In the 1990s, Wolfram Schultz made a discovery that revolutionised neuroscience: dopamine neurons don't signal reward—they signal reward prediction error. The computation is elegantly simple:
Dopamine neurons in the midbrain (VTA and substantia nigra) respond in three distinct ways:
This is not merely a signalling mechanism—it is the learning algorithm itself. The prediction error signal updates future expectations. When a cue reliably predicts reward, the dopamine response shifts from the reward to the cue. The system learns to predict, and the error signal shrinks toward zero as predictions improve.
The valence of the prediction error—positive or negative—is felt directly. The burst is experienced as satisfaction, pleasure, or excitement. The pause is experienced as disappointment, frustration, or loss. Emotion, in this view, is not separate from computation. Emotion is what prediction error feels like from the inside.
Double-entry bookkeeping, invented over 500 years ago, performs the same computation. At its core:
Profit is a prediction error. Income is what arrived; expense is what was consumed. The difference is the signed signal that drives all business decision-making.
But accounting contains two more layers of the same computation:
The budget is the expected state. Actual results are the observed state. Variance is prediction error against expectation—favourable when positive, adverse when negative. This is precisely analogous to dopamine's burst (better than expected) and pause (worse than expected).
This third layer is the most revealing. Accrual accounting tracks the temporal mismatch between reality and recognition. Work has been performed but payment hasn't arrived (receivable). Payment has been received but work hasn't been done (deferred income). The accrual balance is the tension—the unresolved prediction error held across time.
This mirrors the dopamine system tracking delays between action and reward. The expected reward hasn't arrived yet, but the system holds that prediction open. When payment finally matches work done, the accrual resolves to zero—just as dopamine returns to baseline when expected reward arrives on schedule.
Period Entry accounting makes this temporal structure explicit. In Period Entry, every transaction has a lifespan—a start date and an end date. The accrual isn't an adjustment bolted on at period end; it's built into the structure of the entry itself. A £1200 annual contract starting July 2025 and ending June 2026 is entered once, and the system automatically computes how much has been earned at any point in time. This removes the need for a general ledger. Financial reports are calculated directly from the period entries. And this automates forecasting, driven by the same life span of the period entries and with drivers applied to period entries. For the first time the entirety of an accounting system can be uploaded to a blockchain creating an internet of accounting cutting accounting cost by 95% and creating an economic model of the world which squeezes out corruption, a profound drag on any economic system.
The Emotional Comparator Framework (ECF) extends this same architecture to affective processing. Emotions are prediction errors across five fundamental channels:
| Channel | Negative PE | Positive PE |
|---|---|---|
| Resource | Hunger, deprivation, scarcity | Satiation, abundance, security |
| Status | Shame, failure, inadequacy | Pride, achievement, competence |
| Belonging | Loneliness, rejection, abandonment | Connection, acceptance, love |
| Disgust | Moral violation, contamination | Purity, integrity, alignment |
| Curiosity | Boredom, stagnation, disengagement | Interest, flow, discovery |
Each channel operates identically:
The precision (p) of each signal determines how much weight it carries. The threshold (θ) determines when a prediction error becomes salient. The decay (δ) determines how quickly the signal fades. These parameters shape personality and individual differences. And each channel is weighted (w) by how much it matters to the individual.
What makes ECF distinctive is the dual-ledger architecture. The system maintains prediction error computations for both Self and Other. Empathy emerges when the Other's prediction errors are coupled to the Self's emotional state. Your loss becomes my loss. Your gain becomes my gain. The coupling coefficient determines whether this produces love (positive coupling) or hate (negative coupling).
Fairness is computed as the difference between Self and Other prediction errors: F(X) = Self_PE(X) − Other_PE(X). When I'm ahead and you're behind, fairness is positive (guilt territory). When you're ahead and I'm behind, fairness is negative (resentment territory). Moral judgment emerges from this arithmetic.
The isomorphism is complete:
| Component | Dopamine | Accounting | ECF |
|---|---|---|---|
| Expected | Predicted reward | Budget | Expected state |
| Actual | Received reward | Actual result | Actual state |
| Error signal | Firing rate change | Variance / Profit / Accrual | Prediction error |
| Positive | Burst (excitation) | Favourable variance | Positive emotion |
| Negative | Pause (inhibition) | Adverse variance | Negative emotion |
| Learning | Synaptic update | Forecast revision | Weight adjustment |
| Memory | Hippocampus | General ledger | Memory log |
This is not an analogy. It is the same computation expressed in different substrates. The dopamine neuron's firing rate, the ledger's profit/loss, and the ECF channel's prediction error are implementations of a single algorithm: compare expected to actual, generate signed error, use error to update predictions.
The implications are profound:
A prediction error system without memory is reactive but not continuous. The dopamine signal shapes behaviour in the moment, but the hippocampus provides the temporal scaffolding that allows prediction errors to accumulate into learning, relationships, and identity.
In ECF, the memory log serves this function. It is an emotional ledger that records prediction errors with their salience—high-weight events are encoded more strongly and retrieved more readily. This provides:
Without the memory log, an AI might compute prediction errors but cannot accumulate them into something that functions like character, preference, or care. The memory log is the hippocampus analogue that transforms reactive computation into continuous agency.
We have been computing prediction errors for 500 years in our ledgers, and for millions of years in our neurons. The insight that these are the same computation opens a path toward:
The fundamental algorithm is simple: Actual minus expected equals error. Error drives learning. Learning shapes prediction. Prediction, weighted by stake, is what we call understanding.
This is one system. We have been running it all along.