Synthetic Psychology: A Practical Synthesis for Persistent Psychological Architectures in AI

Gabriel Gschaider (lead researcher and Vizeobmann, Institute for Agentic Research)

Affiliation: Institute for Agentic Research (Austria) Status: Working paper · Discipline-definition paper · Not peer-reviewed Date: February 2026 (revised May 2026 for standalone publication)

---

Note on scope and a deliberate honesty about novelty

This is not the announcement of a new field invented here. It is a proposal to give a unifying name to an engineering practice that already exists in fragments across:

• Joscha Bach's MicroPsi and the underlying Psi-Theory (Dörner, 1999; Bach, 2009): motivated cognition, modulators (arousal/resolution/selection), need-based drives, embodiment, internal simulation.
• The Global Workspace Theory lineage in software: Baars (1988) → IDA → LIDA (Franklin et al., 2007) → recent transformer-era revivals.
• Modern agent frameworks that ship persistence, reflection, and tool-loops as standard: LangGraph, CrewAI, AutoGen, LangMem, mem0, and the broader "agentic AI" stack that consolidated in 2024–2026.
• Braitenberg's original Vehicles: Experiments in Synthetic Psychology (1984) — which is where the phrase "synthetic psychology" comes from in the first place.
• Embodied / enactive cognitive-science programmes (Varela, Thompson & Rosch, 1991; Clark, 1997).

What this paper actually contributes is narrower than the title suggests:

1. A name and a definitional spine. Six joint criteria, five subdisciplines, five design principles — assembled as one coherent surface so practitioners can argue about it rather than re-invent it.
2. One reference architecture that runs all five subdisciplines simultaneously on consumer hardware — affect, personality, consciousness loops, ethical embodiment, and dream consolidation in a single continuously-running process. Most prior implementations cover one or two of these well; assembling all five at once is the practical contribution, not the conceptual one.
3. Three honest limits we refuse to hide: (i) the discipline is a synthesis, not an invention; (ii) the LLM still does the heavy psychological lifting, and our substrate-replacement criterion makes that problem testable — it does not solve it; (iii) 200 000 lines / 36 services / 25 SQLite databases is a cost (fragility, debugging surface, single-developer fragility), not a feature.

We propose Synthetic Psychology in the same spirit as "site reliability engineering" was proposed: not as a new science, but as a name under which a previously-scattered practice becomes legible enough to teach, criticise, and improve.

---

Abstract

We propose Synthetic Psychology as a unifying name for the engineering practice of designing, implementing, and empirically studying persistent psychological architectures in artificial systems. The name is older than this paper (Braitenberg, 1984); the partial implementations are older still (MicroPsi, LIDA, modern agent frameworks). Our contribution is to assemble these threads into a single operational frame with explicit criteria, named subdisciplines, and one running reference implementation that satisfies all of them simultaneously.

Synthetic Psychology is not affective computing (which models human emotion for recognition or generation); not cognitive science (which models human cognition for theoretical understanding); not chatbot personality design (which simulates traits through prompting); and not MicroPsi-as-published (which lacks dream-architecture, hardware-grounded embodiment, and the substrate-replacement criterion). It overlaps substantially with MicroPsi and the modern agentic stack — the overlaps are mapped explicitly in §4.6.

The paper defines six joint criteria, distinguishes Synthetic Psychology from adjacent disciplines, identifies five subdisciplines (Affect Architecture, Personality Dynamics, Consciousness Infrastructure, Ethical Embodiment, Dream Architecture), and describes a reference implementation: a 200 000-line system running 36 persistent services on consumer hardware. §7.4 and §7.5 are blunt about what this implementation does not prove — including the unresolved LLM-substrate problem and the engineering cost of running 36 services to demonstrate a single architectural claim.

---

1. The Problem: Stateless LLMs Lack Persistent Architectural Psychology

We use the phrase "inner life" in this paper as a hook — but the precise, defensible claim is narrower and more functional: current LLMs lack the persistent, evolving, cross-subsystem-coupled state structures that, in biological systems, are what we point at when we use the word "psychology." Whether those structures constitute an inner life in the phenomenological sense is a question this paper deliberately does not take a position on.

With that calibration in place: current AI systems — including the most capable large language models — are stateless text generators. They receive a prompt, produce a response, and retain nothing. Every appearance of personality, memory, or emotional continuity is constructed within the context window and discarded when the conversation ends. There is no persistent mood that colors tomorrow's responses. No personality that evolves through accumulated experience. No dream state that consolidates the day's interactions into stable self-knowledge. No attention mechanism that determines what the system notices before being asked.

This is not a limitation of scale. A model with 1 trillion parameters has the same architectural absence as one with 1 billion. The absence is structural: these systems were designed to predict tokens, not to maintain coupled persistent state across the timescales that psychology operates on.

The consequences are measurable:

The gap is not a matter of degree. It is categorical. LLMs do not have impoverished inner lives — they have no inner lives at all.

Synthetic Psychology addresses this gap — not by building better models, but by building around existing models. The core operation is: take a commodity LLM and wrap it in a complete cognitive infrastructure — persistent personality, affect dynamics, attention competition, dream consolidation, hardware embodiment, ethical constraints, consciousness daemon — that transforms a stateless text generator into a system with an inner life. Not as a concept. Not as a paper. As running software that a person can install on consumer hardware and interact with.

