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

Halbrügge, Marc

doi:10.2478/v10229-011-0008-2

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Cognitive models can be applied to a variety of real-world tasks, for example to predict usability in smartphone interaction (Prezenski and Russwinkel, 2014 , 2016b ), air traffic control (Taatgen, 2001 ; Smieszek et al, 2015 ), or driving behavior (Salvucci, 2006 ). Moreover, cognitive modeling approaches can also be used in microworld scenarios (Halbrügge, 2010 ; Peebles and Banks, 2010 ; Reitter, 2010 ). Not only can microworld scenarios simulate the complexity of the real world, they also have the advantage of being able to control variables.…”

Section: Discussionmentioning

confidence: 99%

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Prezenski¹,

Brechmann

Wolff

et al. 2017

Front. Psychol.

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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.

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Section: Discussionmentioning

confidence: 99%

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Prezenski¹,

Brechmann

Wolff

et al. 2017

Front. Psychol.

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“…Within the same cognitive framework, there are alternatives to this design choice. Halbrügge (2010) describes a model of the DSF task that chooses between alternative estimation and calculation strategies. Halbrügge's model uses ACT-R utility learning to propagate rewards to rules representing those strategies in place of our instance-based learning and blending mechanism.…”

Section: Discussionmentioning

confidence: 99%

Metacognition and Multiple Strategies in a Cognitive Model of Online Control

Reitter

2010

Journal of Artificial General Intelligence

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We present a cognitive model performing the Dynamic Stocks&Flows control task, in which subjects control a system by counteracting a systematically changing external variable. The model uses a metacognitive layer that chooses a task strategy drawn from of two classes of strategies: precise calculation and imprecise estimation. The model, formulated within the ACT-R theory, monitors the success of each strategy continuously using instance-based learning and blended retrieval from declarative memory. The model underspecifies other portions of the task strategies, whose timing was determined as unbiased estimate from empirical data. The model's predictions were evaluated on data collected from novel experimental conditions, which did not inform the model's development and included discontinuous and noisy environmental change functions and a control delay. The model as well as the data show sudden changes in subject error and general learning of control; the model also correctly predicted oscillations of plausible magnitude. With its predictions, the model ranked first among the entries to the 2009 Dynamic Stocks&Flows modeling challenge.

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

Cited by 2 publications

References 10 publications

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

Metacognition and Multiple Strategies in a Cognitive Model of Online Control

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