Human Reaction Time Research Articles

Neurofeedback-induced desynchronization of sensorimotor rhythm elicits pre-movement downregulation of intracortical inhibition that shortens simple reaction time in humans: a double-blind sham-controlled randomized study

Abstract Sensorimotor rhythm event-related desynchronization (SMR-ERD) is associated with the activities of cortical inhibitory circuits in the motor cortex. The self-regulation of SMR-ERD through neurofeedback training has demonstrated that successful SMR-ERD regulation improves motor performance. However, the training-induced changes in neural dynamics in the motor cortex underlying performance improvement remain unclear. Here, we hypothesized that SMR-neurofeedback based on motor imagery reduces cortical inhibitory activities during motor preparation, leading to shortened reaction time due to the repetitive recruitment of neural populations shared with motor imagery and movement preparation. To test this, we conducted a double-blind, sham-controlled study on twenty-four participants using neurofeedback training and pre- and post-training evaluation for simple reaction time tests and cortical inhibitory activity using short-interval intracortical inhibition (SICI). The results showed that veritable neurofeedback training effectively enhanced SMR-ERD in healthy male and female participants, accompanied by reduced simple reaction times and pre-movement SICI. Furthermore, SMR-ERD changes correlated with changes in pre-movement cortical disinhibition, and the disinhibition magnitude correlated with behavioral changes. These results suggest that SMR-neurofeedback modulates cortical inhibitory circuits during movement preparation, thereby enhancing motor performance.

The effects of communication topology on mixed traffic flow: modelling and analysis

This paper aims to evaluate the combined impact of the communication topology (CT) and vehicular spatial distribution (SD) on mixed traffic of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs). A generalized car-following (CF) model is presented to capture mixed traffic dynamics, integrating the effects of the CT, human reaction time, information delay and the optimal velocity changes with memory. Then, the linear stable condition of the model is derived using the perturbation method. Finally, extensive simulations are conducted under different CTs (i.e., multiple-predecessor leader following (MPLF) topology, predecessor following (PF) topology, and predecessor-leader following (PLF) topology) and six kinds of SDs. The results reveal that the MPLF topology demonstrates the best steady performance (i.e., stability) and dynamic performance (i.e., smoothness), and that the SD where CAVs are concentrated at the front of the traffic flow shows superior dynamic performance.

Realizing smart scanning transmission electron microscopy using high performance computing.

Scanning Transmission Electron Microscopy (STEM) coupled with Electron Energy Loss Spectroscopy (EELS) presents a powerful platform for detailed material characterization via rich imaging and spectroscopic data. Modern electron microscopes can access multiple length scales and sampling rates far beyond human perception and reaction time. Recent advancements in machine learning (ML) offer a promising avenue to enhance these capabilities by integrating ML algorithms into the STEM-EELS framework, fostering an environment of active learning. This work enables the seamless integration of STEM with High-Performance Computing (HPC) systems. This integration is facilitated by our developed server software, written in Python, which acts as a wrapper over DigitalMicrograph (version 3.5) hardware modules to enable remote computer interactions. We present several implemented workflows that exemplify this integration. These workflows include sophisticated techniques such as object finding and deep kernel learning. Through these developments, we demonstrate how the fusion of STEM-EELS with ML and HPC enhances the efficiency and scope of material characterization for all of STEM available globally having Gatan, Inc. image filter installed on them. The codes are available on GitHub.

Time to consider time: Comparing human reaction times to dynamical signatures from recurrent vision models on a perceptual grouping task

Time to consider time: Comparing human reaction times to dynamical signatures from recurrent vision models on a perceptual grouping task

Leveraging Perspective Transformation for Enhanced Pothole Detection in Autonomous Vehicles.

