Training time estimation to improve alarm reactions

Abstract

Prior researchers have demonstrated that training may be an effective strategy for improving operator reactions to alarm systems with less than perfect reliability. Of the training strategies tested, recognition of temporal patterns in prior sensor activations seems to offer the greatest promise for improving the speed and appropriateness of subsequent alarm reactions. The current research was completed to clarify which of three temporal interval training methods leads to the most appropriate alarm reactions. Fifty-six undergraduates evaluated whether alarms occurring after sensor activations were true or false, based on elapsed time between the sensor signals and the alarm signals. Participants completed five training sessions to learn to estimate time intervals using simple repetition training, performance feedback, or performance feedback plus subdivision cues. Contrary to expectations, results indicated that participants did not benefit differentially from temporal interval training. Differences between pre- and posttest interval estimation performance was similar among groups, and training groups performed comparably when reacting to signals. Participants generally focused on advertised alarm system reliability, responding more appropriately and more quickly to lower reliability alarms. Future researchers and designers should replicate these findings with realistic tasks and real-world complex task operators to determine their generalization.