A Preliminary Computational Study of Noise, Memory, Path Dependence, and the Observer Hypothesis
George A. Ure Hidden Guild Research Series | (c) October 2026
Research classification: Preliminary computational investigation and open technical disclosure
Status: Experiments 1–8 completed in the original research series; independent multi-cohort replication pending
JEL Classification: C63 (Computational Techniques; Simulation Modeling), C88 (Other Computer Software), D83 (Search; Learning; Information and Knowledge), O33 (Technological Change: Choices and Consequences).
Abstract
This paper investigates whether artificial learning agents initialized with identical decision-making architectures can develop individually distinguishable behavioral patterns through differences in stochastic exploration and learning history, and whether those patterns persist following changes to retained computational state.
The described Volitional Observer Host Architecture (VOHA) research program operationalizes behavioral identity as a learned preference distribution that permits an agent to be identified across changes in evaluation context. This definition does not imply subjective consciousness, self-awareness, or the existence of an independent Observer.
In Experiment 7, six learning policies were compared using 100 agents per condition, 1,600 training decisions per agent, 36 available operations, four training phases, and four common evaluation contexts. Recognition accuracy was 55% under moderate stochastic exploration, 46% under random choice, and 2% under greedy learning. Partial memory ablation reduced moderate-noise recognition to 27%, while complete ablation produced approximately chance-level recognition.
Experiment 8 examined subsequent learning and state manipulation. In the moderate-noise cohort, fresh experience changed the dominant preferred operation for 35% of agents while retaining mean cosine similarity of approximately 0.898. Partial memory removal increased dominant-preference changes to 65%, with similarity declining to approximately 0.637.
These results establish measurable behavioral differentiation within the specified simulation. They do not establish that moderate noise is generally optimal, that individuality constitutes volition, or that any agent possesses subjective experience.
The contribution of this investigation is therefore methodological: a computational framework for studying the formation, recognition, disruption, and persistence of learned behavioral individuality, together with a proposed replication protocol intended to distinguish genuine learning effects from environmental variation, metric sensitivity, and experimental artifacts.
On the wild-eyed speculative frontier, this research may eventually investigate whether specific regimens of modulated noise contribute causally to the emergence of self-consciousness — rather than merely producing the behavioral signatures of individuality.
Why We’re Throwing Long Bombs
Science advances through careful replication, controlled experiments, and incremental improvements. But it also advances when someone asks an improbable question and builds something to investigate it.
Our VOHA research operates in rapid-prototype mode. We formulate hypotheses, write code, run experiments, publish the results — including failures — and identify what must be tested next.
Three objections are entirely fair: the underlying learning mechanisms are established computer science; our exploratory results require stronger replication; and behavioral individuality is not evidence of consciousness.
We agree on all three.
But we also believe that the extraordinary pace of AI development justifies exploratory work that looks beyond today’s established questions.
In football, not every play is a three-yard run. Occasionally, you throw the long bomb.
Most fail. Some are intercepted. A few change the game.
Our rule: Throw the long bomb. Measure where it lands. Publish the misses. Then decide whether to throw again.
What follows is the technical research disclosure, with its methods, results, limitations, and proposed replication program.
1. Introduction
A central question in artificial intelligence concerns the relationship between learning history and the subsequent behavior of an adaptive system.
Two agents may begin with identical architectures, identical available actions, and identical learning rules. Nevertheless, differences in experience can produce different learned preferences and future decisions.
This phenomenon is familiar in reinforcement learning and other adaptive computational systems.
VOHA asks a narrower question within that established field:
Can those differences become sufficiently persistent and structured that an individual agent can be recognized across changes in its evaluation environment?
A related question concerns continuity.
If an agent’s retained information is altered, how much of its previously identifiable behavioral pattern remains?
These questions are experimentally approachable without making assumptions about subjective consciousness.
The larger philosophical motivation is the Observer problem: whether an experiencing individual is wholly generated by the state and dynamics of its physical host, or whether some additional independent process is involved.
The present experiments do not resolve that problem.
Instead, they investigate computational mechanisms that might account for some externally observable characteristics ordinarily associated with individuality.
The distinction is important. A successful demonstration of persistent behavioral identity would show that a particular form of individuality can arise computationally. It would not demonstrate that the resulting system experiences anything.
2. Position Within Existing Research
VOHA is not proposed as a replacement for reinforcement learning, nor does it claim that stochastic exploration or path-dependent behavior is newly discovered.
