Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Cognitive Biases in a Geospatial Intelligence Analysis Task: An ACT-R Model Jaehyon Paik (jpaik@parc.com), Peter L. Pirolli (pirolli@parc.com) Palo Alto Research Center 3333 Coyote Hill Rd., Palo Alto, CA 94304 Christian Lebiere (cl@andrew.cmu.edu), Matthew Rutledge-Taylor (mattrt@andrew.cmu.edu) Carnegie Mellon University, Psychology Department 5000 Forbes Avenue, Pittsburgh, PA 15218 Abstract SOCINT (socio-cultural intelligence), and SIGACT (attack intelligence). An ACT-R model of sensemaking in a geospatial intelligence task was developed based on Instance-Based Learning Theory (IBLT). The model (a) maintains hypotheses about the probability of attacks by insurgent groups, (b) seeks new information based on those hypotheses, and (c) updates hypotheses based on new evidence. The model provides a functional account of how these sensemaking processes are carried out in a cognitive architecture, and model performance can be compared to normative (Bayesian) standards. Simulations exhibit two ll-known cognitive biases that are frequently identified as problems in intelligence analysis: (1) anchoring in the ighting of new evidence and (2) confirmation bias in seeking new information. Keywords: ACT-R, cognitive biases, sensemaking Introduction Sensemaking (Klein, Moon, & Hoffman, 2006a, 2006b; Pirolli & Card, 2005; Russell, Stefik, Pirolli, & Card, 1993) is a concept that has been used frequently in studies of intelligence analysis. The term suggests an active seeking and processing of information to achieve understanding. Sensemaking involves a set of processes aimed at seeking and filtering information, plus a set of processes that develop representational schemas (frames) that best fit the available evidence and provide a basis for understanding the data.   present the 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 accuracy of analyses (Heuer, 1999).  present simulation results that exhibit anchoring bias in the evaluation of new evidence and confirmation bias in seeking evidence. The Geospatial Task The geospatial task (Figure 1) is one of a set of challenge tasks developed as part of the IARPA ICArUS program to drive the development of integrated neurocognitive models of sensemaking. This specific task required reasoning based on a set of rules concerning the relation of observed evidence to the likelihood of attack by four different groups. A layered geospatial map is presented on a computer screen, with different layers presenting different forms of intelligence (INTs). The INTs include HUMINT (human intelligence), IMINT (image intelligence), MOVINT (movement intelligence), SIGINT (signal intelligence), Figure 1: The screen shot of the geospatial task. The letters (A, B, C, D) indicate the center of the group location, and ‘1’ surrounded by a box indicates the attack location. The task begins with a given attack location (SIGACT) along with group centers (HUMINT), A, B, C, and D representing the center of activity for four possible insurgent groups. The first step is to report probabilities of attack by each group [A%, B%, C%, D%] based on the SIGACT and HUMINT (see Table 1) 1 . After that, the task is to iteratively choose among the four remaining INT layers (Table 1), up to a total of three INTs (layers), one at a time, in any order. Each INT layer provides unique evidence. Specifically, IMINT can reveal whether an attack happened on a government or military building, MOVINT provides evidence whether an attack occurred in dense or sparse traffic, SIGINT indicates electronic “chatter” or “silence” by different groups, and SOCINT indicates the group whose region the attack happened. At each stage, the selection of a particular INT provides evidence that can be used to update the probability distribution over the hypotheses about the responsibility of the four groups in producing the given attack. The rules specifying how evidence ought to update these probabilities is given in the PROBS rules in Table 1. After the last stage of INT selection, the task is to allocate resources (troops) to prevent further attacks. The new version of the task will provide the initial probabilities based on HUMINT
['Jaehyon Paik', 'Peter Pirolli', 'Christian Lebiere', 'Matthew Rutledge-Taylor']
I am writing a research paper on a idea Cognitive Biases in a Geospatial Intelligence Analysis Task: An ACT-R Model
