The vulnerability of rules in complex work environments: dynamism and uncertainty pose problems for cognition

Many complex work environments rely heavily on cognitive operators using rules. Operators sometimes fail to implement rules, with catastrophic human, social and economic costs. Rule-based error is widely reported, yet the mechanisms of rule vulnerability have received less attention. This paper examines rule vulnerability in the complex setting of airline transport operations. We examined ‘the stable approach criteria rule’, which acts as a system defence during the approach to land. The study experimentally tested whether system state complexity influenced rule failure. The results showed increased uncertainty and dynamism led to increased likelihood of rule failure. There was also an interaction effect, indicating complexity from different sources can combine to further constrain rule-based response. We discuss the results in relation to recent aircraft accidents and suggest that ‘rule-based error’ could be progressed to embrace rule vulnerability, fragility and failure. This better reflects the influence that system behaviour and cognitive variety have on rule-based response. Practitioner Summary: In this study, we examined mechanisms of rule vulnerability in the complex setting of airline transport operations. The results suggest work scenarios featuring high uncertainty and dynamism constrain rule-based response, leading to rules becoming vulnerable, fragile or failing completely. This has significant implications for rule-intensive, safety critical work environments

Event prototypes in airline transport operations

Evidence from accident reports indicate that pilots are not always able to categorise events in real-time, which can lead to delayed or inappropriate response. The prototype view proposes that category judgements are influenced by the clearest and best cases of category membership. Flight crew may not always experience events and situations in their prototypical forms. We outline future research on event prototypes in airline transport operations in order to develop better explanations of event judgements amongst flight crew

Understanding pilot response to flight safety events using categorisation theory

Categorisation theory explains our ability to recognise events in terms of a similarity overlap between either a prototypical, ideal case or a stored exemplar derived from experience. Evidence from aviation accident reports indicate that pilots are not always able to recognise flight safety events in real-time and this can lead to undesirable pilot behaviour. Flight safety events may not always arise in recognisable formats, especially as rare and unusual cue combinations are possible. Correspondence with prototypes or exemplars may be weak, creating borderline cases and harming recognition. In this article we extend categorisation theory to develop a new framework which characterises flight safety events. We model three case studies using the new framework to demonstrate how categorisation theory can be used to understand flight safety events of different types. Finally, we propose a roadmap for future research and discuss how categorisation theory could be applied to training or the organisation of flight crew reference material to improve response to inflight event

Penguins, birds, and pilot knowledge: can an overlooked attribute of human cognition explain our most puzzling aircraft accidents?

Objective: We extend the theory of conceptual categories to flight safety events, to understand variations in pilot event knowledge. Background: Experienced, highly trained pilots sometimes fail to recognize events, resulting in procedures not being followed, damaging safety. Recognition is supported by typical, representative members of a concept. Variations in typicality (“gradients”) could explain variations in pilot knowledge, and hence recognition. The role of simulations and everyday flight operations in the acquisition of useful, flexible concepts is poorly understood. We illustrate uses of the theory in understanding the industry-wide problem of nontypical events. Method: One hundred and eighteen airline pilots responded to scenario descriptions, rating them for typicality and indicating the source of their knowledge about each scenario. Results: Significant variations in typicality in flight safety event concepts were found, along with key gradients that may influence pilot behavior. Some concepts were linked to knowledge gained in simulator encounters, while others were linked to real flight experience. Conclusion: Explicit training of safety event concepts may be an important adjunct to what pilots may variably glean from simulator or operational flying experiences, and may result in more flexible recognition and improved response. Application: Regulators, manufacturers, and training providers can apply these principles to develop new approaches to pilot training that better prepare pilots for event diversit

