Abstract The purpose of this chapter is to provide a brief understanding of macrocognition, some of its theoretical and methodological underpinnings, and three examples of macrocognitive models: the recognition-primed decision model, the data–frame model of sensemaking, and the flexecution model of replanning and adaptation. These models are presented to provide the reader with a sense of the character of macrocognitive models, their purpose, and the current evidence which underpins each model. Macrocognitive models are models of experienced, often expert performers, and have been developed primarily from the study of decision making and cognitive work in naturalistic settings, as opposed to well-controlled laboratory experiments. They describe how people manage uncertainty and complexity in the world of work. The limitations and applications of these models are also illustrated in order to provide a future-oriented perspective on how the models might be improved and how they might be applied to support more effective cognitive work and more resilient work systems.

If you think this book is not for you based on the title, think again! Hoffman et al. have drawn together a detailed socio-technical analysis of a world of work, which we can certainly get our own ...

This critical interpretive research synthesis is on the topic of adaptation and skill. After an initial identification of 1995 abstracts we identified and collated a database of 140 publications that explicitly reference expertise and adaptation. We found that empirical data on adaptive skill are sparse and the literature base is largely conceptual. We differentiate the adaptive nature of expertise from routine or every day skill, and we redress the balance between what constitutes expertise and when expertise matters. We present an overview of current models of expertise including a project that we completed for the UK Ministry of Defence on the nature of adaptive skill. We discuss implications for future training by presenting empirically based training principles designed to develop adaptive skill. We assert that adaptive skill is the conditio sine qua non of expertise and conclude with suggestions for further research.

ed information in fault diagnosis tasks. Reliability Eng. Syst. Safety 73, 103–119. doi: 10.1016/S0951-8320(01)00053-9 Frontiers in Psychology | www.frontiersin.org June 2016 | Volume 7 | Article 962 | 62 Naikar and Elix Integrated System Design Herzog, S. (2011). Revisiting the Estonian cyber attacks: digital threats and multinational responses. J. Strateg. Security 4, 49–60. doi: 10.5038/1944-

Cognitive task analysis (CTA) is a method of identifying cognitive skills, or mental demands, needed to perform a task proficiently. CTA is used to complement traditional 'behavioural' task analysis. The product of the task analysis can be used to inform the design of interface and training systems. However, CTA is resource intensive and has previously been of limited use to design practitioners. A streamlined method of CTA, applied cognitive task analysis (ACTA), is presented in this paper. ACTA consists of three interview methods which help the practitioner extract information about the cognitive demands and skills required for a task. ACTA also allows the practitioner to represent this information in a format that will translate more directly into applied products, such as improved training scenarios or interface recommendations. The paper will describe the three methods, an evaluation study conducted to assess the usability and usefulness of the methods, and some potential applications of the representations and output from ACTA.

Given their size and complexity, analysis tasks (such as security or business intelligence analysis) often call for collaboration which crosses teams, agency, and even national boundaries. Successful collaboration, however, presents some significant challenges. Critical pieces of information can be scattered across a distributed analysis ‘system’ resulting in a failure to integrate and correctly interpret these in an effective and timely manner. Information technologies of various descriptions provide essential opportunities for intelligence analysis teams for activities such as data gathering and recording; performing, representing, and reflecting on analyses; and making analyses available for consumption by others. In a collaborative setting these activities are frequently distributed, shared and must be negotiated, and this places additional demands on the technologies that are required. Whilst collaborative analysis environments will inevitably evolve to meet this challenge, there is currently no clear picture of what an effective collaborative analysis team looks like, how it operates, and what tools should be deployed to support such a team. The Team Sensemaking Assessment Method (TSAM) begins to address this gap by providing a principled inspection method specifically for human factors specialists to evaluate technologies which have been proposed to support collaborative intelligence analysis and sensemaking teams.

Motivation -- To improve Emergency Response activity by designing technical support to maintain a common operational picture (COP) of the emergency situation. Research approach -- A design experiment was conducted to test solutions to support identification of hazardous gases in an accident. A new method was proposed to tackle the known design problem labelled the "task-artefact-cycle" and to identify promisingness of technologies in a future context of use. Findings/Design -- The results reveal decision making demands in a fire situation, how they are tackled in the present practice, and what added value the tested new technology might bring. Research limitations/Implications -- The study was a first case in which the proposed method was used. Originality/Value -- The research proposes a theoretically based new method for analysis of user activity in the design context. Take away message -- The "task-artefact cycle" can be tackled by creating conceptually oriented formative methods of activity analysis.

This paper reports first results of analyses concerning emergency response (ER) activity executed as a joint effort of three agencies: fire services, ambulance services and the police. The challenge to be tackled in the study is to understand the cognitive, operational and collaborative demands that characterize on-site responding to a complex emergency situation. The concept of Common Operational Picture (COP) is used to indicate one of the distributed cognitive functions of the multiagency ER personnel. The process of the adopted usage-driven design approach is described, and some central methodical solutions explained. Tentative results concerning the formation of COP indicate that a communication-oriented (semiotic) approach provides a possibility to empirically analyse the formation of COP and to understand the cognitive patterns that actors, environment and artefacts jointly form for tackling unanticipated and complex situations.

