<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>We present a cognitive model performing the Dynamic Stocks&amp;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&amp;Flows modeling challenge.</p></abstract>

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.

Metacognition and Multiple Strategies in a Cognitive Model of Online Control

Department of Psychology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA

Journal of Artificial General Intelligence

{"article-title":"Metacognition and Multiple Strategies in a Cognitive Model of Online Control"}

We present a cognitive model performing the Dynamic Stocks&Flows control task, in which subjects control a system by counteracting a systematically changing...

Metacognition and Multiple Strategies in a Cognitive Model of Online Control

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 20 - 37

DOI: https://doi.org/10.2478/v10229-011-0007-3

Schlüsselwörter
Cognitive Modeling, Control, Metacognition, Instance-Based Learning, Blending, ACT-R

This content is open access.

Metacognition and Multiple Strategies in a Cognitive Model of Online Control

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 20 - 37

DOI: https://doi.org/10.2478/v10229-011-0007-3

SchlüsselwörterCognitive Modeling, Control, Metacognition, Instance-Based Learning, Blending, ACT-R

This content is open access.

Schlüsselwörter
Cognitive Modeling, Control, Metacognition, Instance-Based Learning, Blending, ACT-R