TY - GEN
T1 - How does manipulation of secondary task scheduling affect human performance?
AU - Tremoulet, Patrice D.
AU - Stibler, Kathleen M.
AU - Craven, Patrick
AU - Barton, Joyce
AU - Gifford, Adam
AU - Regli, Susan Harkness
PY - 2006
Y1 - 2006
N2 - Systems that can track and intelligently adapt to changes in users' cognitive capacities may help to improve human performance. The experiment reported here was designed to assess the benefit of placing neuro-physiological sensors on users to provide data about their cognitive states that can drive performance mitigations. A test group of 16 participants performed a primary task while monitoring for alerts (secondary task) under four conditions: no-mitigation (alerts presented as they arrive), random mitigation (system randomly alters the presentation times of alerts), sensor-driven mitigation (system uses sensor data to influence alert presentation times), and user-driven mitigation (participants pressed a button to influence the alert presentation times). Participants completed no-mitigation and user-driven mitigation conditions faster than sensor-driven and random mitigation conditions, but their accuracy scores did not differ significantly across the four conditions. This is likely due to a ceiling effect: participants' accuracy scores exceeded 90% in every condition. Future work should investigate the possibility that user-driven mitigation may, in some cases, improve performance better than sensor-driven mitigation.
AB - Systems that can track and intelligently adapt to changes in users' cognitive capacities may help to improve human performance. The experiment reported here was designed to assess the benefit of placing neuro-physiological sensors on users to provide data about their cognitive states that can drive performance mitigations. A test group of 16 participants performed a primary task while monitoring for alerts (secondary task) under four conditions: no-mitigation (alerts presented as they arrive), random mitigation (system randomly alters the presentation times of alerts), sensor-driven mitigation (system uses sensor data to influence alert presentation times), and user-driven mitigation (participants pressed a button to influence the alert presentation times). Participants completed no-mitigation and user-driven mitigation conditions faster than sensor-driven and random mitigation conditions, but their accuracy scores did not differ significantly across the four conditions. This is likely due to a ceiling effect: participants' accuracy scores exceeded 90% in every condition. Future work should investigate the possibility that user-driven mitigation may, in some cases, improve performance better than sensor-driven mitigation.
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U2 - 10.1177/154193120605001749
DO - 10.1177/154193120605001749
M3 - Conference contribution
AN - SCOPUS:44349117518
SN - 9780945289296
T3 - Proceedings of the Human Factors and Ergonomics Society
SP - 1945
EP - 1948
BT - Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, HFES 2006
PB - Human Factors and Ergonomics Society Inc.
T2 - 50th Annual Meeting of the Human Factors and Ergonomics Society, HFES 2006
Y2 - 16 October 2006 through 20 October 2006
ER -