Perceptual Errors Research Articles

A mixed-equilibrium model of individual and household activity–travel choices in multimodal transportation networks

This paper develops a novel household-oriented activity-based mixed-equilibrium model for estimating individual and household activity–travel choices in multimodal transportation networks with interactions between private car and public transit modes. In the novel model, household members with heterogeneous errors of perception on the time-dependent utility of different activity types make daily joint/solo activity–travel choices in a mixed-equilibrium manner, which maximizes either perceived household utility or perceived individual utility. A logit-based stochastic choice model is developed to capture the mixed equilibrium with heterogeneous errors of perception and used to predict the choices of alternative joint activity–travel paths (JATPs) on a supernetwork platform. Based on this stochastic JATP choice model, the mixed-equilibrium model is formulated as an equivalent variational inequality (VI) problem and solved using a modified diagonalization method. This converts the time-dependent activity–travel scheduling problem into an equivalent static traffic assignment problem on JATPs. The conditions required for the existence and uniqueness of a solution to the equivalent VI problem in terms of a JATP flow pattern are also identified. Numerical examples are provided to illustrate the model’s merits and its applications for examining the effect of the coronavirus disease 2019 (COVID-19) pandemic.

Melody, not Beat Perception, Predicts Rhythmic Error Detection

The purpose of this study was to examine the relationships among beat perception, error detection, and musical experience. We presented monophonic rhythms using a piano timbre along with two measures of beat perception (Harvard Beat Finding and Interval Test [BFIT] and Goldsmiths Beat Alignment Test) and a measure of melodic error detection. College musicians’ ( N = 43) ability to detect rhythm errors was not significantly correlated to their ability to perceive beat alignment (Goldsmiths test) or tempo change (BFIT). Age was related to performance on only one of the measures, the BFIT test. A regression model yielded pitch error detection as the only significant predictor of rhythmic error detection. We suggest that college musicians already possess a requisite ability for beat processing that allows them to perform error detection. The lack of relationship between beat perception and rhythmic error detection is explained by this requisite ability in the population, and we promote future research for pitch and rhythm processing as it relates to rhythm perception or performance.

Distortion corrected tomographic near-eye displays using light field optimization.

Several multifocal displays have been proposed to provide accurate accommodation cues. However, multifocal displays have an undesirable feature, which is especially emphasized in near-eye displays configuration, that the field of views (FOVs) of the virtual planes change over depth. We demonstrate that this change in FOV causes image distortions, which reduces overall image quality, and depth perception error due to the variation of image sizes according to depths. Here, we introduce a light field optimization technique to compensate for magnification variations among the focal planes. Our approach alleviates image distortions, especially noticeable in the contents with large depth discontinuity, and reconstructs the image size to precise depths, while maintaining a specific tolerance length for the target eye relief. To verify the feasibility of the algorithm, we employ this optimization method for the tomographic near-eye display system to acquire the optimal image and backlight sequences for a volumetric scene. In general, we confirm that the structural similarity index measure of reconstructed images against ground truth increases by 20% when the eye relief is 15mm, and the accommodation cue is appropriately stimulated at the target depth with our proposed method.

How does Dockless bike sharing serve users in Nanjing, China? User surveys vs. trip records

Dockless bike sharing (DBS), as an emerging bike sharing scheme, effectively promotes active travel and improves the mobility of users. Travel behavior analysis of DBS users is a key basis of service improvement, and the data sources include user surveys and trip records. User surveys are widely applied but have the drawback of perceptual errors. Thus, there is a compelling need for exploring travel behavior using automatically-collected trip records. This study examines DBS users' travel frequency and purpose with five semi-annual user surveys and trip record data in Nanjing, China. User surveys reflect user perception of travel behavior, while trip records can be used to calculate actual travel frequency and infer travel purpose. The results show that perceptual travel frequency based on user surveys has a central tendency, and actual travel frequency based on trip records follows a heavy-tailed distribution. Older people tend to underestimate their travel frequency, but other age groups tend to overestimate their frequency. In addition, a dockless bike sharing's travel purpose inferring (DBS-TPI) model is proposed to infer the travel purpose based on trip record data and points of interest data. The DBS-TPI model is reliable and transferable under different scenarios, which could complement user surveys. This study provides insights into improving data collection for better understanding of travel behaviors among DBS users.

