The off-line effect of affective touch on multisensory integration and tactile perceptual accuracy during the somatic signal detection task

Affective touch refers to the emotional and motivational facets of tactile sensation and has been linked to the activation of a specialised system of mechanosensory afferents (the CT system), that respond optimally to slow caress-like touch. Affective touch has been shown to play an important role in the building of the bodily self: the multisensory integrated global awareness of one’s own body. Here we investigated the effects of affective touch on subsequent tactile awareness and multisensory integration using the Somatic Signal Detection Task (SSDT). During the SSDT, participants were required to detect near-threshold tactile stimulation on their cheek, in the presence/absence of a concomitant light. Participants repeated the SSDT twice, before and after receiving a touch manipulation. Participants were divided into two groups: one received affective touch (CT optimal; n = 32), and the second received non-affective touch (non-CT optimal; n = 34). Levels of arousal (skin conductance levels, SCLs) and mood changes after the touch manipulation were also measured. Affective touch led to an increase in tactile accuracy, as indicated by less false reports of touch and a trend towards higher tactile sensitivity during the subsequent SSDT. Conversely, non-affective touch was found to induce a partial decrease in the correct detection of touch possibly due to a desensitization of skin mechanoreceptors. Both affective and non-affective touch induced a more positive mood and higher SCLs in participants. The increase in SCLs was greater after affective touch. We conclude that receiving affective touch enhances the sense of bodily self therefore increasing perceptual accuracy and awareness. Higher SCLs are suggested to be a possible mediator linking affective touch to a greater tactile accuracy. Clinical implications are discussed.

Reward priming differentially modulates enhancement and inhibition in auditory decision-making

In cognitive sciences, rewards, such as money and food, play a fundamental role in individuals' daily lives and well-being. Moreover, rewards that are irrelevant to the task alter individuals' behavior. However, it is unclear whether explicit knowledge of reward irrelevancy has an impact on reward priming enhancements and inhibition. In this study, an auditory change-detection task with task-irrelevant rewards was introduced. The participants were informed explicitly in advance that the rewards would be given randomly. The results revealed that while inhibition related to reward priming only occurred when the participants were explicitly informed about rewards, implicit instruction thereof resulted in enhancement and inhibition associated with reward priming. These findings highlight the contribution of explicit information about rewards associated with auditory decisions.

Active Working Memory and Simple Cognitive Operations

Working memory is thought to serve as a buffer for ongoing cognitive operations, even in tasks that have no obvious memory requirements. This conceptualization has been supported by dual-task experiments, in which interference is observed between a primary task involving short-term memory storage and a secondary task that presumably requires the same buffer as the primary task. Little or no interference is typically observed when the secondary task is very simple. Here, we test the hypothesis that even very simple tasks require the working memory buffer, but interference can be minimized by using activity-silent representations to store the information from the primary task. We tested this hypothesis using dual-task paradigm in which a simple discrimination task was interposed in the retention interval of a change detection task. We used contralateral delay activity (CDA) to track the active maintenance of information for the change detection task. We found that the CDA was massively disrupted after the interposed task. Despite this disruption of active maintenance, we found that performance in the change detection task was only slightly impaired, suggesting that activity-silent representations were used to retain the information for the change detection task. A second experiment replicated this result and also showed that automated discriminations could be performed without producing a large CDA disruption. Together, these results suggest that simple but non-automated discrimination tasks require the same processes that underlie active maintenance of information in working memory.

Respiration aligns perception with neural excitability

Recent studies from the field of interoception have highlighted the link between bodily and neural rhythms during action, perception, and cognition. The mechanisms underlying functional body-brain coupling, however, are poorly understood, as are the ways in which they modulate behaviour. We acquired respiration and human magnetoencephalography (MEG) data from a near-threshold spatial detection task to investigate the trivariate relationship between respiration, neural excitability, and performance. Respiration was found to significantly modulate perceptual sensitivity as well as posterior alpha power (8 - 13 Hz), a well-established proxy of cortical excitability. In turn, alpha suppression prior to detected vs undetected targets underscored the behavioural benefits of heightened excitability. Notably, respiration-locked excitability changes were maximised at a respiration phase lag of around -30° and thus temporally preceded performance changes. In line with interoceptive inference accounts, these results suggest that respiration actively aligns sampling of sensory information with transient cycles of heightened excitability to facilitate performance.