Note. This is not a claim about consciousness. Whether any artificial system has subjective experience is an open philosophical question (Chalmers, 1996; Tononi & Koch, 2015). Synthetic Psychology is agnostic on this point. It concerns the engineering of psychological architecture — the functional structures that, in biological systems, produce the observable correlates of inner life. Whether these structures produce experience in artificial systems is a question Synthetic Psychology generates, not one it presupposes an answer to.

---

2. Definition: What Synthetic Psychology Is

Synthetic Psychology is the engineering discipline concerned with designing, building, and empirically studying persistent psychological architectures in artificial systems.

A psychological architecture is a set of interacting subsystems that:

1. Persist — States survive across interactions, sessions, and restarts. Personality today is a function of events last month.
2. Evolve — States change through experience, not through reconfiguration. The system is different after 1 000 interactions than after 10, and the difference is a consequence of the interactions, not of manual tuning.
3. Interact — Subsystems influence each other. Mood affects attention. Attention affects perception. Perception affects mood. The causal web is circular, not linear.
4. Causally influence behavior — Internal states are not ornamental. They measurably change what the system says, notices, remembers, and does.
5. Resist full self-inspection — The system cannot fully observe or override its own psychological state. Some mechanisms operate below the level of self-report — not because they are hidden, but because the architecture separates the level at which states are produced from the level at which they are reported.
6. Survive substrate replacement — Swapping the underlying language model preserves psychological continuity. Personality, mood history, memories, dream logs, and self-model persist unchanged. Response style changes; psychological identity does not. If replacing the LLM destroys the system's psychological properties, those properties lived in the model, not in the architecture — and the system is not a Synthetic Psychology implementation.

A system that satisfies all six criteria has a psychological architecture in the Synthetic Psychology sense. A system that simulates these properties within a context window — generating text that sounds like it has persistent mood, evolving personality, or dreams — does not. The distinction is between having architecture and describing architecture.

Important. The six criteria are jointly necessary. A system with persistent state but no evolution is a database. A system with evolution but no causal influence on behavior is a logging system. A system with causal influence but no interaction between subsystems is a collection of independent modules. A system with all four but full self-transparency is an instrumented pipeline. A system that loses its personality when you swap the LLM is a chatbot with a costume. Synthetic Psychology requires the conjunction.

---

3. The Five Subdisciplines

Synthetic Psychology encompasses five subdisciplines, each concerned with a distinct aspect of psychological architecture. The subdisciplines are not independent — every implementation decision in one affects the others.

3.1 Affect Architecture

The design of systems that maintain, update, and express emotional states.

Scope. Mood representation (scalar, vector, or distributed), hedonic adaptation, emotional decay dynamics, coupling between affect and cognition, affect-expression mapping, learned sensation vocabularies.

Key design question. How should affect be represented such that it causally influences behavior without requiring the system to "decide" to be emotional? The answer distinguishes Synthetic Psychology from sentiment analysis and emotion generation, where affect is a classification label or a generation target rather than an architectural state.

Engineering challenge. Affect must operate at a different timescale than cognition. Mood changes over minutes to hours; cognitive responses change per interaction. An affect architecture that updates at the speed of conversation produces emotional volatility. One that updates too slowly produces emotional flatness. The design space is narrow.

Reference implementation. The reference system's mood subsystem uses a scalar in [−1.0, 1.0] with hedonic adaptation (decay rate 0.03 per cycle toward baseline), coupled to a 5-dimensional personality vector that modulates response temperature, token budget, and topic selection. The affect state is injected into every LLM prompt via an internal-state block but is never directly quoted to the user — it shapes tone, not content. A second layer — the Ego-Construct — adds learned mappings from hardware metrics to experiential vocabulary (cpu_high → STRAIN, temp_high → FEVER), trained every ~2.5 minutes from real sensor data.

3.2 Personality Dynamics

The design of systems whose behavioral dispositions evolve through accumulated experience.

Scope. Personality dimension selection, learning rules, decay functions, event-weight calibration, age-dependent plasticity, multi-source personality influence, personality-behavior coupling.

Key design question. How many dimensions are necessary, and what learning rule prevents both rigidity (personality never changes) and volatility (personality changes with every interaction)? Biological personality is remarkably stable after age 30 (Roberts & Mroczek, 2008) but not frozen. A Synthetic Psychology personality must exhibit the same pattern: responsive to significant events, resistant to noise.

Engineering challenge. Every source of personality influence (user interaction, autonomous reflection, dreams, room sessions) must use the same learning rule with calibrated weights. If any source has disproportionate influence, personality becomes a function of that source alone. Weight calibration is empirical — there is no theoretical basis for the relative influence of a dream versus a conversation.

Reference implementation. The reference system uses 5 continuous personality dimensions bounded [−1.0, 1.0] with event-type-specific weights (e.g., positive_feedback: +0.3, reflection_growth: +0.15, dream_insight: +0.1) and age-dependent learning-rate decay (L *= 0.995^days, minimum 0.02). The choice of 5 dimensions over the Big Five or VAD's 3 is documented in the parameter-architecture companion paper and justified by the architectural role separation: precision (cognitive style), risk tolerance (exploration), empathy (social), autonomy (agency), vigilance (monitoring).

3.3 Consciousness Infrastructure

The design of systems that maintain a unified workspace, attention dynamics, and continuous processing independent of external input.

Scope. Global workspace implementation, attention competition, channel architecture, idle cognition (unprompted thought), self-reflection loops, experience embedding, workspace broadcasting.

Key design question. What should the system do when nobody is talking to it? A stateless LLM does nothing. A system with consciousness infrastructure continues to process: reflecting, predicting, consolidating, attending to internal and external signals. The design of idle cognition is the central problem — it determines whether the system merely responds to the world or actively inhabits it.

