Perceptual Cycle Research Articles

Using Perceptual Cycle Model and Text Mining to Investigate Ambulance Traffic Crashes

Ambulance crashes constitute a matter of utmost concern within public health, posing potential risks to both patients and emergency responders. Despite this critical importance, investigating the underlying causes of these collisions is difficult because of the scarcity of comprehensive and relevant datasets. To bridge this research gap and gain valuable insights, the present study embarked on a mission to shed light on the causative factors behind ambulance-related crashes. To achieve this objective, this study adopted a meticulous approach, collecting narrative descriptions from ten special investigation reports published by the National Highway Traffic Safety Administration. These reports were selected as they offered in-depth accounts of real-life ambulance crashes, rendering them an invaluable resource for analyzing the multifaceted aspects leading to such incidents. Central to this investigation was the utilization of the Perceptual Cycle Model (PCM), a well-established and comprehensive framework that facilitates a systematic examination of the various stages leading to a crash. The study examined the key influential factors associated with ambulance crashes by employing PCM and text mining. The results reveal diverse factors contributing to ambulance crashes, including varied causes, driver actions, and post-crash scenarios, providing a holistic understanding of road safety. The outcomes of this study will bolster the safety of ambulance operations, safeguard patients and personnel, and ensure the efficient delivery of life-saving emergency services to those in need.

Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver’s situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects’ SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving.

Case Study on the Traffic Collision Patterns of E-Scooter Riders

In recent years, nonmotorized transportation and micromobility have become increasingly popular in urban areas, and bike sharing and e-scooter usage are more affordable and accessible than ever before. However, users of these modes face several significant challenges, including risky riding behavior and the danger of being involved in collisions or near collisions with other motorists. Because of a lack of relevant data sets, it is difficult to comprehend the underlying causes of e-scooter collisions. This study collected narrative descriptions of 24 e-scooter collisions that occurred in Texas in 2021. The perceptual cycle model (PCM) was applied to these narrative crash reports to analyze the causation patterns of the crash occurrences. The PCM mechanism was applied independently to fatal and injury collisions, and the results were then compared with Haddon’s matrix to develop policy recommendations. The novel approach proposed in this study can be widely utilized by road safety professionals to understand the causal relationships behind other key road safety issues.

A Study of Vulnerable Road Users’ Behaviors Using Schema Theory and the Perceptual Cycle Model

China is currently in a rapid urbanization phase, and road traffic accidents occur frequently, with vulnerable road users often being easily injured. Traditional road traffic safety research often focuses on environmental and structural safety issues or considers human factors as the cause of accidents. This study organized 30 vulnerable road users to travel in a quadrangular road area in the Wudaokou area of Beijing, collected language data from the subjects for analysis, and attempted to apply schema theory and the perceptual cycle model from the field of cognitive psychology to analyze the perception and decision-making processes of vulnerable road users, thus discovering accident risks in the traffic environment and their underlying causes from the perspective of vulnerable road users. The study found that factors such as disorderly placement of shared bicycles, food delivery vehicles occupying the road, damaged road infrastructure, and unreasonable road design affect traffic safety and order, and proposes targeted improvement suggestions.

Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g. difficulties monitoring automated systems) for human operators, with their ‘remoteness’ in shore-side control centres exacerbating issues. This paper aims to investigate underlying decision-making processes of operators of uncrewed vehicles using the theoretical foundation of the Perceptual Cycle Model (PCM). A case study of an Unmanned Aerial Vehicle (UAV) accident has been chosen as it bears similarities to the operation of MASS through means of a ground-based control centre. Two PCMs were developed; one to demonstrate what actually happened and one to demonstrate what should have happened. Comparing the models demonstrates the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when operating from remote control centres. Practitioner Summary: To investigate underlying decision-making processes of operators of uncrewed vehicles using the Perceptual Cycle Model, by using an UAV accident case study. The findings showed the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when monitoring uncrewed systems from remote control centres.

