A novel approach to measure the self-generated complexity of human-machine interaction is presented. A complexity measure that relies solely on information-theoretic quantities is defined. An efficient estimation procedure for the complexity measure is introduced. The theoretical framework is validated by a study of human interaction with electronic map displays. Thirty experienced master mariners participated. The task was to search for multiple vessel symbols under time pressure. Samples from the visual scanpath, as well as manual responses, were acquired. The workloads due to time pressure, the number of symbol clusters on the display, and the map scenario were systematically varied. The results of an analysis of variance (ANOVA, /spl alpha/=0.05) show a significant complexity decrease when the time pressure (or workload) is increased. The workload effect on the complexity of master mariners' visual scanpath was stronger than on their manual response. There was also a significant effect of the number of symbol clusters on the display: a display with two clusters showed a significantly higher search complexity for manual response than a nonclustered display.

/pdf/self-generated-complexity-and-human-machine-interaction-4ppxwy56pw.pdf

Self-generated complexity and human-machine interaction

A quantitative complexity theory of human–computer interaction is presented and validated by means of laboratory experiments. Based on the seminal work of Grassberger in theoretical physics, a complexity measure is introduced. The measure is termed the Effective Measure Complexity and has three main advantages. First, it relies solely on information-theoretic quantities, which are intimately connected with the concept of complexity and not randomness. Second, it is model independent and can be estimated efficiently from data. Third, the estimates can be derived from behavioural patterns in terms of observable interaction events alone. Subjective ratings or psychophysiological measurements can be included but are not mandatory. In order to explain the theory, a simple and easy to generalise example in mobile human–computer interaction is presented. Furthermore, the external validity of the complexity measure is studied in laboratory experiments. The experimental task was to search for multiple targets on a...

Complexity assessment of human–computer interaction

In den letzten Jahren ist der Funktionsumfang von Softwareanwendungen immer starker angestiegen. Solche hochfunktionellen Anwendungen (HFA) sind sehr komplex geworden, weil sie die Bedurfnisse groser und heterogener Gruppen von Benutzern mit unterschiedlichem Kenntnisstand, unterschiedlicher Erfahrung und auch fur verschiedene Aufgabenbereiche zufrieden stellen mussen (Fischer 2001). Trotzdem sind leicht benutzbare Benutzungsschnittstellen (UIs) nicht weit verbreitet  trotz der Berucksichtigung eines benutzerzentrierten Entwicklungsprozesses (DIN EN ISO 13407) oder allgemeiner Gestaltungskriterien fur interaktive Softwaresysteme (DIN EN ISO 9241-10). Daher besteht weiterhin ein Bedarf an intelligenteren Benutzungsschnittstellen, die Novizen ebenso wie Experten unterstutzen, ihre Aufgaben durchzufuhren (Lau 1999).

/pdf/antizipative-modellierung-des-benutzerverhaltens-mit-hilfe-w902ol9hzb.pdf

Antizipative Modellierung des Benutzerverhaltens mit Hilfe von Aktionsvorhersage-Algorithmen

A novel approach to measure the complexity of human-computer interaction is presented. A complexity measure is defined, which relies on information-theoretic quantities such as block entropy. An efficient estimation procedure for the complexity measure is introduced. The estimation is based on variable length Markov chains and is using the well-known Shannon guessing game. The theoretical framework is validated by a study of user interaction with electronic map displays. The interaction task was to search for multiple vessel symbols under time pressure. 30 experienced master mariners participated as users. Samples from both users' visual scanpath and manual responses were acquired. The workload due to time pressure and the number of symbol clusters on the display were varied systematically. The results of an ANOVA (/spl alpha/=0.05) show a significant complexity decrease for manual response when the time pressure (or workload) is increased. The workload effect on the complexity of users' visual scanpath was stronger than on his manual response. The complexity of visual perception contributed to 85% of the overall complexity. There was also a significant effect of the number of symbol clusters on the display: a display with 2 clusters showed a significantly higher search complexity for manual response than a non-clustered display.

Complexity and human-computer interaction

Im Herbst 2000 hat die VolkswagenStiftung die Nachwuchsforschergruppe MoDyS am Zentrum Mensch-Maschine-Systeme eingerichtet. Das Forschungsziel ist die Entwicklung und Bewertung von Methoden zur Modellierung und Simulation in dynamischen Mensch-Maschine-Systemen. Obwohl dies die Modellbildung sowohl der technischen Komponenten als auch der Bedienung und der Voraussetzungen dafur einschliest, liegt der Schwerpunkt der Arbeiten in der Gruppe auf der Entwicklung neuer Methoden fur die Modellierung der kognitiven Vorgange beim Bedienen. Es ist das Ziel, die Schnittstellen der Mensch-Maschine-Interaktion in Hinblick auf Effektivitat und Effizienz zu optimieren.

/pdf/editorial-zum-themenheft-modellierung-und-simulation-in-27xih3ho90.pdf

Editorial zum Themenheft Modellierung und Simulation in Mensch-Maschine-Systemen

A novel approach to stochastic operator modeling for multiple target search tasks Is introduced. Unlike the large majority of experiments in literature the focus Is on modeling and prediction of the sequence of detected and identified targets, rather than the prediction of time consumption. We investigate the prediction accuracy of three stochastic operator models: (1) a "classic" Markov Chain of first order, (2) a factorial Hidden Markov Model, and (3) a Variable-Length Markov Chain, The parameters are estimated with the help of empirically acquired traces from search experiments with 37 experienced operators. Five search scenarios with a varying number of targets were studied. Moreover, the prediction accuracy of two deterministic, but semantic models are Investigated: (4) a nearest-neighbor estimator and (5) a nearest-neighbor estimator with perfect procedural memory. Both semantic models do not rely on the empirical data, but Integrate task-specific a-priori knowledge. The results show a significantly highest prediction accuracy of the nearest-neighbor estimator with perfect memory, while the memoryless estimator has the lowest accuracy. These findings are Independent of the number of targets and Indicate the remarkable ability of the operator to memorize already attended targets. The Variable-Length Markov Chain has the significantly highest prediction accuracy among the stochastic models. Finally, a regression formula of the prediction accuracy depending on the number of targets is derived.

Stochastic operator models for multiple target search tasks

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Stochastic operator models for multiple target search tasks