EMERGENT VENTURES SCHMIDT APPLICATION EMAIL DRAFT Subject: Independent researcher, addiction neuroscience: CCT model, three preprints in review, seeking flexible funding for experimental validation Emergent Ventures team, The Conjunctive Consolidation Threshold model is a tripartite pharmacological framework for preventing reward-memory encoding in addiction. It couples dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast into a single ODE system, calibrated with Bayesian MCMC against a literature screen of 1,847 records. All five pre-registered hypotheses were confirmed, with posterior super-additivity of 13 to 22 percentage points across model versions. Three sole-authored preprints are under review at peer-reviewed journals, and a co-authored paper is under review at Alcohol (Elsevier). This work is contrarian by design. The field treats addiction as a dopamine problem; the CCT model argues that memory consolidation, not reward signaling alone, is the intervention target. That claim is testable, and the model specifies exactly which receptor populations to manipulate and when. What it needs now is experimental validation, collaboration with labs that can run rodent or human protocols, and time. Traditional grants do not fund independent pre-PhD researchers with this profile. Emergent Ventures does. I am a Nigerian pharmacist and computational researcher, 29, enrolled in the M.Sc. Digital Health program at Hasso Plattner Institute / University of Potsdam starting Winter 2026/27. I have endorsements from Kent Berridge (Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), and Marcelo Mattar (NYU). A grant of $25,000 to $50,000 would fund a validation collaboration, conference travel, and computational infrastructure for the next 12 months. The full research statement, preprints, and code are available on request. I am available for a call at any time. Eniola Ayodele Olutogun ORCID: 0009-0001-9272-6735 GitHub: github.com/AmunRaPtah zyco.org RESEARCH STATEMENT The CCT model addresses a specific failure in addiction research: the field has no quantitative framework that explains why some drug exposures produce persistent reward memories while others do not. Dopamine-centric theories dominate, but they cannot account for the temporal dynamics of memory consolidation or the role of affective state at the moment of exposure. The model is a system of coupled ordinary differential equations with three axes. The first axis is dopaminergic reward prediction error, modeled with a temporal difference learning rule. The second is NMDAR-dependent long-term potentiation in the mesolimbic pathway, modeled with a calcium-based plasticity rule. The third is affective contrast, the difference between the drug-induced state and the pre-drug baseline, modeled as a gating variable. The three axes interact through a conjunctive threshold: memory encoding occurs only when all three exceed their respective thresholds within a narrow time window. This is the core contrarian claim. Blocking any single axis is insufficient; the threshold must be crossed on all three simultaneously. The model was calibrated with Bayesian MCMC using PyMC's DEMetropolisZ sampler, with 14 free parameters and literature-elicited priors from a systematic screen of 1,847 records. All five pre-registered hypotheses (H1 through H5) were confirmed. Posterior analysis shows super-additivity of 13 to 22 percentage points across model versions, meaning the combined effect of multi-axis intervention exceeds the sum of single-axis effects. This has direct therapeutic implications: combination pharmacotherapy targeting dopamine, NMDA, and affective state may be dramatically more effective than current single-target approaches. Three sole-authored preprints are under review: one at International Addiction Research and Therapy, one at Progress in Neuro-Psychopharmacology and Biological Psychiatry, and one at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier). All code, data, and pre-registration documents are on OSF and Zenodo. The next step is experimental validation. The model makes specific, falsifiable predictions about the timing and combination of pharmacological interventions that should prevent reward-memory formation. I am in discussion with collaborators at the University of Michigan and Princeton about designing rodent protocols to test these predictions. A grant from Emergent Ventures would fund travel for these collaborations, access to computational resources for model refinement, and living expenses during the critical pre-enrollment period before my M.Sc. program begins. Why Emergent Ventures specifically: this is a high-risk, high-reward bet on an independent researcher with no institutional backing. The model is contrarian, the methods are rigorous, and the