A Functional Model of Sensemaking in a Neurocognitive Architecture
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Authors
Lebiere, Christian
Pirolli, Peter
Thomson, Robert
Jaehyon, Paik
Rutledge-Taylor, Matthew
Staszewski, James
Anderson, John R.
Issue Date
2013
Type
journal-article
Language
Keywords
cognitive modeling , bias , intelligence analysis , cognitive architecture , heuristics
Alternative Title
Abstract
Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.</jats:p>
Description
Citation
Lebiere, Christian, Peter Pirolli, Robert Thomson, Jaehyon Paik, Matthew Rutledge-Taylor, James Staszewski, and John R. Anderson. "A functional model of sensemaking in a neurocognitive architecture." Computational intelligence and neuroscience 2013, no. 1 (2013): 921695.
Publisher
Wiley
License
Journal
Volume
Issue
PubMed ID
ISSN
1687-5265
1687-5273
1687-5273