How do differences in native language affect out-of-body experiences?

Out-of-body experiences are scientifically inducible cognitive phenomena attracting global attention due to their application in the Metaverse and medical care. Despite previous studies suggesting that one's native language influences one's cognition, the out-of-body experiences of humans with different native languages have not been investigated separately. This study replicated an experiment from a 2007 study to investigate whether differences in native language affect the ability to have scientifically induced out-of-body experiences. A total of 19 age-matched native English and Japanese speakers completed the experiment in two blocks. Thereafter, their experiences were evaluated using questionnaires, and their responses were compared. Importantly, no significant differences between the English and Japanese native-speaker conditions were found. The results showed that out-of-body experiences were induced similarly in both groups, suggesting that people can have out-of-body experiences as a response to similar stimuli, regardless of their native language. However, differences in participants' introspective reports suggested that their experiences may differ qualitatively, possibly, due to the different linguistic backgrounds. The elucidation of the mechanisms of science-assisted out-of-body experiences that consider different cultural and cognitive characteristics, such as native language, could lead to the investigation of their applications in the borderless Metaverse and medicine.

Hierarchies of description enable understanding of cognitive phenomena in terms of neuron activity.

One objective of neuroscience is to understand a wide range of specific cognitive processes in terms of neuron activity. The huge amount of observational data about the brain makes achieving this objective challenging. Different models on different levels of detail provide some insight, but the relationship between models on different levels is not clear. Complex computing systems with trillions of components like transistors are fully understood in the sense that system features can be precisely related to transistor activity. Such understanding could not involve a designer simultaneously thinking about the ongoing activity of all the components active in the course of carrying out some system feature. Brain modeling approaches like dynamical systems are inadequate to support understanding of computing systems, because their use relies on approximations like treating all components as more or less identical. Understanding computing systems needs a much more sophisticated use of approximation, involving creation of hierarchies of description in which the higher levels are more approximate, with effective translation between different levels in the hierarchy made possible by using the same general types of information processes on every level. These types are instruction and data read/write. There are no direct resemblances between computers and brains, but natural selection pressures have resulted in brain resources being organized into modular hierarchies and in the existence of two general types of information processes called condition definition/detection and behavioral recommendation. As a result, it is possible to create hierarchies of description linking cognitive phenomena to neuron activity, analogous with but qualitatively different from the hierarchies of description used to understand computing systems. An intuitively satisfying understanding of cognitive processes in terms of more detailed brain activity is then possible.

The HEURECA method: Tracking multiple phase coupling dynamics on a single trial basis.

BACKGROUND: Although acquisition techniques have improved tremendously, the neuroscientific understanding of complex cognitive phenomena is still incomplete. One of the reasons for this shortcoming may be the lack of sophisticated signal processing methods. Complex cognitive phenomena usually involve various mental subprocesses whose temporal occurrence varies from trial to trial. Mostly, these mental subprocesses require large-scale integration processes between multiple brain areas that are most likely mediated by complex, non-linear phase coupling mechanisms. Consequently, a spatiotemporal analysis of complex, multivariate phase synchronization patterns on a single trial basis is necessary. NEW METHOD: This paper introduces the HEURECA method (How to Evaluate and Uncover Recurring EEG Coupling Arrangements) that enables the dynamic detection of distinguishable multivariate functional connectivity states in the electroencephalogram. HEURECA adaptively divides a trial into segments of quasi-stable phase coupling topographies and assigns similar topographies to the same synchrostate cluster. RESULTS: HEURECA is evaluated by means of simulated data. The results show that it reliably reconstructs a time series of recurring phase coupling topographies and successfully gathers them into clusters of interpretable neural synchrostates. The advantages and unique features of HEURECA are further illustrated by investigating the popular complex cognitive phenomenon insight. COMPARISON WITH EXISTING METHODS: Unlike existing methods, HEURECA detects complex phase relationships between more than two signals and is applicable to single trials. CONCLUSIONS: Since HEURECA is applicable to all kinds of circular data, it not only provides new insights into insight, but also into a variety of other phenomena in neuroscience, physics or other scientific fields.