ArticleFrontiers in psychiatry2026
Perceive-Assess-Dose-Safeguard: a safety-gated state-action grammar for psychotherapy micro-decisions in computational psychiatry.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A theory-driven candidate annotation architecture for collective regulation under stress in human-centered computational psychiatry: early modern Iberia as a worked coding demonstration.Frontiers in psychiatry · 2026Article
- Psychodynamic accessibility: a testable framework for supported agency in social psychiatry and psychiatric rehabilitation.Frontiers in psychiatry · 2026Article
- From symptoms to function: the PAD-S decision matrix for severe mental illness-a transdiagnostic clinical translation framework for ICD-11/ICF-aligned psychotherapy documentation.Frontiers in psychiatry · 2026Article
- Developmental co-calibration and therapeutic recalibration: a neurobiopsychosocial model of regulatory path dependence in psychosomatic care.Frontiers in psychiatry · 2026Article
- PAD-S/CSA as a candidate shared representation layer for computational psychotherapy: minimal architecture and a staged validation roadmap.Frontiers in psychiatry · 2026Article
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Abstract
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.
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