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A Lattice Theory Approach to the Structure of Mental Models
Philosophical Transactions B
Abstract
Lattice theory is proposed to provide a formalism for the knowledge base used as a mental model by the operator of a complex system. The ordering relation 'greater than or equal to' is interpreted as 'is caused by', and the lattice becomes a representation of the operator's causal hypotheses about the system. A given system can be thought of causally in different ways (purposes, mechanics, physical form, etc.). Each gives rise to a separate lattice. These are related to each other and to an objective description of the structure and function of the physical system by homomorphic mappings. Errors arise when nodes on the mental lattices are not connected in the same way as the physical system lattice; when the latter changes so that the mental lattice no longer provides an accurate map, even as a homomorphism; or when inverse one-to-many mapping gives rise to ambiguities. Some suggestions are made about the design of displays and decision aids to reduce error.
... with a similar purpose [197]: Programmers construct and use mental maps of source code and its functionality. Experienced programmers have a large set of different mental maps of the source code at their disposal-and can reuse them-to solve programming and maintenance tasks. ...
... That is, it could be relevant to model a predicate in an abstraction state if it can be relevant for one of the operations that can follow in the control flow. For our example, the common vocabulary is defined by the 197 Common Vocabulary ...
Whenever software faults can endanger human life, property, or the environment, the absence of faults must be ensured with utmost care and the best technologies available. Evidence is needed showing that all requirements are satisfied and that the risk of faults is reduced. One technique to conduct such a verification task—composed of the software to verify, the specification to check, and a model of the environment—is software model checking. To conduct a verification task with a model checker, different models of the task are constructed. We distinguish between two types of task models: syntactic task models and semantic task models, which define the respective syntactic structure (control flow) and semantic structure (state transitions, invariants) of the verification task. When constructing such models, we can observe that similar structures and substructures reappear within and among different verification tasks. For example, the same assertions to check can appear in different functions, or the same predicate can be part of different invariants to describe sets of program states. Similarities that appear during the model construction process can be the result of solving similar reasoning problems, often solved using computationally expensive procedures (as typical for model checking), over and over again. Not reusing results of solving similar problems, not having a means for conducting repeated efforts automatically, or not trying to reduce the number of similar reasoning efforts, is a waste of precious resources. To address these problems, we present a common conceptual and technical foundation for sharing syntactic and semantic task artifacts for reuse, within and among verification runs. Both the syntactic construction of a verification task and the construction of its semantic model—which describes all possible behaviors and states—are covered. We study how commonalities and regularities in the task models can be taken into account to facilitate the process of sharing task artifacts for reuse, and to make the overall verification process more efficient and effective. We introduce abstract transducers as the theoretical foundation of this thesis: a type of finite-state transducers with an inherent notion of abstraction for states, the input alphabet, and its output alphabet. Abstracting these transducers allows us to widen both the set of input words for that they produce output and the sets of output words. Abstract transducers are instantiated as task artifact transducers to map from program structures to task artifacts to share. We show that the notion of abstraction provides a means for increasing the scope for that task artifacts are shared for reuse. We present two instances of task artifact transducers: Yarn transducers and precision transducers. We use Yarn transducers for providing code to weave into the control-flow structure of a computer program, and present the Loom analysis as a means for orchestrating the weaving process. Precision transducers provide a means for sharing abstraction precisions for reuse, thus aid in defining the level of abstraction of a semantic task model. For both types of transducers, we provide empirical evidence on their practical applicability, for example, to verify Linux kernel modules, and show that they can help in increasing the verification performance.
... There is a consensus in the literature that experience and context both have a considerable influence on mental model development (Johnson-Laird, 1983;Moray, 1990;Bainbridge, 1992;Revell & Stanton, 2012), therefore this link has been exploited in AV driver training studies to improve drivers' mental models for the automation. In the majority of the AV driver training studies investigated, drivers were given a written or verbal description about the capabilities and limitations of the automation, what the automation does, how and when to activate and deactivate the automation and how to perform a takeover request (e.g. ...
