False Positives vs. False Negatives: The Effects of Recovery Time and Cognitive Costs on Input Error Preference
Ben Lafreniere, Tanya R. Jonker, Stephanie Santosa, Mark Parent, Michael Glueck, Tovi Grossman, Hrvoje Benko, Daniel Wigdor
Abstract
Existing approaches to trading off false positive versus false negative errors in input recognition are based on imprecise ideas of how these errors affect user experience that are unlikely to hold for all situations. To inform dynamic approaches to setting such a tradeoff, two user studies were conducted on how relative preference for false positive versus false negative errors is influenced by differences in the temporal cost of error recovery, and high-level task factors (time pressure, multi-tasking). Participants completed a tile selection task in which false positive and false negative errors were injected at a fixed rate, and the temporal cost to recover from each of the two types of error was varied, and then indicated a preference for one error type or the other, and a frustration rating for the task. Responses indicate that the temporal costs of error recovery can drive both frustration and relative error type preference, and that participants exhibit a bias against false positive errors, equivalent to ∼1.5 seconds or more of added temporal recovery time. Several explanations for this bias were revealed, including that false positive errors impose a greater attentional demand on the user, and that recovering from false positive errors imposes a task switching cost.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c3e31faf-2094-4126-b497-4d4c3ebe3095Cited by top-tier papers7
- Exploring Spatial UI Transition Mechanisms with Head-Worn Augmented RealityFeiyu Lu, Yan XuCHI 2022 · 52 citations
- To Err is AI: Imperfect Interventions and Repair in a Conversational Agent Facilitating Group Chat DiscussionsHyo Jin Do, Ha Kyung Kong, Pooja Tetali, Jaewook Lee et al.CSCW 2023 · 31 citations
- Optimizing the Timing of Intelligent Suggestion in Virtual RealityDifeng Yu, Ruta Desai, Ting Zhang, Hrvoje Benko et al.UIST 2022 · 22 citations
- Exploring Visualizations for Precisely Guiding Bare Hand Gestures in Virtual RealityXizi Wang, Ben Lafreniere, Jian ZhaoCHI 2024 · 13 citations
- RIDS: Implicit Detection of a Selection Gesture Using Hand Motion Dynamics During Freehand Pointing in Virtual RealityTing Zhang, Zhenhong Hu, Aakar Gupta, Chi-Hao Wu et al.UIST 2022 · 9 citations
Builds on1
Related papers
- Detecting Input Recognition Errors and User Errors using Gaze Dynamics in Virtual RealityNaveen Sendhilnathan, Ting Zhang, Ben Lafreniere, Tovi Grossman et al.UIST 2022 · 32 citations
- Interaction Interferences: Implications of Last-Instant System State ChangesPhilippe Schmid, Sylvain Malacria, Andy Cockburn, Mathieu NancelUIST 2020 · 4 citations
- Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input TechniqueJay Henderson, Tanya R. Jonker, Edward Lank, Daniel Wigdor et al.UbiComp 2022 · 3 citations
- Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech RecognitionKimi Wenzel, Nitya Devireddy, Cam Davidson, Geoff KaufmanCHI 2023 · 24 citations
- Leveraging Error Correction in Voice-based Text Entry by Talk-and-GazeKorok Sengupta, Sabin Bhattarai, Sayan Sarcar, I. Scott MacKenzie et al.CHI 2020 · 14 citations
