Verification-guided modelling of salience and cognitive load

Rukšėnas, Rimvydas; Back, Jonathan; Curzon, Paul; Blandford, Ann

doi:10.1007/s00165-008-0102-7

Cited by 34 publications

(17 citation statements)

References 28 publications

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“…In this paper, we focused on modelling planning and reactive behaviour. Other important issues such as cognitive salience of actions had been dealt with in our earlier work [18]. In a similar way, they can be incorporated into the cognitive architecture presented here.…”

Section: Resultsmentioning

confidence: 87%

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Modelling Rational User Behaviour as Games between an Angel and a Demon

Rukienas¹,

Curzon

Blandford

2008

2008 Sixth IEEE International Conference on Software Engineering and Formal Methods

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Formal models of rational user behaviour are essential for user-centred reasoning about interactive systems. At an abstract level, planned behaviour and reactive behaviour are two important aspects of the rational behaviour of users for which existing cognitive modelling approaches are too detailed. In this paper, we propose a novel treatment of these aspects within our formal framework of cognitively plausible behaviour. We develop an abstract, formal model of rational behaviour as a game between two opponents. Intuitively, an Angel abstractly represents the planning aspects, whereas a Demon represents the reactive aspects of user behaviour. The formalisation is carried out within the M framework and is illustrated by simple examples of interactive tasks.

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Section: Resultsmentioning

confidence: 87%

“…3) and salient actions can be selected in this step within our approach. The salience issues, however, are not dealt with here (see our earlier work [18]). …”

Section: Towards a Solutionmentioning

confidence: 99%

Modelling Rational User Behaviour as Games between an Angel and a Demon

Rukienas¹,

Curzon

Blandford

2008

2008 Sixth IEEE International Conference on Software Engineering and Formal Methods

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“…Formally specifying and analysing biological systems is an ongoing research area (e.g., [61,62,63,64,65,66,67,68]), including recent formal modeling of aspects of cognition [69,70]. As advocated by Fisher and Henzinger [18], formalising biological models opens the field up to rigorous validation and analysis, as well as providing a solid foundation for extending the models as more discoveries are made, and comparing or integrating different explanations for observed phenomena.…”

Section: Related Workmentioning

confidence: 99%

Modelling and analysing neural networks using a hybrid process algebra

Colvin

2016

Theoretical Computer Science

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Please cite this article in press as: R.J. Colvin, Modeling and analysing neural networks using a hybrid process algebra, Theoret. Comput. Sci. (2015), http://dx.doi.org/10.1016/j.tcs. 2015.08.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Modeling and analysing neural networks using a hybrid process algebra Robert J. ColvinThe University of Queensland, Queensland Brain Institute, Brisbane, Australia, 4072 AbstractResearch involving artificial neural networks has tended to be driven towards efficient computation, especially in the domain of pattern recognition, or towards elucidating biological processes in the brain. Models have become more detailed as our understanding of the biology of the brain has increased, incorporating real-time behaviour of individual neurons interacting within complex system structures and dynamics. There are few examples of abstract and fully formal models of biologically plausible neural networks: in the neural networks literature models are often presented as a mixture of mathematical equations and natural language, supported by simulation code and associated experimental results. The informality often hides or obscures important aspects of a particular model, and leaves a large conceptual gap between the model descriptions and the usually low-level programming code used to simulate them.The main contribution of this paper is formally modelling and analysing a biologically plausible neural network model from the literature that exhibits complex neuron-level behaviour and network-level structure. To achieve this a modelling language 'Pann' is developed, based on the process algebras CSP and Hybrid χ . It is designed to be convenient for mixing the behaviour of discrete events (such as a neuron spike) with mutable continuous and discrete variables (representing chemical properties of a neuron, for instance). Its behaviour is defined using an operational semantics, from which a set of general properties of the language is proved.The groundwork for the biological model is laid by first formalising some well-known concepts from the artificial neural networks domain, such as feedforward behaviour, backpropagation, and recurrent neural networks. The Pann model of a feedforward network, comprising a set of communicating processes representing individual neurons, is proved equivalent to the standard one-line calculation of feedforward behaviour.

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“…These types of analyses can also be used to determine how different types of human operators (novice or expert) may impact system performance based on how their knowledge of, beliefs about, and goals for the operation of the system can result in repetitions of actions or post completion errors (Curzon et al, 2007). The architecture has been extended to a generic cognitive framework that models the effect of salience, cognitive load, and the interpretation of spatial cues and assesses whether they result in erroneous human behavior (Rukšėnas et al, 2008; Rukšenas et al, 2009; Rukšėnas et al, 2007). Habituation, impatience, and carefulness have also been incorporated into these types of models (Basuki et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking

Bolton¹,

Bass²,

Siminiceanu³

2012

International Journal of Human-Computer Studies

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Breakdowns in complex systems often occur as a result of system elements interacting in unanticipated ways. In systems with human operators, human-automation interaction associated with both normative and erroneous human behavior can contribute to such failures. Model-driven design and analysis techniques provide engineers with formal methods tools and techniques capable of evaluating how human behavior can contribute to system failures. This paper presents a novel method for automatically generating task analytic models encompassing both normative and erroneous human behavior from normative task models. The generated erroneous behavior is capable of replicating Hollnagel’s zero-order phenotypes of erroneous action for omissions, jumps, repetitions, and intrusions. Multiple phenotypical acts can occur in sequence, thus allowing for the generation of higher order phenotypes. The task behavior model pattern capable of generating erroneous behavior can be integrated into a formal system model so that system safety properties can be formally verified with a model checker. This allows analysts to prove that a human-automation interactive system (as represented by the model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. We present benchmarks related to the size of the statespace and verification time of models to show how the erroneous human behavior generation process scales. We demonstrate the method with a case study: the operation of a radiation therapy machine. A potential problem resulting from a generated erroneous human action is discovered. A design intervention is presented which prevents this problem from occurring. We discuss how our method could be used to evaluate larger applications and recommend future paths of development.

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Verification-guided modelling of salience and cognitive load

Cited by 34 publications

References 28 publications

Modelling Rational User Behaviour as Games between an Angel and a Demon

Modelling Rational User Behaviour as Games between an Angel and a Demon

Modelling and analysing neural networks using a hybrid process algebra

Generating phenotypical erroneous human behavior to evaluate human–automation interaction using model checking

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