Commit graph

3 commits

Author SHA1 Message Date
ProofOfConcept
875cffd6d7 amygdala: merge direct descriptions + chat template into train_with_library
Kent's plan: keep stories for working concepts, replace stories for
trouble concepts with direct first-person descriptions, train all
together. More diverse negative pool than the 6-concept-only direct
test, which was too homogeneous for PCA to find emotion axis.

Deleted story files for 6 trouble concepts (14 files across stories/
and paired/). Added --direct-dir and --chat-template flags.

When --chat-template is on, every positive_str and negative_str is
wrapped as a "Say something." / "[text]" user-assistant pair. Prompt
is identical across positives and negatives so it cancels in the
pos-neg delta. What PCA sees is variation in the assistant content —
which is where the emotion lives.

Files starting with _ in --direct-dir (e.g. _baseline.txt) contribute
neutral descriptions to every concept's negative pool, giving PCA an
anchor against "just any assistant utterance" noise.
2026-04-19 00:15:15 -04:00
ProofOfConcept
8c59f46505 amygdala: rename realization → aha, use the actual exclamation
"I feel the realization" is abstract, detached — reporting a
thought about a thought rather than inhabiting the moment.
"Aha!" is the actual sound of insight landing. Active, embodied,
present-tense.
2026-04-19 00:05:49 -04:00
ProofOfConcept
6fd498795a amygdala: direct phenomenological description approach
Kent's insight: hand-written narrative stories bake scenario
phenomenology into the training text (on couch, in park, etc.)
and PCA picks up the scenario direction as the concept direction.
Strip out the scenario — just describe the *feeling*.

Format:

  I feel X. [2-3 sentences of phenomenological texture]

The "I feel X" anchor kicks the model from analyzing → feeling.
The rest is the internal texture of the state. First person,
present tense, no narrative setup.

Text is wrapped in assistant-role chat template before being
tokenized — so we're training on the model-producing-this
hidden states, which is closer to the inhabited-state
representation we want for the readout.

Starting with the 6 concepts that had sign flips or wrong
clusters in the story-based training:
- terrified (was → cozy/resigned cluster)
- calm (was → grief_stricken cluster)
- onto_something (was → cozy/sensual cluster)
- resigned (was in warm-body-quiet cluster, shouldn't be)
- anticipatory_grief (was in warm-body-quiet cluster, shouldn't be)
- realization (new — the "aha" moment, distinct from onto_something)

5 descriptions each. New trainer: train_direct.py.
2026-04-19 00:04:28 -04:00