A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process

The disruption of critical components in a transportation network can bring about severe network performance degradation and requires a relatively long period to recover, which would lead to commuters’ day-to-day route choice adjustment. Under disruptions, there would be greater travel time variability (objective uncertainty) and travelers’ perception error uncertainty (subjective uncertainty) in the transportation network. However, no vulnerability analysis method in the literature can consider the day-to-day network performance fluctuation under uncertainties. In this paper, we develop a new day-to-day dynamic network vulnerability analysis approach that allows the consideration of day-to-day network performance fluctuation based on a new day-to-day dynamic model considering both objective travel time uncertainty and subjective perception error uncertainty. Compared to most existing day-to-day models that either adopt User Equilibrium (UE) or Logit-based route choice criterion, the new day-to-day model has two advantages: (1) the Weibit model is used to capture travelers’ subjective perception error uncertainty, which does not have the perfect information assumption in the UE model, or the identically distributed perception error assumption in the Logit model; and (2) the mean-excess travel time (METT) concept is used to capture the objective travel time uncertainty, which handles the inconsideration of travel time variability in most day-to-day models while remaining computational tractability. Based on the proposed day-to-day dynamic model, we develop a new component importance metric for network vulnerability analysis. This new metric characterizes the post-disruption day-dependent consequences to alleviate the limitation of only assessing the final static equilibrium consequence as in the existing studies of vulnerability analysis. Numerical examples are provided to demonstrate the features of the proposed day-to-day dynamic model and the new component importance metric, as well as their applicability in identifying the critical bridges in the Winnipeg network. The proposed approach provides a new decision support tool for planners and managers in assessing the consequences of disruptions, identifying the critical components, and determining the recovery schedules after disruptions.

Charles W. Bergquist (1942–2020)

Charles W. Bergquist (1942–2020)

Modeling Parameters Affecting Electrocution Accidents: An 8-Year Study in Tehran Province

Introduction: Electrocution is one of the most important accidents that lead to death. Considering the factors related to the electrocution accidents can reduce the incidence of this type of accidents and their consequences. This study was designed and conducted with the aim of analyzing and modeling the parameters affecting electrocution accidents. Method: This study was a retrospective descriptive-analytical study that was conducted in Tehran province in 2019-2020. This study was performed on electrocution accidents over a period of 8 years (2011-2018). The data collection tool included the report of 220 cases studied in Tehran Labor Office. In this study, multinomial logistic regression analysis has been used. Significance level in this study was considered 0.05. Results: The findings of modeling the factors affecting the consequences of electrocution accidents showed that six parameters including age, work experience, education, time of accident, unsafe acts and the set of causes of accidents had a significant relationship with the type of damage caused by electrocutions (p<0.005). The greatest impact on the consequences of electrocution accidents were related to unsafe acts , the causes of the accident and the time of the accident per day with an odds ratio of 4.8, 2.19 and 1.84, respectively. Conclusion: The results indicated that electrocution accidents and their consequences can be affected by various parameters and factors and their severity can be intensified or reduced due to the role of these factors. The most important factors influencing the consequences were included unsafe acts such as perceptual errors and skill-based errors.

Cooperative Intersection with Misperception in Partially Connected and Automated Traffic.

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.

Transfer Reinforcement Learning for Autonomous Driving

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

Even if it might not be true, evidence cannot be false

Wordly internalists claim that while internal duplicates always share the same evidence, our evidence includes non-trivial propositions about our environment. It follows that some evidence is false. Worldly internalism is thought to provide a more satisfying answer to scepticism than classical internalist views that deny that these propositions about our environment might belong to our evidence and to provide a generally more attractive account of rationality and reasons for belief. We argue that worldly internalism faces serious difficulties and that its apparent advantages are illusory. First, it cannot adequately handle some not terribly strange cases of perceptual error. Second, it cannot explain why one should plan to use their evidence to update their beliefs. The second issue allows us to explain why cases of misplaced certainty do not require us to introduce false evidence into our views and that why the alleged advantage of worldly internalism in resisting sceptical pressures is illusory.

