How researchers use PsychHypo

Before you commit IRB time, recruitment dollars, or a year of measurement, PsychHypo helps you map the hypothesis space around a construct. You enter the population, mechanism, and method you're considering — for example mindfulness and rumination in adults with GAD measured by EMA — and the engine returns three to five falsifiable hypotheses with supporting papers, suggested designs, and the dominant risks.

Researchers use this moment to compare alternative operationalizations side by side: behavioral task versus self-report, between-subject versus within-subject, single-session versus longitudinal. Each hypothesis arrives with an experimental outline you can hand to a methods advisor without rewriting from scratch. The point is not to replace your design intuition — it is to surface the design choices you would otherwise discover only at preregistration, when changes are expensive.

Grant aims sections live or die on three properties: novelty, mechanistic specificity, and falsifiability. PsychHypo is built to pressure-test exactly these dimensions. You describe your proposed line of work in research vocabulary — population, construct, method, and the question you want to answer — and the engine returns hypotheses grounded in literature it actually retrieved from OpenAlex and Europe PMC.

For R01 and R21 submissions, PIs use the engine to draft initial aims, then run the adversarial stress test to anticipate reviewer critiques. For NIMH applications, the system surfaces translational angles connecting psychiatric symptoms to neurobiological mechanisms. For NSF SBE proposals, it identifies social and cognitive frameworks already represented in the literature. The output is not a finished aims page — it is a structured starting point that compresses weeks of literature scoping into an afternoon.

PhD students use PsychHypo before committee meetings to pressure-test their proposed aims. You enter your construct, your population, and the method you plan to use. The engine returns the hypothesis as you'd frame it, plus the strongest objections a skeptical committee member would raise: alternative explanations, methodological vulnerabilities, prior failed attempts, and the single study most likely to falsify the claim.

This is the rigorous pushback graduate students rarely get from a single advisor. By running the stress test before your committee does, you arrive prepared with answers to the hardest questions. Postdocs use it the same way before job talks and chalk talks. The engine never tells you the project is doomed — it tells you exactly where the risk sits so you can either revise the design or speak directly to the risk in your proposal.

Psychology and psychiatry face one of the most severe replication crises in science. PsychHypo addresses this at the hypothesis stage — before underpowered designs, vague constructs, or untestable claims make it into a study. Every generated hypothesis is falsifiable by construction, with an experimental outline that includes realistic sample sizes, effect sizes from the cited literature, and explicit operationalizations.

The engine flags constructs known to be poorly operationalized, surfaces prior null findings when the cited literature contains them, and recommends preregistration-ready designs. For replication studies specifically, you can enter the original finding and ask PsychHypo to retrieve the surrounding literature including failed replications you may have missed. The goal is not to claim certainty about which findings will replicate — it is to build new studies that are harder to fail to replicate.

Upload a manuscript PDF and PsychHypo reads it against the broader literature. Three analysis types target three moments of the writing process: compare to literature surfaces what is genuinely novel and what existing work the draft may have under-cited; find contradictions identifies internal inconsistencies, methodological concerns, and claims not fully supported by the data; suggest experiments proposes three to five follow-up studies grounded in your draft.

Researchers use this to catch the issues reviewers would catch — under-cited related work, conclusions that overreach the results, claims of novelty that ignore a 2024 paper from another lab — before submission. Each analysis takes about 90 seconds and is saved to your private workspace. Uploads are never used to train models and never shared outside your account.

Psychological science increasingly requires combining methods: behavioral tasks paired with fMRI, ecological momentary assessment paired with longitudinal follow-up, computational modeling layered on top of clinical measures. PsychHypo is designed to generate hypotheses that span these combinations rather than treating them as separate studies.

When you describe a cross-method project — for example RPE signaling in TRD measured by fMRI and modeled with a Q-learning framework, or rumination in GAD measured by EMA and resting-state connectivity — the engine returns hypotheses with experimental outlines that integrate both methods, name the specific analyses, and identify where the two data streams converge or diverge. This is the kind of integrative design that single-method tools rarely surface and that grant reviewers reward.