Road conditions, often degraded by insufficient maintenance or adverse weather, significantly contribute to accidents, exacerbated by the limited human reaction time to sudden hazards like potholes. Early detection of distant potholes is crucial for timely corrective actions, such as reducing speed or avoiding obstacles, to mitigate vehicle damage and accidents. This paper introduces a novel approach that utilizes perspective transformation to enhance pothole detection at different distances, focusing particularly on distant potholes. Perspective transformation improves the visibility and clarity of potholes by virtually bringing them closer and enlarging their features, which is particularly beneficial given the fixed-size input requirement of object detection networks, typically significantly smaller than the raw image resolutions captured by cameras. Our method automatically identifies the region of interest (ROI)-the road area-and calculates the corner points to generate a perspective transformation matrix. This matrix is applied to all images and corresponding bounding box labels, enhancing the representation of potholes in the dataset. This approach significantly boosts detection performance when used with YOLOv5-small, achieving a 43% improvement in the average precision (AP) metric at intersection-over-union thresholds of 0.5 to 0.95 for single class evaluation, and notable improvements of 34%, 63%, and 194% for near, medium, and far potholes, respectively, after categorizing them based on their distance. To the best of our knowledge, this work is the first to employ perspective transformation specifically for enhancing the detection of distant potholes.

Auditory Noise Facilitates Lower Visual Reaction Times in Humans.

Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports.

3D-ElectroZip touch: multi-directional haptic feedback with electro-ribbon zipping actuators

This paper introduces the 3D-ElectroZip Touch, a multi-directional force feedback device developed using dielectrophoretic liquid zipping actuation. The device is designed to stimulate the sense of normal and shear forces by moving in vertical and multiple horizontal directions. Its performance, including displacement, force, and actuation time has been characterized under input voltages from 6 kV to 9 kV, demonstrating a displacement range of 5–11 mm, force range of 0.05 N to 0.22 N, power output ranges from 0.08 W to 0.077 W, and maximum energy efficiency of 72%. The 3D-ElectroZip Touch shows a quick response to the input signal, moving its contact panel by 1.1 mm in 70 ms (36.5% of human reaction time) at 9 kV. Comparing its basic characteristics with the skin sensitivity and the lightweight, compliance, and scalability of the 3D-ElectroZip Touch technology show its high potential to be exploited in tactile displays and wearable haptic feedback devices.

The attentive reconstruction of objects facilitates robust object recognition.

Humans are extremely robust in our ability to perceive and recognize objects-we see faces in tea stains and can recognize friends on dark streets. Yet, neurocomputational models of primate object recognition have focused on the initial feed-forward pass of processing through the ventral stream and less on the top-down feedback that likely underlies robust object perception and recognition. Aligned with the generative approach, we propose that the visual system actively facilitates recognition by reconstructing the object hypothesized to be in the image. Top-down attention then uses this reconstruction as a template to bias feedforward processing to align with the most plausible object hypothesis. Building on auto-encoder neural networks, our model makes detailed hypotheses about the appearance and location of the candidate objects in the image by reconstructing a complete object representation from potentially incomplete visual input due to noise and occlusion. The model then leverages the best object reconstruction, measured by reconstruction error, to direct the bottom-up process of selectively routing low-level features, a top-down biasing that captures a core function of attention. We evaluated our model using the MNIST-C (handwritten digits under corruptions) and ImageNet-C (real-world objects under corruptions) datasets. Not only did our model achieve superior performance on these challenging tasks designed to approximate real-world noise and occlusion viewing conditions, but also better accounted for human behavioral reaction times and error patterns than a standard feedforward Convolutional Neural Network. Our model suggests that a complete understanding of object perception and recognition requires integrating top-down and attention feedback, which we propose is an object reconstruction.