Locus coeruleus neurons encode the subjective difficulty of triggering and executing actions

The brain stem noradrenergic nucleus locus coeruleus (LC) is involved in various costly processes: arousal, stress, and attention. Recent work has pointed toward an implication in physical effort, and indirect evidence suggests that the LC could be also involved in cognitive effort. To assess the dynamic relation between LC activity, effort production, and difficulty, we recorded the activity of 193 LC single units in 5 monkeys performing 2 discounting tasks (a delay discounting task and a force discounting task), as well as a simpler target detection task where conditions were matched for difficulty and only differed in terms of sensory-motor processes. First, LC neurons displayed a transient activation both when monkeys initiated an action and when exerting force. Second, the magnitude of the activation scaled with the associated difficulty, and, potentially, the corresponding amount of effort produced, both for decision and force production. Indeed, at action initiation in both discounting tasks, LC activation increased in conditions associated with lower average engagement rate, i.e., those requiring more cognitive control to trigger the response. Decision-related activation also scaled with response time (RT), over and above task parameters, in line with the idea that it reflects the amount of resources (here time) spent on the decision process. During force production, LC activation only scaled with the amount of force produced in the force discounting task, but not in the control target detection task, where subjective difficulty was equivalent across conditions. Our data show that LC neurons dynamically track the amount of effort produced to face both cognitive and physical challenges with a subsecond precision. This works provides key insight into effort processing and the contribution of the noradrenergic system, which is affected in several pathologies where effort is impaired, including Parkinson disease and depression.

Research on Time Series Anomaly Detection: Based on Deep Learning Methods

Time series anomaly detection has always been an important research direction. The early time series anomaly detection methods are mainly statistical methods and machine learning methods. With the powerful functions of deep neural network being continuously mined by researchers, the effect of deep neural network in anomaly detection task has been significantly better than the traditional methods. In view of the continuous development and application of deep neural networks such as transformer and graph neural network (GNN) in time series anomaly detection in recent years, the body of research lacks a comparative evaluation of deep learning methods in recent years. This paper studies various deep neural networks suitable for time series, which are divided into three categories according to anomaly detection methods. The evaluation is conducted on public datasets. By analyzing the evaluation criteria, this paper discusses the performance of each model, as well as the problems and development direction in the field of time series anomaly detection in the future. This study found that in the time series anomaly detection task, transformer is suitable for dealing with long-time series prediction, and studying the graph structure of time series may be the best way to deal with time series anomaly detection in the future

Vehicle and pedestrian target detection in auto driving scene

To improve the target detection accuracy and speed of autonomous driving in various weather environments and small target traffic senarios,an improved YOLOV4 target detection model based on CSPDarknet45_G backbone network is proposed in this paper.By adding a new DBG module which consists of DArknetConv2D + BN + GELU activation function,this model is enhanced in generalization ability and accuracy. We also improved Res unit residual module to enhance shallow features fusing with deep feathers and reduced the number of neurons in the CSP module to simplify the module structure.The K-Means++ clustering algorithm is introduced to obtain the size of the prior box used for target detection to satisfy the data set in this paper. In the captured target vehicle image data set, the model detection result shows that the improved YOLOV4 model achieve an average detection accuracy of 90.45%, a recall of 94.37%, and an FPS of 50 frames per second when the IOU is taken as 0.5, which meet the real-time and accuracy of the detection task in this paper.

Response-locked classification image analysis of perceptual decision making in contrast detection

In many situations, humans make decisions based on serially sampled information through the observation of visual stimuli. To quantify the critical information used by the observer in such dynamic decision making, we here applied a classification image (CI) analysis locked to the observer's reaction time (RT) in a simple detection task for a luminance target that gradually appeared in dynamic noise. We found that the response-locked CI shows a spatiotemporally biphasic weighting profile that peaked about 300 ms before the response, but this profile substantially varied depending on RT; positive weights dominated at short RTs and negative weights at long RTs. We show that these diverse results are explained by a simple perceptual decision mechanism that accumulates the output of the perceptual process as modelled by a spatiotemporal contrast detector. We discuss possible applications and the limitations of the response-locked CI analysis.

Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine’s environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.