Vertical slice

Macaque RDM Confidence Wagering

Kiani-Shadlen macaque random-dot motion confidence slice with source-data summaries for motion-strength accuracy and sure-target choice from the Khalvati-Kiani-Rao Nature Communications source-data ZIP.

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Macaque RDM Confidence · accuracy by motion strength

Per-monkey accuracy across motion strengths from the Khalvati-Kiani-Rao source-data summary. The full source-row CSV is ~242 MB so the cell uses the slice's pre-aggregated accuracy summary (a few dozen rows, one per measure × monkey × motion strength).

Pyodide + scipy run locally; first load downloads ~12 MB.

Macaque RDM Confidence · accuracy by motion strength

Pyodide 0.26.4

Per-monkey accuracy across motion strengths from the Khalvati-Kiani-Rao source-data summary. The full source-row CSV is ~242 MB so the cell uses the slice's pre-aggregated accuracy summary (a few dozen rows, one per measure × monkey × motion strength).

Python (df is a pandas DataFrame loaded from the trials CSV; ⌘/Ctrl-Enter to run)
Click run to start

Runs entirely in your browser. First click downloads ~10 MB of Pyodide and pandas; subsequent runs reuse the cached runtime.

Findings extracted

Curves attached to this slice, on shared signed-evidence axes.

Metrics

Source rows 174,160
Accuracy rows 100,694
Sure-target rows 73,466
Confidence rows 13

Slice profile

Operational variables for cross-slice comparison.

Species
non-human-primate
Modality
visual
Stimulus metric
absolute motion coherence
Evidence type
stochastic-motion
Choice type
wagering
Response modality
saccade
Source data level
figure-source-data
Analysis outputs
sure-target probability and accuracy summaries
Data scope
Nature Communications figure source-data rows across two macaques
Canonical axis
absolute motion strength because signed direction is unavailable

Reproduce locally

Copy this recipe to rebuild the slice's artifacts on your machine at the exact commit deployed here.

Reproduce locally

Run the slice pipeline at this commit

git clone https://github.com/aeronjl/behavtaskatlas.git
cd behavtaskatlas
git checkout ec14ee5
uv sync --extra rdm
uv run behavtaskatlas macaque-rdm-confidence-wager-download
uv run behavtaskatlas macaque-rdm-confidence-wager-harmonize
uv run behavtaskatlas macaque-rdm-confidence-wager-analyze
uv run behavtaskatlas macaque-rdm-confidence-wager-report

Extras group rdm pulls in the analysis dependencies for this family. Recipe pinned to commit ec14ee5 so a teammate runs the same code path the deploy used.

Artifacts

Slice has a generated local report; the analysis pipeline ran cleanly and produced trial-level output, plots, and provenance.