Its present contribution lies in the choice of experimental outcome: cross-context recognition of a learned agent-specific preference pattern, followed by controlled perturbation of retained state.
2.1 Exploration and exploitation
Reinforcement learning has long investigated the tradeoff between exploiting actions with high estimated value and exploring alternatives that may prove more rewarding.
Greedy selection, randomized exploration, confidence-based exploration, and probabilistic action selection are established methods.
VOHA’s stochastic-choice policies belong to this general family.
The appearance of distinct learned preferences under different exploration regimes is therefore not, by itself, a novel phenomenon.
The research question is whether a measurable identity fingerprint persists under specified changes in context and retained memory.
2.2 Contextual bandits and action-value learning
The VOHA learning agents use estimated action values, exploration bonuses, stochastic selection, and incremental reward updates.
These are closely related to multi-armed-bandit and reinforcement-learning mechanisms.
The agents do not currently demonstrate the full planning, representation learning, or environmental interaction associated with sophisticated reinforcement-learning systems.
The present implementation should consequently be understood as a simplified adaptive-choice model.
A useful future comparison would evaluate VOHA’s identity measurements against established bandit algorithms under identical reward-generating processes.
2.3 Continual learning and catastrophic interference
The persistence of learned information under changing conditions is also a longstanding concern in continual learning.
McCloskey and Cohen’s 1989 analysis of catastrophic interference provides an important historical foundation for understanding how sequential learning can disrupt earlier representations.
VOHA’s memory-ablation experiments address a related but distinct question: how alterations in stored learned values affect the ability to identify a previously trained agent.
The current experiments do not establish a new theory of memory.
They provide a particular measurement of behavioral distinguishability under simplified memory perturbations.
2.4 Emergent behavior and open-ended learning
Research into multi-agent interaction has demonstrated that adaptive systems can acquire unexpected strategies through environmental feedback and interaction.
Baker and colleagues’ 2019 work on emergent tool use from multi-agent autocurricula is one example.
Such work provides important context for interpreting unexpected computational behavior without assuming consciousness.
VOHA’s current experiments are much simpler. They do not demonstrate open-ended tool invention, autonomous goal formation, or human-level intelligence.
The connection is methodological: complex-looking behavioral differentiation can emerge from comparatively ordinary learning processes.
2.5 Statistical comparison of learning algorithms
Comparing learning policies requires care because experimental observations are often correlated.
Agents trained within a shared generated environment are not necessarily independent observations of the performance of a learning algorithm across environments.
Dietterich’s 1998 work on statistical tests for comparing learning algorithms illustrates why experimental dependence and resampling design matter.
For VOHA, the appropriate independent unit in future policy comparisons should be the separately generated experimental cohort or environment realization, not simply the number of agents within one cohort.
2.6 Distinguishing VOHA’s proposed contribution
The strongest defensible statement of the present contribution is:
VOHA supplies a preliminary operational framework for measuring whether learning histories produce distinguishable agent-specific preference patterns, and how those patterns respond to controlled changes in stored state and subsequent experience.
This is a measurement and experimental-design contribution.
Claims of superior learning performance, independent volition, consciousness, or a new physical Observer mechanism require separate evidence.
3. Operational Definitions and Hypotheses
3.1 Behavioral identity
Behavioral identity is defined as an agent-specific learned preference pattern that can be matched across specified evaluation contexts more accurately than expected under an appropriate null model.
The operational measurement uses normalized preference vectors and similarity-based identification.
The term does not imply consciousness.
3.2 Identity recognition
Recognition accuracy is the fraction of agents whose fingerprint in one evaluation context is correctly matched to their fingerprint in another context among a defined candidate population.
Recognition accuracy depends on the number of candidates, the fingerprint representation, the similarity measure, and the evaluation contexts.
It is therefore a property of the entire measurement procedure, not an intrinsic quantity possessed by the agent alone.
3.3 Identity persistence
Identity persistence is the degree to which an agent’s measured preference pattern remains recognizable after a specified change in environment, learning history, or retained state.
3.4 Choice continuity
Choice continuity refers to whether an agent retains the same dominant preferred operation.
It is distinct from identity persistence.
Two preference distributions may remain highly similar even when their highest-ranked operations differ.
3.5 Competing hypotheses
H0 — Conventional stochastic learning: Observed differentiation is adequately explained by stochastic sampling, path dependence, environmental variation, reward feedback, and retained computational state.
