| """Post-hoc sensitivity split of B1 by trial duration (Appendix H). |
| |
| python analysis/07_audio_context_study/03_duration_split.py |
| |
| The claim being reproduced: splitting B1's raters at the cell's median trial |
| duration (a criterion independent of the ratings), the faster half yields a |
| singular variance-components fit with ~zero stimulus variance, while the |
| slower half's decision threshold matches the audio baseline. |
| |
| Duration is the trial's totalTaskDurationMs from the Firestore export |
| (data/audio_context_study/trial_durations_audiocontext_onevoice.csv), one value per |
| trial; a rater's speed is the median of their trial durations. The split is at |
| the median of those per-rater medians. Variance components use the same model |
| as scale_usage.py / the paper's ICC: |
| |
| Rating ~ presented_first + trial_z + (1|AudioClip) + (1|ParticipantID) |
| |
| and the threshold is the trial-level mean gap on non-tie trials, as in |
| threshold_summary.py. Reported for both halves, alongside the audio-baseline |
| threshold on the same 30 prompts for reference. This is a post-hoc sensitivity |
| check, not a pre-specified exclusion rule. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "common")) |
|
|
| from _condition_contrasts import trial_gaps |
| from _lme4 import lmer_varcomp |
|
|
| ROOT = Path(__file__).resolve().parent.parent.parent |
| VOICE_DATA = ROOT / "data/voice_study" |
| CONTEXT_DATA = ROOT / "data/audio_context_study" |
| DERIVED = ROOT / "results/07_audio_context_study" |
|
|
|
|
| def durations() -> pd.Series: |
| """(ParticipantID, PromptID) -> trial duration in seconds.""" |
| t = pd.read_csv(CONTEXT_DATA / "trial_durations_audiocontext_onevoice.csv") |
| return (t.set_index(["ParticipantID", "PromptID"])["TaskDurationMs"] |
| .div(1000.0).rename("seconds")) |
|
|
|
|
| def varcomp(g: pd.DataFrame) -> dict: |
| g = g.dropna(subset=["Rating", "TrialNumber", "AudioClip"]).copy() |
| g["pf"] = g["PresentedFirst"].astype(int) |
| sd = g["TrialNumber"].std() |
| g["trial_z"] = (g["TrialNumber"] - g["TrialNumber"].mean()) / (sd if sd else 1) |
| return lmer_varcomp( |
| g, "Rating ~ pf + trial_z + (1|AudioClip) + (1|ParticipantID)") |
|
|
|
|
| def threshold(g: pd.DataFrame) -> tuple[float, int]: |
| x = trial_gaps(g)["gap"].dropna() |
| return float(x.mean()), len(x) |
|
|
|
|
| def main() -> None: |
| b1 = pd.read_csv(CONTEXT_DATA / "long_audiocontext_onevoice_raw.csv") |
| b1 = b1[~b1["IsAttentionCheck"].astype(bool)].dropna(subset=["Rating"]) |
| |
| |
| analyzed = set(pd.read_csv(CONTEXT_DATA / "long_audiocontext_onevoice_normalized.csv") |
| ["ParticipantID"]) |
| b1 = b1[b1["ParticipantID"].isin(analyzed)] |
| b1["Arm"] = "B1" |
| dur = durations() |
|
|
| per_rater = (b1.drop_duplicates(["ParticipantID", "PromptID"]) |
| .join(dur, on=["ParticipantID", "PromptID"])) |
| speed = per_rater.groupby("ParticipantID")["seconds"].median().dropna() |
| b1 = b1[b1["ParticipantID"].isin(speed.index)] |
| cut = speed.median() |
| fast = speed[speed <= cut].index |
| slow = speed[speed > cut].index |
| print(f"{len(speed)} raters with durations; median of per-rater medians " |
| f"{cut:.1f}s -> fast {len(fast)}, slow {len(slow)}") |
|
|
| rows = [] |
| for name, ids in (("fast half", fast), ("slow half", slow)): |
| g = b1[b1["ParticipantID"].isin(ids)] |
| thr, n = threshold(g) |
| vc = varcomp(g) |
| print(f"\n{name}: {g['ParticipantID'].nunique()} raters, " |
| f"median trial {speed[ids].median():.0f}s") |
| print(f" threshold {thr:.1f} ({n} non-tie trials)") |
| print(" variance components: " + |
| ", ".join(f"{k}={v:.2f}" for k, v in vc.items())) |
| if vc.get("AudioClip", 1) < 1e-6: |
| print(" -> singular fit: stimulus variance is zero") |
| rows.append(dict(group=name, raters=g["ParticipantID"].nunique(), |
| median_trial_s=speed[ids].median(), threshold=thr, |
| non_tie_trials=n, v_stim=vc.get("AudioClip"), |
| v_rater=vc.get("ParticipantID"), v_resid=vc.get("Residual"))) |
|
|
| |
| import _audio_context_replication as C |
| prompts = set(pd.read_csv(ROOT / "stimuli/audio_context_study/prompts.csv")["PromptID"]) |
| base = C.load_arms(prompts) |
| base = base[base["Arm"] == "ORIGINAL af_heart"] |
| thr, n = threshold(base) |
| print(f"\naudio baseline (same 30 prompts): threshold {thr:.1f} " |
| f"({n} non-tie trials)") |
| rows.append(dict(group="audio baseline", threshold=thr, non_tie_trials=n)) |
| pd.DataFrame(rows).to_csv(DERIVED / "duration_split.csv", index=False) |
| print("-> results/07_audio_context_study/duration_split.csv") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