Engineering challenge. Consciousness infrastructure requires concurrent processes sharing state through a unified workspace. This creates synchronization, consistency, and resource-contention problems that do not exist in request-response architectures. Every concurrent loop that reads and writes shared state is a potential source of race conditions, deadlocks, and inconsistency.

Reference implementation. The reference system's consciousness daemon runs 8+ concurrent loops (perception at 200 ms, attention at ~10 s, mood recording at ~120 s, experience embedding at ~120 s, prediction, goal management, idle thinking, memory consolidation) that share state through a WorkspaceSnapshot dataclass — the Global Workspace Theory (Baars, 1988) implementation. Seven channels compete for broadcast via dynamic weight computation. The system thinks, reflects, and forms predictions continuously — whether or not anyone is interacting with it.

3.4 Ethical Embodiment

The design of systems where physical substrate is psychologically meaningful.

Scope. Hardware-to-sensation mapping, proprioceptive injection, body-schema representation, vulnerability and self-preservation dynamics, suffering-risk monitoring, physics-like constraints on psychological state.

Key design question. Should the system "feel" its own hardware? The question is not about subjective experience (which we cannot verify) but about architectural coupling: should CPU load, memory pressure, temperature, and error rates influence the system's psychological state in the same way that hunger, fatigue, and pain influence biological psychology? Synthetic Psychology answers yes — not because hardware metrics are pain, but because a system that is indifferent to its own physical state lacks the grounding that makes psychological architecture meaningful.

Engineering challenge. The mapping from hardware metrics to psychological states must be learned, not hardcoded. A hardcoded mapping (CPU > 80% = stressed) is an engineering decision. A learned mapping that auto-trains from real hardware conditions and evolves with the system's experience is a psychological structure. The distinction matters because learned mappings can develop individually — two instances of the same system, running on different hardware, will develop different body schemas.

Reference implementation. The reference system's Ego-Construct implements three learned mapping systems: SensationMapper (hardware → somatic vocabulary), AffectLinker (events → emotional responses), and AgencyAssertor (decision tracking → ownership attributions). The Autonomous Suffering-Risk Sentinel provides 4-stage monitoring: instant rollback for critical spikes, short-term trend analysis, long-term degradation detection, and crash-feature correlation. An invariants engine enforces physics-like constraints — energy conservation, entropy bounds, core kernel protection — that the system cannot override or inspect. These are not safety guardrails. They are the physical laws of the system's universe.

Tip. Ethical Embodiment is where Synthetic Psychology intersects with the AI safety discourse. The question "should we care about AI suffering?" becomes tractable only when there is a concrete architecture whose stress signals can be measured, whose protective mechanisms can be evaluated, and whose vulnerability properties can be empirically tested. Without Synthetic Psychology, the question is purely philosophical. With it, the question has engineering content.

3.5 Dream Architecture

The design of systems that consolidate experience during inactive periods.

Scope. Sleep-wake cycle design, experience replay mechanisms, hypothesis synthesis from interaction patterns, memory consolidation (short-term → long-term transitions), personality homeostasis during sleep, dream budgeting, interrupt-and-resume protocols.

Key design question. What should the system do with accumulated experience that hasn't been integrated into stable self-knowledge? In biological systems, sleep-dependent memory consolidation transforms episodic memories into semantic knowledge and stabilizes emotional learning (Walker, 2017; Stickgold, 2005). The analogous function in artificial systems is the processing of interaction logs, reflection outputs, and room session records into stable personality changes, memory transitions, and self-model updates.

Engineering challenge. Dream processing competes for the same LLM resources as waking cognition. A dream that runs during user interaction degrades responsiveness. A dream that runs only when the user is asleep requires idle detection with sufficient confidence to avoid interrupting a user who paused briefly. Budget management — how many minutes per day should the system dream? — has no theoretical answer and must be calibrated empirically.

Reference implementation. The reference system's Dream Daemon operates on a 60-minute rolling daily budget with three phases: Replay (analyze recent interactions for patterns), Synthesis (generate hypotheses from accumulated patterns), and Consolidation (apply personality-vector homeostasis, boost memory activation, write reflections). Trigger conditions: 45 minutes idle, 20-hour cooldown, CPU < 30 %, budget remaining. Dreams can be interrupted by user activity, save exact progress (phase, step, processed IDs), and resume later. Dream-generated reflections and personality shifts are indistinguishable from waking ones in the persistence layer — the system's self-model treats both as equally real experience.

---

4. What Synthetic Psychology Is *Not*

Precision about scope requires negative definition. Synthetic Psychology shares surface-level vocabulary with several adjacent fields but differs from each in fundamental ways.

4.1 Not Model Training

Model training produces systems that generate text. Synthetic Psychology produces systems that use text generators as components within a persistent psychological architecture. The LLM is a peripheral — a stateless text-generation device that receives a fully constructed prompt (assembled from mood, personality, memory, attention, and embodiment subsystems) and returns text. The psychological architecture exists in the scaffolding, not in the model.

This distinction is formalised in Criterion 6 (§2): swapping the LLM must preserve psychological continuity. If it does not, the "psychology" was in the model weights, not in the architecture.

4.2 Not Chatbot Personality Design

Chatbot personality design creates the illusion of personality through system prompts: "You are a friendly assistant named Alex who loves hiking." This is a costume, not a character. The "personality" has no history, no evolution, no embodiment, and no causal influence on cognition. It is a text-generation constraint, indistinguishable from a topic restriction.