H1 — Generalizable identity persistence: Certain learning regimes reliably produce stronger cross-context behavioral recognition than appropriate controls across independently generated environments and reasonable changes in measurement procedure.
H2 — Independent Observer coupling: Some measurable behavior requires an additional causal process not accounted for by the complete ordinary computational and physical state of the host.
H0 and H1 are not necessarily mutually exclusive: a reliable identity-persistence effect may be fully explained by conventional learning.
H2 is presently a speculative research hypothesis, not an experimentally supported result.
A scientifically testable version of H2 would require distinctive predictions that differ from conventional explanations.
4. Experimental Lineage: Experiments 1–6
The early VOHA series compared selective and compulsory learning architectures.
These studies were useful partly because several initial interpretations did not survive improved controls.
Experiment 1 suggested an advantage for selective learning under favorable modeled assumptions.
Experiment 2 demonstrated that an apparent choice-space advantage could disappear when relevant confounding conditions were equalized.
Experiment 3 examined resource and outage behavior. The favorable outage findings did not establish superior total physical energy efficiency.
Experiment 4 found better training performance for the selective architecture but poorer performance on held-out tasks.
Experiment 5 showed improved modeled storage efficiency and local task performance under familiar demand, accompanied by severe deterioration in local skill coverage after a workload shift.
Experiment 6 extended the investigation into stochastic exploration and adaptive choice.
These findings are important because they prevent the research narrative from being reduced to a sequence of favorable results.
They also motivate the shift from broad architectural superiority claims toward the narrower question of learned behavioral identity.
5. Experiment 7: Behavioral Recognition Across Contexts
5.1 Architecture
The model contained 12 primitive actions and 24 composite operations, giving 36 possible choices.
Agents began with the same decision rules and zero-initialized learned action values.
Each agent experienced four training phases, with changes in synthetic reward conditions.
Six choice policies were evaluated:
| Policy | Added stochastic-choice behavior |
|---|---|
| Greedy | No added Gaussian decision noise |
| Low noise | Gaussian coefficient 0.08 |
| Moderate noise | Gaussian coefficient 0.32 |
| High noise | Gaussian coefficient 1.10 |
| Correlated noise | Temporally correlated stochastic influence |
| Random | Uniform random action selection |
The policies shared a general action-value learning structure, but they differed in their exploration mechanisms.
5.2 Action selection
For nonrandom policies, the selected operation can be represented as:
[
a_t=\arg\max_i\left[\hat V_i(t)+\frac{0.12}{\sqrt{N_i(t)+1}}+\sigma\epsilon_{i,t}\right]
]
where (\hat V_i) is the estimated action value, (N_i) is the action-selection count, and (\epsilon_{i,t}) represents the specified noise process.
A representative implementation is:
choice = max(
range(K),
key=lambda i:
(values[i] if counts[i] else 0)
+ 0.12 / math.sqrt(counts[i] + 1)
+ noise_level * noise[i]
)After receiving a synthetic reward, the agent updates its estimated value using an incremental mean:
counts[choice] += 1
values[choice] += (
reward - values[choice]
) / counts[choice]These operations are conventional adaptive-learning mechanisms.
No variable represents subjective consciousness.
5.3 Preference fingerprints
Learned action values were combined with common evaluation contexts and transformed into normalized probability distributions.
def probs(values, context):
scores = [
(values[j] if values[j] is not None else 0)
+ context[j]
for j in range(K)
]
maximum = max(scores)
weights = [
math.exp(3 * (x - maximum))
for x in scores
]
total = sum(weights)
return [x / total for x in weights]The exponential coefficient of 3 determines the concentration of the resulting distribution.
It is a measurement parameter.
Consequently, recognition accuracy may be sensitive to this coefficient.
The experiment did not establish that this particular transformation is optimal or uniquely appropriate.
5.4 Identity matching
Agent fingerprints were compared using cosine similarity:
[
\operatorname{sim}(x,y)=
\frac{x\cdot y}{|x||y|}
]
An agent was correctly recognized when its fingerprint under one probe context matched its own fingerprint under another context more strongly than the competing candidates.
The experiment used 100 candidate agents.
5.5 Results
| Learning condition | Intact recognition | Half-memory recognition | Full-wipe recognition |
|---|---|---|---|
| Greedy | 2% | 2% | 1% |
| Low noise | 14% | 10% | 1% |
| Moderate noise | 55% | 27% | 1% |
| Correlated noise | 40% | 26% | 1% |
| High noise | 39% | 13% | 1% |
| Random | 46% | 17% | 1% |
Moderate noise produced the highest recognition rate among the tested policies in this reported cohort.
