Analysing Cognitive Behaviour using LOTOS and Mexitl

Bowman, Howard; Faconti, Giorgio P.

doi:10.1007/s001650050045

Cited by 21 publications

(10 citation statements)

References 4 publications

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“…An alternative approach is to specify users the way they are [BBD00]. This idea underlies approaches based on formal user modelling [DBD + 98,BoF99,DuD99]. By representing the cognitive structures of the user in a generic way, formal user models specify how user behaviour is generated.…”

Section: Related Workmentioning

confidence: 99%

Verification-guided modelling of salience and cognitive load

et al. 2009

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Abstract. Well-designed interfaces use procedural and sensory cues to increase the cognitive salience of appropriate actions. However, empirical studies suggest that cognitive load can influence the strength of those cues. We formalise the relationship between salience and cognitive load revealed by empirical data. We add these rules to our abstract cognitive architecture, based on higher-order logic and developed for the formal verification of usability properties. The interface of a fire engine dispatch task from the empirical studies is then formally modelled and verified. The outcomes of this verification and their comparison with the empirical data provide a way of assessing our salience and load rules. They also guide further iterative refinements of these rules. Furthermore, the juxtaposition of the outcomes of formal analysis and empirical studies suggests new experimental hypotheses, thus providing input to researchers in cognitive science.

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Section: Related Workmentioning

confidence: 99%

Verification-guided modelling of salience and cognitive load

et al. 2009

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“…They also have not specifically focused on predicting performance times using GOMS, but rather are using it as a formal hierarchical task model. Bowman and Faconti [11] formally specify a cognitive architecture using the process calculus LOTOS, and then apply a temporal interval logic to analyse constraints, including timing ones, on the information flow and transformation between the different cognitive subsystems. Their approach is more detailed than ours, which abstracts from those cognitive processes.…”

Section: Related Workmentioning

confidence: 99%

Combining Human Error Verification and Timing Analysis

Rukšėnas

Curzon

Blandford

et al. 2008

Engineering Interactive Systems

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Abstract. Designs can often be unacceptable on performance grounds. In this work, we integrate a GOMS-like ability to predict execution times into the generic cognitive architecture developed for the formal verification of human error related correctness properties. As a result, formal verification and GOMS-like timing analysis are combined within a unified framework. This allows one to judge whether a formally correct design is also acceptable on performance grounds, and vice versa. We illustrate our approach with an example based on a KLM style timing analysis.

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“…It has been argued by a number of researchers [DBD98,Bow99] that formal methods provide a powerful way to specify, evaluate, and verify such systems. As an important component in interactive systems, human operators and their cognitive capabilities also determine the success or the failure of such systems.…”

Section: Introductionmentioning

confidence: 99%

Process algebraic modelling of attentional capture and human electrophysiology in interactive systems

Bowman

Barnard

et al. 2009

Form. Asp. Comput.

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Previous research has developed a formal methods-based (cognitive-level) model of the Interacting Cognitive Subsystems central engine, with which we have simulated attentional capture in the context of Barnard’s key-distractor Attentional Blink task. This model captures core aspects of the allocation of human attention over time and as such should be applicable across a range of practical settings when human attentional limitations come into play. In addition, this model simulates human electrophysiological data, such as electroencephalogram recordings, which can be compared to real electrophysiological data recorded from human participants. We have used this model to evaluate the performance trade-offs that would arise from varying key parameters and applying either a constructive or a reactive approach to improving interactive systems in a stimulus rich environment. A strength of formal methods is that they are abstract and the resulting specifications of the operator are general purpose, ensuring that our findings are broadly applicable. Thus, we argue that new modelling techniques from computer science can also be employed in computational modelling of the mind. These would complement existing techniques, being specifically targeted at psychological level modelling, in which it is advantageous to directly represent the distribution of control.

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Analysing Cognitive Behaviour using LOTOS and Mexitl

Cited by 21 publications

References 4 publications

Verification-guided modelling of salience and cognitive load

Verification-guided modelling of salience and cognitive load

Combining Human Error Verification and Timing Analysis

Process algebraic modelling of attentional capture and human electrophysiology in interactive systems

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