Optimal Tradable Credit Scheme Design with Recommended Credit Price

As an interesting research topic in transportation field, tradable credit scheme (TCS) has been extensively explored in the latest decade. Existing studies implicitly assumed that travelers are clear about the equilibrium credit price and make their trips accordingly. However, this may not be the case in reality, since the credit price is endogenously determined by the credit-trading behavior, especially in the early stages after the implementation of a TCS. Considering travelers’ uncertainty on the equilibrium credit price, this paper aims to investigate the impacts of perception error on credit price and how to accommodate such errors by an appropriate scheme design. Transferring the perception error on credit price to a given and fixed value released by central authority, we first investigate the impacts of recommended credit price under a given TCS. The numerical results imply that it is necessary to simultaneously consider the choice of recommended credit price and charging scheme in TCS design. Regarding this, we combine the goals of social welfare and public acceptance of the scheme and propose a bilevel biobjective programming (BLBOP) model, by which the net economic benefit is maximized while the gap between the recommended and realized credit prices is minimized. Through two numerical examples, it is found that the rise in perception variance could intensify the contradiction effect between the two objectives. Additionally, a nonnegligible price gap must be allowed to occur to maintain the effectiveness of a TCS.

Taking aim at the perceptual side of motor learning: exploring how explicit and implicit learning encode perceptual error information through depth vision.

Motor learning in visuomotor adaptation tasks results from both explicit and implicit processes, each responding differently to an error signal. Although the motor output side of these processes has been extensively studied, the visual input side is relatively unknown. We investigated if and how depth perception affects the computation of error information by explicit and implicit motor learning. Two groups of participants made reaching movements to bring a virtual cursor to a target in the frontoparallel plane. The Delayed group was allowed to reaim and their feedback was delayed to emphasize explicit learning, whereas the camped group received task-irrelevant clamped cursor feedback and continued to aim straight at the target to emphasize implicit adaptation. Both groups played this game in a highly detailed virtual environment (depth condition), leveraging a cover task of playing darts in a virtual tavern, and in an empty environment (no-depth condition). The delayed group showed an increase in error sensitivity under depth relative to no-depth. In contrast, the clamped group adapted to the same degree under both conditions. The movement kinematics of the delayed participants also changed under the depth condition, consistent with the target appearing more distant, unlike the Clamped group. A comparison of the delayed behavioral data with a perceptual task from the same individuals showed that the greater reaiming in the depth condition was consistent with an increase in the scaling of the error distance and size. These findings suggest that explicit and implicit learning processes may rely on different sources of perceptual information.NEW & NOTEWORTHY We leveraged a classic sensorimotor adaptation task to perform a first systematic assessment of the role of perceptual cues in the estimation of an error signal in the 3-D space during motor learning. We crossed two conditions presenting different amounts of depth information, with two manipulations emphasizing explicit and implicit learning processes. Explicit learning responded to the visual conditions, consistent with perceptual reports, whereas implicit learning appeared to be independent of them.

Induced astigmatism biases the orientation information represented in multivariate electroencephalogram activities.

A small physical change in the eye influences the entire neural information process along the visual pathway, causing perceptual errors and behavioral changes. Astigmatism, a refractive error in which visual images do not evenly focus on the retina, modulates visual perception, and the accompanying neural processes in the brain. However, studies on the neural representation of visual stimuli in astigmatism are scarce. We investigated the relationship between retinal input distortions and neural bias in astigmatism and how modulated neural information causes a perceptual error. We induced astigmatism by placing a cylindrical lens on the dominant eye of human participants, while they reported the orientations of the presented Gabor patches. The simultaneously recorded electroencephalogram activity revealed that stimulus orientation information estimated from the multivariate electroencephalogram activity was biased away from the neural representation of the astigmatic axis and predictive of behavioral bias. The representational neural dynamics underlying the perceptual error revealed the temporal state transition; it was transiently dynamic and unstable (approximately 350 ms from stimulus onset) that soon stabilized. The biased stimulus orientation information represented by the spatially distributed electroencephalogram activity mediated the distorted retinal images and biased orientation perception in induced astigmatism.

Integrating art museum visits into the radiology curriculum: A program to encourage engagement, observation and analytic skills among millennial learners

Millennial learners have unique educational preferences and expectations, many of which vary significantly from those of their predecessors. To aid in the preparation and training of the next generation of radiologists, we have been exploring a teaching technique using an art observation training workshop to foster engagement of millennial radiology residents to pay closer attention to image detail, clearly state the observations and synthesize the “story” of the patient's condition. Fine tuning these visual skills is crucial as research shows that over 70% of errors in interpreting diagnostic imaging studies are related to visual perception errors.

O-RADS MRI score: analysis of misclassified cases in a prospective multicentric European cohort.

