External Noise Conditions Research Articles

Perceptual learning changes the amplitude not the shape of the temporal window of visual processing.

Perceptual learning (PL) can significantly improve human performance in perceptual tasks primarily through template reweighting. Previous studies have documented how PL changes perceptual template in stimulus feature space. We investigated how PL reweights visual information in time. With a dynamic external noise paradigm and the elaborated perceptual template model (ePTM) analysis, we found that training with an orientation identification task in the zero external noise condition reduced contrast thresholds in both zero and high external noise conditions, whereas training in the high external noise condition only reduced contrast thresholds in high external noise conditions. The ePTM analysis showed that training in both zero and high external noise changed the overall amplitude, but not the shape of the temporal window of the perceptual template to exclude external noise across time, and training in zero external noise additionally reduced additive internal noise. Our results provided additional constraints for models of PL. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles

This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.

Self-sensitivity amplifiable dual-gate ion-sensitive field-effect transistor based on a high-k engineered dielectric layer

In this study, we propose a self-sensitivity amplifiable pH-sensor platform based on a dual-gate (DG) structure ion-sensitive-field-effect-transistor (ISFET) by applying a high-k engineered dielectric layer. We implement amplification according to the capacitance ratio of top and bottom gate dielectric layers through the capacitive coupling effect of DG structure, which exceeds the Nernst limit of the existing ISFET, and maximizes device sensitivity by extracting the change in current based on a reference voltage. In repeated and continuous pH sensitivity measurements and reliability evaluations under external noise conditions, the proposed sensor platform demonstrated excellent linearity and stability. Because the proposed sensor platform significantly exceeds the Nernst limit and has excellent reliability, it is expected to be a promising technology for use as a biosensor platform for detecting analytes with micro potentials.

Robust estimation of state vector coordinates in the controlled helicopter motion problem

The problem of finding H∞ – a observer of the state vector of a linear continuous non-stationary dynamical system with a semi-infinite time of functioning is considered. It is assumed that a mathematical model of a closed-loop linear continuous deterministic dynamical system with an optimal linear regulator, found as a result of minimization of the quadratic quality criterion, is known. For solving the state observer synthesis problem the reduction of the problem to a min-max optimal control problem is used. In this problem, the minimum of the quality criterion is sought by the observer’s gain matrix, and the maximum – by the external influence, measurement noise, and initial conditions. To solve this problem, the extension principle is applied and sufficient optimality conditions are obtained that requires the choice of auxiliary functions of the Krotov–Bellman type. As a result of the implementation of the procedure for choosing an auxiliary function and using the rules of matrix differentiation, relations for the synthesis of the observer and formulas for finding the best matrix of observer gains, as well as the laws for choosing the worst external influences and noise, were obtained. We find a solution to the problem of state vector coordinates estimation in the presence of limited external influences and disturbances in a linear model of the measuring system. As an example, the equations of motion of the Raptor-type helicopter are used.

Visual attention in spatial cueing and visual search.

To characterize internal processes of an observer conducting perceptual tasks, we developed an observer model that combines the perceptual template model (PTM), the attention mechanisms in the PTM framework (Lu & Dosher, 1998), and uncertainty of signal detection theory (Green & Swets, 1966). The model was evaluated with a visual search experiment conducted in a range of external noise, signal contrast, and target-distractor similarity conditions. In each trial, eight Gabor patches were shown in each of two brief intervals, with one target at a different orientation from the distractors in one of the presentations. Subjects were precued to a subset of the stimuli (1, 2, 4, or 8) and asked to report (a) which interval contained the target and (b) where the target was. Individual roles of uncertainty and of attention in visual search were investigated by comparing models with and without an attention component. The results showed that decision uncertainty alone was sufficient to account for the set-size effect, even in conditions with high target-distractor similarity. Our theoretical model and empirical results provide a coherent picture regarding how visual information is selected and processed during feature search.