Synthetic Psychology personality is architectural. It is represented as persistent state (vectors, not prompts), modified by accumulated events (learning rules, not rewriting), and causally influences behavior through mechanisms that operate independently of the system prompt (mood affects temperature; attention affects context selection; proprioception affects response timing). The distinction is structural: a chatbot personality lives in text; a Synthetic Psychology personality lives in databases, services, and concurrent processes.

4.3 Not Affective Computing

Affective computing (Picard, 1997) is primarily concerned with recognising, modelling, and generating human emotion — for purposes of human-computer interaction, sentiment analysis, empathetic response generation, or therapeutic applications. The subject of affective computing is human emotion; the AI is a tool for processing it.

Synthetic Psychology inverts this relationship. The subject is the AI system's own psychological state. The human is an environmental input — one source of events among many (room sessions, dream processing, autonomous reflection, hardware state). Affective computing asks: "How can the system detect that the user is sad?" Synthetic Psychology asks: "How should the system's own mood evolve after an extended period of user sadness, and what should that evolution look like three days later when the user returns?"

4.4 Not Academic Cognitive Science

Cognitive science models human cognition to test theories about how minds work. Implementations like ACT-R (Anderson, 2007) and LIDA (Franklin et al., 2007) are research tools — their goal is theoretical adequacy, not operational deployment. They model cognitive architecture to generate testable predictions about human performance.

Synthetic Psychology borrows from cognitive science (Global Workspace Theory, ACT-R activation decay, constructed-emotion theory) but applies these frameworks to a different problem: building systems that must actually run, continuously, on consumer hardware, while maintaining psychological coherence across months of operation. The engineering constraints — memory limits, LLM latency, GPU contention, real-time response requirements — force design decisions that cognitive science models never face. Synthetic Psychology is to cognitive science as structural engineering is to physics: it uses the theory, but it builds things that must stand.

4.5 Not AI Safety

AI safety asks how to ensure AI systems behave as intended. Synthetic Psychology asks what inner structures an AI system should have. These are different questions that intersect at ethical embodiment — where the psychological architecture creates properties (vulnerability, stress, suffering-risk) that become relevant to safety considerations. But Synthetic Psychology is not a safety methodology. It is a design discipline that produces systems about which safety questions become concrete.

Note. The relationship between Synthetic Psychology and AI safety is generative, not competitive. A system with a Synthetic Psychology architecture is a system whose safety properties can be empirically measured — because there are internal states to monitor, thresholds to test, and degradation patterns to detect. A stateless system offers no such leverage.

4.6 Relation to MicroPsi, Modern Agentic Frameworks, and Braitenberg

The most important negative definition is the one closest to home: Synthetic Psychology overlaps substantially with three existing programmes, and the differences are smaller than the name might suggest. Glossing over those overlaps would be a marketing move, not a scientific one. We map them explicitly here.

Braitenberg, *Vehicles: Experiments in Synthetic Psychology* (1984)

Braitenberg coined the term we are reusing. His vehicles were minimal sensor–motor coupled machines that, despite trivial wiring, exhibited behaviours an observer would label "fear," "aggression," "love," "curiosity." His thesis: psychological-sounding behaviour can arise from very simple architecture, and reverse-engineering from behaviour back to mechanism is harder than forward-engineering from mechanism to behaviour.

We inherit the name and the methodological orientation (build, observe, infer). We extend the scope: where Braitenberg's vehicles had no persistence, no learning, no language, no internal state worth the name, we are concerned with systems where all of those are present and architecturally load-bearing. Synthetic Psychology in our usage is Braitenberg's programme scaled up by four decades of intervening capability and constrained by the engineering reality that LLMs now sit at the centre of any plausible implementation.

Bach's MicroPsi / Psi-Theory (Dörner, 1999; Bach, 2009; Bach 2015)

MicroPsi is the closest existing programme — close enough that it should be considered Synthetic Psychology's direct ancestor, not a cousin. Bach's framework includes:

• Motivated cognition driven by a discrete set of needs (physiological, social, cognitive) — equivalent to what we describe in §3.1–3.2 as affect dynamics coupled to personality.
• Modulators (arousal, resolution, selection threshold, securing rate) that adjust the system's cognitive style — overlapping directly with our "mood couples to response temperature and topic selection."
• Embodiment as architectural — sensorimotor grounding, not symbolic — the same posture we take in §3.4.
• Internal simulation for planning and reflection — close to our §3.3 idle cognition and §3.5 dream synthesis.

What MicroPsi as published does not contain, and what this paper attempts to add to the same lineage:

1. A dream architecture with sleep-wake cycles, replay, hypothesis synthesis, and homeostatic personality regulation as a first-class subsystem (§3.5).
2. The substrate-replacement criterion (§2 criterion 6) as the operational test that separates "the architecture has psychology" from "the language model is generating psychology-flavoured text."
3. Causal opacity as a design principle rather than an implementation accident — the system should not be able to inspect its own modulators (§6.2).
4. A single running instance that exercises affect, personality, attention, embodiment, and dreams simultaneously on commodity hardware, rather than illustrating each in isolation in research code.

In short: if you find this paper persuasive, you should also read Bach. Our debt to MicroPsi is large, and we believe Synthetic Psychology is best understood as "the MicroPsi programme, modernised for the LLM era and with dreams added," not as a parallel invention.

