Signal-to-Noise Ratio in PVT Performance as a Cognitive Measure of the Effect of Sleep Deprivation on the Fidelity of Information Processing

Abstract

Study objectives: There is a long-standing debate about the best way to characterize performance deficits on the psychomotor vigilance test (PVT), a widely used assay of cognitive impairment in human sleep deprivation studies. Here, we address this issue through the theoretical framework of the diffusion model and propose to express PVT performance in terms of signal-to-noise ratio (SNR).

Methods: From the equations of the diffusion model for one-choice, reaction-time tasks, we derived an expression for a novel SNR metric for PVT performance. We also showed that LSNR-a commonly used log-transformation of SNR-can be reasonably well approximated by a linear function of the mean response speed, LSNRapx. We computed SNR, LSNR, LSNRapx, and number of lapses for 1284 PVT sessions collected from 99 healthy young adults who participated in laboratory studies with 38 hr of total sleep deprivation.

Results: All four PVT metrics captured the effects of time awake and time of day on cognitive performance during sleep deprivation. The LSNR had the best psychometric properties, including high sensitivity, high stability, high degree of normality, absence of floor and ceiling effects, and no bias in the meaning of change scores related to absolute baseline performance.

Conclusions: The theoretical motivation of SNR and LSNR permits quantitative interpretation of PVT performance as an assay of the fidelity of information processing in cognition. Furthermore, with a conceptual and statistical meaning grounded in information theory and generalizable across scientific fields, LSNR in particular is a useful tool for systems-integrated fatigue risk management.

Keywords: central cognition; circadian misalignment; cognitive processing; diffusion model; fatigue risk management.; neuronal processing capacity; performance impairment; psychometrics; psychomotor vigilance test; total sleep deprivation.

Figure 1

Psychomotor vigilance test (PVT) metrics and the effects of sleep deprivation. (a) Illustration of the response time (RT) distribution at baseline (solid) and after a night of total sleep deprivation (dotted). The inset shows the number of responses (lapses) in the right tail of the distribution (RTs ≥ 500 ms) at baseline (BL; solid) and after a night of total sleep deprivation (SD; hatched). Figure adapted from Honn et al. with permission. (b) Illustration of the diffusion model for one-choice, reaction-time tasks. Figure adapted from Ratcliff and Van Dongen with permission. (c–f) PVT metrics (means ± SE) at 2-hr intervals across 38 hr of total sleep deprivation (c: SNR, d: LSNR, e: LSNR_apx, f: number of lapses). The curves in each graph correspond to four different laboratory studies (Study 1: black; Study 2: dark gray; Study 3: light gray; and Study 4: dotted). The insets show the difference (mean and standard error) between sleep deprivation (28–38 hr awake) and the same times of day 24 hr earlier at baseline (4–14 hr awake) in each of the four studies. In the bottom right graph the vertical scales are inverted such that downward consistently corresponds to worse performance on the PVT.