Can Self-Report Tools Help People Translate Affective Signals Without Medicalizing Them?

Gavriel Wayenberg
3 minutes ago
2 min read

One of the quiet problems of contemporary life is that many people feel something before they can explain it.

They may feel unease, overload, attraction, threat, grief, hesitation, excitement, contradiction, or a sense that a situation carries more meaning than the facts immediately show. But the available categories are often too narrow.

Either the feeling is dismissed as “just emotion,” or it is medicalized too quickly.

Between those two reactions lies an important public-interest question:

Can a self-report tool help people translate affective signals into clearer language without turning them into diagnosis?

This is the question behind the FEELLLM Public PoC.

The problem: affect before interpretation

Affective signals are not always irrational. They can carry information about social context, bodily state, memory, fatigue, risk, desire, trauma, habit, environment, or anticipation.

But they are also not automatically true.

The challenge is to create tools that help people observe and articulate affective experience without granting it false certainty.

This requires a careful middle position:

respect subjective experience;
avoid premature diagnosis;
avoid replacing human judgment;
avoid turning every feeling into an algorithmic conclusion.

What FEELLLM explores

FEELLLM is a public self-report experiment by Ajinomatrix / Life-X.

Its purpose is to help users translate a difficult feeling or situation into structured language. It does not diagnose, treat, store, or medically interpret the user. It does not claim to know what a person feels better than they do.

Instead, it asks:

What is the person reporting?
What emotional signals are present?
What context seems relevant?
What remains uncertain?
What might be useful to clarify before speaking with a human?

This is a modest but important function.

Why this belongs to ISPCR

ISPCR is interested in the socio-philosophical consequences of emerging technologies. FEELLLM sits precisely at that boundary: AI, self-report, affective language, public reflection, and the ethics of non-medical interpretive tools.

The central issue is not whether AI can “understand feelings.”The better question is whether structured tools can help humans better describe what they themselves are experiencing.

That distinction matters.

A non-medical public experiment

FEELLLM is not therapy. It is not crisis support. It is not a clinical tool.

It is an experiment in structured translation:

feeling → language → reflection → possible human conversation

Used carefully, such tools may help people prepare better conversations with therapists, coaches, friends, family, advisors, or professional support networks.

Used carelessly, they could become overconfident or intrusive.

That is why the public PoC is deliberately framed with limits.