Taking a mixed-methods approach to collision investigation: AcciMap, STAMP-CAST and PCM

Recently, ergonomics and safety researchers have turned their attention towards applying combinations of sociotechnical methods rather than using single methods in isolation. In the current research, a mixed-method approach combining two systems-based methods, Accimaps and the Systems Theoretic Accident Model and Process - Causal Analysis using Systems Theory (STAMP-CAST), and one cognitive approach, the Perceptual Cycle Model (PCM), were employed in analysing a rail-level crossing incident in Bangladesh. Each method was applied individually to investigate the collision, and interventions were proposed corresponding to incident events at different risk management framework levels. The three methods provided different perspectives of the whole picture, together identifying an array of contributory factors. The complementary nature of these methods aided in proposing a comprehensive set of safety recommendations, thereby demonstrating the benefit of a mixed-method approach for collision investigation in low-income settings.

Pilot decision‐making during a dual engine failure on take‐off: Insights from three different decision‐making models

AbstractCommercial airline pilots are required to make efficient, justifiable, and safety‐critical decisions when faced with adverse events such as engine failures. Although these are rare events, the consequences are severe, and the pilot response is critical. This paper reviews pilot decision‐making when faced with a dual engine failure on take‐off using three different decision models; the Recognition Primed Decision Model, Decision Ladders and the Perceptual Cycle Model. In‐depth interviews with eight experienced airline pilots were conducted to capture their decision‐making processes in response to a dual engine failure on take‐off event. The analysis of these interviews using the three different decision models provide recommendations for a proposed decision assistant. The different decision models are discussed in relation to the insight they can bring to developing a future decision assistant tool within the flight deck of commercial aircraft.

Identifying objective behavioural measures of expert driver situation awareness

Efforts to reduce road crash rates depend on a clear understanding of the factors that contribute to driver risk. Not all drivers are at equivalent risk. It is critical to identify the factors that contribute to the development of expertise in the driving environment. The rapid development of a driver’s situation awareness (SA) is central to the safe performance of the driving task. Therefore, SA must be clearly operationalised in order to better assess its role in the development of expertise. This study employs an existing scheme based on the Perceptual Cycle Model (PCM) used for post hoc incident analysis and adapts it to the driving context. We attempted to correlate performance on coded verbalisations indicative of SA with non-invasive objective gaze metrics. Gaze metrics and the verbal counts were shown to differentiate between both expert and experienced (non-expert) drivers, but these measures failed to correlate with one another. Findings indicate differences in the way expert and experienced drivers update their schema of the driving task, with equivalent effort required to do so. The novel adaptation demonstrated in this paper allows for a domain-specific assessment of SA which reliably differentiates between drivers of varying expertise levels. Although selected gaze metrics were shown to be inadequate predictors of SA, additional analysis demonstrated key differences in gaze content. Combined, these findings enhance an understanding of expert SA development contributing to reduced crash risk.

Generating Design Requirements for Flight Deck Applications: Applying the Perceptual Cycle Model to Engine Failures on Take-off

ABSTRACT Engine failure events during critical phases of flight are a rare yet very real possibility on commercial aircraft. The response of the pilots to these events is vital to minimizing possible adversity. New technologies are in development that provide enhanced information on the status of the aircraft engine after sustaining engine damage in order to guide pilot’s decision making. It is important that this enhanced information is aligned with the requirements of the user. This paper describes how user input can effectively be incorporated early on in the design process of new technologies to understand how current systems may be improved. The Perceptual Cycle Model (PCM) is applied to in-depth interview reports from commercial airline pilots on their responses to different engine failure events caused by bird-strikes to the engine. Application of the PCM demonstrates where current processes may be better supported to enhance pilot decision making from such events. From this, areas where improvements could be made using future avionic systems are presented, with user-led design recommendations.