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Objective.Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different objects. While dynamics of object recognition and decision making are usually ignored in object recognition models, here we proposed a fully spiking hierarchical model, explaining the process of object recognition from information representation to making decision.Approach.Coupling a deep neural network and a recurrent attractor based decision making model beside using spike time dependent plasticity learning rules in several convolutional and pooling layers, we proposed a model which can resemble brain behaviors during an object recognition task. We also measured human choices and reaction times in a psychophysical object recognition task and used it as a reference to evaluate the model.Main results.The proposed model explains not only the probability of making a correct decision but also the time that it takes to make a decision. Importantly, neural firing rates in both feature representation and decision making levels mimic the observed patterns in animal studies (number of spikes (p-value < 10-173) and the time of the peak response (p-value < 10-31) are significantly modulated with the strength of the stimulus). Moreover, the speed-accuracy trade-off as a well-known characteristic of decision making process in the brain is also observed in the model (changing the decision bound significantly affect the reaction time (p-value < 10-59) and accuracy (p-value < 10-165)).Significance.We proposed a fully spiking deep neural network which can explain dynamics of making decision about an object in both neural and behavioral level. Results showed that there is a strong and significant correlation (r= 0.57) between the reaction time of the model and of human participants in the psychophysical object recognition task.

Interpretability is in the Eye of the Beholder: Human Versus Artificial Classification of Image Segments Generated by Humans Versus XAI

The evaluation of explainable artificial intelligence is challenging, because automated and human-centred metrics of explanation quality may diverge. To clarify their relationship, we investigated whether human and artificial image classification will benefit from the same visual explanations. In three experiments, we analysed human reaction times, errors, and subjective ratings while participants classified image segments. These segments either reflected human attention (eye movements, manual selections) or the outputs of two attribution methods explaining a ResNet (Grad-CAM, XRAI). We also had this model classify the same segments. Humans and the model largely agreed on the interpretability of attribution methods: Grad-CAM was easily interpretable for indoor scenes and landscapes, but not for objects, while the reverse pattern was observed for XRAI. Conversely, human and model performance diverged for human-generated segments. Our results caution against general statements about interpretability, as it varies with the explanation method, the explained images, and the agent interpreting them.

Incorporating DRAGON12-light trainer board in an introductory microprocessors course

The HCS12 microcontroller and DRAGON12-Light Trainer boards are extensively utilized in microprocessor system design education. This paper details the rationale, approach, and outcomes from implementing the DRAGON12-Light Trainer board in teaching an upper-level undergraduate microprocessors course at Oakland University. The course’s primary goal is for students to acquire the skills necessary to design both hardware and software for microprocessor-based systems, with applications across various industries. The paper assesses the effectiveness of employing the Motorola HCS12 microcontroller for its real-world relevance and integrates the Freescale CodeWarrior IDE v5.1 environment for software development. This research uniquely contributes by measuring the improvement in students’ practical skills, specifically in hardware and software design using assembly and C programming, through hands-on lab assignments. It reports on the development of students’ abilities to engage in microprocessor-based system design and critically evaluates the applied aspects of pedagogy by incorporating the DRAGON12-Light Trainer in lab exercises, such as those involving ’Measuring Human Reaction Time’ and ’Servo motor interfacing.’ Quantitative results, derived from student surveys and assessments, indicate significant improvements in students’ programming competencies. The paper provides statistical results showcasing the increase in students’ self-rated confidence levels in assembly and C language programming before and after course completion. Additionally, qualitative insights are discussed, reflecting students’ experiences and the perceived applicability of the skills they acquired. These results underscore the pedagogical value of integrating practical training devices like the DRAGON12-Light Trainer board into microprocessors curricula.

Dynamic EEG changes during exposure to noise at different levels of loudness and sharpness

