CounterpartsCounterpartsThe Memory Field GuideDirectory
EncodingAI: partial

Emotional memory

The moments that mattered are the ones you keep. Feeling is the encoder's thumb on the scale.

lunch with Dana
  • eventshe tells you about her new job
  • feltan ordinary catch-up
the next six months
the lunch6 months
starting strength
fades to half in
5 weeks
small talk ▸ starts weak, fades fast · six months later: barely a trace

In the brain

Emotion is the encoder’s thumb on the scale. Arousing events are remembered better and longer: the amygdala modulates hippocampal consolidation, and stress hormones flag an event as important as it’s laid downMcGaugh 2000. It’s why a flashbulb memory survives while a thousand ordinary Tuesdays vanish — and emotional memories don’t just start stronger, they decay more slowly.

The model

Two effects, not one: an affective weight on initial encoding strength, and a reduced decay rate. Emotion changes both the height and the slope of the forgetting curve — a fact most implementations quietly drop.

In AI

Usually collapsed into a single salience axis. DeWix weights adjectives “reflecting the emotional context”; Dynamic Affective Memory Management keeps an affective dimension in a numeric salience profile; Counterparts records feelings at encoding and lets the strongest one slow a memory’s fading. But almost nothing else lets emotion change the decay rate the way the amygdala does — it’s treated as starting weight only.

The map

Emotional salience axis — partially anticipated (KR claim 2; Dynamic Affective Memory).

3 prior-art references

1 post-priority (field context, not prior art)

Emotion-as-initial-weight is partially claimed. Emotion-as-slower-forgetting — a distinct decay rate for what mattered — is largely open.

How an AI memory can do this

Every AI memory makes a choice about this mechanism, whether or not its makers name it.

Counterparts is an AI memory modeled on human memory; its home page shows each of its mechanisms, this one included. See it on the Counterparts home

The directory rates 12 AI memory systems on the same eleven mechanisms, in plain language and with sources.

Compare them side by side

References

Sources

  1. J. L. McGaugh (2000). Memory — a century of consolidation (amygdala modulation of emotional memory). Science, 287.

From The Memory Field Guide: every source, the full landscape of patents, papers and open-source systems, and the method behind it.