Abstract
This chapter is looking for the biologically inspired oscillating agent modeling. In other words, it searches for the experimental neuroscience-based evidences of the oscillatory nature of social agents as approximations of real humans. In this regard, we noticed from the neuroscience domain, that basic mind states, which directly influence human behavior, can be characterized by the specific brainwave oscillations. For the experimental validation (or disproof) of the biologically inspired OSIMAS paradigm we have designed a framework of EEG (electroencephalography)-based experiments. Initial baseline individual tests of spectral cross-correlations of EEG-recorded brainwave patterns for some mental states have been provided in this chapter. Preliminary experimental results do not refute the main OSIMAS postulates.
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Notes
- 1.
Some electroencephalographic (EEG) experimental evidences provided in the sections below led to an idea of interpreting human basic behavioral patterns in terms of mind states, which can be characterized by unique electromagnetic power spectral density distributions in EEG channels (McFadden 2002).
- 2.
For instance, contextual (implicit) information spread in social media (like propaganda, political campaigns, information wars, etc), network models of the diffusion of innovations, models of self-excitatory wave propagation in social media, etc.
- 3.
We assume that some information can be broadcasted (shared as in the modern mass media communication channels) by agent to the entire system of other agents.
- 4.
Similarly, like we cannot see, smell or hear an air (as medium), through which other things and people can be seen, heard or smelled. Nerveless, this medium exists, despite the fact that our senses do not perceive it directly.
- 5.
In the early 90s were widely discussed similar all-pervasive contextual information field ideas referred then as nonlocal ‘mind field’ along the lines that (Grinberg-Zylberbaum and Ramos 1987) and (Orme-Johnson et al. 1982) (Orme-Johnson 1981) found some experimental evidences for existence of experimental evidences of interbrain direct communication at a distance.
- 6.
Experimental testing of the H0(2) hypothesis is foreseen in the prospective research.
- 7.
Recordings took place in the laboratory premises of the Meditation Research Institute (MRI) in SRSG (Rishikesh, India). Recorded signals were analyzed with the EEGLab software package, which was used to remove artifacts, filter data and analyze it.
- 8.
Figure 4.4 illustrates data of only 3 of the 10 participants. They were randomly chosen. However, similar tendencies were observed for other participants too. In this way, we outlined a direction for further thorough investigation based on the specific spatial locations.
- 9.
We included γ frequency range in order to embrace whole brainwaves region.
- 10.
We carried this out for a comparison between the different mental states by calculating the total differences (summing the activations in all EEG channels) for different brainwaves, see Eq. 4.3. However, absolute values of power spectral density distributions for the different states and persons varied significantly, see Figs. 4.3 and 4.4. Therefore, we converted absolute measures to conditional units [c.u.] so that estimates of differences would fit in the normalized scale [0–10]. We assumed that a significant difference is beyond 3.3 c.u., because an average standard deviation ±2σ for the 95 % confidence level reaches ±1.1 c.u. (see Table 4.2), which is one third of the face value. Beyond this level, relatively the values make 95 % confidence level meaningless as the signal is dissipated in the noise, i.e., noise and signal ratio is too high. Some related details are provided in the other article (Plikynas et al. 2014).
- 11.
Figure 4.7 illustrates analyses of the differences in brainwave activations for the appropriate frequency ranges in Med.-Thin. states of two experienced meditators, who were depicted due to the longest meditation practice. For the baseline testing we needed the most coherent participants in terms of ability to stay in the meditative state. As we have mentioned before, the habitual EEG analyses usually explores differences of EEG activations for two head-maps only (Radin 2004; Standish et al. 2004b; Travis and Arenander 2006a; Wackermann et al. 2003). Hence, these two participants were chosen purely for illustrative reasons to visualize results of our measuring methodic (see Eqs. 4.1–4.3).
- 12.
Emmanuel Haven’s and Andrei Khrennikov’s recent monograph “Quantum Social Science” forms one of the very first contributions in a very novel area of related research, where information processing in social systems is formalized with the mathematical apparatus of quantum mechanics (Haven and Khrennikov 2013).
- 13.
We chose these basic mind states (BMS)—sleeping, wakefulness, thinking, and resting. We make use of the fact that each BMS has characteristic brainwave pattern, which can be identified using power spectral density (PSD) distribution analyses (Müller et al. 2008). At the level of neural ensembles, synchronized activity of large numbers of neurons give rise to macroscopic oscillations, i.e., brainwaves, which can be observed in the electroencephalogram (EEG). For instance: (i) delta range (frontal high amplitude waves; frequency range up to 4 Hz) is mostly associated with deep sleep and deep meditative states; (ii) theta range (found in various locations; frequency range 4–8 Hz) is mostly associated with relaxed, meditative, and creative states (Lutz et al. 2004; Travis and Arenander 2006b); (iii) alpha range (both sides posterior regions of head; frequency range 8–12 Hz) is mostly associated with reflective, sensory and motor activities; (iv) beta range (both sides, symmetrical distribution, most evident frontally, low-amplitude waves; frequency range 12–30 Hz) is mostly associated with active thinking, focus, hi alert, anxious states; (v) gamma range (frequency range approximately 30–100 Hz) is mostly associated with brain binding into a coherent system for the purpose of complex cognitive or motor functions.
- 14.
In quantum mechanics, the wave function describes the quantum state of a particle and how it behaves in terms of its wave-like nature. Although human mind consists from billions of particles, we assume that in BMS prevails some sort of coherence mechanism, which binds these particles and makes them oscillate coherently (in unison) as one. Therefore, in a most simplified way we assume that wave function in principle can be used as approximation for the representation of BMS.
- 15.
Despite a century of clinical use, the underlying origins of EEG rhythms have remained a mystery. However, microtubule quantum vibrations (e.g. in the megahertz frequency range) appear to interfere and produce much slower EEG “beat frequencies” in the range 4–70 Hz. Clinical trials of brief brain stimulation—aimed at microtubule resonances with megahertz mechanical vibrations using transcranial ultrasound—have shown reported improvements in people mood (Hameroff and Penrose 2014b).
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Plikynas, D. (2016). From Baseline Individual to Social Neurodynamics: Experimental Framework. In: Introducing the Oscillations Based Paradigm. Springer, Cham. https://doi.org/10.1007/978-3-319-39040-6_4
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DOI: https://doi.org/10.1007/978-3-319-39040-6_4
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