To retrospectively review the causes of categorization errors using O-RADS-MRI score and to determine the presumptive causes of these misclassifications. EURAD database was retrospectively queried to identify misclassified lesions. In this cohort, 1194 evaluable patients with 1502 pelvic masses (277 malignant / 1225 benign lesions) underwent standardized MRI to characterize adnexal masses with histology or 2 years' follow-up as a reference standard. An expert radiologist reviewed cases with two junior radiologists and lesions termed misclassified if malignant lesion was scored ≤ 3, a benign lesion was scored ≥ 4, the site of origin was incorrect, or a non-adnexal mass was incorrectly categorized as benign or malignant. There were 139 / 1502 (9.2%) misclassified masses in 116 women including 109 adnexal and 30 non-adnexal masses. False-negative cases corresponded to 16 borderline or invasive malignant adnexal masses rated score ≤ 3 (16 / 139, 11.5%). False-positive cases corresponded to 88 benign masses were rated score 4 (67 / 139, 48.2%) or 5 (18 / 139,12.9%) or considered suspicious non-adnexal lesions (3 / 139, 2.2%). Misclassifications were only due to origin error in 12 adnexal masses (8 benign, 4 malignant) (8.6%, 12 / 139) and 23 non-adnexal masses (18 benign, 5 malignant,16.5%, 23 / 139) perceived respectively as non-adnexal and adnexal masses. Interpretive error (n = 104), failure to recognize technical insufficient exams (n = 9), and perceptual errors (n = 4) were found. Most interpretive was due to misinterpretation of solid tissue or incorrect assignment of mass origin. Eighty-four out of 139 cases were correctly reclassified by the readers with strict adherence to the score rules. Most errors were due to misinterpretation of solid tissue or incorrect assignment of mass origin. • Prospective assignment of O-RADS-MRI score resulted in misclassification of 9.25% of sonographically indeterminate pelvic masses. • Most errors were interpretive (74.8%) due to misinterpretation of solid tissue as defined by the lexicon or incorrect assignment of mass origin. • Pelvic inflammatory disease is a common source of misclassification (8.9%) (12 / 139).

ID: 3521790 DOES ARTIFICIAL INTELLIGENCE IMPROVE NEOPLASMS DETECTION RATE FOR COLONOSCOPY? - A SINGLE CENTER PILOT STUDY

Removal of neoplastic polyps during total colonoscopy reduces the mortality and morbidity of colorectal cancer. However, one-fourth of polyps are missed and some of these lesions are considered to be a cause of post-colonoscopy colorectal cancer. To reduce the perceptual error of colonoscopy, a number of computer-aided detection (CADe) systems are reported; however, its clinical efficacy is still uncertain. In this study, we conducted a prospective, non-randomized, single-center pilot study to clarify whether the CADe system improves the neoplasms detection rate.

POMDP-Based Candy Server:Lessons Learned from a Seven Day Demo

An autonomous robot must decide a good strategy to achieve its long term goal, despite various types of uncertainty. The Partially Observable Markov Decision Processes (POMDPs) is a principled framework to address such a decision making problem. Despite the computational intractability of solving POMDPs, the past decade has seen substantial advancement in POMDP solvers. This paper presents our experience in enabling on-line POMDP solving to become the sole motion planner for a robot manipulation demo at IEEE SIMPAR and ICRA 2018. The demo scenario is a candy-serving robot: A 6-DOFs robot arm must pick-up a cup placed on a table by a user, use the cup to scoop candies from a box, and put the cup of candies back on the table. The average perception error is ∼3cm (≈ the radius of the cup), affecting the position of the cup and the surface level of the candies. This paper presents a strategy to alleviate the curse of history issue plaguing this scenario, the perception system and its integration with the planner, and lessons learned in enabling an online POMDP solver to become the sole motion planner of this entire task. The POMDP-based system were tested through a 7 days live demo at the two conferences. In this demo, 150 runs were attempted and 98% of them were successful. We also conducted further experiments to test the capability of our POMDP-based system when the environment is relatively cluttered by obstacles and when the user moves the cup while the robot tries to pick it up. In both cases, our POMDP-based system reaches a success rate of 90% and above.

The origin of Vierordt's law: The experimental protocol matters.

