A cognitive prosthesis for complex decision-making

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies – using simplified as opposed to analytic decision rules – have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called “IMAGE” to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed

A Finite State Automaton Representation And Simulation Of A Data/Frame Model Of Sensemaking

This thesis presents the application of a finite state automaton (FSA) to analytic modeling of Data/Frame Model (DFM) of sensemaking. A FSA is chosen for the DFM simulation because of its inherent characteristics to mimic changes in system behaviors and transitional states akin to the dynamic information changes in dynamic and unstructured emergencies. It also has the ability to capture feedback and loops, transitions, and spatio-temporal events based on iterative processes of an individual or a group of sensemakers. The thesis has exploited the human-driven DFM constructs for analytical modeling using Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software system. Sensemaking times, problem stage time (PST), and nodeto-node (NTN) transition times serve as the major performance factors. The results obtained show differences in sensemaking times based on problem complexity and information uncertainty. An analysis of variance (ANOVA) statistical analysis, for three developed fictitious scenarios with different complexities and Hurricane Katrina, was conducted to investigate sensemaking performance. The results show that sensemaking performance was significant with an F (3,177) of 16.78 and probability value less than 0.05, indicating an overall effect of sensemaking information flow on sensemaking. Tukey’s Studentized Range Test shows the significant statistical differences between the complexities of Hurricane Katrina (HK) and medium complexity scenario (MC), HK and low complexity scenario (LC), high complexity scenario (HC) and LC, and MC and LC

Value visualization in Product Service Systems preliminary design

Emerging from a study in the European aerospace industry, this paper identifies a gap in the way value-related information is communicated to designers of hardware in the preliminary stages of Product Service System (PSS) design. To fit this gap a Lifecycle Value Representation Approach, named LiVReA, that uses color-coded 3D CAD models to enable value information to be translated into visual features, is presented. Such approach aims at enhancing designers' awareness of the value contribution of an early design concept on the overall PSS offer by complementing requirements-based information with criteria reflecting the fulfillment of customers and system value. The paper details the development of the approach, its underlying rationale, the results of preliminary validation activities and the potential for industrial application in the light of the currently available PSS representation tool

A Decision Support System For The Intelligence Satellite Analyst

The study developed a decision support system known as Visual Analytic Cognitive Model (VACOM) to support the Intelligence Analyst (IA) in satellite information processing task within a Geospatial Intelligence (GEOINT) domain. As a visual analytics, VACOM contains the image processing algorithms, a cognitive network of the IA mental model, and a Bayesian belief model for satellite information processing. A cognitive analysis tool helps to identify eight knowledge levels in a satellite information processing. These are, spatial, prototypical, contextual, temporal, semantic, pragmatic, intentional, and inferential knowledge levels, respectively. A cognitive network was developed for each knowledge level with data input from the subjective questionnaires that probed the analysts’ mental model. VACOM interface was designed to allow the analysts have a transparent view of the processes, including, visualization model, and signal processing model applied to the images, geospatial data representation, and the cognitive network of expert beliefs. VACOM interface allows the user to select a satellite image of interest, select each of the image analysis methods for visualization, and compare ‘ground-truth’ information against the recommendation of VACOM. The interface was designed to enhance perception, cognition, and even comprehension to the multi and complex image analyses by the analysts. A usability analysis on VACOM showed many advantages for the human analysts. These include, reduction in cognitive workload as a result of less information search, the IA can conduct an interactive experiment on each of his/her belief space and guesses, and selection of best image processing algorithms to apply to an image context

Addressing the Challenges in NextGen Decision Making

DTFAWA-10-X-80005, Annex 9NASA provided a broad overview of flight crew decision making and training challenges expected to result from the implementation of NextGen automation, including decision support automation. Recommendations included the following human factors recommendations and caveats for the design of future flight deck systems: - Pilots Need Accurate Mental Models of Automated Systems - Systems Awareness Is Key to Situation Awareness - Changes Must Be Highlighted - CRM \u2018Monitor and Challenge\u2019 Philosophy for Flight Crew Must Also Apply to Flight Deck Automatio

A Bayesian Abduction Model For Sensemaking

This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS

Teacher Thoughts on Infographics as Alternative Assessment: A Post-Secondary Educational Exploration

This qualitative phenomenological case study is designed to investigate the learning outcomes, lived experiences, and perceptions of eight post-secondary teachers participating in a sketch-based infographic development training program. This research is designed to assess the viability of infographics as a learning and assessment strategy, providing insight into the application of infographics to the post-secondary education environment, and informing the development of an instructional and assessment model with prescriptive conditions for usage and training. This research provides much needed empirical support for specific applications of visualization tools in the post-secondary learning environment with a specific focus on teacher perspectives providing additional insight into visual skill development, learning environment considerations, training requirements and support implications associated with infographics. This study revealed five (5) major themes associated with the use of infographics as alternative assessment in post-secondary education. These five interconnected themes include Using Infographics, Teaching Infographics, Developing Infographics, Assessing with Infographics and Infographics and Learning. A prescriptive model and approach for using, teaching, developing, and assessing infographics in post-secondary educational settings is presented

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions