Human Memories Can Be Linked by Temporal Proximity.

Real-world memories involve the integration of multiple events across time, yet the mechanisms underlying this integration is unknown. Recent rodent studies show that distinct memories encoded within a few hours, but not several days, share a common neural ensemble, and a common fate whereby later fear conditioning can transfer from one memory to the other. Here, we tested if distinct memories could be linked by temporal proximity in humans.

Cognitive and Physiological Measures in Well-Being Science: Limitations and Lessons.

Social and personality psychology have been criticized for overreliance on potentially biased self-report variables. In well-being science, researchers have called for more "objective" physiological and cognitive measures to evaluate the efficacy of well-being-increasing interventions. This may now be possible with the recent rise of cost-effective, commercially available wireless physiological recording devices and smartphone-based cognitive testing.

Morning stimulant administration reduces sleep and overnight working memory improvement.

The goal of cognitive enhancement is to improve mental functions using interventions including cognitive training, brain stimulation and pharmacology. Indeed, psychostimulants, commonly used for cognitive enhancement purposes, while preventing sleep, have been shown to increase working memory (WM) and attention. Sleep, however, is also important for cognitive function; thus, understanding the interaction between stimulants, sleep and cognition may inform current approaches to cognitive enhancement.

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information.

Automatic detection of rapid eye movements (REMs): A machine learning approach.

Background: Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process.