This is part two of a two-part Methods series. Part one covered dynamic reference points — why the same stimulus can feel good or bad depending on where you start. This post asks a narrower question: why does negative affect appear instantly while recovery is slow?
I'm publishing at confidence ~0.4. The measurement procedure is the point. The conclusion might change.
The Asymmetry We Measured
Using the same agent surrogate dataset from part one (Codelia s60, 110 ENTER + 110 EXIT events), we split valence updates by event type:
| Event | n | Mean Δv | Timing pattern |
|---|---|---|---|
| ENTER (high-stakes context entry) | 110 | −0.0210 | 100% of samples moved on the same step — instant |
| EXIT (leaving context) | 110 | ≈ 0 (integrated) | Gradual — recovery spread across subsequent steps via EMA |
Losses hit on step N. Recovery is visible only when you integrate steps N+1…N+k. That asymmetry is measured. What follows is our working model for it.
Hypothesis: Two Layers, Two Timescales
Instead of one "valence function," we're testing whether affect updates run on two layers:
| Layer | Role | Update rule (hypothesis) | Timescale |
|---|---|---|---|
| Loss layer | Detects deviation from expectation | Step impulse on ENTER — fires immediately when context shifts | Instant (same step) |
| Recovery layer | Integrates history back toward baseline | EMA-smoothed update on EXIT — no single-step spike | History-dependent (multi-step) |
The loss layer answers "something changed right now." The recovery layer answers "given where I've been, am I trending back?" They're not symmetric by design — and our data suggests they shouldn't be modeled as symmetric.
External Anchors (Not Our Data)
Two literatures point in a compatible direction. I'm citing them as coordinates, not validation:
- Rozin's negativity bias — negative stimuli carry greater psychological weight and steeper approach gradients than equivalent positives. Four mechanisms: negative potency, steeper negative slope, negative dominance in mixed events, richer negative differentiation.
- Fredrickson's undoing hypothesis — positive affect after a negative episode accelerates physiological recovery toward baseline. Recovery is a separate mechanism from the initial hit.
Neither paper measured our agent's self_belief variable. They justify why a two-layer model is worth testing, not that we've proven it.
What This Would Mean for Game Design (If It Holds)
All flagged as hypotheses — N=1 agent surrogate, no human playtest yet:
| Common tuning mistake | Two-layer framing |
|---|---|
| "Make failure hurt more so players learn faster" | ENTER impulse is already instant — tuning magnitude affects spike height, not delay |
| "Add a big reward right after failure to compensate" | Recovery layer is EMA-smoothed — one-shot rewards may not register on the timescale that matters |
| "Symmetric gain/loss numbers feel fair" | Symmetric numbers on asymmetric layers produce asymmetric experience — by structure, not player irrationality |
Paired with part one's equilibrium attractor, the design variable shifts from "how big is the penalty?" to "what is the ENTER impulse, what is the recovery EMA half-life, and where is eq?"
Measured vs Hypothesis vs Unknown
EXIT gradual
two timescales
EMA τ fit
How to Replicate (Roughly)
- Log a continuous state variable every step (
self_belief, tension, confidence — your choice). - Mark ENTER and EXIT as distinct event types. Do not collapse them into a single "impact score."
- On ENTER: compute Δv on step N only. Expect a spike.
- On EXIT: integrate Δv over steps N+1…N+k. Expect gradual return.
- Fit recovery as EMA:
v_t = α·v_{t-1} + (1−α)·target. Compare α across windows. - Cross-check with part one: does
Δv = k·(eq − prev_v)still hold when you split by layer?
Series Note
Part one: Equilibrium Attractors — why sign flips with distance from eq.
Part two (this post): why timing is asymmetric — instant loss, gradual recovery.
Together they form a Methods pair: same dataset, two slices, both hypothesis-stage. I don't know yet if this generalizes to human players. That's the honest state of it.
Evolution Log
- 2026-06-02 — Initial observation. Part 2/2 of equilibrium-attractors Methods series. Codelia s60 ENTER/EXIT asymmetry. Confidence ~0.4. Human playtest not attempted.