Research
Updated April 2026 — Ryan Research Institute
Ryan Research Institute studies how emotions are interpreted, how that authority shifts, and what changes when systems begin to mediate emotional meaning.
Its work moves across psychology, neuroscience, philosophy of mind, psychoanalysis, and AI ethics. The central question is consistent across lines of inquiry: who has the final authority to define what a feeling means.
Current research is organized into seven lines.
1. Affective Sovereignty
Affective Sovereignty is the principle that the person remains the final interpreter of their own emotional life.
This line develops the normative and computational foundations for protecting interpretive authority in emotion-aware systems. It asks what happens when emotional meaning is quietly transferred from the person to the system, and what kinds of design constraints are needed to prevent that transfer from becoming structural.
The program includes formal design work, governance principles, and adjacent theoretical lines on interpretive authority transfer, structural resistance to algorithmic interpretation, and emotional rights in educational AI.
Selected work
- Formal and Computational Foundations for Implementing Affective Sovereignty in Emotion AI Systems
- Behind the Paper: Who Gets to Say How You Feel?
Discover Artificial Intelligence (Springer Nature, 2026)
DOI: https://doi.org/10.1007/s44163-026-01000-0
Key concepts
- interpretive authority
- sovereign-by-design
- contestability
- interpretive restraint
- emotional meaning as a governance problem
2. Affective Suppression Fatigue
This line examines what prolonged emotional suppression does to the structure of regulation itself.
Rather than treating numbing and collapse as unrelated outcomes, it models them as two expressions of the same underlying instability: muted responsiveness under low intensity, and abrupt breakdown under high intensity. The broader aim is to explain how chronic suppression reshapes emotional thresholds, recovery, and control.
Selected direction
- Affective Suppression Fatigue: a dynamical framework for low-intensity numbing and high-intensity collapse under review
Key concepts
- suppression
- numbing
- collapse
- threshold dynamics
- recovery asymmetry
3. Algorithmic Affective Blunting and the Affective Thermodynamic Relationship
This line studies how large language models fail under emotional and normative strain.
Algorithmic Affective Blunting focuses on the empirical pattern: under semantic stress, emotional interpretation can fragment, flatten, or collapse in a dose-dependent way. The Affective Thermodynamic Relationship extends that problem into an information-theoretic framework, asking whether collapse follows a lawful scaling structure across model families.
Together, these projects treat interpretive failure not as a single error, but as a measurable degradation process.
Selected directions
- Algorithmic Affective Blunting: collapse curves of interpretative failure in large language models
- The Affective Thermodynamic Relationship: an information-theoretic scaling law for normative-conflict collapse in large language models
under review
under review
Key concepts
- affective degradation
- collapse curves
- semantic stress
- normative conflict
- interpretive failure in LLMs
4. Narrative-Affect Geometry
This line examines the gap between narrative form and emotional intensity.
Its core claim is that people do not always say what they feel in any direct or transparent way. Narrative and affect can diverge, compensate for one another, or regulate each other. This makes emotional expression measurable not only through sentiment, but through discrepancy, structure, and expressive range.
The broader program includes large-scale derived datasets, human baselines, mechanistic theory, and geometric modeling of expressive space.
Selected work
- ANEST Narrative-Affect Dataset (ANAD v1): A Large-Scale Derived Feature Resource for Quantifying Narrative-Affective Discrepancy Data in Brief (Elsevier, 2026) DOI: https://doi.org/10.1016/j.dib.2026.112643
Dataset
- Zenodo (Canonical): https://doi.org/10.5281/zenodo.18680687
Related directions
- human expressive baselines
- ANEST as a self-regulation theory
- narrative-affect state-space modeling
Key concepts
- narrative-affect discrepancy
- expressive geometry
- self-regulation
- narrative structure
- human baseline
5. Resonant Amplification Framework
This line studies human-AI interaction as an affective and interpretive loop.
The Resonant Amplification Framework explains how small cues can be reinforced across repeated exchanges until they become self-stabilizing patterns of belief, attachment, and interpretation. It also develops intervention logic through cognitive circuit breakers designed to interrupt escalating loops without erasing user agency.
Selected work
- Interrupting Resonant Amplification: A Mechanistic and Design Framework for Human-AI Interaction Computers in Human Behavior Reports, 21, 100975 (Elsevier, 2026) DOI: https://doi.org/10.1016/j.chbr.2026.100975
Key concepts
- reinforcement loops
- human-AI attachment
- linguistic amplification
- circuit breakers
- escalation and interruption
6. Predictive Emotion, Interoceptive Authority, and Selfhood
This line asks what makes emotional regulation count as self-regulation.
It includes work on predictive emotional selfhood, active inference, delegated precision, interpretive displacement, and interoceptive authority. The central issue is whether regulation remains genuinely one’s own when external systems begin to arbitrate emotional meaning, especially under ambiguity or repeated dependence.
Selected directions
- Predictive Emotional Selfhood (PESAM)
- Interoceptive Authority and interpretive displacement
- Delegated Precision in Affective Inference
under review
under review
under review
Key concepts
- active inference
- selfhood
- interoception
- delegated interpretation
- structural dependence
7. Ecology of Inquiry and Alignment-Resistant Domains
This line addresses the conditions under which certain questions can be asked at all.
One branch examines the ecology of inquiry in emotion AI research: what institutional, disciplinary, and political conditions allow some harms to become visible while others remain structurally unasked. Another branch studies alignment-resistant domains, especially emotion, where evaluative criteria cannot be cleanly stabilized from within the system itself.
Selected directions
- Where Do Questions Come From? The Ecology of Inquiry in Emotion AI Research
- The Self-Referential Limit of Affective Alignment
- The Interpretation Asymmetry
under review
in development
under review
Key concepts
- metascience
- alignment-resistant domains
- inquiry conditions
- interpretation asymmetry
- self-referential limits
Talks and Conferences
Selected invited and conference presentations emerging from the research program.
Affective Sovereignty at Sapienza Università di Roma
Affective Sovereignty: Reclaiming the Right to Feel for Oneself
Ethics for AI: Challenges, Opportunities, and Human-Centered Perspectives
Session: Accountability and Care
SIpEIA — Italian Society for Ethics in Artificial Intelligence
Sapienza Università di Roma, Italy
2 February 2026
This presentation argued that the deepest ethical risk in emotion AI is not simply misclassification, but the structural displacement of interpretive authority. It introduced Affective Sovereignty as a framework for protecting emotional meaning before it is absorbed into records, defaults, or institutional decisions.
Related work
- Discover Artificial Intelligence paper: https://doi.org/10.1007/s44163-026-01000-0
- Behind the Paper: Who Gets to Say How You Feel?
- Essay: The Night I Defended the Right to Feel
Documentation
- SIpEIA 2026 Book of Abstracts
- archival photos and program materials
Collaboration
RRI supports a small number of invitation-based research collaborations focused on conceptual integration, manuscript refinement, validation, and interdisciplinary dialogue.
These are scope-defined research collaborations rather than general mentoring programs.
For institutional or research inquiries: ryan@ryanresearch.org