Dataset Information

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

ABSTRACT: 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.

SUBMITTER: Prezenski S

PROVIDER: S-EPMC5543095 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Publications

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

Prezenski Sabine S Brechmann André A Wolff Susann S Russwinkel Nele N

Frontiers in psychology 20170804

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 co ...[more]

PMID: 28824512

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Project description:Preserving culture heritage cellulose acetate-based historical films is a challenge due to the long-term instability of these complex materials and a lack of prediction models that can guide conservation strategies for each particular film. In this work, a cellulose acetate degradation model is proposed as the basis for the selection of appropriate strategies for storage and conservation for each specimen, considering its specific information. Due to the formulation complexity and diversity of cellulose acetate-based films produced over the decades, we hereby propose a hybrid modeling approach to describe the films degradation process. The problem is addressed by a hybrid model that uses as a backbone a first-principles based model to describe the degradation kinetics of the pure cellulose diacetate polymer. The mechanistic model was successfully adapted to fit experimental data from accelerated aging of plasticized films. The hybrid model considers then the specificity of each historical film via the development of two chemometric models. These models resource on gas release data, namely acetic acid, and descriptors of the films (manufacturing date, AD-strip value and film type) to assess the current polymer degradation state and estimate the increase in the degradation rate. These estimations are then conjugated with storage conditions (e.g., temperature and relative humidity, presence of adsorbent in the film's box) and used to feed the mechanistic model to provide the required time degradation simulations. The developed chemometric models provided predictions with accuracy more than 87%. We have found that the storage archive as well as the manufacturing company are not determining factors for conservation but rather the manufacturing date, off gas data as well as the film type. In summary, this hybrid modeling was able to develop a practical tool for conservators to assess films conservation state and to design storage and conservation policies that are best suited for each cultural heritage film.

Dataset Information

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

Publications

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

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