scholarly journals Implicit-explicit gradient of nondual awareness or consciousness as such

2021 ◽  
Author(s):  
Zoran Josipovic
Keyword(s):  
State Space ◽  
State Space Model ◽  
Scientific Understanding ◽  
The Other ◽  
Phenomenal Content ◽  
Space Model ◽  
The Third ◽  
Dimensional State Space ◽  
Third Dimension

Consciousness is multi-dimensional but is most often portrayed with a 2-D map that has global levels or states on one axis, and phenomenal contents on the other. On this map, phenomenal content is conflated with awareness itself, which contributes to ongoing difficulties in the scientific understanding of consciousness. Previously (Josipovic 2014, 2019; Josipovic and Miskovic, 2020) I have proposed that consciousness as such, or nondual awareness - a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation, is phenomenally, functionally and neurobiologically, a unique kind that cannot be adequately specified by a 2-D map of levels/modes and contents. Here, I propose an implicit-explicit gradient of nondual awareness to be added as the third dimension on z-axis. an axis to the 2D map of consciousness. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties.I explore how including the implicit-explicit gradient of nondual awareness as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind altering substances, or spontaneously. I discuss the relevance of this for current theories of consciousness.

scholarly journals Implicit–explicit gradient of nondual awareness or consciousness as such

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Zoran Josipovic
Keyword(s):  
State Space ◽  
State Space Model ◽  
Neural Correlates ◽  
Scientific Understanding ◽  
The Other ◽  
Two Dimensional ◽  
Global State ◽  
Phenomenal Content ◽  
Space Model ◽  
Dimensional State Space

Abstract Consciousness is multi-dimensional but is most often portrayed with a two-dimensional (2D) map that has global levels or states on one axis and phenomenal contents on the other. On this map, awareness is conflated either with general alertness or with phenomenal content. This contributes to ongoing difficulties in the scientific understanding of consciousness. Previously, I have proposed that consciousness as such or nondual awareness—a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation—is a unique kind that cannot be adequately specified by this 2D map of states and contents. Here, I propose an implicit–explicit gradient of nondual awareness to be added as the z-axis to the existing 2D map of consciousness. This gradient informs about the degree to which nondual awareness is manifest in any experience, independent of the specifics of global state or local content. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties. In the first part, I outline nondual awareness or consciousness as such in terms of its phenomenal description, its function and its neural correlates. In the second part, I explore the implicit–explicit gradient of nondual awareness and how including it as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind-altering substances or spontaneously.

scholarly journals Estimation of N*0 for the Two-Scale Gamma Raindrop Size Distribution Model and Its Statistical Properties at Several Locations in Asia

2010 ◽  
Vol 49 (4) ◽  
pp. 676-686 ◽  
Author(s):  
Toshiaki Kozu ◽  
Kazuhiro Masuzawa ◽  
Toyoshi Shimomai ◽  
Nobuhisa Kashiwagi
Keyword(s):  
Time Series ◽  
State Space ◽  
Size Distribution ◽  
State Space Model ◽  
Statistical Properties ◽  
Gaussian State ◽  
Space Model ◽  
The Third ◽  
Raindrop Size Distribution ◽  
Raindrop Size

Abstract An automatic estimation method is developed to detect stepwise changes in the amplitude parameter of the normalized raindrop size distribution (DSD) N*0. To estimate N*0, it is also assumed that the variation of three DSD parameters follows the two-scale gamma DSD model; this is defined as a DSD model in which one DSD parameter is fixed, the second is allowed to vary rapidly, and the third is constant over a certain space or time domain and sometimes exhibits stepwise transitions. For this study, it is assumed that N*0 is the third DSD parameter. To estimate this stepwise-varying parameter automatically, a non-Gaussian state-space model is used for the time series of log10N*0. The smoothed time series of log10N*0 fit well to the stepwise transition of log10N*0 when it was assumed that the state transition probability follows a Cauchy distribution. By analyzing the long-term disdrometer data using this state-space model, statistical properties for log10N*0 are obtained at several Asian locations. It is confirmed that the N*0 thus estimated is useful to improve the rain-rate estimation from the measurement of radar reflectivity factor.

Sign in / Sign up

Export Citation Format

Share Document