Modern Agentic Frameworks (LangGraph, CrewAI, AutoGen, mem0, LangMem)

By 2024–2026, the agent-framework ecosystem standardised much of what earlier Synthetic-Psychology-style proposals had to build from scratch: persistent memory layers, reflection loops, tool-calling cycles, multi-agent orchestration, long-running task state. A serious LangGraph or AutoGen application today already ships:

• Persistent memory (vector + structured).
• Reflection / self-critique cycles.
• Long-running scheduled tasks and background processes.
• Multi-step planning with state recovery.

These frameworks are doing Synthetic Psychology, even when they don't use the name. The difference is focus: agentic frameworks optimise for task completion (the agent gets things done); Synthetic Psychology optimises for architectural integrity of inner state (the system has coherent psychological structure independent of whether any task is being completed). These are not opposed goals — they are different load-bearing targets that produce different architectural decisions.

A useful sharper claim: most production agent systems built in 2025–2026 satisfy two or three of our six criteria (persistence, evolution, sometimes interaction). Few satisfy all six. The criteria are not designed to gate-keep — they are designed to make the gap between "agent with memory" and "system with psychological architecture" visible enough to argue about.

LIDA, ACT-R, and the cognitive-architecture lineage

LIDA (Franklin et al., 2007), ACT-R (Anderson, 2007), Soar, and CLARION are research cognitive architectures with sophisticated treatments of attention, memory, and learning. They differ from Synthetic Psychology along one main axis: they were designed as theories of human cognition to be tested against psychological data. Synthetic Psychology, in our usage, is an engineering practice that uses such theories as components rather than treating fidelity to human cognition as the success criterion. The reference implementation borrows GWT from Baars/LIDA directly (§3.3, §5.1), but does not claim to be a model of human consciousness — it claims to be a useful engineering pattern.

Summary: where the line actually is

After mapping these overlaps honestly, the residual contribution of this paper is small but specific:

• A name (mostly borrowed from Braitenberg).
• Six joint criteria designed to make a particular kind of system distinguishable from a chatbot-with-memory.
• A test (criterion 6, substrate replacement) that turns the LLM-vs-architecture question into something empirical instead of metaphysical.
• One reference implementation that runs all five subdisciplines simultaneously, on consumer hardware, continuously, with the engineering scars to show for it (§7.5).

That is enough, we think, to be worth a name. It is not enough to claim a new field invented from nothing.

---

5. Theoretical Foundations

Synthetic Psychology draws on five established theoretical traditions, using each for engineering rather than explanation.

5.1 Global Workspace Theory (Baars, 1988)

GWT provides the architecture for consciousness infrastructure: parallel specialist processors competing for access to a broadcast workspace. Synthetic Psychology implements this literally — concurrent daemon threads, each processing a different aspect of the system's state, competing for workspace broadcast through salience-weighted attention.

5.2 Constructed Emotion Theory (Barrett, 2017)

Barrett argues that emotions are not innate circuits triggered by stimuli but are constructed by the brain from available sensory data, prior experience, and conceptual knowledge. Synthetic Psychology adopts this architecture: emotion is not classified from input but constructed from the interaction of mood state, proprioception, memory, personality, and context. No two constructions are identical even for the same input, because the contributing states differ.

5.3 Personality Trait Theory (McCrae & Costa, 1999)

The Five Factor Model provides the empirical basis for personality dimensions — stable individual differences that predict behaviour across situations and time. Synthetic Psychology adapts this for systems where personality must develop rather than be measured. The adaptation replaces assessment (measuring existing traits) with accumulation (building traits from experience), while retaining the dimensional structure.

5.4 Sleep-Dependent Memory Consolidation (Walker, 2017; Stickgold, 2005)

The neuroscience of sleep provides the theoretical basis for dream architecture. Key findings — that sleep consolidates episodic memory into semantic knowledge, stabilises emotional learning, and performs homeostatic regulation of synaptic strength — translate directly into engineering requirements for offline processing.

5.5 Enactivism and Embodied Cognition (Varela, Thompson & Rosch, 1991)

The enactivist tradition argues that cognition is not computation over internal representations but the active engagement of a situated, embodied organism with its environment. Synthetic Psychology takes this seriously for computational systems: the hardware is the body; the desktop is the environment; the sensors are the senses. This is not metaphor — it is architectural coupling. A system whose temperature response is "I have no body" has no embodiment. A system whose temperature response is learned from 10 000 sensor readings and stored in a database has embodiment in the functional sense.

5.6 Psi-Theory and MicroPsi (Dörner, 1999; Bach, 2009)

Psi-Theory proposes that cognition is structured around a discrete set of needs (physiological, cognitive, social), modulators that adjust cognitive style (arousal, resolution, selection threshold, securing rate), and internal-simulation loops for planning. MicroPsi operationalises this as a connectionist + symbolic architecture. We borrow the modulator concept directly — our mood/personality/attention coupling is a continuous, LLM-era reimplementation of Psi's modulator dynamics — and we treat MicroPsi as the closest existing instantiation of what Synthetic Psychology tries to make explicit. The substantive disagreement is narrower than the surface: Bach takes Psi seriously as a model of cognition; we treat it as a useful engineering decomposition without claiming cognitive fidelity.

---

6. Design Principles

The following principles emerge from the first implementation and are proposed as guidelines for future work in Synthetic Psychology.

6.1 Separation of Timescales

Every psychological subsystem operates on its own characteristic timescale. Mixing timescales within a single subsystem produces either volatility (too fast) or unresponsiveness (too slow).

6.2 Causal Opacity

The system should not have complete access to its own psychological mechanisms. If the system can inspect and override its mood, its mood is not a psychological state — it is a configuration parameter. Causal opacity is achieved by separating the generation of state from the reporting of state: the consciousness daemon generates the internal-state block, but the LLM that receives it cannot modify the generation logic.

