Marc Halbrügge

Exploration for Understanding in Cognitive Modeling

Exploration for Understanding in Cognitive Modeling The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments - may achieve an improved understanding of architectures and models - if there is more emphasis on systematic sensitivity and necessity analyses during model development, evaluation, and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge, exploring the complex interactions among architectural mechanisms, knowledge-level strategy variants, and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees, combined with parallelization on high performance computing clusters, to enable large scale, simultaneous search and exploration.

Predicting task execution times by deriving enhanced cognitive models from user interface development models

Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.

Predicting user error for ambient systems by integrating model-based UI development and cognitive modeling

With the move to ubiquitous computing, user interfaces (UI) are no longer bound to specific devices. While this problem can be tackled using the model-based UI development (MBUID) process, the usability of the device-specific interfaces is still an open question. We are presenting a combined system that integrates MBUID with a cognitive modeling framework in order to provide usability predictions at development time. Because of their potential impact, our focus within usability problems lies on user errors. These are captured in a cognitive model that capitalizes on meta-information provided by the MBUID system such as the abstract role of a UI element within a task sequence (e.g., input, output, command). The free parameters of the cognitive model were constrained using data from two previous studies. A validation experiment featuring a new application and UI yielded an unexpected error pattern that was nonetheless consistent with the model predictions.

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task

Keep it simple - A case study of model development in the context of the Dynamic Stocks and Flows (DSF) task This paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing computational cognitive models for human behavior during an open ended control task. Participants in the modeling competition were provided with a simulation environment and training data for benchmarking their models while the actual specification of the competition task was withheld. To meet this challenge, the cognitive model described here was designed and optimized for generalizability. Only two simple assumptions about human problem solving were used to explain the empirical findings of the training data. In-depth analysis of the data set prior to the development of the model led to the dismissal of correlations or other parametric statistics as goodness-of-fit indicators. A new statistical measurement based on rank orders and sequence matching techniques is being proposed instead. This measurement, when being applied to the human sample, also identifies clusters of subjects that use different strategies for the task. The acceptability of the fits achieved by the model is verified using permutation tests.

How Can Cognitive Modeling Benefit from Ontologies? Evidence from the HCI Domain

Cognitive modeling as a method has proven successful at reproducing and explaining human intelligent behavior in specific laboratory situations, but still struggles to produce more general intelligent capabilities. A promising strategy to address this weakness is the addition of large semantic resources to cognitive architectures. We are investigating the usefulness of this approach in the context of human behavior during software use. By adding world knowledge from a Wikipedia-based ontology to a model of human sequential behavior, we achieve quantitatively and qualitatively better fits to human data.The combination of model and ontology yields additional insights that cannot be explained by the model or the ontology alone.

User Experience Measurement of a Static Website Compared to a Responsive Website Using AttrakDiff Mini

The increasing use of mobile devices with internet access poses new challenges to website design. Because of the frequent introduction of new devices and the variety of their respective form factors, the website’s quality of experience can hardly be maintained across the range of devices. A promising solution to this problem is responsive web design. Responsive websites adapt to the display resolution of the currently used device. While responsive design has been embraced by the engineering community, its benefits regarding user experience are still lacking empirical evidence. This paper presents a user study that sheds light on the effect of a responsive redesign of a large company’s website on its quality of experience.