No abstract

Our findings indicate a significant influence of mild HH on a number of outcome measures at altitudes above 10K, where operational restrictions are well established. In contrast, there was no clear influence of HH on performance at lower altitudes (i.e., 8K and 10K). The occurrence of HH symptoms and the decrements in target identification latency and accuracy at 8K and 10K may negatively impact flight performance and require further study.Bouak F, Vartanian O, Hofer K, Cheung B. Acute mild hypoxic hypoxia effects on cognitive and simulated aircraft pilot performance. Aerosp Med Hum Perform. 2018; 89(6):526-535.

The dorsal and ventral aspects of the prefrontal cortex (PFC) are the two regions most consistently recruited in divergent thinking tasks. Given that frontal tasks have been shown to be vulnerable to sleep loss, we explored the impact of a single night of sleep deprivation on fluency (i.e., number of generated responses) and PFC function during divergent thinking. Participants underwent functional magnetic resonance imaging scanning twice while engaged in the Alternate Uses Task (AUT) – once following a single night of sleep deprivation and once following a night of normal sleep. They also wore wrist activity monitors, which enabled us to quantify daily sleep and model cognitive effectiveness. The intervention was effective, producing greater levels of fatigue and sleepiness. Modeled cognitive effectiveness and fluency were impaired following sleep deprivation, and sleep deprivation was associated with greater activation in the left inferior frontal gyrus (IFG) during AUT. The results suggest that an intervention known to temporarily compromise frontal function can impair fluency, and that this effect is instantiated in the form of an increased hemodynamic response in the left IFG.

There is evidence to suggest that successful lying necessitates cognitive effort. We tested this hypothesis by instructing participants to lie or tell the truth under conditions of high and low working memory (WM) load. The task required participants to register a response on 80 trials of identical structure within a 2 (WM Load: high, low) × 2 (Instruction: truth or lie) repeated-measures design. Participants were less accurate and responded more slowly when WM load was high, and also when they lied. High WM load activated the fronto-parietal WM network including dorsolateral prefrontal cortex (PFC), middle frontal gyrus, precuneus, and intraparietal cortex. Lying activated areas previously shown to underlie deception, including middle and superior frontal gyrus and precuneus. Critically, successful lying in the high vs. low WM load condition was associated with longer response latency, and it activated the right inferior frontal gyrus—a key brain region regulating inhibition. The same pattern of activation in the inferior frontal gyrus was absent when participants told the truth. These findings demonstrate that lying under high cognitive load places a burden on inhibition, and that the right inferior frontal gyrus may provide a neural marker for successful lying.

N-back working memory (WM) tasks necessitate the maintenance and updating of dynamic rehearsal sets during performance. The delayed matching-to-sample (dMTS) task is another WM task, which in turn involves the encoding, maintenance, and retrieval of stimulus representations in sequential order. Because both n-back and dMTS engage WM function, we hypothesized that compared to a control task not taxing WM, training on the n-back task would be associated with better performance on dMTS by virtue of training a shared mental capacity. We tested this hypothesis by randomly assigning subjects (N = 43) to train on either the n-back (including 2-back and 3-back levels) or an active control task. Following training, dMTS was administered in the fMRI scanner. The n-back group performed marginally better than the active control group on dMTS. In addition, although the n-back group improved more on the less difficult 2-back level than the more difficult 3-back level across training sessions, it was improvement on the 3-back level that accounted for 21% of the variance in dMTS performance. For the control group, improvement in training across sessions was unrelated to dMTS performance. At the neural level, greater activation in the left inferior frontal gyrus, right posterior parietal cortex, and the cerebellum distinguished the n-back group from the control group in the maintenance phase of dMTS. Degree of improvement on the 3-back level across training sessions was correlated with activation in right lateral prefrontal and motor cortices in the maintenance phase of dMTS. Our results suggest that although n-back training is more likely to improve performance in easier blocks, it is improvement in more difficult blocks that is predictive of performance on a target task drawing on WM. In addition, the extent to which training on a task can transfer to another task is likely due to the engagement of shared cognitive capacities and underlying neural substrates—in this case WM.