Unified theories of cognition
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The cost structure of sensemaking
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
Image and brain: the resolution of the imagery debate
Image and brain: the resolution of the imagery debate
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Making Sense of Sensemaking 1: Alternative Perspectives
IEEE Intelligent Systems
Making Sense of Sensemaking 2: A Macrocognitive Model
IEEE Intelligent Systems
SAL: an explicitly pluralistic cognitive architecture
Journal of Experimental & Theoretical Artificial Intelligence - Pluralism and the Future of Cognitive Science
Pictorial representations of routes: chunking route segments during comprehension
Spatial cognition III
The Role of the Basal Ganglia --Anterior Prefrontal Circuit as a Biological Instruction Interpreter
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
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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 hypothesisupdating 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.