Now you see it, now you don’t: dynamism amplifies the typicality effect

Some safety events do not stabilise in a coherent state, presenting with transient or intermittent features. Such dynamism may pose problems for human performance, especially if combined with non-typical stimuli that are rarely encountered in everyday work. This may explain undesirable pilot behaviour and could be an important cognitive factor in recent aircraft accidents. Sixty-five airline pilots tested a real-world typicality gradient, composed of two cockpit events, a typical event, and a non-typical event, across two different forms of dynamism, a stable, single system transition, and an unstable, intermittent system transition. We found that non-typical event stimuli elicited a greater number of response errors and incurred an increased response latency when compared to typical event stimuli, replicating the typicality effect. These performance deteriorations were amplified when a form of unstable system dynamism was introduced. Typical stimuli were unaffected by dynamism. This indicates that dynamic, non-typical events are problematic for pilots and may lead to poor event recognition and response. Typical is advantageous, even if dynamic. Manufacturers and airlines should evolve pilot training and crew procedures to take account of variety in event dynamics

A new facet of category theory: cognitive disadvantage and its implications for safety in the cockpit

The typicality effect suggests typical category members provide a cognitive advantage, such as being quicker and easier to recognise and describe. The reverse effect has not been explored in an applied environment. Non-typical flight safety events appear to pose problems for pilots, leading to delayed recognition and ineffective use of checklists. Fifty-six airline pilots completed an experiment that tested a real-world typicality gradient, comparing pilot performance on a group of four non-typical events against four randomly selected events. Non-typical flight safety events elicited a greater number of response errors and a greater response latency when compared with a random selection of safety events. We specify and measure cognitive disadvantage and suggest innovations in pilot education, such as locating troublesome events and improving recognition guidance. Our new findings can be used to better prepare pilots for event diversity and inform safety in other work systems of interest to ergonomics

Automation transparency and the design of intelligent aircraft engine interfaces

© The Authors.In this article we report progress on a programme of research to implement intelligent engine systems in civil aircraft. Modern turbofan engines capture data about their performance and health during flight. Until now, this information has remained hidden from the flight deck. Our research will examine how best to communicate these new information sources to the flight deck to deliver intelligent assistance in understanding engine health and offering choices to minimise disruption should an engine develop a fault that affects performance. We have adopted automation transparency as a key design pillar to ensure that flight crew have an appropriate understanding of the reasoning of the intelligent system under different operating conditions. User-centred design will inform the degree to which the different interface elements are transparent, informing the balance between the provision of information necessary to ensure safe and efficient performance. Currently, there is significant uncertainty as to whether automation transparency can confer a performance advantage in all cases. Our research will empirically investigate different levels of automation transparency to validate performance

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets

Land Cover Mapping using Digital Earth Australia

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of QueenslandandNewSouthWales. Thiswasachievedbyprogressivelyandhierarchicallycombining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over95%(forhydroperiodandfractionalcover).Thechangesidentifiedincludemangrovediebackin the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy

Living Earth:Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development

Earth Observation (EO) has been recognised as a key data source for supporting the United Nations Sustainable Development Goals (SDGs). Advances in data availability and analytical capabilities have provided a wide range of users access to global coverage analysis-ready data (ARD). However, ARD does not provide the information required by national agencies tasked with coordinating the implementation of SDGs. Reliable, standardised, scalable mapping of land cover and its change over time and space facilitates informed decision making, providing cohesive methods for target setting and reporting of SDGs. The aim of this study was to implement a global framework for classifying land cover. The Food and Agriculture Organisation’s Land Cover Classification System (FAO LCCS) provides a global land cover taxonomy suitable to comprehensively support SDG target setting and reporting. We present a fully implemented FAO LCCS optimised for EO data; Living Earth, an open-source software package that can be readily applied using existing national EO infrastructure and satellite data. We resolve several semantic challenges of LCCS for consistent EO implementation, including modifications to environmental descriptors, inter-dependency within the modular-hierarchical framework, and increased flexibility associated with limited data availability. To ensure easy adoption of Living Earth for SDG reporting, we identified key environmental descriptors to provide resource allocation recommendations for generating routinely retrieved input parameters. Living Earth provides an optimal platform for global adoption of EO4SDGs ensuring a transparent methodology that allows monitoring to be standardised for all countrie