A general instance-based learning framework for studying intuitive decision-making in a cognitive architecture.
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Authors
Thomson, Robert
Lebiere, Christian
Anderson, John R.
Staszewski, James
Issue Date
2015-09
Type
journal-article
Language
Keywords
cognitive architecture , cognitive modeling , Decision Making , heuristics
Alternative Title
Abstract
Cognitive architectures (e.g., ACT-R) have not traditionally been used to understand intuitive decision-making; instead, models tend to be designed with the intuitions of their modelers already hardcoded in the decision process. This is due in part to a fuzzy boundary between automatic and deliberative processes within the architecture. We argue that instance-based learning satisfies the conditions for intuitive decision-making described in
Kahneman and Klein (2009), separates automatic from deliberative processes, and provides a general mechanism for the study of intuitive decision-making. To better understand the role of the environment in decision-making, we describe biases as arising from three sources: the mechanisms and limitations of the human cognitive architecture, the information structure in the task environment, and the use of heuristics and strategies to adapt performance to the dual constraints of cognition and environment. A unified decision-making model performing multiple complex reasoning tasks is described according to this framework.
Description
Citation
Thomson, Robert, Christian Lebiere, John R. Anderson, and James Staszewski. "A general instance-based learning framework for studying intuitive decision-making in a cognitive architecture." Journal of Applied Research in Memory and Cognition 4, no. 3 (2015): 180-190.
Publisher
American Psychological Association (APA)
License
Journal
Volume
Issue
PubMed ID
ISSN
2211-369X
2211-3681
2211-3681