Effect of prewarning on increase in reaction time in an auditory monitoring task.

1973 ◽  
Vol 101 (2) ◽  
pp. 378-380 ◽  
Author(s):  
Hans O. Lisper ◽  
Lennart Melin ◽  
Per O. Sjoden
1972 ◽  
Vol 34 (2) ◽  
pp. 439-444 ◽  
Author(s):  
Hans-Olof Lisper ◽  
Anders Kjellberg ◽  
Lennart Melin

5 Ss were required to respond as rapidly as possible to auditory signals of threshold, 34, 48, and 88 db intensity, mixed within the same 2-hr. session. Reaction time increased over time for all signal intensities, but the increase was larger for the threshold signal. There were two kinds of increase, one independent of signal intensity transferring the entire distribution toward longer reaction times. The other kind of increase was dependent on signal intensity and increased the number of long reaction times for the threshold signal.


1964 ◽  
Vol 48 (1) ◽  
pp. 13-15 ◽  
Author(s):  
J. S. Kidd ◽  
Angelo Micocci

1969 ◽  
Vol 29 (3) ◽  
pp. 815-823 ◽  
Author(s):  
Paul D. Jacobs ◽  
Roger E. Kirk

Male and female Ss performed a two-component monitoring task under 3 conditions of stress, No-stress, Task-related Stress, and Task-unrelated Stress. Dependent variables were reaction time, cumulative responses, and cumulative response errors. The results indicated faster reaction time under conditions of Task-related Stress than during the other two conditions. Differences in cumulative responses and cumulative response errors also occurred over monitoring periods, suggesting improved vigilance with practice. The results were interpreted as indicative of a “protective-adaptive” response to stress, during which S protects himself by adapting within his response repertoire to stressors.


2021 ◽  
Author(s):  
Frederic Dehais ◽  
Simon Ladouce ◽  
Ludovic Darmet ◽  
Tran-Vu Nong ◽  
Giuseppe Ferraro ◽  
...  

The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots' intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1 score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.


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