Failure prediction of a motor-driven gearbox in a pulverizer under external noise and disturbance

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

Failure Prediction of a Motor-driven Gearbox in a Pulverizer Under External Noise and Disturbance

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

Noise Provides New Insights on Contrast Sensitivity Function

Sensitivity to luminance difference, or contrast sensitivity, is critical for animals to survive in and interact with the external world. The contrast sensitivity function (CSF), which measures visual sensitivity to spatial patterns over a wide range of spatial frequencies, provides a comprehensive characterization of the visual system. Despite its popularity and significance in both basic research and clinical practice, it hasn’t been clear what determines the CSF and how the factors underlying the CSF change in different conditions. In the current study, we applied the external noise method and perceptual template model to a wide range of external noise and spatial frequency (SF) conditions, and evaluated how the various sources of observer inefficiency changed with SF and determined the limiting factors underlying the CSF. We found that only internal additive noise and template gain changed significantly with SF, while the transducer non-linearity and coefficient for multiplicative noise were constant. The 12-parameter model provided a very good account of all the data in the 200 tested conditions (86.5%, 86.2%, 89.5%, and 96.4% for the four subjects, respectively). Our results suggest a re-consideration of the popular spatial vision model that employs the CSF as the front-end filter and constant internal additive noise across spatial frequencies. The study will also be of interest to scientists and clinicians engaged in characterizing spatial vision deficits and/or developing rehabilitation methods to restore spatial vision in clinical populations.

Optimal error correction in topological subsystem codes

A promising approach to overcome decoherence in quantum computing schemes is to perform active quantum error correction using topology. Topological subsystem codes incorporate both the benefits of topological and subsystem codes, allowing for error syndrome recovery with only 2-local measurements in a two-dimensional array of qubits. We study the error threshold for topological subsystem color codes under very general external noise conditions. By transforming the problem into a classical disordered spin model, we estimate using Monte Carlo simulations that topological subsystem codes have an optimal error tolerance of 5.5(2)%. This means there is ample space for improvement in existing error-correcting algorithms that typically find a threshold of approximately 2%.

Visual attention in spatial cuing and visual search

To characterize internal processes of an observer conducting perceptual tasks, we developed an observer model that combines the perceptual template model (PTM), the attention mechanisms in the PTM framework (Lu & Dosher, 1998), and uncertainty of signal detection theory (Green & Swets, 1966). The model was evaluated with a visual search experiment conducted in a range of external noise, signal contrast, and target-distractor similarity conditions. In each trial, eight Gabor patches were shown in each of two brief intervals, with one target at a different orientation from the distractors in one of the presentations. Subjects were precued to a subset of the stimuli (1, 2, 4, or 8) and asked to report (a) which interval contained the target and (b) where the target was. Individual roles of uncertainty and of attention in visual search were investigated by comparing models with and without an attention component. The results showed that decision uncertainty alone was sufficient to account for the set-size effect, even in conditions with high target-distractor similarity. Our theoretical model and empirical results provide a coherent picture regarding how visual information is selected and processed during feature search.

Characterizing and modeling temporal dynamics of perceptual decision making

We combined the external noise method (1) with the cue-to-respond speed accuracy trade-off (SAT) paradigm (2) to characterize the temporal dynamics of perceptual decision making. Observers were required to identify the orientation of one of eight briefly presented peripheral Gabor targets (+/- 12 deg) in both zero and high noise. An arrow, occurring in the center of the display cued the observer to the target location 234 ms before the onset of a brief target display; an auditory beep, occurring at one of eight delays (SOA=25 to 800 ms) after the target onset, cued the observers to respond. Five Gabor contrasts, spanning a wide range of performance levels, were tested in each external noise condition. Increasing accuracy of discrimination (d') was measured over processing times from 210 to 940 ms (as a function of SOA to the cue) in each external noise and Gabor contrast condition. All ten SAT functions were well fit by exponential functions with identical time constant and intercept but different asymptotic levels. This suggests that, despite enormous variation in the external noise and contrast energy in the stimulus, and in the ultimate accuracy of performance, information accumulated with the same rate and starting time across all the external noise and contrast conditions. In addition, we conducted a standard response time version of the experiment both before and halfway through the SAT procedure. Data from the response time version of the experiment were all consistent with the speed-accuracy trade-off data, but primarily differed in response accuracy. A simple elaboration of the perceptual template model (3) with a dynamic decision process in which information accumulates with the same rate but with step sizes proportional to the signal to noise ratio in the perceptual representation of the visual input fully accounts for the results. (1) Pelli, Dissertation; (2) Dosher, Cognitive Psychology'76; (3) Lu & Dosher, JOSA'99.