In 1868, Karl Vierordt discovered one type of errors in time perception-an overestimation of short duration and underestimation of long durations, known as Vierordt's law. Here we reviewed the original study in its historical context and asked whether Vierordt's law is a result of an unnatural experimental randomization protocol. Using iterative Bayesian updating, we simulated the original results with high accuracy. Importantly, the model also predicted that a slowly changing random-walk sequence produces less central tendency than a random sequence with the same durations. This was validated by a duration reproduction experiment from two sequences (random and random walk) with the same sampled distribution. The results showed that trial-wise variation influenced the magnitude of Vierordt's law. We concluded that Vierordt's law is caused by an unnatural yet widely used experimental protocol.

An Approach for Individualized Cochlear Frequency Mapping Determined From 3D Synchrotron Radiation Phase-Contrast Imaging.

Cochlear implants are traditionally programmed to stimulate according to a generalized frequency map, where individual anatomic variability is not considered when selecting the centre frequency of stimulation of each implant electrode. However, high variability in cochlear size and spatial frequency distributions exist among individuals. Generalized cochlear implant frequency maps can result in large pitch perception errors and reduced hearing outcomes for cochlear implant recipients. The objective of this work was to develop an individualized frequency mapping technique for the human cochlea to allow for patient-specific cochlear implant stimulation. Ten cadaveric human cochleae were scanned using synchrotron radiation phase-contrast imaging (SR-PCI) combined with computed tomography (CT). For each cochlea, ground truth angle-frequency measurements were obtained in three-dimensions using the SR-PCI CT data. Using an approach designed to minimize perceptual error in frequency estimation, an individualized frequency function was determined to relate angular depth to frequency within the cochlea. The individualized frequency mapping function significantly reduced pitch errors in comparison to the current gold standard generalized approach. This paper presents for the first time a cochlear frequency map which can be individualized using only the angular length of cochleae. This approach can be applied in the clinical setting and has the potential to revolutionize cochlear implant programming for patients worldwide.

Errors on a Speech-in-Babble Sentence Recognition Test Reveal Individual Differences in Acoustic Phonetic Perception and Babble Misallocations.

The ability to recognize words in connected speech under noisy listening conditions is critical to everyday communication. Many processing levels contribute to the individual listener's ability to recognize words correctly against background speech, and there is clinical need for measures of individual differences at different levels. Typical listening tests of speech recognition in noise require a list of items to obtain a single threshold score. Diverse abilities measures could be obtained through mining various open-set recognition errors during multi-item tests. This study sought to demonstrate that an error mining approach using open-set responses from a clinical sentence-in-babble-noise test can be used to characterize abilities beyond signal-to-noise ratio (SNR) threshold. A stimulus-response phoneme-to-phoneme sequence alignment software system was used to achieve automatic, accurate quantitative error scores. The method was applied to a database of responses from normal-hearing (NH) adults. Relationships between two types of response errors and words correct scores were evaluated through use of mixed models regression. Two hundred thirty-three NH adults completed three lists of the Quick Speech in Noise test. Their individual open-set speech recognition responses were automatically phonemically transcribed and submitted to a phoneme-to-phoneme stimulus-response sequence alignment system. The computed alignments were mined for a measure of acoustic phonetic perception, a measure of response text that could not be attributed to the stimulus, and a count of words correct. The mined data were statistically analyzed to determine whether the response errors were significant factors beyond stimulus SNR in accounting for the number of words correct per response from each participant. This study addressed two hypotheses: (1) Individuals whose perceptual errors are less severe recognize more words correctly under difficult listening conditions due to babble masking and (2) Listeners who are better able to exclude incorrect speech information such as from background babble and filling in recognize more stimulus words correctly. Statistical analyses showed that acoustic phonetic accuracy and exclusion of babble background were significant factors, beyond the stimulus sentence SNR, in accounting for the number of words a participant recognized. There was also evidence that poorer acoustic phonetic accuracy could occur along with higher words correct scores. This paradoxical result came from a subset of listeners who had also performed subjective accuracy judgments. Their results suggested that they recognized more words while also misallocating acoustic cues from the background into the stimulus, without realizing their errors. Because the Quick Speech in Noise test stimuli are locked to their own babble sample, misallocations of whole words from babble into the responses could be investigated in detail. The high rate of common misallocation errors for some sentences supported the view that the functional stimulus was the combination of the target sentence and its babble. Individual differences among NH listeners arise both in terms of words accurately identified and errors committed during open-set recognition of sentences in babble maskers. Error mining to characterize individual listeners can be done automatically at the levels of acoustic phonetic perception and the misallocation of background babble words into open-set responses. Error mining can increase test information and the efficiency and accuracy of characterizing individual listeners.