Automated Vehicles (AVs) are expected to bring many benefits to society (e.g. improved safety, increased capacity, reduced fuel use and emissions). However, there are also many challenges with AVs. These include issues associated with drivers’ trust, mental models of the automation’s capabilities and limitations and manual driving skill degradation. Therefore, solutions are needed to enhance the benefits and eliminate the challenges with AVs. One solution is driver training. Current training for drivers of AVs is limited to an owner’s manual and most drivers do not read their owner’s manual. Therefore, this thesis sought to understand the training needs for drivers of a Level 4 AV and to design, develop and evaluate a comprehensive training programme to address these needs. A grounded theory approach was used to identify nine key themes in AV driver training. These themes were applied to currently deployed training programmes, five AV collisions and IAM RoadSmart’s Advanced Driver Course to demonstrate the validity and relevance of these themes to AVs and driver training. A Training Needs Analysis (TNA) was conducted to establish the tasks and competencies that drivers need to safely operate the Level 4 AV. This TNA identified 7 main tasks, 25 sub-tasks, 2428 operations and 105 training needs and was used to develop an online video-based training resource and a training package for the safe activation of the Level 4 AV. Evaluation studies demonstrated short-term benefits of these training programmes over no training (more correct decisions, better activation behaviours) and owner’s manuals (more appropriate mental models, reduced mental demand), however the long-term retention benefits and applications to Level 5 AVs and other transport domains must be explored. This thesis should encourage further research into the development of better training for drivers of AVs, so that clear benefits of AVs can be realised without the challenges.
... The Lattice theory of mental models for complex systems (Moray, 1988(Moray, , 1990(Moray, , 1998(Moray, , 1996 explains why a non-expert view serves as a source of evidence during the theorizing process. This theory postulates that human beings construct models of causal relationships to explain how a particular system works. ...
Purpose
This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena.
Design/methodology/approach
This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research.
Findings
This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts.
Research limitations/implications
From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage.
Practical implications
This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts.
Social implications
This study is particularly important for institutions with limited access to costly databases, labs and researchers.
Originality/value
Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.
... Train driving, in both traditional and modern forms, carries substantial cognitive workload demands, for instance through continuous visual monitoring of the environment (Naweed, 2014). As train drivers develop and rely on mental models to reduce their cognitive workload as their driving skills increase (Moray, Groeger, & Stanton, 2017), expectation bias and habituation in driving can also lead to errors, for instance through strong-but-wrong assumptions about the environment in which they are driving (Moray, 1990). ...
The road environment has changed markedly over the years. Train horns are ostensibly used to alert road users and are typically mandatory on approach to railway level crossings. However, they have increasingly been seen as a nuisance. Beyond their negative aspects, a study has yet to comprehensively evaluate train horn effectiveness and understand if they remain beneficial and relevant in the contemporary environment. Hence, this study aimed to provide evidence on the actual use of train horns. Field observations were conducted at 54 railway crossings across four Australian States. The effects of level crossing type, location, and environmental conditions were investigated in relation to train horn loudness as objectively measured at the crossing. Results revealed that train horns were not always used, presenting an issue for passive level crossings. However, when sounded, train horn use was redundant and insufficiently loud at level crossings equipped with bells. Taken together, current train horn practice was found to be highly variable and dependent on crossing type, remoteness, and individual train drivers, thus resulting in inconsistent warnings and raising important implications for standardisation.
... There is a consensus in the literature that experience and context both have a considerable influence on mental model development (Bainbridge, 1992;Johnson-Laird, 1983;Moray, 1990;Revell & Stanton, 2012), therefore this link has been exploited in AV driver training programmes in order to improve drivers' mental model of the automation. In the majority of the AV driver training programmes investigated, drivers were given a written or verbal description about the capabilities and limitations of the automation, what the automation does, how and when to activate and deactivate the automation and how to perform a takeover request (e.g. ...
Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver’s inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight “key” themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.
... Neither the inspector nor the mechanic had a correct mental model about the pitch control mechanism; they missed the chance to detect the anomaly because they skipped the procedure, including FDR calibration. Operators may organize their knowledge of system components and interrelationships on different levels of detail; these may differ considerably from the actual system and may even constitute wrong knowledge (Moray 1990). The inability to see several cues about the presence of FDR might be connected with attention tunnelling, as the focus was limited to the tension adjustment. ...
Aircraft maintenance is a critical success factor in the aviation sector, and incorrect maintenance actions themselves can be the cause of accidents. Judgemental errors are the top causal factors of maintenance-related aviation accidents. This study asks why judgemental errors occur in maintenance. Referring to six aviation accidents, we show how various biases contributed to those accidents. We first filtered aviation accident reports, looking for accidents linked to errors in maintenance judgements. We analysed the investigation reports, as well as the relevant interview transcriptions. Then we set the characteristics of the actions behind the accidents within the context of the literature and the taxonomy of reasons for judgemental biases. Our results demonstrate how various biases, such as theory-induced blindness, optimistic bias, and substitution bias misled maintenance technicians and eventually become the main cause of a catastrophe. We also find these biases are interrelated, with one causing another to develop. We discuss how these judgemental errors could relate to loss of situation awareness, and suggest interventions to mitigate them.
