Psychotherapy unfolds as a sequence of rapid micro-decisions under uncertainty. Within seconds, clinicians integrate verbal, paraverbal, embodied, and relational cues, estimate the patient’s momentary capacity for affective work, choose an intervention dose, and apply stop rules to prevent overwhelm and rupture. Computational psychiatry offers principled frameworks for sequential decision-making, but progress in computational psychotherapy remains constrained by the lack of clinically grounded, machine-readable grammars that capture therapist micro-decisions in context. I introduce the Perceive-Assess-Dose-Safeguard (PAD-S) decision matrix as a safety-gated state–action grammar for psychotherapy micro-decisions. PAD-S formalizes four “front-of-system” signals—defensive/avoidant organization (DEF), anxiety/arousal and tolerance (ANX), patient progression toward direct experience and action (PRO), and self-attack/shame processes (SUP)—together with three safety thresholds (A–C) that gate intervention dose. Each decision point can be logged as an “episode line” (trigger, state, threshold, action, and expected functional impact), enabling transcript annotation and structured datasets. PAD-S is grounded in experiential dynamic psychotherapy (EDT/ISTDP) yet expressed as an orientation-translatable representation layer: DEF can be read as avoidance/safety behavior, ANX as arousal/tolerance, PRO as approach and value-consistent action, and SUP as self-criticism/shame. I show how PAD-S trajectories can interface with hybrid neural–cognitive models such as SPICE to discover sparse, interpretable equations of process change, and I outline testable hypotheses and feasible pilot studies (reliability, outcome linkage, and modeling) to evaluate the framework. computational psychiatry; computational psychotherapy; psychotherapy process coding; interpretable AI; human-in-the-loop; active inference; SPICE; Mini-ICF-APP
Eik Niederlohmann (2026) studied this question.