From a psychological standpoint, noise can disturb human mental performance in the workplace. While loudness and sharpness of noise are usable factors for evaluating the subjective effects of noise exposure, there has been comparatively less focus on their influence on human reaction time and the dynamic changes in brain waves. Therefore, this study aimed to investigate the temporal changes in the theta beta ratio (TBR) and reaction time (RT) before and during exposure to different noise loudness and sharpness levels. Eight different loudness and sharpness levels of noise were generated and broadcast. Sixty-four men performed the Integrated Visual and Auditory (IVA) test both before and during exposure to the generated noise, and their brain waves were recorded simultaneously. The main step of the IVA test lasted approximately 12 min and consisted of 250 visual and 250 auditory stimuli presented randomly. The highest difference in TBR before and during exposure to various loudness levels was associated with 65 phon at 250 Hz and 75 phon at 4000 Hz, with values of 0.86 and 0.75, respectively. This difference equaled 0.69 for the 1.23 acum sharpness level at a frequency of 4000 Hz. In addition, the sharpness of 3.29 acum at a frequency of 8000 Hz led to the highest difference equaling 0.71 in TBR between before and during exposure to various sharpness levels. The most significant difference in RT was observed before and during exposure to lower loudness levels (65 and 75 phon), resulting in 405.75 and 347.5 ms, respectively. This highest difference was also observed for sharpness levels of 1.23 (391.4 ms) and 3.29 acum (427.4 ms) compared to other sharpness levels. Furthermore, attention deficit increased with longer exposure times to all noise levels. The results showed a statistically significant correlation between TBR and RT during exposure to all noises. Specifically, lower loudness at frequencies of 250 and 4000 Hz and higher sharpness at frequencies of 4000 and 8000 Hz had the most significant impact on the attention parameter. In psychoacoustics studies, it is important to consider both the frequency and duration of exposure in addition to assessing the loudness and sharpness level of noise.

Fragility of Delay Margin Against Platoon Size in a Connected Vehicle System; Network Design and Multiple Trials

Abstract Linear stability of a car following model is investigated considering time delays due to human reaction times and communication delays. In multiple trials, we generate networks of randomly arranged legacy and autonomous vehicles, each vehicle type respecting some connectivity rules. Next, the largest amount of delay the vehicle network can tolerate without becoming unstable is computed. This delay, also known as the delay margin (DM), can become sensitive with respect to platoon size under certain connectivity rules. We demonstrate this ‘fragility’ property, and next propose a new network-design policy with which DM can be made robust against platoon size.

The scaling of mental computation in a sorting task

Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how human reaction times scale with increasing input complexity. In the current work, we investigate the algorithms underlying human cognitive processes. Computer science characterizes algorithms by their time and space complexity scaling with problem size. We propose to use participants’ reaction times to study how human computations scale with increasing input complexity. We tested this approach in a task where participants had to sort sequences of rectangles by their size. Our results showed that reaction times scaled close to linearly with sequence length and that participants learned and actively used latent structure whenever it was provided. This behavior was in line with a computational model that used the observed sequences to form hypotheses about the latent structures, searching through candidate hypotheses in a directed fashion. These results enrich our understanding of plausible cognitive models for efficient mental sorting and pave the way for future studies using reaction times to investigate the scaling of mental computations across psychological domains.

Measuring Human Perception to Improve Open Set Recognition.

The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms. Human perception as measured by the methods and procedures of visual psychophysics from psychology provides an additional data stream for algorithms that need to manage novelty. For instance, measured reaction time from human subjects can offer insight as to whether a class sample is prone to be confused with a different class - known or novel. In this work, we designed and performed a large-scale behavioral experiment that collected over 200,000 human reaction time measurements associated with object recognition. The data collected indicated reaction time varies meaningfully across objects at the sample-level. We therefore designed a new psychophysical loss function that enforces consistency with human behavior in deep networks which exhibit variable reaction time for different images. As in biological vision, this approach allows us to achieve good open set recognition performance in regimes with limited labeled training data. Through experiments using data from ImageNet, significant improvement is observed when training Multi-Scale DenseNets with this new formulation: it significantly improved top-1 validation accuracy by 6.02%, top-1 test accuracy on known samples by 9.81%, and top-1 test accuracy on unknown samples by 33.18%. We compared our method to 10 open set recognition methods from the literature, which were all outperformed on multiple metrics.

