An ACT-R Model of Sensemaking in Geospatial Intelligence Tasks
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Authors
Paik, Jaehyon
Pirolli, Pete
Dong, Wei
Lebiere, Christian
Thomson, Robert
Issue Date
2013
Type
Conference presentations, papers, posters
Language
Keywords
Instance-Based Learning , ACT-R , Sensemaking , anchoring bias , cognitive bias
Alternative Title
Abstract
We developed an ACT-R model of sensemaking in geospatial intelligence tasks based on two widely used learning processes in ACT-R: instance-based learning and reinforcement learning. This map-based task requires users to select (make visible) layers that visualize different types of intelligence, and to revise probability estimates about which groups might commit a future attack. The model (a) evaluates the gains to be made by selecting layers during the simulation, (b) selects layers based on the evaluation of all layers, and (c) adjusts probability estimates of the threats
posed by all groups based on new evidence. The model exhibits layer-selection patterns that are comparable to participants (N = 45) studied on this task and both model and people deviate from a rational model based on greedy maximization of expected information gain. The model also exhibits an anchoring bias in updating belief probabilities based on revealed evidence, which corresponds to the average participant.
Description
Citation
Paik, Jaehyon, Peter Pirolli, Wei Dong, Christian Lebiere, and Robert Thomson. "An ACT-R Model of Sensemaking in Geospatial Intelligence Tasks." In Proceedings of the 22nd Annual Conference on Behavior Representation in Modeling and Simulation. 2013.
Publisher
BRiMS Annual Conference