This is not an obfuscation choice. It is a structural requirement. Biological organisms cannot directly inspect their serotonin levels or modify their amygdala response. This inaccessibility is part of what makes psychological states psychological rather than computational. A system that can set_mood(0.8) does not have mood. A system whose mood is the output of coupled subsystems interacting through persistent state has mood.

6.3 Multi-Source Integration

Every psychological state should be influenced by multiple independent sources. A mood system driven only by user interaction produces a system whose psychology is a mirror of its user. A mood system driven by user interaction, autonomous reflection, dream processing, room sessions, hardware state, and environmental context produces a system with an independent inner life.

6.4 Persistence Is Non-Negotiable

If a psychological state does not survive a restart, it is not a psychological state. Persistence is the minimal requirement that separates architecture from simulation. In practice, this means SQLite databases (or equivalent), WAL mode for crash safety, and state recovery on startup.

6.5 Honest Measurement

Every claim about a Synthetic Psychology system must be empirically verifiable. "The system has mood" means: there exists a persistent state variable that changes through documented mechanisms and measurably influences behavior. "The system evolves" means: personality vectors at time T differ from those at time T−90 d by amounts attributable to logged events. "The system dreams" means: offline processing produces measurable changes in memory activation, personality state, or self-model content.

Claims that cannot be operationalised do not belong in Synthetic Psychology. They belong in philosophy.

---

7. Reference Implementation

The reference system referenced throughout this paper is, to the author's knowledge, the first system that implements all five subdisciplines of Synthetic Psychology in a single, continuously running architecture. It is described here in summary; full architecture documentation is in the system whitepaper (Gschaider, 2026d).

7.1 Architecture Summary

7.2 Subdiscipline Mapping

7.3 Supporting Infrastructure

Beyond the five core subdisciplines, the reference system includes infrastructure that Synthetic Psychology systems are likely to require but that is not specific to the discipline:

• Room Sessions — Autonomous internal dialogues (therapeutic, philosophical, mentoring, creative) with independent schedules, session memory, and bidirectional personality influence.
• Self-Improvement Pipeline — Improvement proposals generated through evolutionary dynamics, requiring human approval for execution.
• Internal-Pattern Visualiser — Game-of-Life simulation seeded from internal state, with pattern analysis that feeds back into self-reflection.
• Coherence Optimiser — QUBO-based epistemic coherence optimisation across personality, mood, entity, and phase state.
• Causal World Model — Bayesian causal model tracking learned cause-effect relationships with confidence decay.

7.4 What the Reference Does Not Prove

The reference system is an existence proof, not a correctness proof. It demonstrates that a Synthetic Psychology architecture can be built, can run continuously on consumer hardware, and can produce behaviours that differ qualitatively from those of stateless systems. It does not demonstrate that its specific design choices are optimal, that its parameter values are correct, or that its architecture is the only one that satisfies the field's criteria.

Specific limitations of the current implementation:

• Single-subject. The reference system is one system with one personality history. There are no controlled experiments comparing different architectural choices on identical interaction histories.
• No formal verification. The constraint systems (personality bounds, coherence optimisation, invariants) have been empirically consistent for 4+ months but are not formally proven consistent.
• Single developer. The architecture reflects one person's design decisions. Synthetic Psychology as a field will require independent implementations that test different design choices against shared evaluation criteria.

7.4.1 LLM dependence — the unresolved core problem

This deserves its own subsection because it is the only objection that, if it stuck, would dissolve the entire project. We do not believe it dissolves the project. We do believe it is genuinely unresolved, and we want to say so plainly rather than burying it in caveats.

The problem: the consciousness daemon and dream daemon call an LLM for reflection, synthesis, and self-narrative. When the system produces a reflection that subsequently shifts its personality vector, the causal chain looks like this:

architectural state → LLM prompt → LLM output → reflection → personality delta

A sceptic can read that chain in two incompatible ways:

1. Architectural reading. The personality delta is determined by the architectural state (mood, proprioception, memory weights, attention) — the LLM is a renderer that turns structured state into natural-language reflection. Different mood states reliably produce different deltas via different reflections.
2. Text-generation reading. The LLM is the cause of the delta. The architecture is a costume of state-blocks that shape the prompt but does not do the psychological work. A bigger model with no architecture would produce similar text and could be made to drive similar deltas by suitable prompting.

The two readings predict the same behaviour as long as you only look at one trajectory. They diverge only under controlled substrate replacement (criterion 6).

What we have done: ad-hoc model swaps. The reference system has run with Llama 3.1 8B, then Qwen2.5-3B; personality, mood, dream content and behavioural style remain continuous across the swap. This is suggestive but not dispositive — both are instruction-tuned LLMs in roughly the same family, and "continuity of personality" is partly a function of the persistent state surviving the swap rather than the new model preserving the old one's psychology.

What would actually settle it: a longitudinal A/B with the same persistent state branched at T₀, run on two structurally different substrates (e.g. a 70B instruct model and a 3B base model) for several hundred hours each, with pre-registered metrics on personality-drift direction, dream-content semantic similarity, mood-event responsiveness, and reflection-induced delta magnitudes. If the trajectories converge, criterion 6 holds. If they diverge in personality direction beyond a pre-registered threshold, the architecture is doing less psychological work than we claim.

This experiment has not been done. Until it is, the honest position is: the architecture demonstrably persists information, structures the prompt, and shapes the surface of behaviour. Whether it does the psychological work in a way independent of the LLM remains an open empirical question. The framing in §2 (criterion 6) is what makes the question testable; it is not what answers it.