Car following models for alleviating the degeneration of CACC function of CAVs in weak platoon intensity

ABSTRACT To alleviate the performance degeneration of the cooperative adaptive cruising control (CACC) of connected and automated vehicles (CAVs) with low market penetration rates (MPRs), it is necessary to resolve the performance degeneration of CAVs operating at weak mixed-platoon intensities for any-MPR CAVs. A car following model (PMCC) for CAVs is proposed, accounting for human reaction time, communication delay, traffic information memory, and vehicle-to-vehicle (V2V) communication. Linear stability analysis was conducted to validate the stability of mixed platoons at weak platoon intensities for different CAV following models. Numerical simulations were conducted to simulate weak-intensity mixed platoons for different CAV following models operating at high and low equilibrium velocities. The results suggest that PMCC exhibits wider stability equilibrium intervals under different theoretical time headway scenariosand performs well at dissipating the speed fluctuation propagation and minimizing the speed fluctuation amplitude.

Come together: A unified description of the escalator capacity.

We investigate a variety of aspects related to the simulation of passenger dynamics on escalators, mainly focusing on the discrepancy between the 'theoretical' and the 'practical' capacity that is observed for these facilities. The structure of the paper is twofold. In the first part, we introduce a space-continuous model to describe the transition of agents from walking on the plain to standing on the escalator. In the second part, we use numerical findings from simulations to study important measures like minimum distances between the standing agents and average occupancies of the escalator steps. One of the most important results obtained in this paper is a generalized analytical formula that describes the escalator capacity. We show that, apart from the conveyor speed, the capacity essentially depends on the time gap between entering passengers which we interpret as human reaction time. Comparing simulation results with corresponding empirical data from field studies and experiments, we deduce a minimum human reaction time in the range of 0.15s-0.30s which is in perfect agreement with results from social psychology. With these findings, it is now possible to determine accurately the relationship between the capacity and the speed of an escalator, allowing a science-based performance evaluation of buildings with escalators.

Optimum Real Time Estimation and Interfacing of Braking Points for Racing Car

To provide additional on-track support for racing drivers, a novel real-time braking point estimation system was developed and tested to improve overall lap time performance. The Formula-Student competition was chosen as testing environment because of direct access to a real car and, hence, to all the data needed for this study, which can be fully disclosed at the same time. To estimate the optimum braking point, a simulation of the track section ahead is performed using a multidimensional approach to model not only longitudinal and lateral acceleration, but also combined acceleration scenarios. A lap identification system as well as live track analysis tools were developed in order to obtain track data that cannot be measured directly. To evaluate the overall grip conditions, a tire model, using both a wheel load and a two-track model, is implemented, which includes a dynamic calibration feature designed to adapt the system to the changing conditions of the circuit. It can be seen that the simulation provides a detailed profile of the maximum permitted velocities for every point in front of the real vehicle, and that this can be used to create a reliable prediction of the braking points along the track. Furthermore, a concept based on physiological fundamentals was developed and investigated in terms of human reaction times in order to meet the requirements of an intuitive human-machine interface. Our results demonstrate that it can be successfully used to automatically compensate for driver delays, improve the trust in the system and ensure safe operation. We conclude that our new system can reduce lap times, depending on the length and number of curves, by a significant amount of time (tens of a second).

Human reaction time measurement using a smartphone

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Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D, and augmented reality.

The introduction of Augmented Reality (AR) has attracted several developments, although the people's experience of AR has not been clearly studied or contrasted with the human experience in 2D and 3D environments. Here, the directional task was applied in 2D, 3D, and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted was also done. Research question: Are the main differences between 2D, 3D, and AR able to be predicted using Markov chains? Methods: A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants attached to the arranged RT for the theory of Markov chain probability. A Markov chain analysis was performed on the participants' data. Subsequently, the way certain factors influenced participants RT amongst the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statistically tested using ANOVA. Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D tasks with the variance predicted with the current state. Moreover, a clear explanation of delayed RT in every environment was done. Mood and coffee did not show significant differences in RTs on a simplified videogame. Gender differences were found in 3D, where endogenous directional goals are in 3D, but no gender differences appeared in AR where exogenous AR buttons can explain the larger RT that compensate for the gender difference. Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions in different environments and gender evolve from the dynamics of preceding cognitive activity can fit and improve neurocomputational models.