Matching Items (10)
- All Subjects: Cognitive Psychology
- Genre: Doctoral Dissertation
- Creators: Cooke, Nancy J.
- Member of: ASU Electronic Theses and Dissertations
Preoperative team briefings have been suggested to be important for improving team performance in the operating room. Many high risk environments have accepted team briefings; however healthcare has been slower to follow. While applying briefings in the operating room has shown positive benefits including improved communication and perceptions of teamwork, most research has only focused on feasibility of implementation and not on understanding how the quality of briefings can impact subsequent surgical procedures. Thus, there are no formal protocols or methodologies that have been developed.
The goal of this study was to relate specific characteristics of team briefings back to objective measures of team performance. The study employed cognitive interviews, prospective observations, and principle component regression to characterize and model the relationship between team briefing characteristics and non-routine events (NREs) in gynecological surgery. Interviews were conducted with 13 team members representing each role on the surgical team and data were collected for 24 pre-operative team briefings and 45 subsequent surgical cases. The findings revealed that variations within the team briefing are associated with differences in team-related outcomes, namely NREs, during the subsequent surgical procedures. Synthesis of the data highlighted three important trends which include the need to promote team communication during the briefing, the importance of attendance by all surgical team members, and the value of holding a briefing prior to each surgical procedure. These findings have implications for development of formal briefing protocols.
Pre-operative team briefings are beneficial for team performance in the operating room. Future research will be needed to continue understanding this relationship between how briefings are conducted and team performance to establish more consistent approaches and as well as for the continuing assessment of team briefings and other similar team-related events in the operating room.
Cyber threats are growing in number and sophistication making it important to continually study and improve all dimensions of cyber defense. Human teamwork in cyber defense analysis has been overlooked even though it has been identified as an important predictor of cyber defense performance. Also, to detect advanced forms of threats effective information sharing and collaboration between the cyber defense analysts becomes imperative. Therefore, through this dissertation work, I took a cognitive engineering approach to investigate and improve cyber defense teamwork. The approach involved investigating a plausible team-level bias called the information pooling bias in cyber defense analyst teams conducting the detection task that is part of forensics analysis through human-in-the-loop experimentation. The approach also involved developing agent-based models based on the experimental results to explore the cognitive underpinnings of this bias in human analysts. A prototype collaborative visualization tool was developed by considering the plausible cognitive limitations contributing to the bias to investigate whether a cognitive engineering-driven visualization tool can help mitigate the bias in comparison to off-the-shelf tools. It was found that participant teams conducting the collaborative detection tasks as part of forensics analysis, experience the information pooling bias affecting their performance. Results indicate that cognitive friendly visualizations can help mitigate the effect of this bias in cyber defense analysts. Agent-based modeling produced insights on internal cognitive processes that might be contributing to this bias which could be leveraged in building future visualizations. This work has multiple implications including the development of new knowledge about the science of cyber defense teamwork, a demonstration of the advantage of developing tools using a cognitive engineering approach, a demonstration of the advantage of using a hybrid cognitive engineering methodology to study teams in general and finally, a demonstration of the effect of effective teamwork on cyber defense performance.
This increasing role of highly automated and intelligent systems as team members has started a paradigm shift from human-human teaming to Human-Autonomy Teaming (HAT). However, moving from human-human teaming to HAT is challenging. Teamwork requires skills that are often missing in robots and synthetic agents. It is possible that adding a synthetic agent as a team member may lead teams to demonstrate different coordination patterns resulting in differences in team cognition and ultimately team effectiveness. The theory of Interactive Team Cognition (ITC) emphasizes the importance of team interaction behaviors over the collection of individual knowledge. In this dissertation, Nonlinear Dynamical Methods (NDMs) were applied to capture characteristics of overall team coordination and communication behaviors. The findings supported the hypothesis that coordination stability is related to team performance in a nonlinear manner with optimal performance associated with moderate stability coupled with flexibility. Thus, we need to build mechanisms in HATs to demonstrate moderately stable and flexible coordination behavior to achieve team-level goals under routine and novel task conditions.
The American Heart Association (AHA) estimates that there are approximately 200,000 in-hospital cardiac arrests (IHCA) annually with low rates of survival to discharge at about 22%. Training programs for cardiac arrest teams, also termed code teams, have been recommended by the Institute of Medicine (IOM) and in the AHA's consensus statement to help improve these dismal survival rates. Historically, training programs in the medical field are procedural in nature and done at the individual level, despite the fact that healthcare providers frequently work in teams. The rigidity of procedural training can cause habituation and lead to poor team performance if the situation does not match the original training circumstances. Despite the need for team training, factors such as logistics, time, personnel coordination, and financial constraints often hinder resuscitation team training. This research was a three-step process of: 1) development of a metric specific for the evaluation of code team performance, 2) development of a communication model that targeted communication and leadership during a code blue resuscitation, and 3) training and evaluation of the code team leader using the communication model. This research forms a basis to accomplish a broad vision of improving outcomes of IHCA events by applying conceptual and methodological strategies learned from collaborative and inter-disciplinary science of teams.
Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.
The 21st-century professional or knowledge worker spends much of the working day engaging others through electronic communication. The modes of communication available to knowledge workers have rapidly increased due to computerized technology advances: conference and video calls, instant messaging, e-mail, social media, podcasts, audio books, webinars, and much more. Professionals who think for a living express feelings of stress about their ability to respond and fear missing critical tasks or information as they attempt to wade through all the electronic communication that floods their inboxes. Although many electronic communication tools compete for the attention of the contemporary knowledge worker, most professionals use an electronic personal information management (PIM) system, more commonly known as an e-mail application and often the ubiquitous Microsoft Outlook program. The aim of this research was to provide knowledge workers with solutions to manage the influx of electronic communication that arrives daily by studying the workers in their working environment. This dissertation represents a quest to understand the current strategies knowledge workers use to manage their e-mail, and if modification of e-mail management strategies can have an impact on productivity and stress levels for these professionals. Today’s knowledge workers rarely work entirely alone, justifying the importance of also exploring methods to improve electronic communications within teams.
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
Node-link diagrams are widely used to visualize the relational structure of real world datasets. As identical data can be visualized in infinite ways by simply changing the spatial arrangement of the nodes, one of the important research topics of the graph drawing community is to visualize the data in the way that can facilitate people's comprehension. The last three decades have witnessed the growth of algorithms for automatic visualization. However, despite the popularity of node-link diagrams and the enthusiasm in improving computational efficiency, little is known about how people read these graphs and what factors (layout, size, density, etc.) have impact on their effectiveness (the usability aspect of the graph, e.g., are they easy to understand?). This thesis is comprehensive research to investigate the factors that affect people's understanding of node-link diagrams using eye-tracking methods. Three experiments were conducted, including 1) a pilot study with 22 participants to explore the layout and size effect; 2) an eye tracking experiment with 43 participants to investigate the layout, size and density effect on people's graph comprehension using abstract node-link diagram and generic tasks; and 3) an eye tracking experiment with the same participants to investigate the same effects using a real visualization analytic application. Results showed that participants' spatial reasoning ability had significant impact on people's graph reading performance. Layout, size, and density were all found to be significant effects under different task circumstances. The applicability of the eye tracking methods on visualization evaluation has been confirmed by providing detailed evidence that demonstrates the cognitive process of participants' graph reading behavior.
The wood-framing trade has not sufficiently been investigated to understand the work task sequencing and coordination among crew members. A new mental framework for a performing crew was developed and tested through four case studies. This framework ensured similar team performance as the one provided by task micro-scheduling in planning software. It also allowed evaluation of the effect of individual coordination within the crew on the crew's productivity. Using design information, a list of micro-activities/tasks and their predecessors was automatically generated for each piece of lumber in the four wood frames. The task precedence was generated by applying elementary geometrical and technological reasoning to each frame. Then, the duration of each task was determined based on observations from videotaped activities. Primavera's (P6) resource leveling rules were used to calculate the sequencing of tasks and the minimum duration of the whole activity for various crew sizes. The results showed quick convergence towards the minimum production time and allowed to use information from Building Information Models (BIM) to automatically establish the optimal crew sizes for frames. Late Start (LS) leveling priority rule gave the shortest duration in every case. However, the logic of LS tasks rule is too complex to be conveyed to the framing crew. Therefore, the new mental framework of a well performing framer was developed and tested to ensure high coordination. This mental framework, based on five simple rules, can be easily taught to the crew and ensures a crew productivity congruent with the one provided by the LS logic. The case studies indicate that once the worst framer in the crew surpasses the limit of 11% deviation from applying the said five rules, every additional percent of deviation reduces the productivity of the whole crew by about 4%.
Intuitive decision making refers to decision making based on situational pattern recognition, which happens without deliberation. It is a fast and effortless process that occurs without complete awareness. Moreover, it is believed that implicit learning is one means by which a foundation for intuitive decision making is developed. Accordingly, the present study investigated several factors that affect implicit learning and the development of intuitive decision making in a simulated real-world environment: (1) simple versus complex situational patterns; (2) the diversity of the patterns to which an individual is exposed; (3) the underlying mechanisms. The results showed that simple patterns led to higher levels of implicit learning and intuitive decision-making accuracy than complex patterns; increased diversity enhanced implicit learning and intuitive decision-making accuracy; and an embodied mechanism, labeling, contributes to the development of intuitive decision making in a simulated real-world environment. The results suggest that simulated real-world environments can provide the basis for training intuitive decision making, that diversity is influential in the process of training intuitive decision making, and that labeling contributes to the development of intuitive decision making. These results are interpreted in the context of applied situations such as military applications involving remotely piloted aircraft.