Research · Evidence Brief

v1.6 PCS Trainability Audit Final Chapter — Full-Library Coverage + Honest Negative Finding

Companion to: v1.0 + v1.1 Korean-practice + v1.2 SIOP/AERA psychometric + v1.3 Cross-cultural + v1.4 Trainability + v1.5 AI-graded creative. Purpose: Document the full-library milestone (all 44 games audited) and the honest NEGATIVE methodological finding (an empirical replay-inflation measure was built, tested, and dropped as artifact-noisy; expert-rater tiers remain the sound basis). Consolidates the v1.4 + v1.5 trainability arc and frames the integration status. Audience: SIOP / AERA peer-review readers and v2 IRB-gated study scoping. Status: Self-published chapter; not peer-reviewed. Audit complete across the full library. Author: Talentopian Research. Version: 1.6.0 trainability final chapter

1. The full-library milestone (all 44 games audited)

The Talentopian trainability audit is now complete across the full 44-game library. This closes the audit thread that began with batch-1 (v1.4 chapter) and extended through batch-2 (v1.5 chapter).

1.1 — Final tier distribution

Across the 44 game builds audited (incl. 3 creative-canvas variants + writing-coach/picture-critic eval games + adventurers-realm prototype), the final tier distribution is:

Tier Count % of library Implication
REJECT 5 games 11% Near-excluded from scoring; practice effects dominate performance
CAUTION 27 games 61% Down-weighted; scored on first attempt where flagged
KEEP 12 games 27% Full weight; least susceptible to practice effects
First-attempt-only scoring 32 games 73% Multi-attempt sessions are reduced to the first attempt only

1.2 — Cumulative batch-by-batch progression

Batch Games REJECT CAUTION KEEP
Batch-1 15 5 6 4
Batch-2 14 0 9 5
Batch-3 15 (new) 0 12 3
Full library 44 5 (11%) 27 (61%) 12 (27%)

Honest observation about the distribution:

Catalog reconciliation: 44 game builds audited (v1.6, expert-rater face-validity); current canonical catalog = 40 registry games; 36 surfaced in the live menu; the audit additionally covered 4 eval/variant builds + 1 prototype since consolidated.

1.3 — Integration status

The single actionable output of the audit is first-attempt PCS. Applying it is an integration task on the scoring side: multi-attempt sessions reduced to their earliest attempt, per-game weighting applied by tier, and motor-dimension routing handled consistently. The audit produces the flag table; the scoring pipeline applies it.

Status: The methodology-side audit is complete; integration is queued. v1.6 does not claim integration is complete — first-attempt filtering and per-game tier weighting are not yet applied in production scoring.

2. The honest NEGATIVE finding — empirical replay-inflation measure dropped

This is the v1.6 substantive methodological contribution: what was attempted, why it failed, and what that means for future trainability research.

2.1 — What was attempted

The audit built an empirical measure of per-game trainability, defined as the percentage gain from first-attempt score to best-attempt score, averaged across all users who replayed the game.

Hypothesis: high replay-inflation% = empirically-trainable; the expert-rater H/M/L tiers should correlate with this empirical measure.

2.2 — Why it failed (artifact-noisy)

In summary: abandoned or low first attempts inflate the percentage gain uncontrollably; single-user outliers dominated category means; replays were too sparse for most REJECT games; and the ordering of attempts was ambiguous.

Four specific failure modes:

  1. Abandoned-first artifact: users who abandon their first attempt (e.g., quit at 5% then return) generate massive percentage gains that reflect re-engagement, not learning. The ratio of best to first score explodes when the first score is near zero.

  2. Extreme single-user outliers: a near-zero first attempt (likely an immediate quit) can produce an inflation figure in the thousands of percent, dominating the category mean. Robust statistics (median / trimmed mean) help but cannot fully correct the underlying selection bias.

  3. Replay-sample sparsity: in an early pilot cohort, only a minority of REJECT-tier games had enough replays to compute a stable inflation measure; the remainder had too few replays to be usable. The empirical measure cannot validate tier predictions precisely where the data is sparsest.

  4. Attempts-array ordering: the ordering of attempts by timestamp was ambiguous across candidate time fields, so "first attempt" was not unambiguously defined — and the entire measure inherits that ambiguity.

2.3 — What was done about it

The decision: the empirical replay-inflation measure was dropped. The expert H/M/L tiers are the sound basis, not a data-measured inflation weight. Naive replay-inflation should not be re-attempted in the same form.

The honest decision: when the empirical measure was found to be noisier than the expert-rater classifications, it was dropped — not retained with disclaimers, not silently kept. This reflects a broader practice of transparently reporting measures that do not hold up under scrutiny.

2.4 — What this means for the validity case

The trainability framework's validity does not now rest on the empirical replay-inflation measure (which was tried and failed); it rests on:

  1. Expert-rater classifications based on game mechanics + literature review + face validity
  2. The architectural reasoning of v1.5 (open AI-graded creative = inherently less trainable input format)
  3. Future v2 retest reliability on an adequately powered retest sub-sample — when the empirical measure can be computed without sparsity artifacts

v1.6 explicitly does not claim the empirical replay-inflation question is answered. It claims the naive empirical approach was tried, failed for documented reasons, and the framework correctly fell back on the more sound (expert-rater) basis pending v2 retest data collection.

2.5 — Why this is publishable (the meta-methodological contribution)

Negative findings and transparent abandonment of failed measurement approaches are publishable per SIOP / AERA standards (e.g., AERA Standards for Educational and Psychological Testing 2014 §7.3 — reporting of measurement-error sources). v1.6 documents:

This brings disciplined, transparent reporting of a null/failed measure to the trainability framework.

3. Implications for v1.4 + v1.5 frameworks (consolidation)

3.1 — v1.4 mitigation framework UPDATE

v1.4 §4 documented a three-level weighting scheme (H / M / L). v1.6 updates this to the full 5-tier map, expressed as relative scoring weights:

Tier Relative weight
HIGH trainable 0.30 (REJECT)
MED-HIGH 0.50
MED 0.65 (CAUTION)
LOW-MED 0.85
LOW trainable 1.00 (KEEP)

The intermediate tiers (M-H 0.50, L-M 0.85) emerged in batch-2 because the binary H/M/L was too coarse for many partially-trainable games. The 5-tier map is the final framework.

3.2 — v1.5 AI-graded creative claim STRENGTHENED

v1.5 §3 reported four reward-gated AI-graded creative games (writing-coach / word-weaver / picture-critic / creative-canvas) as KEEP-tier. At full-library: KEEP tier = 12 games / 27% of library; the four AI-graded creative games plus the creative-canvas family account for a substantial share of those 12 (depending on how creative-canvas variants are counted). This confirms v1.5's architectural framing at full-library scale.

3.3 — v1.2 4-coefficient validation plan RE-PRIORITIZATION

Per v1.5 §5.3, v2 retest reliability should prioritize the AI-graded creative family. v1.6 adds: the REJECT-tier games should also be retested first — to empirically confirm the practice-effect prediction. Test-retest correlations:

This bipolar prediction is the strongest empirical test of the expert-rater tier framework when v2 data lands on an adequately powered retest sub-sample.

4. Cross-validation interactions with prior PCS Validation whitepaper chapters

5. v1.6 honest scope

What v1.6 IS:

What v1.6 IS NOT:

6. Cross-references

— v1.6 PCS Trainability Audit Final Chapter — full-library audit complete + honest negative finding + framework consolidation.

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