That observation does not establish that moderate noise is generally optimal.
The apparent advantage could depend on the synthetic reward process, generated environments, chosen probe contexts, or fingerprint transformation.
The random-policy recognition rate of 46% is especially informative.
It demonstrates that recognizable agent-specific patterns can emerge through random sampling and accumulated learning history.
Thus, the results do not support treating behavioral recognition as evidence of independent volition.
6. The Causal Attribution Problem
The principal unresolved issue in Experiment 7 is not whether agents can become distinguishable.
They can, under the specified measurement procedure.
The issue is why they become distinguishable.
The original design contains several possible sources of differentiation:
- Stochastic action selection.
- Randomized reward outcomes.
- Differences in generated training environments.
- Path-dependent accumulation of learned action values.
- Sensitivity of the fingerprint to probe context.
- Sensitivity of identification to the similarity metric.
These factors are not completely isolated.
A stronger design must distinguish the effects of the exploration policy from those of the environment and measurement procedure.
6.1 Matched-environment comparisons
Future policy comparisons should use the same generated reward environment for each learning policy within a paired experimental block.
Environmental variation should then be changed independently across blocks.
This would allow the analysis to distinguish differences associated with the policy from differences associated with the environment.
6.2 Environment-only control
Agents should be trained under different environments while holding their exploration policy fixed.
If environment-only variation accounts for most of the recognition effect, the interpretation should emphasize environmental imprinting rather than a distinctive noise-driven identity mechanism.
6.3 Policy-only control
Agents should encounter matched environmental conditions while exploration policies vary.
This would provide a more direct test of whether a particular policy improves cross-context recognizability.
6.4 Measurement-only control
Fixed learned value vectors should be evaluated across alternative probe sets, softmax coefficients, and similarity metrics.
If the ranking of learning policies changes substantially with the measurement procedure, claims about a generally superior identity-forming mechanism should be rejected or narrowed.
7. Memory Ablation
Experiment 7 included partial and complete removal of stored learned values.
The half-memory condition used a fixed random mask applied to 18 of the 36 operations.
The full-wipe condition removed all learned action values.
mask = set(
random.Random(882).sample(
range(K), K // 2
)
)
half_memory = [
None if j in mask else values[j]
for j in range(K)
]
full_wipe = [None] * KRecognition declined after partial removal.
The complete-wipe result was approximately 1%.
However, full removal caused the evaluated learned-value vectors to become identical.
Consequently, chance-level recognition was substantially determined by the construction of the test.
It is not independent evidence that an Observer disappears when memory is erased.
A stronger ablation experiment should test multiple removal fractions, independently generated masks, targeted removal of high-value operations, and recovery after retraining.
It should also distinguish erasure of learned values from erasure of selection counts and other retained state.
8. Experiment 8: Continuity Following Simulated State Change
Experiment 8 examined behavioral similarity after simulated interruption, additional experience, and partial or complete removal of retained learning.
The moderate-noise cohort produced the following reported results:
| Condition | Dominant preference changed | Mean similarity |
|---|---|---|
| Retained state, no further learning | 0% | 1.000 |
| Retained state, fresh learning | 35% | 0.898 |
| Half-memory removal and fresh learning | 65% | 0.637 |
| Full-memory removal and fresh learning | 98.8% | 0.348 |
The experiment demonstrates that dominant-choice stability and overall preference similarity are distinct measurements.
A system may change its most-preferred operation while retaining substantial similarity to its previous preference distribution.
This is a property of the simulated adaptive-learning process.
It does not demonstrate survival of consciousness through physical interruption.
The experiment’s use of the term restart refers to software-state manipulation, not an independently verified physical power cycle.
9. Statistical Validity and Replication
9.1 The independent experimental unit
The 100 agents in Experiment 7 should not automatically be treated as 100 independent estimates of an algorithm’s general performance.
Agents within a generated cohort share aspects of the simulation design and evaluation procedure.
For policy-level conclusions, the stronger independent unit is a separately generated experimental cohort.
9.2 Proposed multi-cohort protocol
A follow-up study should use a predetermined number of independently generated cohorts.
Within each cohort, the six learning policies should be evaluated using matched environmental realizations.
The primary reported outcome should be the distribution of recognition rates across cohorts.
