Matching Items (8)
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- All Subjects: human-computer interaction
- Genre: Academic theses
- Genre: Doctoral Dissertation
- Creators: Cooke, Nancy J.
- Status: Published
Description
Research on priming has shown that exposure to the concept of fast food can have an effect on human behavior by inducing haste and impatience (Zhong & E. DeVoe, 2010). This research suggests that thinking about fast food makes individuals impatient and strengthens their desire to complete tasks such as reading and decision making as quickly and efficiently as possible. Two experiments were conducted in which the effects of fast food priming were examined using a driving simulator. The experiments examined whether fast food primes can induce impatient driving. In experiment 1, 30 adult drivers drove a course in a driving simulator after being exposed to images by rating aesthetics of four different logos. Experiment 1 did not yield faster driving speeds nor an impatient and faster break at the yellow light in the fast food logo prime condition. In experiment 2, 30 adult drivers drove the same course from experiment 1. Participants did not rate logos on their aesthetics prior to the drive, instead billboards were included in the simulation that had either fast food or diner logos. Experiment 2 did not yielded faster driving speeds, however there was a significant effect of faster breaking and a higher number of participants running the yellow light.
ContributorsTaggart, Mistey. L (Author) / Branaghan, Russell (Thesis advisor) / Cooke, Nancy J. (Committee member) / Song, Hyunjin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Although there are many forms of organization on the Web, one of the most prominent ways to organize web content and websites are tags. Tags are keywords or terms that are assigned to a specific piece of content in order to help users understand the common relationships between pieces of content. Tags can either be assigned by an algorithm, the author, or the community. These tags can also be organized into tag clouds, which are visual representations of the structure and organization contained implicitly within these tags. Importantly, little is known on how we use these different tagging structures to understand the content and structure of a given site. This project examines 2 different characteristics of tagging structures: font size and spatial orientation. In order to examine how these different characteristics might interact with individual differences in attentional control, a measure of working memory capacity (WMC) was included. The results showed that spatial relationships affect how well users understand the structure of a website. WMC was not shown to have any significant effect; neither was varying the font size. These results should better inform how tags and tag clouds are used on the Web, and also provide an estimation of what properties to include when designing and implementing a tag cloud on a website.
ContributorsBanas, Steven (Author) / Sanchez, Christopher A (Thesis advisor) / Branaghan, Russell (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The International Standards Organization (ISO) documentation utilizes Fitts’ law to determine the usability of traditional input devices like mouse and touchscreens for one- or two-dimensional operations. To test the hypothesis that Fitts’ Law can be applied to hand/air gesture based computing inputs, Fitts’ multi-directional target acquisition task is applied to three gesture based input devices that utilize different technologies and two baseline devices, mouse and touchscreen. Three target distances and three target sizes were tested six times in a randomized order with a randomized order of the five input technologies. A total of 81 participants’ data were collected for the within subjects design study. Participants were instructed to perform the task as quickly and accurately as possible according to traditional Fitts’ testing procedures. Movement time, error rate, and throughput for each input technology were calculated.
Additionally, no standards exist for equating user experience with Fitts’ measures such as movement time, throughput, and error count. To test the hypothesis that a user’s experience can be predicted using Fitts’ measures of movement time, throughput and error count, an ease of use rating using a 5-point scale for each input type was collected from each participant. The calculated Mean Opinion Scores (MOS) were regressed on Fitts’ measures of movement time, throughput, and error count to understand the extent to which they can predict a user’s subjective rating.
Additionally, no standards exist for equating user experience with Fitts’ measures such as movement time, throughput, and error count. To test the hypothesis that a user’s experience can be predicted using Fitts’ measures of movement time, throughput and error count, an ease of use rating using a 5-point scale for each input type was collected from each participant. The calculated Mean Opinion Scores (MOS) were regressed on Fitts’ measures of movement time, throughput, and error count to understand the extent to which they can predict a user’s subjective rating.
ContributorsBurno, Rachael A (Author) / Wu, Bing (Thesis advisor) / Cooke, Nancy J. (Committee member) / Branaghan, Russell (Committee member) / Arizona State University (Publisher)
Created2015
Description
Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions.
ContributorsHsiung, Chi-Ping (M.S.) (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
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.
ContributorsAlomari, Jamil (Author) / Mayyas, AbdRaouf (Thesis advisor) / Cooke, Nancy J. (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.
ContributorsMcNabb, Jaimie Christine (Author) / Gray, Dr. Rob (Thesis advisor) / Branaghan, Dr. Russell (Committee member) / Becker, Dr. Vaughn (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
ContributorsDemir, Mustafa, Ph.D (Author) / Cooke, Nancy J. (Thesis advisor) / Bekki, Jennifer (Committee member) / Amazeen, Polemnia G (Committee member) / Gray, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.
ContributorsNiichel, Madeline Kathleen (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2019