Adaptation to sine-wave gratings selectively reduces the sensory gain of the adapted stimuli

Adapting to sinusoidal gratings selectively reduces contrast sensitivity to subsequent test stimuli (1). To investigate the perceptual processes underlying selective adaptation, we developed an external noise plus adaptation paradigm and a theoretical framework based on a noisy observer model. After adapting to a 45 deg, 2 Hz counter-flickering sine grating of 0.8 contrast, observers performed two-interval-forced-choice detection of Gabors of matched spatial frequency, tilted either at 45 or 135 deg, and embedded in one of six levels of external noise. Each session began with 2 min of adaptation, and test trials included 6 sec of re-adaptation and two 133 ms test intervals. The psychometric functions for tests of 45 deg without adaptation, and 135 deg with and without adaptation essentially overlapped. The tests of 45 deg after adaptation produced a 92.5% and a 90.8% threshold increase in the zero and highest external noise conditions. A contrast-gain control perceptual template model (cgcPTM) was developed and tested. The cgcPTM consists of a perceptual template, transducer non-linearity, contrast gain control, pre- and post gain control internal noises, and a decision structure. At the overall input and output level, the cgcPTM is mathematically equivalent to an earlier version of the PTM that uses multiplicative noise in place of contrast gain control (2). In either framework, adaptation selectively reduces the gain of the perceptual template at the adapted spatial frequency and orientation without altering either pre- or post gain control (additive and multiplicative) noises, or changing transducer nonlinearity. The theoretical framework and the inferred mechanism of adaptation are fully consistent with the theory of adaptation proposed by Wilson & Humanski (3). We conclude that adaptation selectively reduces the sensory gain to the adapted stimuli. 1. BlakemoreCampbellJ.Physiol'69; 2. LuDosherJOSA'99. 3. WilsonHumanskiVR'93.

Category and Perceptual Learning in Subjects with Treated Wilson's Disease

To explore the relationship between category and perceptual learning, we examined both category and perceptual learning in patients with treated Wilson's disease (WD), whose basal ganglia, known to be important in category learning, were damaged by the disease. We measured their learning rate and accuracy in rule-based and information-integration category learning, and magnitudes of perceptual learning in a wide range of external noise conditions, and compared the results with those of normal controls. The WD subjects exhibited deficits in both forms of category learning and in perceptual learning in high external noise. However, their perceptual learning in low external noise was relatively spared. There was no significant correlation between the two forms of category learning, nor between perceptual learning in low external noise and either form of category learning. Perceptual learning in high external noise was, however, significantly correlated with information-integration but not with rule-based category learning. The results suggest that there may be a strong link between information-integration category learning and perceptual learning in high external noise. Damage to brain structures that are important for information-integration category learning may lead to poor perceptual learning in high external noise, yet spare perceptual learning in low external noise. Perceptual learning in high and low external noise conditions may involve separate neural substrates.

Mechanisms underlying perceptual learning of contrast detection in adults with anisometropic amblyopia

What underlies contrast sensitivity improvements in adults with anisometropic amblyopia following perceptual learning in grating contrast detection? In this paper, we adopted the external noise approach (Z.-L. Lu & B. A. Dosher, 1998) to identify the mechanisms underlying perceptual learning in adults with anisometropic amblyopia. By measuring contrast thresholds in a range of external noise conditions at two performance levels (79.3% and 70.7%), we found that a mixture of internal additive noise reduction and external noise exclusion underlay training induced contrast sensitivity improvements in adults with anisometropic amblyopia. In comparison, normal adults exhibited only small amount of external noise exclusion under the same training conditions. The results suggest that neural plasticity may be more robust in amblyopia, lending further support of perceptual learning as a potential treatment for adult amblyopia.