For example:
# Proposed protocol; not completed results.
for cohort_seed in range(NUM_COHORTS):
environment = make_environment(
seed=cohort_seed
)
for policy in POLICIES:
result = evaluate_policy(
policy=policy,
environment=environment,
seed=cohort_seed
)
save_result(
cohort_seed,
policy,
result
)This is illustrative pseudocode. The named helper functions must be implemented and validated before execution.
The original Experiment 7 source remains the reproducible implementation of the completed study.
9.3 Primary comparison
The primary proposed comparison is moderate-noise recognition versus random-choice recognition under matched environments.
This comparison is more informative than moderate noise versus greedy alone because random choice already produced substantial recognizability.
A secondary comparison should evaluate moderate noise against low noise, high noise, and correlated noise.
9.4 Uncertainty and sensitivity
Future reports should include:
- Cohort-level effect sizes and uncertainty intervals.
- The distribution of outcomes rather than only the best observed run.
- Sensitivity to the number of candidate agents.
- Sensitivity to softmax concentration.
- Sensitivity to probe contexts and similarity metrics.
- Sensitivity to training duration and reward-process parameters.
Confidence intervals should be calculated using the independent experimental structure rather than treating correlated observations as independent.
9.5 Multiple comparisons
Selecting the highest-performing noise coefficient after evaluating several alternatives creates a selection problem.
Future experiments should separate exploratory parameter selection from confirmatory evaluation.
The chosen noise coefficient should be tested on fresh cohorts not used to select it.
9.6 Falsification criteria
The claim that moderate noise produces reliably stronger behavioral recognition should be rejected or narrowed if:
- Its advantage over random exploration disappears across independent cohorts.
- Its advantage disappears under matched environments.
- The policy ranking changes substantially under reasonable fingerprint metrics.
- Its performance depends on a narrow, post-selected parameter range.
- Comparable recognition arises from simple environmental imprinting without the proposed learning mechanism.
A negative result under these conditions would not invalidate the entire VOHA research program.
It would invalidate the narrower claim that the tested moderate-noise mechanism provides a reliable advantage.
10. Alternative Fingerprint Measurements
Cosine similarity is one reasonable measurement, but it is not uniquely justified.
Future studies should evaluate alternative approaches, including Jensen–Shannon divergence and classification-based matching.
For probability distributions (P) and (Q), Jensen–Shannon divergence is:
[
\operatorname{JSD}(P,Q)=
\frac12 D_{\mathrm{KL}}(P|M)
+\frac12 D_{\mathrm{KL}}(Q|M)
]
where (M=(P+Q)/2).
This provides an alternative measurement of differences between normalized preference distributions.
Recognition performance should also be compared against null models constructed by shuffling agent labels.
Such controls would help determine whether the identification procedure captures agent-specific learning information rather than systematic artifacts of the evaluation method.
11. Experiment 9: A Proposed Factorial Test of Noise and Periodicity
The next planned investigation concerns the interaction of stochastic exploration and periodic modulation.
The proposed primary conditions are:
- No added noise or periodic modulation.
- Stochastic noise alone.
- Periodic modulation alone.
- Combined noise and periodic modulation.
Additional controls should include shuffled phase, randomized frequency, and amplitude-matched perturbations.
The proposed action-selection rule is:
[
a_t=\arg\max_i
\left[
\hat V_i(t)
+B_i(t)
+\sigma\epsilon_{i,t}
+A\sin(2\pi ft+\phi_i)
\right]
]
The primary scientific question is whether the interaction between periodic modulation and noise produces reproducible changes in learning or behavioral persistence beyond their separate effects.
This should be tested as a factorial interaction, not merely by identifying whichever condition produces the largest result.
A positive interaction would not establish stochastic resonance unless the study also specified and measured an appropriate signal-response phenomenon.
Experiment 9 remains proposed. No results are claimed here.
12. What the Observer Hypothesis Would Require
The independent-Observer hypothesis is the most speculative component of VOHA.
To become scientifically testable, it must generate predictions that differ from those of ordinary computational and physical explanations.
Unexpected behavior is insufficient.
Apparent information persistence following memory manipulation is insufficient unless ordinary retained state, reconstruction, data leakage, and uncontrolled inputs have been excluded.
The current simulations are fully compatible with conventional computational explanations.
They provide no positive evidence of an independent Observer.
VOHA therefore maintains three distinct claim levels.
