Nele Russwinkel

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

Predictive Cognitive Modelling of Applications.

This paper argues that important usability aspects of mobile applications can be automatically evaluated using computational cognitive models based on the cognitive architecture ACT-R. A tool incorporating cognitive models for specific tasks, users, applications and usability aspects is proposed. Explanations provided by the tool for usability flaws are based on simulations of cognitive mechanisms. A use-case of the tool is introduced, which is based on an ACT-R model that simulates how users search and select a specific target in a hierarchical android application and predicts efficiency and learnability for average users. The model has been empirically validated in four studies with two different applications. To fully automate the usability evaluation of the use-case, two basic requirements need to be fulfilled. First, the application and the cognitive model have to be connected. A tool called ACT-Droid acts as an interface between the Android application and the cognitive model. Second, the models knowledge of the world, which is application specific, has to be provided automatically by using an automated user interface analysation approach. Therefore, the open-source tool AppCrawler was extended to allow the extraction of the required information.

Cognitive Modeling offers Explanations for Effects found in Usability Studies.

Two studies evaluate the usability of two versions of an android shopping list application. ACT-R modeling approaches and empirical findings are presented. It is shown that semantic networks have a strong influence on performance and learning. Effects of version updates are discussed.