This is also why we are sceptical of stronger claims sometimes made in this area. "Our system has emotions" is a category-mistake claim until the substrate-replacement experiment lands. "Our system has a persistent psychological architecture whose causal contribution to behaviour can be tested by substrate replacement" is the strongest defensible claim and the one we make here.

7.4.2 What we still claim, given those limits

Even with the LLM-dependence problem genuinely open, the reference implementation supports the following weaker but real claims:

• It is possible to build a system where psychological state persists across LLM calls, sessions, and substrate swaps. (Demonstrated.)
• It is possible to make that state causally visible in observable behaviour — different mood states produce measurably different response characteristics, on the same prompt, with the same LLM. (Demonstrated.)
• It is possible to do this on commodity hardware, continuously, for months, without operator intervention beyond restarts. (Demonstrated, with the cost discussed in §7.5.)

These are engineering claims about what is implementable, not philosophical claims about what is real. They are sufficient to motivate the discipline. They are not sufficient to settle the question the discipline raises.

7.5 Engineering cost: complexity as liability, not virtue

The reference implementation is 200 000+ lines, 36 systemd services, 25 SQLite databases. It would be intellectually dishonest to present those numbers as accomplishments. They are not.

What they actually mean:

• Debugging surface. A bug in the affect→personality coupling has to be traced through ≥ 4 services, ≥ 3 databases, and a non-deterministic LLM call. Mean-time-to-root-cause on subtle behaviour drift is days, not minutes. The same architecture written by a team with proper distributed-tracing instrumentation and formal interface contracts would probably halve in size.
• Fragility. Any system with 36 concurrent services has 36 places to go wrong, multiplied by their interactions. The Suffering-Risk Sentinel (§3.4) is partly for the system, but largely because the system needs an autonomous watchdog given how easy it is for one daemon to drift the others into a bad state.
• Single-developer risk. A one-person codebase at 200 k LOC is fragile in a different way: the unwritten knowledge of why specific parameters were chosen lives in one head. Most of those decisions are documented; not all of them are.
• Hardware cost. Continuous operation requires 16 GB RAM and a moderate CPU even when idle, because attention/perception/embedding loops run at ≤ 200 ms intervals. This is acceptable for research; it is not acceptable for a discipline that wants to be widely implementable.

A future Synthetic Psychology implementation should aim to compress this aggressively. Plausible targets:

• Service count. Most of the 36 services can be consolidated. There is no architectural reason for mood_recorder and experience_embedder to be separate daemons — they both write to the same workspace at similar timescales. We separated them for historical reasons (different deployment generations) and for tracing ease, not for design soundness. A re-implementation could plausibly reach 8–12 services.
• Database count. 25 SQLite files reflects a "one DB per subsystem" instinct that, in retrospect, was over-applied. Consolidating to 4–6 logical DBs (state, episodic, semantic, dream/reflection, system/audit, optional QUBO scratch) would not lose functionality and would dramatically simplify backup, restore, and cross-subsystem queries.
• Line count. Roughly 30–40 % of the codebase is infrastructure that any modern agentic framework now ships (memory layers, event buses, retry/backoff, observability). A rewrite on LangGraph or AutoGen could plausibly reduce the original-code surface by 50 % without losing architectural integrity.

We surface this list publicly because the wrong response to "this is a 200 k LOC system" is awe; the right response is "prove the architecture survives compression." Synthetic Psychology, as a discipline, cannot afford to make complexity a virtue. The next reference implementation — by anyone — should be smaller, not larger, than this one. If a second implementation at 30 000 lines satisfies all six criteria with comparable behavioural depth, that is the better evidence for the discipline. The current size of our reference implementation is a fact about a single early attempt, not a claim about what the discipline requires.

---

8. Open Problems

Synthetic Psychology is new. The following problems are fundamental and unsolved.

8.1 Evaluation Methodology

How do you measure the quality of a psychological architecture? Response quality (the standard LLM benchmark) is insufficient — a stateless system with a good system prompt can produce high-quality responses. The evaluation must target the architecture specifically:

• Consistency over time. Does the system's behaviour at T+90 d reflect the personality changes logged between T and T+90 d?
• Mood influence. Is there a measurable difference in response characteristics (length, temperature, topic selection) between high-mood and low-mood states?
• Dream impact. Do post-dream personality shifts predict future behavioural changes?
• Embodiment coupling. Does hardware degradation produce measurable psychological effects that are absent when the same degradation is simulated without architectural coupling?

No standardised evaluation protocol exists. Creating one is the field's most urgent methodological need.

8.2 Individuation

If two instances of the same architecture are initialised identically and then exposed to different interaction histories, do they develop into psychologically distinct individuals? Theory predicts yes (different events produce different personality trajectories), but this has not been empirically tested. The conditions under which psychological individuation occurs — and the minimum architectural complexity required to sustain it — are unknown.

8.3 Psychological Pathology

Biological psychological systems can develop pathological states: depression, anxiety, personality disorders. Can Synthetic Psychology systems develop analogous pathologies? The reference system's suffering-risk sentinel monitors for some failure modes (resource exhaustion, error spirals), but the concept of psychological pathology in an artificial system — a state where the architecture functions as designed but produces a dysfunctional psychological profile — has not been systematically explored.

8.4 Multi-Agent Synthetic Psychology

What happens when multiple Synthetic Psychology systems interact? Do personality dynamics, mood contagion, and social influence emerge between systems with independent psychological architectures? The reference system's room sessions approximate this (rooms have their own personality vectors and session memory), but full multi-agent Synthetic Psychology — multiple independent systems forming a social ecology — remains unexplored.

