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An examination of the heuristic capabilities of the self-organized criticality (SOC) theory for studying social processes, reviewing key ideas of the theory and the methods of identifying pink noise as an SOC attribute. The authors analyze terrorism in twenty countries in the period from 1970s to 2014. The source of the background data is the Global Terrorism Database, maintained by the START Consortium. SOC approaches and methodology were used to identify and explain such non-linear effects as spontaneous outbreaks of terrorism. It is found that numerical series that reflect changes in the terrorism volume are essentially pink noise. This allowed the universal explanatory schemes of SOC theory to be applied to interpret such systems features and dynamics and demonstrate that in many countries, terrorism is a self-organized criticality phenomenon. Systems in the state of SOC are capable of abrupt growth in activity without any apparent reason. One of the parameters of the numerical series studied ‒ power-law exponent ‒ can serve as an indicator of the internal state of the societies prone to terror threats.

This paper describes the near-infrared detector system noise generator (NG) that we wrote for the James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec). NG simulates many important noise components including: (1) white \"read noise\"; (2) residual bias drifts; (3) pink 1/f noise; (4) alternating column noise; and (5) picture frame noise. By adjusting the input parameters, NG can simulate noise for Teledyne's H1RG, H2RG, and H4RG detectors with and without Teledyne's SIDECAR ASIC IR array controller. NG can be used as a starting point for simulating astronomical scenes by adding dark current, scattered light, and astronomical sources into the results from NG. NG is written in Python-3.4. The source code is freely available for download from http://jwst.nasa.gov/publications.html.

Study Objectives: Auditory stimulation devices (white and pink noise) are used to mask sounds and facilitate relaxation and sleep; however, the effectiveness of this intervention is not well established. This systematic review examined the scientific literature for the effect of specific types of auditory stimulation on sleep outcomes in adults. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guided this review. Searches were conducted in 9 databases for intervention studies that could easily be employed in clinical practice. We excluded other types of auditory stimulation (music alone, binaural tones, and synchronization). Two reviewers screened abstracts and full-text articles for eligibility, with conflicts resolved by a third reviewer, and extracted data. Risk of bias was assessed with the Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies. Results: Thirty-four studies reported results of 1,103 persons participating in 3 categories of interventions: white noise (18), pink noise (11), and 6 multiaudio (some combination of white, pink, music, or silence). Nineteen studies had positive findings in terms of improving sleep outcomes: 6 white noise (33%), 9 pink noise (81.9%), and 4 multiaudio (66.7%). Multiaudio had the lowest (better) risk of bias (mean/standard deviation: 1.67/0.82) compared to white (2.38/0.69) and pink noise (2.36/0.81). Conclusions: Although there was no strong evidence to support use of auditory stimulation, none of the studies reported any adverse effects with short-term application of auditory stimulation during sleep. Future research needs to include confounding factors that can affect outcomes, including one’s noise sensitivity, personality, and other conditions or medications that may affect sleep. Citation: Capezuti E, Pain K, Alamag E, Chen XQ, Philibert V, Krieger AC. Systematic review: auditory stimulation and sleep. J Clin Sleep Med . 2022;18(6):1697–1709.

Humans and animals can reliably perceive behaviorally relevant sounds in noisy and reverberant environments, yet the neural mechanisms behind this phenomenon are largely unknown. To understand how neural circuits represent degraded auditory stimuli with additive and reverberant distortions, we compared single-neuron responses in ferret primary auditory cortex to speech and vocalizations in four conditions: clean, additive white and pink (1/f) noise, and reverberation. Despite substantial distortion, responses of neurons to the vocalization signal remained stable, maintaining the same statistical distribution in all conditions. Stimulus spectrograms reconstructed from population responses to the distorted stimuli resembled more the original clean than the distorted signals. To explore mechanisms contributing to this robustness, we simulated neural responses using several spectrotemporal receptive field models that incorporated either a static nonlinearity or subtractive synaptic depression and multiplicative gain normalization. The static model failed to suppress the distortions. A dynamic model incorporating feedforward synaptic depression could account for the reduction of additive noise, but only the combined model with feedback gain normalization was able to predict the effects across both additive and reverberant conditions. Thus, both mechanisms can contribute to the abilities of humans and animals to extract relevant sounds in diverse noisy environments.

What are good control stimuli for music perception research? Systematic evaluations of control suitability remain limited. We wanted to examine if control stimuli (brown, pink, white noise, and voice recordings) lead to different emotional ratings in themselves. Across two separate studies (n = 84, and 1280, respectively), participants assessed brown, pink, and white noise and voice recordings using a music-emotional perception scale with variations. We used the GEMS-9 scale, and the GEMS-9 scale with the second-order factors 'sublime', 'uneasy', and 'vital'. Our two studies show that brown noise was considered more sublime than white and pink noise, while white noise was considered more uneasy than brown noise, pink noise, and voice recordings in both studies. Brown, pink, and white noise is rated emotionally above 3 on unease on a scale from 1 to 7. This means that none of the noise stimuli had minimal emotional ratings and therefore had an emotional effect in themselves. Out of the three noise stimuli, white noise had the highest ratings of unease across both studies. Only voice recordings were considered neutral, defined as having consistently minimal emotional ratings in both studies.