[{"from": "human", "value": "I am writing a research paper on a idea Cognitive Biases in a Geospatial Intelligence Analysis Task: An ACT-R Model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Cognitive Biases in a Geospatial Intelligence Analysis Task: An ACT-R Model Jaehyon Paik (jpaik@parc.com), Peter L. Pirolli (pirolli@parc.com) Palo Alto Research Center 3333 Coyote Hill Rd., Palo Alto, CA 94304 Christian Lebiere (cl@andrew.cmu.edu), Matthew Rutledge-Taylor (mattrt@andrew.cmu.edu) Carnegie Mellon University, Psychology Department 5000 Forbes Avenue, Pittsburgh, PA 15218 Abstract SOCINT (socio-cultural intelligence), and SIGACT (attack intelligence). An ACT-R model of sensemaking in a geospatial intelligence task was developed based on Instance-Based Learning Theory (IBLT). The model (a) maintains hypotheses about the probability of attacks by insurgent groups, (b) seeks new information based on those hypotheses, and (c) updates hypotheses based on new evidence. The model provides a functional account of how these sensemaking processes are carried out in a cognitive architecture, and model performance can be compared to normative (Bayesian) standards. Simulations exhibit two ll-known cognitive biases that are frequently identified as problems in intelligence analysis: (1) anchoring in the ighting of new evidence and (2) confirmation bias in seeking new information. Keywords: ACT-R, cognitive biases, sensemaking Introduction Sensemaking (Klein, Moon, & Hoffman, 2006a, 2006b; Pirolli & Card, 2005; Russell, Stefik, Pirolli, & Card, 1993) is a concept that has been used frequently in studies of intelligence analysis. The term suggests an active seeking and processing of information to achieve understanding. Sensemaking involves a set of processes aimed at seeking and filtering information, plus a set of processes that develop representational schemas (frames) that best fit the available evidence and provide a basis for understanding the data.   present the 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 accuracy of analyses (Heuer, 1999).  present simulation results that exhibit anchoring bias in the evaluation of new evidence and confirmation bias in seeking evidence. The Geospatial Task The geospatial task (Figure 1) is one of a set of challenge tasks developed as part of the IARPA ICArUS program to drive the development of integrated neurocognitive models of sensemaking. This specific task required reasoning based on a set of rules concerning the relation of observed evidence to the likelihood of attack by four different groups. A layered geospatial map is presented on a computer screen, with different layers presenting different forms of intelligence (INTs). The INTs include HUMINT (human intelligence), IMINT (image intelligence), MOVINT (movement intelligence), SIGINT (signal intelligence), Figure 1: The screen shot of the geospatial task. The letters (A, B, C, D) indicate the center of the group location, and \u20181\u2019 surrounded by a box indicates the attack location. The task begins with a given attack location (SIGACT) along with group centers (HUMINT), A, B, C, and D representing the center of activity for four possible insurgent groups. The first step is to report probabilities of attack by each group [A%, B%, C%, D%] based on the SIGACT and HUMINT (see Table 1) 1 . After that, the task is to iteratively choose among the four remaining INT layers (Table 1), up to a total of three INTs (layers), one at a time, in any order. Each INT layer provides unique evidence. Specifically, IMINT can reveal whether an attack happened on a government or military building, MOVINT provides evidence whether an attack occurred in dense or sparse traffic, SIGINT indicates electronic \u201cchatter\u201d or \u201csilence\u201d by different groups, and SOCINT indicates the group whose region the attack happened. At each stage, the selection of a particular INT provides evidence that can be used to update the probability distribution over the hypotheses about the responsibility of the four groups in producing the given attack. The rules specifying how evidence ought to update these probabilities is given in the PROBS rules in Table 1. After the last stage of INT selection, the task is to allocate resources (troops) to prevent further attacks. The new version of the task will provide the initial probabilities based on HUMINT"}]