Approaches to cognitive modeling in dynamic systems control
Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made towards assessing individual differences in control performance, the cognitive processes under...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
29 November 2017
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| In: |
Frontiers in psychology
Year: 2017, Volume: 8 |
| ISSN: | 1664-1078 |
| DOI: | 10.3389/fpsyg.2017.02032 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3389/fpsyg.2017.02032 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.02032/full 
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| Author Notes: | Daniel V. Holt and Magda Osman |
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| 520 | |a Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made towards assessing individual differences in control performance, the cognitive processes underlying exploration and control of dynamic systems are not yet well understood. In this perspectives article we examine the contribution of different approaches to modeling cognition in dynamic systems control, including instance-based learning, heuristic models, complex knowledge-based models and models of causal learning. We conclude that each approach has particular strengths in modeling certain aspects of cognition in dynamic systems control. In particular, Bayesian models of causal learning and hybrid models combining heuristic strategies with reinforcement learning appear to be promising avenues for further work in this field. | ||
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