Temperature as Precision: A First-Person Account of Hypomanic Cognition through the Lens of Active Inference

This commentary reinterprets the precision engineering framework proposed by Friston (2023) through the lens of first-person experience in Bipolar II Disorder (BP2). As a BP2 patient and computer science student, the author models cognitive fluctuations across hypomanic, mixed, depressive, and euthymic episodes using a Temperature (τ) framework, where τ ≈ 1/precision. The paper proposes a three-axis model ― τ (Temperature) × V (Valence) × σ (Volatility) ― to capture the phenomenology of precision failures. Five retrospectively analyzed hypomanic thought trajectories demonstrate convergence to a single grandiosity attractor despite diverse initial seeds. Key observations include: the non-uniform nature of precision reduction in hypomania (question generation remains intact while answer validation collapses); the self-censorship filter as a manifestation of precision-weighted prediction error suppression; and differential pharmacological responses interpretable as τ modulation versus output suppression. These observations suggest that precision engineering is not only a theoretical construct but a subjectively experienced phenomenon, and that computational psychiatry may benefit from systematic integration of first-person autoethnographic data.