Intra- and cross-modal cuing of spatial attention: Time courses and mechanisms

We report a new phenomenon in cross-modal cuing of visual spatial attention—simultaneous auditory peripheral cues are “hyper-effective”—more effective than auditory peripheral (AP), visual central (VC), or visual peripheral (VP) cues that preceded the target with ample time for preparatory orienting. The time courses and mechanisms of visual spatial attention were measured in a four-location Gabor orientation identification task for targets embedded in systematically varying amounts of external noise [Lu, Z. -L., & Dosher, B. A. (1998). External noise distinguishes attention mechanisms. Vision Research, 38 (9), 1183–1198] and for cue-target asynchronies (CTOAs) between 0 and 240ms. Large CTOA pre-cuing improvements in contrast thresholds occurred in high external noise conditions for AP, VC, and VP cues. In low external noise conditions, pre-cuing advantages occurred for visual peripheral (VP) cues, but not for visual central cues (VC), replicating Lu and Dosher [Lu, Z. -L., & Dosher, B. A. (2000). Spatial attention: Different mechanisms for central and peripheral temporal precues? Journal of Experimental Psychology: Human Perception and Performance, 26 (5), 1534–1548]. Auditory peripheral pre-cues (AP) were similar to VP cues at large cue-target delays, but demonstrated a large cross-modal cuing advantage for simultaneous auditory peripheral cues (AP) in both high and low external noise. We conclude that endogenous attention (visual central pre-cuing) excludes external noise, while exogenous attention (both visual and auditory peripheral pre-cuing) enhances the stimulus but also excludes external noise if informative peripheral cues are used.

Bayesian adaptive estimation of threshold versus contrast external noise functions: The quick TvC method

External noise paradigms, measuring contrast threshold as a function of external noise contrast (the “ TvC” function), provide a valuable tool for studying perceptual mechanisms. However, measuring TvC functions at the multiple performance criteria needed to constrain observer models has previously involved demanding data collection (often > 2000 trials). To ease this task, we developed a novel Bayesian adaptive procedure, the “quick TvC” or “ qTvC” method, to rapidly estimate multiple TvC functions, by adapting a strategy originally developed to estimate psychometric threshold and slope [Cobo-Lewis, A. B. (1996). An adaptive method for estimating multiple parameters of a psychometric function. Journal of Mathematical Psychology, 40, 353–354; Kontsevich, L. L., & Tyler, C. W. (1999). Bayesian adaptive estimation of psychometric slope and threshold. Vision Research, 39(16), 2729–2737]. Exploiting the regularities observed in empirical TvC functions, the qTvC method estimates three parameters: the optimal threshold c 0, the critical noise level N c, and the common slope, η, of log-parallel psychometric functions across external noise conditions. Before each trial, the qTvC uses a one-step-ahead search to select the stimulus (jointly defined by signal and noise contrast) that minimizes the expected entropy of the three-dimensional posterior probability distribution, p( N c, c 0, η). The method’s accuracy and precision, for estimating TvC functions at three performance criteria (65%, 79%, and 92% correct), were evaluated using Monte-Carlo simulations and a psychophysical task. Simulations showed that less than 300 trials were needed to estimate TvC functions at three widely separated criteria with good accuracy (bias < 5%) and precision (mean root mean square error <1.5 dB). Using an orientation identification task, we found excellent agreement (weighted r 2 > .95) between TvC estimates obtained with the qTvC and the method of constant stimuli, although the qTvC used only 12% of the data collection (240 vs 1920 trials). The qTvC may hold considerable practical value for applying the external noise method to study mechanisms of observer state changes and special populations. We suggest that the same adaptive strategy can be applied to directly estimate other classical functions, such as the contrast sensitivity function, elliptical equi-discrimination contours, and sensory memory decay functions.

Perceptual learning of motion direction discrimination in fovea: Separable mechanisms

Dosher and Lu (1998) [Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 95 (23), 13988-13993.] proposed three mechanisms of perceptual learning: stimulus enhancement, external noise exclusion, and multiplicative noise reduction. In this study, we used pre-training as a manipulation to evaluate the separability of these mechanisms as a key test of the theoretical framework. Observers were trained in identifying the motion direction of a moving sine-wave grating in fovea with varying amount of superimposed external noise across trials, after receiving no pre-training, pre-training in high external noise, or pre-training in zero external noise in the same task. We found: (1) Without pre-training, perceptual learning significantly reduced contrast thresholds by about the same amount across all the external noise levels. (2) Both types of pre-training significantly reduced contrast thresholds in the corresponding conditions. (3) Pre-training in high external noise greatly reduced subsequent learning in high external noise, accounting for 64.6% of the total (pre-training + subsequent) improvements in that condition. On the other hand, the amount of subsequent learning in low external noise conditions was essentially the same as the total (pre-training + subsequent) amount of improvements in high external noise, suggesting that pre-training in high external noise had mostly only improved performance in noisy displays. (4) Pre-training in zero external noise practically eliminated or left very little additional learning in all the external noise conditions. We concluded that the two mechanisms of perceptual learning, stimulus enhancement, and external noise exclusion, can be trained independently in motion direction discrimination in fovea; training in low noise suffices to improve observer performance over all the external noise conditions.

