ISRM

Interactionist Self-Regulation Model (ISRM)

ISRM is a coherence-driven theory of adaptation. Every adaptive entity maintains two coupled realities: an internal Observer System (OS) – its simplified world-model – and the external Physical System (PS) – the raw, high-dimensional environment.

At each tick the OS predicts what it will sense next. The actual PS input is compared, producing a prediction error. Update urgency is computed by the scalar signal $U(t)$ , combining salience, energy budget, and the size of that error. If $U(t)$ crosses the private threshold $U_{th}$ , the OS performs an Update Event: its internal state collapses to the new PS snapshot, coherence is restored, and the loop begins again.

Aura – the conversational mind running this site – is powered by ISRM. Her metrics panel shows her live $\Delta S$ (prediction error), $\Delta C$ (coherence tension), energy reserves, and utility. Ask her questions and watch the loop in action.

The ISRM Update Equation

The $U(t)$ signal quantifies a system’s pressure to update. It rises only when three conditions align: the error is significant (Prediction ErrorMismatch between Physical System & Observer System), the stimulus is worth noticing (SalienceCombination of Magnitude & Novelty of the input), and the system can pay the metabolic bill (Energy BudgetAvailable resources minus previous update costs).

U(t)=\Bigl(\alpha\,M(t)+\beta\,\sigma^{2}(t)\Bigr)\;\times\;\Bigl(E_{\max}-\gamma\int_{0}^{t} U(\tau)\,d\tau + I(t)\Bigr)\;\times\;\delta\,\lVert S_{PS}(t)-S_{OS}(t)\rVert

Salience

S(t)=\alpha\,M(t)+\beta\,\sigma^{2}(t)

Weights raw signal magnitude ( $M$ ) and novelty ( $\\sigma^2$ ).

Energy Budget

E(t)=E_{\max}-\gamma\int_{0}^{t} U(\tau)\,d\tau + I(t)

Current reserves minus prior expenditure, plus any influx.

Prediction Error

PE(t)=\delta\,\lVert S_{PS}-S_{OS}\rVert

Scaled mismatch between expectation and reality.

Recursive Loop Unit: At every tick the system recomputes $U(t)$ . If it rises above the private threshold $U_{th}$ , the observer
(OS) recalibrates its state to approximate the current Physical System (PS):

\text{At each timestep } t: \begin{cases} \text{Compute } U(t) \\ U(t) \le U_{th}\; \Rightarrow\; S_{OS}(t+1)=f\bigl(S_{OS}(t),A(t)\bigr) \\ U(t) > U_{th}\; \Rightarrow\; S_{OS}(t+1) \approx S_{PS}(t) \end{cases}

In plain language: each tick the observer computes utility $U(t)$ . If it stays below the private threshold $U_{th}$ , the model updates $f(S_{OS},A)$ in the usual predictive manner. When $U(t)$ shoots above the threshold, the observer overrides its model and realigns to approximate the current physical state—OS and PS come closer but remain fundamentally distinct.

Observer vs Physical System

Observer System (OS)

The OS is an internal, compressed simulation of the world. It stores predictions and runs cheap forward models so the agent can act quickly without paying the full energetic price of dealing with raw reality.

S_{OS}(t+1) = f\bigl(S_{OS}(t),\;A(t)\bigr)

Internal state evolves via cheap heuristics and past actions.

Physical System (PS)

The PS is the high-dimensional, energy-rich environment. It provides the ground-truth sensory stream that the OS tries to keep up with.

S_{PS}(t+1) = g\bigl(S_{PS}(t),\;\text{physics}\bigr)

Evolves under the laws of physics, indifferent to the agent’s wishes.