| Summary: | Augmented cognition, which refers to real-time modifications to a human–system interface to improve performance and includes dynamic task environments with automated adaptations, can serve to protect against performance impairment under challenging work conditions. However, the effectiveness of augmented cognition as a countermeasure for performance impairment due to sleep loss is unknown. Here, in a controlled laboratory study, an adaptive version of a Change Signal task was administered repeatedly to healthy adults randomized to 62 h of total sleep deprivation (TSD) or a rested control condition. In the computerized task, a left- or right-facing arrow was presented to start each trial. In a subset of trials, a second arrow facing the opposite direction was presented after a delay. Subjects were to respond within 1000 ms of the trial start by pressing the arrow key corresponding to the single arrow (<i>Go</i> trials) or to the second arrow when present (<i>Change</i> trials). The <i>Change Signal Delay</i> (<i>CSD</i>)—i.e., the delay between the appearance of the first and second arrows—was shortened following incorrect responses and lengthened following correct responses so that subsequent <i>Change</i> trials became easier or harder, respectively. The task featured two distinct <i>CSD</i> dynamics, which produced relatively stable low and high error rates when subjects were rested (<i>Low</i> and <i>High Error Likelihood</i> trials, respectively). During TSD, the <i>High Error Likelihood</i> trials produced the same, relatively high error rate, but the <i>Low Error Likelihood</i> trials produced a higher error rate than in the rested condition. Thus, sleep loss altered the effectiveness of the adaptive dynamics in the Change Signal task. A principal component analysis revealed that while subjects varied in their performance of the task along a single dominant dimension when rested, a second inter-individual differences dimension emerged during TSD. These findings suggest a need for further investigation of the interaction between augmented cognition approaches and sleep deprivation in order to determine whether and how augmented cognition can be relied upon as a countermeasure to performance impairment in operational settings with sleep loss.
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