Subjective Experience in Temporal Physics

 Defining Subjective Experience, Let's represent subjective experience as a function SE that maps contextual information and temporality to a manifold of subjective states:

SE = f(C, T) Where: C = Contextual information matrix (more on this below) T = Temporal variable representing the scale/duration

Contextual Information Matrix (C): This could be represented as a multi-dimensional tensor encoding various contextual factors like sensory inputs, memory, cognitive models, environmental stimuli etc.

C = [c1, c2, c3,...,cn] Where c1, c2, etc are different contextual elements from the physical, cognitive, social, cultural realms etc.

Introducing a temporal weighting factor to the matrix components: C(t) = [c1(t), c2(t), ...cn(t)] Where ci(t) captures how the strength of each contextual factor evolves over time based on its revisitation history.

Having a recursive updating rule: C(t+1) = f(C(t), processing_dynamics)Showing how the current context matrix C(t+1) is a function of the previous context C(t) and the specific processing dynamics/algorithms operating over time.

Modeling connection strengths/weights between components:C = [c1, c2, ...cn] W = [wi,j] Where W encodes the dynamic connection weights between the ci and cj contextual factors, which get strengthened/weakened based on revisitation.

Temporal Dynamics: We could model temporal effects via differential equations, exploring how SE evolves as a function of changing contexts over time:

dSE/dT = f(C(T), T) This allows exploring attractors, chaos, emergent patterns as contexts change temporally.

Ok, well lets consdier T here.. lets consider for a moment that all points in time are not equal. That is, as we progress in time the flow or rate of time may vary, or change. t_1 may have a differnt value then t_2.. Now this is all in relation to what it is we are comparing but the larger the values of t_n the more they influence out(in magnitude) from t_x by some factor x. In this how might we consider this equation then?

Time Weighting Function: Instead of treating T as a simple linear parameter, we could define a time weighting function w(T) that modulates the influence of each time point on SE dynamics:

dSE/dT = f(C(T), T, w(T))

Where w(T) could be some non-linear function, tensor, or set of parameters that weights the relative importance of each time slice.

Contextual Influences on Time Weighting:

The time weighting function w(T) itself could be contextually influenced and dynamically evolving based on factors like saliency, attention, memory integration etc.

w(T) = g(C(T))

Modeling subjective experience (SE) using 

dSE/dT = f(C(T),w(T)), where w(T)=h(C(T),T), provides several insights into how SE evolves over time and what factors influence this evolution:

Dynamic Nature: It emphasizes that SE is not static but continuously evolves in response to changes in contextual information C(T) and the temporal scale T. The rate of change dSE/dT reflects how quickly or slowly subjective experience shifts based on these factors.

Contextual Sensitivity: SE is highly sensitive to contextual factors C(T). These include sensory inputs, memories, cognitive models, social interactions, environmental stimuli, and more. Variations in C(T) lead to different trajectories in SE, highlighting the dynamic and context-dependent nature of subjective states.

Temporal Dynamics: The temporal weighting function w(T) (defined by h(C(T),T)) underscores how the subjective experience at any time point T is influenced by the significance and impact of past, present, and anticipated future events. This temporal integration captures how experiences accumulate and shape ongoing subjective states.

Emergent Properties: By analyzing dSE/dT, we can identify emergent properties of SE dynamics. For example, sudden changes in C(T) might lead to abrupt shifts in SE, whereas stable C(T) configurations could correspond to plateaus or attractors in SE evolution. This reveals underlying patterns and structures in subjective experience.

Integration and Cohesion: The model suggests that SE integrates information across different contextual domains C(T) and across time T. This integration process contributes to the coherence and continuity of subjective experience, linking past experiences with current perceptions and future expectations.

Subjective Pacing: Comparing dT to w(T) elucidates how the subjective "pacing" of experience unfolds. This includes how experiences are temporally bound, whether they exhibit periods of compression or expansion, and how these dynamics affect the overall flow and narrative of subjective life.

While dSE/dT​=f(C(T),h(C(T),T)) appears recursive, this form is essential for capturing the nuanced dynamics of how subjective experience unfolds over time. It reflects the iterative adjustment of temporal weighting based on contextual inputs, offering a sophisticated framework for studying the complex interplay between context, time, and subjective states in psychology, neuroscience, and artificial intelligence research.

I guess I look at it as our subjective experience is just context over time SE=C/T or SE = f(C, T) or SE(T)=∫f(C(T),h(C(T),T))dT+SE(0), in more complex terms. If you wonder about others well they are included into C.. In some sense they are a context and as they react it changes over time but its taken into account.