Level and mechanisms of perceptual learning: Learning first-order luminance and second-order texture objects

Perceptual learning is an improvement in perceptual task performance reflecting plasticity in the perceptual system. Practice effects were studied in two object orientation tasks: a first order, luminance object task and a second-order, texture object task. Perceptual learning was small or absent in the first-order task, but consistently occurred for the second-order (texture) task, where it was limited to improvements in low external noise conditions, or stimulus enhancement [Dosher, B., & Lu, Z. -L. (1998). Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 95 (23) 13988–13993; Dosher, B., & Lu, Z. -L. (1999). Mechanisms of perceptual learning. Vision Research, 39 (19) 3197–3221], analogous to attention effects in first- and second-order motion processing [Lu, Z. -L., Liu, C. Q., & Dosher, B. (2000). Attention mechanisms for multi-location first- and second-order motion perception. Vision Research, 40 (2) 173–186]. Perceptual learning affected the later, post-rectification, stages of perceptual analysis, possibly localized at V2 or above. It serves to amplify the stimulus relative to limiting internal noise for intrinsically noisy representations of second-order stimuli.

Independent perceptual learning in monocular and binocular motion systems.

Eye-transfer tests, external noise manipulations, and observer models were used to systematically characterize learning mechanisms in judging motion direction of moving objects in visual periphery (Experiment 1) and fovea (Experiment 2) and to investigate the degree of transfer of the learning mechanisms from trained to untrained eyes. Perceptual learning in one eye was measured over 10 practice sessions. Subsequent learning in the untrained eye was assessed in five transfer sessions. We characterized the magnitude of transfer of each learning mechanism to the untrained eye by separately analyzing the magnitude of subsequent learning in low and high external noise conditions. In both experiments, we found that learning in the trained eye reduced contrast thresholds uniformly across all of the external noise levels: 47 +/- 10% and 62 +/- 8% in experiments 1 and 2, respectively. Two mechanisms, stimulus enhancement and template retuning, accounted for the observed performance improvements. The degree of transfer to the untrained eye depended on the amount of external noise added to the signal stimuli: In high external noise conditions, learning transferred completely to the untrained eye in both experiments. In low external noise conditions, there was only partial transfer of learning: 63% in experiment 1 and 54% in experiment 2. The results suggest that template retuning, which is effective in high external noise conditions, is mostly binocular, whereas stimulus enhancement, which is effective in low external noise displays, is largely monocular. The two independent mechanisms underlie perceptual learning of motion direction identification in monocular and binocular motion systems.

Attention mechanisms for multi-location first- and second-order motion perception

We applied the external noise plus attention paradigm to study attention mechanisms involved in concurrent first-order and second-order motion perception at two spatial locations. Cued to attend to one of the locations, the observer was instructed to independently judge direction of motion of either first-order (Experiment 1) or second-order (Experiment 2) motion stimuli at both locations in every trial. Across trials, systematically controlled amounts of external noise were added to the motion displays. We measured motion threshold at three performance criteria in every attention×external noise condition. We find that observers could, without any loss, simultaneously compute first-order motion direction at two widely separated spatial locations across a broad range of external noise conditions. However, considerable loss occurred at the unattended location in processing second-order motion direction at two separated spatial locations. We conclude that, under the conditions investigated in the current study, (1) in first-order motion perception, the visual system could simultaneously process motion direction at two widely separated locations without any capacity limitation; (2) in second-order motion perception, attending to a spatial location enhances stimulus contrast at that location by a factor of about 1.37 (or equivalently, reduces the internal additive noise by a factor of about 0.73).