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. 1. Introduction We present a computational cognitive model, developed in the ACT-R architecture [1, 2], of several core information-foraging and hypothesis-updating processes involved in a complex sensemaking task. Sensemaking [3–6] is a concept that has been used to define a class of activities and tasks in which there is an active seeking and processing of information to achieve understanding about some state of affairs in the world. Complex tasks in intelligence analysis and situation awareness have frequently been cited as examples of sensemaking [3–5]. Sensemaking, as in to make sense, implies an active process to construct a meaningful and functional representation of some aspects of the world. A variety of theories and perspectives on sensemaking have been developed in psychology [3, 4], human-computer interaction , information and library science , and in organizational science . In this paper we present a cognitive model of basic sensemaking processes for an intelligence analysis task. A major concern in the intelligence community is the impact of cognitive biases on the
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