1 / 49

Biased v erticality percepts in motion and pattern vision

December 17th, 2007. Biased v erticality percepts in motion and pattern vision. M aaike de Vrijer Pieter Medendorp Jan van Gisbergen. Introduction. Visual stability and spatial perception.

derora
Télécharger la présentation

Biased v erticality percepts in motion and pattern vision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. December 17th, 2007 Biased verticality percepts in motion and pattern vision Maaike de Vrijer Pieter Medendorp Jan van Gisbergen

  2. Introduction Visual stability and spatial perception • Maintaining a veridical percept of allocentricvisualorientations despite changes in eye, head and body orientation

  3. Introduction Visual stability

  4. Introduction Subjective Visual Vertical (SVV) • Aubert (1861) was first to observe substantial errors in the perception of world-centered orientation of visual lines Errors of undercompensation

  5. Introduction A-effect E-effect A-effect E-effect Subjective Visual Vertical (SVV) response error [deg] B roll angle [deg] from: Van Beuzekom, Medendorp & Van Gisbergen, 2001

  6. Introduction Errors in SVV ≠ Errors in body tilt percept subjective vertical tilt estimates response error [deg] roll angle [deg] from: Van Beuzekom, Medendorp & Van Gisbergen, 2001

  7. Introduction Subjective Motion Vertical (SMV) • Can the brain compensate for head tilt when judging the spatial direction of motion? • If so, does the same pattern of errors occur as in the perception of line orientation? • Not trivial: motion and pattern vision involve different neural areas

  8. Methods Methods

  9. Methods Vestibular chair

  10. Methods Two tasks Motion task Line task Polarized line Random dot pattern of 30% coherence

  11. Methods Experimental run

  12. Methods Experimental setup • Eight subjects (7 male, 1 female), aged 31 ± 13 years • 13 tilt angles: -120º to 120º with steps of 20º • Total of 26 conditions (13 tilts x 2 tasks) • Each condition was measured 12 times in a single run • Task: adjust motion or line to world vertical

  13. Results Results

  14. Results Compensation for static head tilt in both tasks One subject Compensation angle β [deg] All subjects Tilt angle ρ [deg]

  15. Results SMV Errors = SVV Errors Tilt angle ρ [deg] Thus: common signal processing!

  16. Results What causes systematic errors if body tilt signal and retinal signal are unbiased? Models for verticality perception Mittelstaedt’s idiotropic model (1983) Bayesian model

  17. Modeling Modeling

  18. Modeling Utricle and saccule contain different numbers of hair cells Mittelstaedt’s idiotropic model

  19. Modeling Idiotropic vector Mittelstaedt’s idiotropic model mt Parameters: S = saccular gain Mz = length idiotropic vector

  20. Modeling Fit results idiotropic model

  21. Modeling Scatter fit idiotropic model

  22. Idiotropic model Modeling • Questions raised by assumptions: • Why would the brain not be able to cope with unequal distribution of hair cells? • Why would the brain not use the unbiased tilt signal?

  23. Modeling Bayesian model • Bayes’ rule states: p(T|S)=p(S|T)p(T) • Optimal estimate of variable T is based on sensory evidence AND on prior knowledge. • Bayesian models have been found useful to explain perceptual bias phenomena P(T) 25º 30º 20º P(S|T) 25º 30º 20º Do verticality errors reflect a Bayesian strategy? 25º 30º 20º

  24. Modeling Single trial Multiple trials Tilt angle Tilt angle Bayesian framework • Sensory head tilt signal: • Provided by several sensory systems like the vestibular system, somato-sensory afferents and proprioception • Accurate but noisy • Prior knowledge: • Small tilt angles are more likely than large angles from: Carandini, 2006

  25. Modeling Result: less noise but biased signal Bayesian model Head tilt signal is unbiased but noisy Noise increases with tilt angle

  26. Modeling Assumptions (1) • Tilt signal is contaminated by Gaussian noise, which increases linearly with tilt angle: σtilt = a0 + a1∙|r| • Prior is normally distributed with μ= 0 andσ = σp • Optimal estimate of tilt is obtained by taking the maximum of the posterior distribution (MAP)

  27. Modeling Assumptions (2) • Visual signal is contaminated by Gaussian noise, which differs in SVV and SMV task: • σvl for line task (SVV) • σvm for motion task (SMV) • Spatial direction of visual stimulus is obtained by summing the retinal direction and the estimated head tilt angle

  28. Modeling Bayesian model fits • The model was fitted to motion and line data simultaneously • Parameters: • a0: Tilt noise at ρ=0º (offset) • a1: Tilt noise increase (slope) • σp:Prior width • σvl:Visual noise in line task • σvm:Visual noise in motion task

  29. Modeling Systematic error fits

  30. Modeling Systematic errors: All subjects Bayesian model and idiotropic model can both account accurately (R2>0.81) for systematic SVV/SMV errors

  31. Modeling Scatter

  32. Discussion Discussion (1) Overestimation of SVV/SMV scatter • Possible underestimation of scatter due to approach of collecting all responses in a single run • Psychophysical measurement of scatter would improve SVV/SMV scatter estimates

  33. Discussion Discussion (2) Errors of undercompensation (A-effects) and errors of over-compensation (E-effects) • This Bayesian model cannot explain E-effects  Additional mechanism • uncompensated counterroll of the eyes • Idiotropic model can account for both types of errors

  34. Discussion Discussion (3) Paradox: Why no large systematic errors in body tilt estimate? • Mittelstaedt model: other sensors • Bayesian model: Precision/Accuracy trade-off • Visual stability • Balance

  35. Conclusions Conclusions • Identical errors in SMV and SVV  shared computational mechanism • Bayesian approach is promising and should be further tested

  36. Preliminary Current projects • Accurate (psychophysical) testing of scatter in SVV at several tilt angles • Accurate (psychophysical) testing of scatter in body tilt percept at several tilt angles • Does noise in tilt percept increase with tilt angle? • Scatter SVV < Scatter tilt percept ? • Does Bayesian model still fit these data?

  37. Preliminary Preliminary results Psychophysical measurement of SVV • Eight subjects (5 male, 3 female) • 9 tilt angles: -120º to 120º with steps of 30º • SVV was measured psychophysically at each tilt angle • Forced-choice task (left/right)

  38. Preliminary One subject

  39. Preliminary All subjects

  40. Preliminary E-effects Fit of systematic errors

  41. Preliminary Fit of scatter Currently, investigations on incorporating ocular counterroll (OCR) to account for E-effects

  42. Preliminary Body tilt estimate Psychometric SVV and body tilt estimate Subjective vertical (SVV)

  43. Thank you!

  44. Extra Idiotropic model • Idiotropic vector M compensates for the distortion at small tilts at the expense of increasing systematic errors for larger tilt angles.

  45. Extra b b Idiotropic model

  46. Extra Ocular counterroll (OCR) • Sinusoidal behaviour • ~10% of roll angle, maximally 10º (on average) at 90º roll tilt • Preliminary: Bayesian fits improved Without OCR With OCR

  47. Extra Ocular counterroll (OCR) Scatter: Without OCR With OCR

More Related