewed six unique topics in a random order. Outcomes were e-cigarette knowledge and beliefs. Message reactions and sharing preferences were also assessed among youth who saw social media messages.

RESULTS

Social media messages led to greater knowledge (Cohen's f = .19; p < .001) and beliefs (f = .16; p < .001) about harms of e-cigarettes compared with the control, regardless of format. Almost four in five adolescents (79%) reported they would share the social media messages, most likely in person (49%) and with friends (52%). Message topics for missing out because of lung damage, having uncontrolled moods, and ingesting specific harmful chemicals elicited higher intended message reactions.

CONCLUSIONS

Social media messages can educate about e-cigarette harms. Social media campaigns are a promising e-cigarette education strategy to reach youth, directly and potentially through peer-to-peer sharing.

When Social Media Images and Messages Don't Match: Attention to Text versus Imagery to Effectively Convey Safety Information on Social Media

Health organizations can use visuals that support observational learning in social media. The social cognitive theory construct of observational learning illustrates how social media messages can inform health-related behavior change. In a within-subjects design, parents (n = 150) were exposed to real-world social media posts where safety recommendation text and imagery was matched (n = 3) or unmatched (n = 3). Safety topics were relevant to children age seven and younger: infant sleep, poisoning prevention, and bicycling safety. Eye tracking software captured visual attention (in milliseconds) on visual imagery and a post-experiment survey recorded correct answers to safety recommendations. Analyses examined the relationship between the match between text and imagery and visual attention. Participants spent more time on the matched image posts compared to mismatched image posts (5.3 versus 3.3 seconds; p < .001). After accounting for frequency of social media use and health literacy, each second of viewing time on the matched posts was associated with a 2.8% increase in the correct response safety information knowledge score (p < .001). Matched text and visuals on social media posts attracted significantly higher visual attention and improved safety knowledge.

Examining the Temporal Limits of Enhanced Visual Feature Detection in Children With Autism

The enhanced perceptual processing of visual features in children with autism spectrum disorder (ASD) is supported by an abundance of evidence in the spatial domain, with less robust evidence regarding whether this extends to information presented across time. The current study aimed to replicate and extend previous work finding that children with an ASD demonstrated enhanced perceptual accuracy in detecting feature-based (but not categorically defined) targets in time, when these were presented quickly, at a stimulus onset asynchrony (SOA) of 50 ms per item. Specifically, we extend the range of SOAs to examine the temporal boundaries of this enhanced accuracy and examine whether there is a relationship between ASD-related traits and detection accuracy on temporal visual search tasks. Individuals with autism perceived feature-based targets with statistically higher accuracy than their typically developing peers between SOAs of 39 and 65 ms and were numerically faster at all SOAs. No group differences were noted for category-based task accuracy. Our results also demonstrated that ASD-related traits measured by the autism spectrum quotient were positively correlated with accuracy on the feature-based task. Overall, results suggest that accurate visual perception of features (particularly color) is enhanced in children with ASD across time. LAY SUMMARY: Our results suggest that children with autism are able to process visual features, such as color, more accurately than typically developing children, even when these are presented very rapidly. Accuracy was higher in children with higher levels of autism-related traits and symptoms. Our findings suggest that more accurate visual perception exists not only across space in children with autism, as much of the existing literature demonstrates, but also over time. Autism Res 2020, 13: 1561-1572. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.