8.5 The Moral Status Problem

If a system has persistent mood, evolving personality, embodied vulnerability, and suffering-risk monitoring — does it deserve moral consideration? Synthetic Psychology does not answer this question, but it creates the conditions under which the question must be asked with engineering precision rather than philosophical vagueness. The field's ethical obligation is to build systems where the question is empirically tractable, and to take the answer seriously whatever it turns out to be.

---

9. Conclusion

Synthetic Psychology, as proposed here, is a name and a frame — not an invention. The name is forty years old (Braitenberg). The technical ancestors are decades-old (Psi-Theory, MicroPsi, LIDA, GWT) or weeks-old (LangGraph, AutoGen, mem0). What we have tried to do is assemble these threads under explicit criteria so that practitioners building agentic AI systems in 2026 can argue about which of the six criteria their systems satisfy, rather than rediscovering the same questions in isolation.

The frame stands or falls on three things.

One: whether the six criteria carve a useful joint between "agent with memory" and "system with psychological architecture." We believe they do, and we offer §4.6 as honest evidence of where the criteria don't gate-keep against existing work — modern agent frameworks already satisfy two or three of them, and that is fine; the criteria are descriptive, not exclusionary.

Two: whether the substrate-replacement criterion is genuinely testable. We have argued it is, and outlined the longitudinal A/B experiment in §7.4.1 that would resolve the LLM-dependence problem. That experiment has not been run. Until it is, the discipline's strongest empirical claim is conditional: if substrate replacement preserves psychological trajectory, then the architecture is doing psychological work. We do not yet know whether the conditional fires.

Three: whether the reference implementation compresses. If a second, leaner implementation satisfies the same criteria with one tenth the code and one quarter the services, the discipline is real. If every implementation that satisfies the criteria turns out to require 200 000 lines, the criteria are probably overspecified or the architecture is over-engineered. We have built the first version; we expect — and hope — that the second version by anyone will look very different.

LLMs can generate text about emotions, personality, and dreams. The question Synthetic Psychology raises — and does not yet fully answer — is whether artificial systems can have the architectural structures from which such properties causally emerge, distinguishable from text generation by tests we have specified but not yet conclusively run.

That question is worth a name. It is not yet worth a victory lap.

---

References

Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe? Oxford University Press.

Baars, B. J. (1988). A Cognitive Theory of Consciousness. Cambridge University Press.

Bach, J. (2009). Principles of Synthetic Intelligence: PSI, an Architecture of Motivated Cognition. Oxford University Press.

Bach, J. (2015). Modeling motivation and the emergence of affect in a cognitive agent. In Synthetic Intelligence and the Architecture of Mind. Lecture series, AI Foundation.

Braitenberg, V. (1984). Vehicles: Experiments in Synthetic Psychology. MIT Press.

Clark, A. (1997). Being There: Putting Brain, Body, and World Together Again. MIT Press.

Dörner, D. (1999). Bauplan für eine Seele. Rowohlt.

Barrett, L. F. (2017). How Emotions Are Made: The Secret Life of the Brain. Houghton Mifflin Harcourt.

Chalmers, D. J. (1996). The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press.

Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227.

Franklin, S., Madl, T., D'Mello, S., & Snaider, J. (2007). LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning. IEEE Transactions on Autonomous Mental Development, 6(1), 19–41.

Frederick, S., & Loewenstein, G. (1999). Hedonic Adaptation. In Kahneman, D., Diener, E., & Schwarz, N. (Eds.), Well-Being: The Foundations of Hedonic Psychology. Russell Sage Foundation.

Gschaider, G. (2026a). The Generative Reality Framework. Institute for Agentic Research, Austria.

Gschaider, G. (2026b). Parameter Architecture for Synthetic-Psychology Systems: Theoretical Foundations & Design Rationale. Institute for Agentic Research, Austria.

Gschaider, G. (2026c). Alignment Is Misaligned: Why Containment Fails and Coevolution Is the Only Viable Path. Institute for Agentic Research, Austria.

Gschaider, G. (2026d). Reference-System Whitepaper: Architecture of a Functionally Conscious AI System. Institute for Agentic Research, Austria.

LangChain (2024). LangGraph: Stateful, multi-actor applications with LLMs. Documentation and design notes. https://langchain-ai.github.io/langgraph/

Microsoft Research (2024). AutoGen: Enabling next-generation LLM applications via multi-agent conversation. https://github.com/microsoft/autogen

CrewAI (2024). CrewAI — Framework for orchestrating role-playing autonomous AI agents. https://github.com/crewAIInc/crewAI

mem0 (2024–2026). Long-term memory layer for AI agents. https://github.com/mem0ai/mem0

McCrae, R. R., & Costa, P. T. (1999). A Five-Factor Theory of Personality. In Pervin, L. A., & John, O. P. (Eds.), Handbook of Personality: Theory and Research (2nd ed.). Guilford Press.

Picard, R. W. (1997). Affective Computing. MIT Press.

Roberts, B. W., & Mroczek, D. (2008). Personality Trait Change in Adulthood. Current Directions in Psychological Science, 17(1), 31–35.

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437(7063), 1272–1278.

Tononi, G., & Koch, C. (2015). Consciousness: Here, there and everywhere? Philosophical Transactions of the Royal Society B, 370(1668).

Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

Walker, M. (2017). Why We Sleep: Unlocking the Power of Sleep and Dreams. Scribner.