An investigation of urban pedestrian behaviour in Bangladesh using the Perceptual Cycle Model

In Bangladesh, pedestrians remain the most neglected road user group in terms of research and safety measures, even though they are involved in nearly 50% of all fatal collisions. In the Dhaka metropolitan area, this statistic rises to around 65%. To reduce the recurrence of such collisions, it is necessary to understand the underlying thought processes of pedestrians, and how pedestrians interact with different road users, particularly when crossing the road. This study contributes to knowledge through the analysis of verbal data from 46 pedestrians, framed in terms of the Perceptual Cycle Model, where cognitive processes are described in terms of three categories: schema (mental templates), action (doing things), and world (environmental information). Concurrent verbal reports were provided by participants while they negotiated a busy area of mixed traffic in the centre of Dhaka city. The analysis revealed some of the factors that affected decision-making (for road crossing behaviour) at the different road sections. Many external factors (e.g., street sellers, rubbish blocking the path, lack of shade and poor pavement condition) prevented the pavement from being used and contributed to riskier road crossing behaviour. Some safety implications and related recommendations are presented.

Modified Perceptual Cycle Generative Adversarial Network-Based Image Enhancement for Improving Accuracy of Low Light Image Segmentation

In recent years, the importance of the semantic segmentation field has been increasingly emphasized because autonomous vehicle and artificial intelligence (AI)-based robot technology are being researched extensively; and methods for accurately recognizing objects are required. Previous state-of-the-art segmentation methods have been proven to be effective for databases obtained during daytime. However, in extremely low light or nighttime environments, the shape and color information of objects are very small or disappear due to an insufficient amount of external light, which makes it difficult to train the segmentation network and significantly degrades performance. In our previous work, segmentation performance in a low light environment was improved using the enhancement-based segmentation method. However, low light images could not be restored precisely and segmentation performance improvement was limited because only per-pixel loss functions were used when training the enhancement network. To overcome these drawbacks, we propose a low light image segmentation method based on a modified perceptual cycle generative adversarial network (CycleGAN). Perceptual image enhancement was performed using our network, which significantly improved segmentation performance. Unlike the existing perceptual loss, the Euclidean distance of the feature maps extracted from the pretrained segmentation network was used. In our experiments, we used low light databases generated from two famous road scene open databases, which are Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), and confirmed that our proposed method shows better segmentation performance in extremely low light environments than the existing state-of-the art methods.

Using the Perceptual Cycle Model and Schema World Action Research Method to generate design requirements for new avionic systems

AbstractThe Schema World Action Research Method (SWARM) has previously been used as a means to explore the underlying decision‐making processes involved in retrospective incident reports. The approach has been fruitful in capturing all interacting processes involved in managing incidents. This paper proposes that SWARM may also be used prospectively within the early phases of the design lifecycle for new behavioral‐based intervention strategies. Six pilot interviews were conducted to explore pilots' perceptual cycle processing when faced with a suspected engine oil leak. The aim was to explore whether there may be any deficiencies within current practise and explore ways in which pilots may be better supported in dealing with abnormal system parameters such as this. A number of design recommendations are proposed for a new avionic system capable of supporting and guiding pilots through the decision‐making process.

Semi-Supervised Face Frontalization in the Wild

Synthesizing a frontal view face from a single nonfrontal image, i.e. face frontalization, is a task of practical importance in a wide range of facial image analysis applications. However, to train the frontalization model in a supervised manner, most existing face frontalization methods rely on the availability of nonfrontal-frontal face pairs (typically from the Multi-PIE dataset) captured in a constrained environment. Such approaches, in return, limit the generalizability of their application to unconstrained scenarios. Unfortunately, although a large amount of in-the-wild face datasets are available, they cannot easily be utilized for face frontalization training since the nonfrontal and frontal facial images are not paired. To train a frontalization network which generalizes well to both constrained and unconstrained environments, we propose a semi-supervised learning framework which effectively uses both (labeled) indoor and (unlabeled) outdoor faces. Specifically, to achieve this goal, this article presents a Cycle-Consistent Face Frontalization Generative Adversarial Network (CCFF-GAN) which consists of both (1) the supervised and (2) the unsupervised components. For (1), we use the indoor paired (labeled) data to learn a roughly accurate frontalization network which may not generalize well to outdoor (in-the-wild) scenarios. For (2), to cope with the generalization issue, the unsupervised part uses the unpaired (unlabeled) images under the perceptual cycle consistency constraint in the semantic feature space to generalize the network from controlled (indoor) to uncontrolled (outdoor) environment. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art face frontalization methods, especially under the in-the-wild scenarios.

