Browsing by Author "Byrne, Michael"

Now showing 1 - 4 of 4
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    Buzz Buzz: Haptic Cuing of Road Conditions in Autonomous Cars for Drivers Engaged in Secondary Tasks
    (2021-04-27) Pandey, Shivam; Byrne, Michael
    Can drivers’ situation awareness during automated driving be maintained using haptic cues which provide information about road and traffic scenarios while the drivers are engaged in a secondary task and without disengaging them from the secondary task? Multiple Resource Theory predicts that using different sensory channels can improve multiple-task performance. Using haptics to provide information avoids the audio-visual channels likely occupied by the secondary task. Drivers played Fruit Ninja as the secondary task while seated in a driving simulator with a Level 4 autonomous system driving. A mixed design was used for the experiment with the presence of haptic cues and the presentation time of situation awareness questions as the between-subjects conditions. Five road and traffic scenarios comprised the within-subjects part of the design. Subjects who received haptic cues had a higher number of correct responses to the situation awareness questions and looked up at the simulator screen fewer times than those who were not provided cues. Subjects did not find the cues to be disruptive and gave good satisfaction scores to the haptic device. Additionally, subjects across all conditions seemed to have performed equally well in playing Fruit Ninja. It appears that haptic cuing can maintain drivers’ situation awareness during automated driving while drivers are engaged in a secondary task. Practical implications of these findings for implementing haptic cues in autonomous vehicles are also discussed.
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    Post-Power Law of Practice: Comparing Static and Dynamical Models of Skill Acquisition
    (2024-04-19) Weeks, Charles Robert; Byrne, Michael
    Traditionally, the power law has been used to describe the trajectory of skill acquisition. Recent research has challenged this ``law,'' suggesting other models may better capture individual-level data. Furthermore, the motor learning and recovery literature suggests dynamical models might better capture non-monotonic behavior or the effect of feedback. This study compares the fits of six models on data from two mirror tracing experiments with different levels of haptic feedback. This includes two power models, the exponential model, a hybrid power and exponential model, and two dynamical models. This research replicated previous findings that the exponential model is better than the power models for individual-level data. The APEX and two dynamical models showed some advantage, but a fit metric penalizing extra parameters (BIC) called the extent of these advantages into question. These results are important as the models with better fits may better represent the cognitive processes involved in skill acquisition.
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    The Generalizability of Cognitive Modeling Parameters
    (2015-07-06) Howie, Nicole; Byrne, Michael; O'Malley, Marcia; Kortum, Philip
    Increased awareness of the importance of usability has stemmed from the realization that customer satisfaction and revenue generation are affected by usability. One method that can be used to evaluate usability is cognitive modeling, which can make quantitative predictions about human performance across different tasks and devices. However, it is unclear if cognitive models can accurately predict older adult performance. Trewin et al. (2012) proposed that parameters used in models for older adults may have to be specific to a task and/or device. The purpose of this research was to determine if task and/or device type must be accounted for in parameters used to model older adult performance in a cognitive architecture. Jastrzembski and Charness (2007) estimated architectural parameters for older adults and were able to successfully predict performance of tasks performed with a mobile phone. The current study investigated if these parameters generalize to different tasks and devices. In the experiment, older (70 years and above) and younger (18 - 39 years old) adults performed two tasks with two different devices. Overall, the results from the behavioral data showed that older adults performed more slowly than younger adults, however older adult’s performance varied across task. Performance differences between older and younger adults due to device were caused by strategy differences. Cognitive models of each task with one device were created using modeling parameters that represented older and younger adults. Then the behavioral data were compared to the models. The models were mainly slower than older and younger adults across each task. The results helped provide evidence that task and/or device type are important and should be incorporated into modeling parameters. However, strategy must be accounted for as well in order to accurately model older adult performance.
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    Workload-Based Task Selection for Automation
    (2023-12-01) Taffese, Tewodros Belete; Byrne, Michael
    Workload is a construct used to understand and predict human-automation performance. For an effective human-autonomy collaboration, it is important to maintain workload at an optimum level. Excessive workload impedes performance, while low workload might lead to boredom, lack of vigilance, and lack of situation awareness. While there is existing research on workload and its impact on automation, there seems to be lack of guidelines regarding which components of a task should be subject to automation. The purpose of this dissertation was to address this gap by providing a framework for task selection in automation. Three experiment was conducted to investigate the efficacy of workload-based task selection for automation. Participants performed the Theatre Defense Task, which is a simulated military operation task. The first experiment compared subtask workloads while participants performed the task without automation, identifying high and low workload subtasks. Workload differences between subtasks were measured using subjective workload ratings and performance-based metrics. In the second experiment, these subtasks were automated and compared in different conditions. The third experiment introduced a design intervention to address observed strategies from the second experiment and to optimize the automation. Participants exhibited higher performance scores in high workload subtask automation compared to low workload subtask automation conditions, especially when the task difficulty was high. The workload rating of different subtasks appeared to be influenced by the automation. The results showed that automation alters human-system interactions. Workload-based task selection demonstrated performance improvement and reduced workload. More importantly, automating high workload subtasks significantly improved and reduced overall workload, while automating low workload components of a task had no impact on performance. This study underscores the significance of exploring the impact of subtask automation on both the overall task and individual subtask performances.