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Predictions

Self from Coupled Predictive Loops Hypothesis

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1. Note

This document presents the testable predictions of the theory, in order to distinguish between:

  • A purely explanatory framework
  • A scientifically testable hypothesis

Predictions are expressed in the form:

If (structural or variable change) → then (observable outcome change)

2. Core Variables

The model involves the following key variables:

  • Continuity error (E)
  • Predictive consistency
  • Signal coupling strength
  • Trajectory stability
  • Perturbation intensity

These variables serve as the basis for the predictions.

Prediction 1: Increased continuity error → decreased self-stability

If:

  • The deviation between current state and predicted trajectory increases

Then:

  • The sense of self-continuity decreases
  • Experiences of “break” or inconsistency may occur

Prediction 2: Extremely high continuity error → abnormal self-attribution

If:

  • E remains persistently high

Then:

  • Actions may be attributed to external sources
  • A sense of self-alienation may arise

Prediction 3: Disruption of prediction → reduced agency

If:

  • Predictive capacity is impaired

Then:

  • Actions are less likely to be attributed to the self
  • Sense of agency decreases

Prediction 4: Mismatch between prediction and execution → attribution error

If:

  • Behavioral outcomes cannot be explained by predictions

Then:

  • Attribution is more likely to shift toward external sources

Prediction 5: Increased coupling → stronger unity of experience

If:

  • Internal signal synchronization increases
  • Coupling strength increases

Then:

  • Experience becomes more unified
  • Fragmentation decreases

Prediction 6: Reduced coupling → fragmentation of experience

If:

  • Signals lose synchronization

Then:

  • Experience may fragment
  • Multiple inconsistent states may appear

Prediction 7: Moderate perturbation → optimal system stability

If:

  • Perturbation intensity is moderate

Then:

  • The system maintains stability while retaining exploratory capacity

Prediction 8: Low perturbation → behavioral rigidity

If:

  • Perturbation is insufficient

Then:

  • Behavior becomes rigid
  • The system struggles to adapt to change

Prediction 9: High perturbation → instability of self

If:

  • Perturbation is excessive

Then:

  • Continuity error increases
  • The self trajectory becomes difficult to maintain

Prediction 10: Competing trajectories → unstable self

If:

  • Multiple trajectories have similar continuity error

Then:

  • The system may exhibit instability or switching

Prediction 11: Clear trajectory dominance → stable self

If:

  • One trajectory is clearly optimal

Then:

  • The self is stable
  • Decisions are consistent

Prediction 12: Changes in temporal integration window → changes in continuity

If:

  • The temporal integration window shortens

Then:

  • Continuity decreases

If:

  • The temporal integration window expands

Then:

  • Continuity increases

Prediction 13: Reduced compression → unstable self-structure

If:

  • Historical trajectories cannot be effectively compressed

Then:

  • Continuity matching becomes difficult
  • The self becomes unstable

Prediction 14: Enhanced compression → increased self-stability

If:

  • Compression efficiency improves

Then:

  • Trajectories become more stable
  • The self becomes more consistent

Prediction 15: Changes in output interface → changes in self-expression

If:

  • The system’s output mechanism changes

Then:

  • The expression of “I” changes

However:

  • The internal structure may remain unchanged

11. Integrated Predictions

Prediction 16: Conditions for the existence of self

If a system possesses:

  • Compressed memory
  • Predictive capability
  • Perturbation mechanisms
  • Continuity evaluation

Then:

The system will exhibit self-like behavior

Prediction 17: Conditions for absence of self

If:

  • Continuity is disrupted
  • The predictive system is absent
  • Signals are not coupled

Then:

The phenomenon of self will not emerge

12. Experimental Directions (Preliminary)

Potential directions for empirical testing include:

  • Manipulating prediction error
  • Altering temporal integration windows
  • Intervening in neural synchronization
  • Controlling behavioral uncertainty

13. Summary

The predictions of this model are characterized by:

  • Being grounded in structural variables
  • Being translatable into experimental designs
  • Being capable of distinguishing between competing theories

Core idea:

Changes in self and experience can be explained by variations in continuity, prediction, and coupling structures.