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Instruction in information structuring improves Bayesian judgment in intelligence analysts.

ABSTRACT: An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts' probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more likely to invariably choose the category to which they assigned the higher probability of a target's membership. The research provides a rare example of evidence-based validation of effectiveness in instruction to improve the statistical assessment skills of intelligence analysts. Such instruction could also be used to improve the assessment quality of other types of experts who are required to integrate statistical information or make probabilistic assessments.

SUBMITTER: Mandel DR

PROVIDER: S-EPMC4389401 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Instruction in information structuring improves Bayesian judgment in intelligence analysts.

Mandel David R DR

Frontiers in psychology 20150408

An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts' probability judgments were more coherent (i.e., more additive and compliant with Bayes the ...[more]

PMID: 25904882

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Project description:ObjectiveRecent studies have reported that skilled tennis players are likely to use proximal body information for anticipating the direction of their opponent's forehand shot. However, in these studies, the visual stimuli did not include visual information about the ball. Skilled players may have used proximal information owing to the lack of distal information. To address this issue, we developed a novel methodological approach using computer graphics (CG) images in which the entire body was presented by a combination of point-light display (i.e., poor graphical information, PLD) and polygons (i.e., rich graphical information). Using our novel methodological approach, we examined whether skilled tennis players use proximal body information when anticipating shot directions.Methods and resultsFifteen skilled tennis players and fifteen novice players tried to anticipate shot directions by observing four CG forehand strokes (ALPOL: all body parts were represented with polygon; RAPLD: racket and arm were represented with PLD; BOPLD: body parts without racket and arm were represented with PLD; and ALPLD: all body parts were represented with PLD). Our intention in creating CG models with such combinations (i.e., RAPLD and BOPLD) was that because of the richer graphical information provided by polygons compared to PLD, the participant's anticipatory judgment would be influenced more by body parts expressed with polygons. The results showed that for skilled players, anticipatory judgment was more accurate when they observed RAPLD than when they observed BOPLD and ALPLD. In contrast, for novice players, there were no differences in the accuracy of anticipatory judgments with the four CG models.ConclusionsOnly skilled players made more accurate anticipatory judgments when body regions were expressed with rich graphical information, and the racket and arm were expressed with poor graphical information. These suggest that skilled players used proximal information to effectively anticipate shot directions.

Project description:BACKGROUND: Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. RESULTS: Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. CONCLUSIONS: In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.

Dataset Information

Instruction in information structuring improves Bayesian judgment in intelligence analysts.

Publications

Instruction in information structuring improves Bayesian judgment in intelligence analysts.

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