Breaking the cycle of frustration: Applying Neisser's Perceptual Cycle Model to drivers of semi-autonomous vehicles

Semi-autonomous cars are already on the road and highly autonomous cars will soon be with us. Little is understood about how drivers will adapt to the changing relationship with their vehicle, but to ensure safety and consumer acceptance, this insight is vital. To this end, an on-road study in a semi-autonomous vehicle was undertaken with six UK drivers. The ‘think aloud’ technique was employed and video and audio footage of their interaction with the vehicle was captured. Neisser's (1976) Perceptual Cycle Model (PCM) was used to analyse the data and three case studies are presented to highlight how poor synergy between driver and semi-autonomous vehicles can occur from the lens of Schema, Action or World information. Seven key design considerations are proposed to ensure a more positive and safer interaction between driver and autonomous vehicle to guide focus by manufacturers. Further evidence for the existence of a ‘counter cycle’ (Plant and Stanton, 2015) within the PCM is found and how this relates to the challenges of using verbal protocals expressed during a fast moving dynamic task is discussed.

Revisiting individual differences in the time course of binocular rivalry.

Simultaneously showing an observer two incompatible displays, one to each eye, causes binocular rivalry, during which the observer regularly switches between perceiving one eye's display and perceiving the other. Observers differ in the rate of this perceptual cycle, and these individual differences have been reported to correlate with differences in the perceptual switch rate for other bistable perception phenomena. Identifying which psychological or neural factors explain this variability can help clarify the mechanisms underlying binocular rivalry and of bistable perception generally. Motivated by the prominent theory that perceptual switches during binocular rivalry are brought about by neural adaptation, we investigated whether perceptual switch rates are correlated with the strength of neural adaptation, indexed by visual aftereffects. We found no compelling evidence for such correlations. Moreover, we did not corroborate previous findings that switch rates are correlated between binocular rivalry and other forms of bistable perception. This latter nonreplication prompted us to perform a meta-analysis of existing research into correlations among forms of bistable perception, which revealed that evidence for such correlations is much weaker than is generally believed. By showing no common factor linking individual differences in binocular rivalry and in our other paradigms, these results fit well with other work that has shown such common factors to be rare among visual phenomena generally.

Situation awareness in young novice ambulance drivers: So much more than driving

BackgroundThe intractable problem of young novice driver road crashes, and the critical role of emergency responders in attending road crashes, is well-recognised as is the critical role of situation awareness skills (SAS, ie. an understanding of ‘what is going on’ in a specific situation). Emergency responders may be young novice drivers and young novice ambulance drivers, therefore SAS will be required for safe road use. This project explored the SA demands upon young novice ambulance drivers (‘drivers’) in Queensland, Australia. MethodA synthesis of literature regarding SAS relevant to drivers was followed by a hierarchical task analysis (HTA) and a perceptual cycle model (PCM) to explicate the complex emergency ambulance driver dispatch and response system and SAS requirements. ResultsInadequate SA is a likely contributor to risks for drivers, patients, and other road users. The HTA revealed a plethora of opportunities for inadequate SAS to negatively impact safety. The PCM highlighted complex environmental information modifies driver ‘world’ schema (eg., medical procedures) which in turn directs their actions (eg., attending to radio/pager) that in a cyclical manner relies upon complex environmental information, etcetera. Discussion and concluding remarksEmergency responder SA appears quite different to ‘ordinary drivers’, suggesting well-developed road-related schema are required before young novice ambulance drivers are behind the wheel in a highly-emotive, time-critical situation. Drivers are not simply ‘driving’; they are engaged in a breadth of tasks while driving (e.g., accessing dynamic case details) which, for safety, rely upon adequate SAS, therefore training programs should target SAS development.

Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016

“Human error” is often implicated as a causal factor in accident investigation yet very little is done to understand ‘why’ such errors occur in the first place. This paper uses the principles of Schema Theory and the Perceptual Cycle Model (PCM) to further explore the circumstances surrounding the fatal Tesla crash in May 2016 in which the driver was fatally injured using team-PCM representations. The preliminary National Highway Traffic Safety Administration accident investigation concluded that the driver of the Tesla Model S was at fault. However, the analysis presented in this paper argues that rather than “driver error”, the underlying cause of this tragic incident could be in fact more akin to a “designer error” implicating the design of the Autopilot feature itself. This is in line with the National Transportation Safety Boards more recent announcement that suggests systems design may have contributed to the crash. It would therefore appear that the drivers expectation of system functionality may not have matched the real life capabilities of the system. This is likely to be a product of inappropriate mental models relating to system function.

Allocating Attention to Detect Motorcycles: The Role of Inattentional Blindness.

To determine whether inattentional blindness (IB) can be used to understand the psychological mechanisms around looked-but-failed-to-see (LBFTS) crashes involving motorcycles Background: IB occurs when an observer looks directly at an object yet fails to see it, thus LBFTS crashes may be a real-world example of IB. The study tests a perceptual cycle model in which motorcycles are detected less frequently because they fall lower on the attentional hierarchy for driving. A driving-related IB task with photographs of driving situations investigated whether an additional stimulus, a taxi or motorcycle, would be more likely to be missed by participants. In Experiments 2 and 3, the "threat value" of objects in the scene were varied to determine the degree to which this influences participants' tendency to notice motorcycles. Participants were twice as likely to miss a motorcycle compared with a taxi. Moreover, participants reported that they would expect to miss a motorcycle on the road. In Experiments 2 and 3, participants modulated their attention to accommodate motorcycles when necessary, suggesting that motorcycles are afforded the lowest level of attentional bandwidth. Inattentional blindness forms a good psychological framework for understanding LBFTS crashes, particularly in the context of attentional set, such that LBFTS crashes occur because motorcycles do not feature strongly in a typical driver's attentional set for driving. The findings here are important because LBFTS crashes can be reduced if we can change the expectations of road users around the presence of motorcycles on the road.

The development of the Schema-Action-World (SAW) taxonomy for understanding decision making in aeronautical critical incidents

The perceptual cycle model (PCM) offers a process-orientated approach to understanding decision making. This approach is distributed in nature as it considers how internally held schemata and external environmental information interact to produce actions and behaviour. This an essential component of any incident reporting system, although it is often lacking. In its current form the PCM only provides a very high-level of explanation. This research utilised data from critical decision making interviews to deconstruct the three high-level categories of the PCM into a 28 item taxonomy. In doing so, we were able to provide a more detailed description of aeronautical critical decision making (ACDM) by demonstrating the relevance of different concepts in different phases of dealing with an incident. The data were used to model the ACDM process. The taxonomy can be used for gaining a comprehensive understanding of both the contextual and cognitive components of decision making, something that existing taxonomies and models often fail to do.

Distributed cognition in Search and Rescue: loosely coupled tasks and tightly coupled roles

The perceptual cycle model (PCM) underpins much Ergonomics research, particularly in a team context, for example in its theoretical underpinning of distributed situation awareness. Despite this, the PCM framework it has not been explicitly applied to explore team processes, which is surprising given the prevalence of teamwork in safety critical systems. This paper explores team processes in the context of search and rescue (SAR) by applying the PCM and an association classification scheme with a network analysis approach utilising the event analysis of systemic teamwork (EAST) method. Data were collected via observations and communication recordings during training flights with SAR crews and were amalgamated into a representative case study. The analysis demonstrates how the SAR team function within a distributed perceptual cycle whereby the actions of one team member become world information for another team member. Advancements to the EAST method are proposed and the implications of the research are discussed.Practitioner Summary: This paper explores the perceptual cycle interactions of SAR crews using a novel EAST approach. The analysis demonstrates how the crew function as a distributed cognitive unit and applications in terms of training and design are discussed.