From OpenWetWare

Revision as of 06:02, 2 November 2009 by Alex O. Holcombe (Talk | contribs)
(diff) ←Older revision | Current revision (diff) | Newer revision→ (diff)
Jump to: navigation, search


Alex Holcombe
Polly Barr
• Charlie Ludowici
• Kim Ransley
• Ingrid Van Tongeren
William Ngiam
Fahed Jbarah
• Patrick Goodbourn


Skills Checklist
Python Programming
Psychopy/VisionEgg Installation Notes
R analysis,plot,stats
Buttonbox with photocell
Programming Cheat Sheets

  • Check gradually varying of two patches, have to judge orientation of second when one is vertical, also look up std dev of Arnold tilt orientation
  • Try independently varying orientation of the two moving/stationary patterns across trials, like Keeble & Nishida

also see temporal precision and action also see Holcombe:InPhaseTask also see Holcombe:ModellingUncertainty also see Holcombe:TemporalLimitsReview

THE BELOW NOTES REFER TO DATA THAT ARE REPORTED IN: Linares, D.L., Holcombe, A.O., & White, A.L. (in press) Where is the moving object now? Reports of instantaneous position show poor temporal precision (σ = 70 ms). Journal of Vision

Holcombe,White,Linares VSS 2008 poster on this topic, data below is subset i think



Radius experiment: 800x600 at 160 Hz (Mitsubishi)

The rest of the experiments: 800x600 at 120 Hz (ViewSonic)

Temporal noise for every subject

Col change: 76 ms Sound: 65 ms Predictive: 86 ms Button press: 64 ms

These slopes are wrong for AH with buttonpress! Image:SlopesNoIntercepts.png, Image:Slopes.png

Buttonpress (sensorimotor synchronization) vs. other tasks

  • is variability consistently less than for other tasks?

Yes for ML, AH, DL poster data, by 20-30 ms. This includes dot-crossing predictive task

For data not in table above, DL in 3 different runs Image:ButtonPressTemporalNoiseDLAHoldnew.png shows low temporal noise, and AH ended up with better temporal noise (i think this was partially a data analysis error; Dani has now fixed it)
Personal tools