potential impact is large. Traditional funding mechanisms require institutional affiliation, preliminary experimental data, or a PhD supervisor. I have none of those, but I have a validated model, three preprints in review, and endorsements from four leading researchers in the field. Emergent Ventures is the only programme I have found that funds this exact profile. The grant would be used as follows: $15,000 for collaboration travel and lab visits, $10,000 for computational infrastructure and cloud computing for model refinement, $10,000 for living expenses during the pre-enrollment period, and $5,000 for conference presentation and publication fees. Total requested: $40,000. I am also prepared to discuss the negative results from my other research lines, which demonstrate my commitment to pre-registration and honest reporting. A cardiotoxicity topology study found that persistent homology features do not beat a plain descriptor baseline (AUROC 0.8426 vs 0.8782), and an interface-topology-for-resistance study found no signal (AUROC 0.425 and 0.485). These results were reported directly rather than reframed. The same rigor applies to the CCT model. CHECKLIST - [ ] Verify current Emergent Ventures application process and submission portal - [ ] Confirm grant amount range and any regional preferences for 2026 - [ ] Prepare PDF copies of all three CCT preprints for attachment - [ ] Prepare PDF copy of the Alcohol (Elsevier) co-authored paper - [ ] Obtain letters of support from Berridge, Gershman, Daw, or Mattar - [ ] Prepare a one-page budget breakdown with specific cost estimates - [ ] Verify ORCID record is current and includes all preprints - [ ] Update GitHub repository with final CCT model code and documentation - [ ] Prepare a 5-minute video summary of the CCT model for reviewer convenience - [ ] Confirm M.Sc. enrollment status and provide proof of admission to HPI/Potsdam EDITOR NOTES - Eligibility risk: Emergent Ventures typically funds individuals, but the programme has shifted focus over time; verify that the current tranche accepts non-AI researchers and that the "General" track is still active. The LessWrong post is from a specific date; check for newer guidance. - The CCT model is the correct research line to lead with for this programme, but the experimental validation claim is aspirational. The profile lists Berridge, Gershman, Daw, and Mattar as endorsers, not confirmed collaborators. The applicant must confirm whether any of these researchers have agreed to design or run validation experiments, or whether this is a hope. Do not present this as confirmed collaboration without verification. - The budget figures ($40,000 total) are invented for this draft. The applicant must replace them with real cost estimates based on actual quotes for travel, cloud computing, and living expenses in Potsdam/Berlin. The M.Sc. program at HPI is tuition-free, but living costs in Potsdam are approximately 900 to 1,200 EUR per month; the living-expense line should reflect the actual gap between current income and expenses. - The applicant's employment history shows a transition from clinical pharmacy to product management at Synthcare (March 2026 onward). This may conflict with the time commitment implied by the research statement. The applicant should clarify how the product manager role interacts with the research timeline, or whether the grant would fund a reduction in work hours. - The German equivalent grade of 1.9 for a 5.1/7.0 CGPA should be verified against the actual conversion used by German institutions, as conversion scales vary. This matters if the M.Sc. program or any future German funding body reviews the application. - The profile lists psyche-twin as an active research line, but it is not mentioned in this application. This is intentional, as it does not fit the Emergent Ventures selection criteria as well as the CCT model. However, the applicant should be prepared to answer questions about it if reviewers find the personal site or GitHub. - The ergofluids project failed its first real-data gate and was reported honestly. This is a strength for the Emergent Ventures application, as it demonstrates scientific integrity. The applicant should be prepared to discuss this explicitly if asked, and should not hide it. - The applicant should verify that all three preprints are still under review and have not been rejected or withdrawn between the profile date and the application date. If any have been rejected, the application should be updated to reflect the current status. - The email draft is 300 words, which fits the 150-300 word range for the fallback process type. If Emergent Ventures has a formal application form, the research statement above should be adapted to their specific questions, and the email draft should be discarded.