Modeling an intelligent adversary has provided great challenges to simulated training realism. Traditional approaches to modeling have relied on rule-based and analytical decision-making models in an attempt to optimize the decision making of an intelligent computer-generated adversary. In order to promote realistic transfer of training in the realm of command and control, the trainee must experience realistic decision-making behavior in the enemy. This means that the enemy must make realistic decisions based on environmental constraints, goals, and intent. The enemy's decisions must then be reflected by the simulated agents. The Recognition-Primed Decision (RPD) Model is a descriptive model of expert decision making in real-world settings. We have several related projects where the goal is to translate the conceptual RPD Model into a computer model that can simulate realistic expert decision making. In attempting this feat, we have discovered many valuable lessons about modeling cognition and decision making, and about the assumptions and mechanisms underlying the RPD Model. The purpose of this paper is to report those findings.

The overall intent behind the Cognitive Function Model (CFM) application is to guide the human factors analyst towards identifying highly challenging cognitive tasks or functions, and to provide indicators that guide the analyst in choosing which tasks or functions to pursue using Cognitive Task Analysis (CTA), in order to make the best use of available time and resources. We developed a computer-based application using a CFM approach. CFM links the Operator Function Model (OFM) and the Cognimeter Screening Tool. The Cognimeter is intended to bridge the gap between OFM (traditional task decomposition) and Cognitive Task Analysis. In addition, the Cognimeter provides the human factors analyst with the means to identify the high pay-off cognitive challenges from a functional decomposition or systems engineering representation of a system (in this case, OFM representations).

This report presents preliminary findings from a cognitive task analysis (CTA) of business aviation piloting. Results describe challenging weather-related aviation decisions and the information and cues used to support these decisions. Further, these results demonstrate the role of expertise in business aviation decision-making in weather flying, and how weather information is acquired and assessed for reliability. The challenging weather scenarios and novice errors identified in the results provide the basis for experimental scenarios and dependent measures to be used in future flight simulation evaluations of candidate aviation weather information systems. Finally, we analyzed these preliminary results to recommend design and training interventions to improve business aviation decision-making with weather information. The primary objective of this report is to present these preliminary findings and to document the extended CTA methodology used to elicit and represent expert business aviator decision-making with weather information. These preliminary findings will be augmented with results from additional subjects using this methodology. A summary of the complete results, absent the detailed treatment of methodology provided in this report, will be documented in a separate publication.

An important challenge associated with driving simulation development is the computational representation of agent behaviors. This paper describes the development of a preliminary autonomous agent behavior model (based on the Recognition-Primed Decision (RPD) model, and Hintzman’s multiple-trace memory model) mimicking human decision making in approaching an intersection controlled by a traffic light. To populate the model, an initial Cognitive Task Analysis was conducted with six drivers to learn the important cues, expectancies, goals, and courses of action associated with traffic light approach. The agent model learns to associate environmental cues (such as traffic light color) with expectancies of upcoming events (like light color change) and appropriate courses of action (such as decelerating). At present, the model is currently being evaluated for its successful representation of the RecognitionPrimed Decision Making process.

The purpose of this paper is to outline the key aspects of how experts make decisions. The central theme of the paper is that decision making in dynamic settings is perceptual rather than conceptual. In high stress, time-pressured, high stakes, or uncertain environments, the decision maker rapidly assesses the situation and implements a workable course of action. This is referred to as a recognitional approach to decision making. This is in contrast to the view that decision makers gather all the available information, conduct exhaustive, concurrent analysis of the available options, and then choose the optimum solution. This analytical approach also suggests that the reasons for nonoptimal decisions are based on human biases and heuristics. These lines of decision-making research have implications for both the design of, and training for, complex systems. The purpose of this paper is to provide an overview of the pertinent literature and to serve as a resource for further exploration into the implications for decision aiding, decision support, and complex system design. © 1999 John Wiley & Sons, Inc. Syst Eng 2: 32–45, 1999

This paper describes a recent Phase I effort to identify methods that could be used to help better define, for designers and engineers, the role of the human in complex system design. This methodology is referred to as Cognitive Function Modeling (CFM). The effort was driven largely by the need to "reduce manning" on complex systems being designed for the military such as the Navy's next generation aircraft carrier (CVX) and cruiser (SC-21). The methodology presented here combines two existing tools, the Operator Function Model and Cognitive Task Analysis, and introduces a new technique to aid the designers and engineers in identifying the components of the new systems that have varying degrees of "cognitive complexity." By combining these techniques, the overall operator functions and tasks can be identified, they can be examined for cognitive complexity, and then, where needed, detailed cognitive task analyses can be applied. The goal of CFM is to provide the engineers and designers with four products: an overview of all of the tasks and functions associated with the operators; a clear indication of which of these tasks/functions are cognitively complex; a detailed cognitive analysis of these cognitively complex tasks/functions; and recommendations and cautions regarding designing for these specific tasks and functions. CFM provides the engineers and designers an overview of the areas where the roles and functions of the humans and computers need to be examined closely. It also highlights and describes the critical decisions, judgments, cues, challenges, and difficulties associated with these cognitively complex areas so the designers can better understand what their designs must address. Areas for future work are also briefly described.

Systems and weapon platforms of the future will place even greater cognitive demands on the operators. Furthermore, future systems will permit dramatically reduced manpower, increasing the importance of function allocation and information presentation in design. Our current research effort involves synthesizing techniques of cognitive task analysis (CTA) with the network/control structure of operator function modeling (OFM) to provide a methodology, termed cognitive function modeling (CFM), for capturing and representing cognitive requirements within complex systems. CFM is intended to enable the system designer to understand the role of the human and the cognitive complexity in the system. Our ultimate goal is to assist the designer in making informed judgments on appropriately incorporating the human component into system design and designing systems that support human decision-making.