A stimulus set and a simulated participant | Noise-only stimuli | Ratings instead of a two-image choice | Matching the InfoVal reference to the design | When some trials were dropped | A masked classification image | Several classification images at once | A CI averaged over participants | Reproducing a number from an earlier version | Run the version that computed it | Recompute with the current version | When the .Rdata file is lost | Checking the settings against the stimulus PNGs

1. Installing | 2. What the method does | 3. Generating stimuli | The .Rdata file is the important output | 4. Collecting responses | 5. Computing one classification image | 6. Scaling | 7. Several participants at once | After autoscale(), look at $scaled | 8. Is there actually signal? | 9. Running the task online | 10. Citing

The stimulus .Rdata file | Written when the stimuli are generated | Added when an informational value is computed | Attributes | Before you write code against the file | The classification image

1. Generate stimuli | 2. Collect (or, here, simulate) responses | 3. Compute the classification image | Next steps