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Browsing by Author "Wickens, Christopher D., advisor"

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    Enhancing visual search performance: investigating cue effectiveness, dual cueing, automation bias, and attentional tunneling in complex search scenes with head-mounted displays
    (Colorado State University. Libraries, 2024) Warden, Amelia C., author; Cleary, Anne, advisor; Wickens, Christopher D., advisor; Graham, Dan, committee member; Arefin, Mohammad, committee member
    In large complex environments, such as urban driving or flying a plane, human attention may be overloaded, leading to negative consequences when encountering expected and unexpected hazards, like pedestrians crossing the street or a cart on the runway. In such situations, the searcher may benefit from attention cues presented with an HMD. The current experiments address gaps in HMD attention cueing by investigating the effectiveness of different cue properties: cue precision, dual-cueing, cue frame-of-reference, and the impact of imperfectly reliable automation. In all three experiments, participants searched for a routine target (cued or uncued) and an uncued, less expected high priority target. Search efficiency was examined across three different platforms with increasing search field sizes and realism: a static search with a 2D wide-angle desktop display (Experiment 1), a static search presented with an augmented-reality head-mounted display (AR-HMD; Experiment 2), and dynamic search in a 3D virtual reality environment (Experiment 3). Search performance benefited from cueing compared to an unaided search in all experiments. Dual-cueing provided the greatest benefit with the AR-HMD when the searcher's field-of-view (FOV) was constrained by the device's FOV because the searcher benefited from a global cue that indicated which direction they could find the locally cued target. While cueing improved search efficiency, cues showed an overall automation bias, with searchers blindly following incorrect automation. This bias was slightly amplified by the dual cue compared to the single cue. Lastly, there was a trend suggesting automation-based attentional tunneling, where the uncued, less expected high priority target was missed. Overall, attention cueing significantly enhances search performance, particularly with dual cues when targets appear outside of the searcher's FOV. But cueing also introduces an automation bias. These findings have design implications for optimizing automated cueing systems for various platforms to enhance hazard detection in real-world large scenes.
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    Estimating variability across numeric and spatial information
    (Colorado State University. Libraries, 2020) Spahr, Kimberly S., author; Clegg, Benjamin A., advisor; Wickens, Christopher D., advisor; Prince, Mark, committee member; Smith, Charles, committee member
    Research has demonstrated the difficulty of estimation and prediction, particularly in complex and uncertain conditions. Specifically, humans lack precision or are biased in making estimates of variability from continuously distributed stimuli, such as hurricane trajectories (spatial information) or sets of random numbers (numeric information). Conversely, people tend to provide calibrated estimates of average behavior for both spatial and numeric stimuli. Using either spatial or numeric stimuli, past studies noted that people tend to underestimate variability but provide accurate mean estimates. Nonetheless, no experiments have utilized both spatial and numeric stimuli to identify the extent to which people estimate variability, and to a lesser extent, mean behavior, across different types of information. This individual differences perspective holds significant implications for training and support in making calibrated decisions under uncertainty. The current study addressed this gap by presenting participants with a spatial task and a numeric task, each of which assessed knowledge and calibration to variability and means. Using cross-task correlational analyses, this study explored the extent to which similar mechanisms might underlie performance in both domains of stimuli. During the spatial task, participants learned the location of varying trajectories, and then reported on the likelihood of possible trajectory endpoints (spatial variability) and the average trajectory. During the numeric task, participants viewed lists of random numbers, and estimated the mean and spread of these lists (numeric variability). A correlational analysis revealed that participants who gave more accurate estimates of variability on the spatial task were not necessarily more accurate when estimating numeric variability. Such findings indicate that different cognitive processes likely support the understanding of variability for different types of information. Additional research is necessary to elucidate which cognitive mechanisms are involved; possible systems include working memory and numeracy. Participants expressed a similar overestimation bias to variability across both tasks. This bias trend does not replicate prior literature for either spatial or numeric information, and future studies will focus on how to induce participants to change their response biases. Finally, mean estimation performance correlated across tasks, meaning that those who were more accurate when estimating spatial means were more likely to accurately estimate numeric means.