Section 1 Man-Vehicle Control.- 1. Preview of Man-Vehicle Control Session.- 2. Some Aspects of Automation in Civil Transport Aircraft.- 3. Techniques for Display/Control System Integration in Supervisory and Monitoring Systems.- 4. Integrated Computer-Generated Cockpit Displays.- 5. Evaluation of Information Displays in Control and Monitoring Tasks.- 6. Man-Machine Relationship in SELTRAC.- 7. Driver Decision-Making Research in a Laboratory Simulation.- 8. Human Performance and Workload in Simulated Landing-Approaches with Autopilot-Failures.- 9. Evaluation of Vigilance Related to Visual Perception.- 10. Workload in Air Traffic Control - A Field Study.- 11. Pilot Workload Analysis Based upon In-Flight Physiological Measurements and Task Analysis Methods.- 12. The Measurement of Human Response in Man-Vehicle Control Situations.- 13. Pilot Workload Analysis.- 14. The Internal Model - What Does It Mean in Human Control?.- Section 2 General Models.- 1. Preview of Models of the Human Monitor/Supervisor.- 2. The Vigilance Increment: An Ideal Observer Hypothesis.- 3. Monitoring and Control of Unreliable Systems.- 4. Detection of Random Process Failures by Human Monitors.- 5. Attention, Control, and Sampling Behaviour.- 6. A Queueing Model of Monitoring and Supervisory Behaviour.- 7. The Model Supervisor Dilemma.- 8. Toward a General Model of Supervisory Control.- 9. The Human Operator Simulator - HOS.- 10. Adaptive Allocation of Decision Making Responsibility between Supervisor and Computer.- 11. Man/Machine Interaction in Adaptive Computer-Aided Control.- Section 3 Process Control.- 1. Preview of Process Control Session.- 2. The Behaviour of Process Operators under Emergency Conditions.- 3. Evaluation of Man-Machine Relationships in U.S. Nuclear Power Plants.- 4. Control Strategies of Novice and Experienced Controllers with a Slow Response System (A Zero-Energy Nuclear Reactor).- 5. Human Performance in Manual Process Control.- 6. Outlines of a Hybrid Model of the Process Plant Operator.- 7. The Man-Robot Interface in Automated Assembly.- 8. The Effect of Cost on the Sampling Behavior of Human Instrument Monitors.- 9. Parallel vs. Serial Instrumentation for Multivariable Manual Control in Control Rooms.- 10. Perceptual Organisation and the Design of the Man-Computer Interface in Process Control.- 11. Process Control - Simple and Sophisticated Display Devices as Decision Aids.- 12. Autonomous I/O-Colour-Screen System for Process Control with Virtual Keyboards Adapted to the Actual Task.- 13. Graphic Video Displays for Process and Man-Vehicle Control.- Section 4 Workshop Reports.- and Summary.- Workshop Discussion Report - Group I.- Workshop Discussion Report - Group II.- Workshop Discussion Report - Group III.- Workshop Discussion Report - Group IV.- Appendices.- Appendix A. Glossary of Special Terms.- Appendix B. Participants.
This book unifies relevant aspects of engineering, operations analysis, human factors, and psychology and discusses the basis of integrated systems design.
Information theory was created for the purpose of studying the communication of messages from one point to another, and since its appearance,14 its focus has remained on the question, “how can the constraint between the two variables X (message sent) and Y (message received) be measured and maximized”? Although the theory was generalized to N dimensions,10,2 and its relation to the analysis of variance noted,9 not much use seems to have been made of the result, perhaps in part because the descriptors “N-dimensional Information Theory” or “Uncertainty Analysis” did not adequately represent what can actually be seen as the analysis of constraints in multivariable systems. In any statistically-analyzable system of several variables interacting in a lively way, some variables (or sets of them) exert effects on others. These effects are reflected statistically as non-independence of the variables involved, and it is this deviation from independence which we indicate by the term “constraint.” We prefer this term to the term “dependence” because the latter suggests dependence of X
on
Y while the former is neutral as to direction. To the extent that the variables are not independent, they are “in communication” with one another, and information theory can be used to analyze the non-independence. In addition, the fluctuation of values taken by any variable can be viewed as a message it sends, a flow of information about itself to all other parts of the system which are “listening.” The view of systems as networks of information transfer leads to quantitative conclusions about system behavior and structure which are somewhat novel and of wide applicability.
Elements of lattice theory
- L A Skornjakov
A lattice theory of mental models of complex systems
- N Moray