<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Journal of Artificial General Intelligence

This paper describes the creation of a cognitive model submitted to the ‘Dynamic Stocks and Flows’ (DSF) modeling challenge. This challenge aims at comparing...

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

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 38 - 51

DOI: https://doi.org/10.2478/v10229-011-0008-2

Schlüsselwörter
Cognitive Modeling, Model Comparison, Sequence Matching, Cluster Analysis, Bootstrap Test

This content is open access.

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

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 38 - 51

DOI: https://doi.org/10.2478/v10229-011-0008-2

SchlüsselwörterCognitive Modeling, Model Comparison, Sequence Matching, Cluster Analysis, Bootstrap Test

This content is open access.

Schlüsselwörter
Cognitive Modeling, Model Comparison, Sequence Matching, Cluster Analysis, Bootstrap Test