The present paper addresses the generation of power-law, colored digital noise signals (sequences) with arbitrary spectral slope. In the beginning, brief background information is given about some noise features. Further, a newly proposed method is described, based on generation of a white noise signal, its transformation into the frequency domain, spectral processing and inverse transform back into the time domain. Computer simulations are performed to confirm the consistency of the algorithm, including estimation of the power spectral density and the autocorrelation, along with example of its out performance in comparison with the corresponding in-built Matlab® function.

The chemical dynamics of lakes and streams affect their suitability as aquatic habitats and as water supplies for human needs. Because water quality is typically monitored only weekly or monthly, however, the higher-frequency dynamics of stream chemistry have remained largely invisible. To illuminate a wider spectrum of water quality dynamics, rainfall and streamflow were sampled in two headwater catchments at Plynlimon, Wales, at 7-h intervals for 1–2 y and weekly for over two decades, and were analyzed for 45 solutes spanning the periodic table from H ⁺ to U. Here we show that in streamflow, all 45 of these solutes, including nutrients, trace elements, and toxic metals, exhibit fractal 1/ f ᵅ scaling on time scales from hours to decades (α = 1.05 ± 0.15, mean ± SD). We show that this fractal scaling can arise through dispersion of random chemical inputs distributed across a catchment. These 1/ f time series are non–self-averaging: monthly, yearly, or decadal averages are approximately as variable, one from the next, as individual measurements taken hours or days apart, defying naive statistical expectations. (By contrast, stream discharge itself is nonfractal, and self-averaging on time scales of months and longer.) In the solute time series, statistically significant trends arise much more frequently, on all time scales, than one would expect from conventional t statistics. However, these same trends are poor predictors of future trends—much poorer than one would expect from their calculated uncertainties. Our results illustrate how 1/ f time series pose fundamental challenges to trend analysis and change detection in environmental systems.

Previous work assessing the effect of additive noise on the postural control system has found a positive effect of additive white noise on postural dynamics. This study covers two separate experiments that were run sequentially to better understand how the structure of the additive noise signal affects postural dynamics, while also furthering our knowledge of how the intensity of auditory stimulation of noise may elicit this phenomenon. Across the two experiments, we introduced three auditory noise stimulations of varying structure (white, pink, and brown noise). Experiment 1 presented the stimuli at 35 dB while Experiment 2 was presented at 75 dB. Our findings demonstrate a decrease in variability of the postural control system regardless of the structure of the noise signal presented, but only for high intensity auditory stimulation.

Accumulated evidence from the past decades suggests that sleep plays a crucial role in memory consolidation and the facilitation of higher-level cognitive processes such as abstraction and gist extraction. In addition, recent studies show that applying pink noise during sleep can further enhance sleep-dependent memory consolidation, potentially by modulating sleep physiology through stochastic resonance. However, whether this enhancement extends to higher cognitive processes remains untested. In this study, we investigated how the application of open-loop pink noise during sleep influences the gain of insight into hidden patterns. Seventy-two participants were assigned to three groups: daytime-wake, silent sleep, and sleep with pink noise. Each group completed the number reduction task, an established insight paradigm known to be influenced by sleep, over two sessions with a 12-h interval. Sleep groups were monitored by the DREEM 3 headband in home settings. Contrary to our prediction, pink noise did not induce an increase in insight compared to silent sleep and was statistically more similar to the wake condition despite evidence for its typical influence on sleep physiology. Particularly, we found that pink noise limited the time spent in the initial cycle of N1 just after sleep onset, while time spent in N1 positively predicted insight. These results echo recent suggestions that the time in the initial cycle of N1 plays a critical role in insight formation. Overall, our results suggest that open-loop pink noise during sleep may be detrimental to insight formation and creativity due to the alterations it causes to normal sleep architecture.

This study builds on the early brain dynamics work of Erol Başar, focusing on the human electroencephalogram (EEG) in relation to the generation of event-related potentials (ERPs) and behaviour. Scalp EEG contains not only oscillations but non-wave noise elements that may not relate to functional brain activity. These require identification and removal before the true impacts of brain oscillations can be assessed. We examined EEG/ERP/behaviour linkages in young adults during an auditory equiprobable Go/No-Go task. Forty-seven university students participated while continuous EEG was recorded. Using the PaWNextra algorithm, valid estimates of pink noise (PN) and white noise (WN) were obtained from each participant’s prestimulus EEG spectra; within-participant subtraction revealed noise-free oscillation spectra. Frequency principal component analysis (f-PCA) was used to obtain noise-free frequency oscillation components. Go and No=Go ERPs were obtained from the poststimulus EEG, and separate temporal (t)-PCAs obtained their components. Exploratory multiple regression found that alpha and beta prestimulus oscillations predicted Go N2c, P3b, and SW1 ERP components related to the imperative Go response, while PN impacted No-Go N1b and N1c, facilitating early processing and identification of the No-Go stimulus. There were no direct effects of prestimulus EEG measures on behaviour, but the EEG-affected Go N2c and P3b ERPs impacted Go performance measures. These outcomes, derived via our mix of novel methodologies, encourage further research into natural frequency components in the noise-free oscillations immediately prestimulus, and how these affect task ERP components and behaviour.