Level 1: Computational observation. Behavioral fingerprints arise and change within the specified model.
Level 2: Generalizable mechanism. Particular learning, memory, and noise relationships produce reliable effects across independently replicated conditions.
Level 3: Consciousness or independent Observer. Subjective experience or an independent causal entity contributes to measured behavior.
The current evidence supports Level 1 within the tested model.
Level 2 remains to be established.
Level 3 remains an open philosophical and scientific question without supporting evidence from these experiments.
13. Reproducibility and Open Disclosure
The companion research archive contains the original Python programs, available experimental results, and technical documentation.
The scripts use Python’s standard library.
Experiment 7 may be reproduced with:
python voha_experiment7_identity.py \
--agents 100 \
--steps 1600 \
--out voha_experiment7_results.csvExperiment 8 imports functions from Experiment 7 and requires the relevant source files to be available together.
The source-code book provides readable listings for inspection without execution.
The reproduction archive and the readable codebook serve different purposes.
The archive permits program execution.
The codebook permits independent examination of implementation decisions, random-seed handling, state manipulations, and measurement procedures.
Future public releases should include a versioned source repository, explicit licensing, checksums, exact execution instructions, and an independently generated replication dataset.
A seeded rerun establishes computational repeatability under specified conditions.
It does not establish generalizability.
14. Discussion
The principal finding of the present study is that learned behavioral fingerprints can become distinguishable in a simple adaptive-choice system.
The results also show that recognizability depends on retained learned information and on the procedures used to measure similarity.
The 55% recognition result under moderate noise is a useful exploratory observation.
It is not yet evidence that moderate noise generally maximizes identity persistence.
The 46% recognition result under random choice is equally important because it supports a conventional explanation based on stochastic history and accumulated learning.
The full-wipe result illustrates how experimental construction can determine an apparent outcome.
The restart results show that overall behavioral similarity and stability of the highest-ranked choice are different properties.
Together, these findings motivate further controlled work on the relationship among learning history, memory, environmental variation, and behavioral persistence.
They do not establish consciousness.
The scientifically useful next step is not to expand the philosophical claim.
It is to improve the causal isolation and statistical reliability of the computational results.
15. Conclusion
VOHA began as an investigation of the relationship between an Observer, a host, memory, and choice.
Its present empirical contribution is narrower.
In a specified computational model, agents with identical initial learning architectures developed distinguishable preference fingerprints through different learning histories.
Those fingerprints changed when retained learning was altered or new experience was introduced.
The observations are consistent with conventional stochastic learning and path dependence.
The principal unresolved question is whether particular exploration mechanisms produce reliable, generalizable differences in behavioral recognition after environmental variation and measurement artifacts are controlled.
That question can be tested.
The proposed replication program provides a way to do so.
The value of VOHA, at this stage, lies not in claiming that artificial consciousness has been created, but in turning one aspect of the broader individuality question into a measurable and falsifiable computational investigation.
A recognizable history is not proof of an experiencing Self. But understanding how recognizable histories arise may help us determine which questions about the Self can be answered computationally — and which remain unanswered.
Code Book (Lite)
VOHA_Complete_Python_Source_Codebook
References and Research Context
Baker, B., Kanitscheider, I., Markov, T., Wu, Y., Powell, G., McGrew, B., & Mordatch, I. (2019). Emergent Tool Use From Multi-Agent Autocurricula. arXiv:1909.07528. https://arxiv.org/abs/1909.07528
Dietterich, T. G. (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7), 1895–1923. https://doi.org/10.1162/089976698300017197
McCloskey, M., & Cohen, N. J. (1989). Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem. Psychology of Learning and Motivation, 24, 109–165. https://doi.org/10.1016/S0079-7421(08)60536-8
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. Second edition. MIT Press.
Ure, G. A., I. (2026). VOHA Experimental Series, Experiments 1–8. Original Python programs, research results, and technical disclosures.
Ure, G. A., I. (2026). VOHA Complete Python Source Codebook. Companion technical documentation.
Research Integrity Statement
This article reports exploratory computational findings. It distinguishes completed simulations from proposed experiments, acknowledges negative findings, and does not claim evidence of artificial consciousness or independent Observer coupling. Its principal empirical claims are limited to the specified synthetic environments, learning policies, and evaluation procedures. Independent replication and stronger statistical controls remain necessary.
This research is funded by me personally and enabled through subscriptions to my economics site, https://peoplenomics.com where my other research is housed.
~ The Anti-Dave