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  • Slide 1
  • Magnitude and time course of illusory translation perception during off-vertical axis rotation Rens Vingerhoets Pieter Medendorp Jan Van Gisbergen
  • Slide 2
  • Contents Introduction - Sensors - Off-vertical axis rotation - Models Methods Results - Verbal estimates - Psychophysical data Model implications Conclusions Contents
  • Slide 3
  • Sensory signals involved in spatial orientation: Visual Cues Semicircular canals Otoliths Somatosensory cues Introduction - Sensors
  • Slide 4
  • The semi-circular canals Sensitive to angular acceleration High-pass filter
  • Slide 5
  • Introduction - Sensors The otoliths Sensitive to acceleration caused by: Gravity Inertial acceleration
  • Slide 6
  • Off-Vertical Axis RotationVertical Axis Rotation Introduction What is off-vertical axis rotation (OVAR)?
  • Slide 7
  • Rotation in yaw about an axis that is tilted relative to the direction of gravity. Stimulation of both otoliths and canals Introduction Left Ear Down (LED)Right Ear Down (RED)Nose Up (NU) Nose Down (ND)
  • Slide 8
  • What causes this percept? Left Ear Down (LED) Right Ear Down (RED)Nose Up (NU) Nose Down (ND) Introduction What happens during OVAR? Otolith signal from tilt interpreted as translation? LED ND NU RED NDND LED RED NU R
  • Slide 9
  • Introduction Otolith Disambiguation Neural strategy for otolith disambiguation: Filter hypothesis Acceleration
  • Slide 10
  • Introduction Otolith Disambiguation Neural strategy for otolith disambiguation: Canal-Otolith interaction Acceleration Rotation
  • Slide 11
  • Introduction research question Do these models apply to self-motion perception during OVAR? To check this quantitative data is required
  • Slide 12
  • Methods Experimental setup Picture of vestibular chair
  • Slide 13
  • Methods Experimental setup 6 subjects 2 series (only clockwise rotation) - Tilt series: 0, 15 and 30 deg tilt at 30 deg/s - Speed series: 20, 30, 40 and 50 deg/s at 15 deg tilt Each experimental condition consisted of 18-20 runs of 180 s each Subjects indicated verbally when cone illusion started Subjects reported the perceived radius Self-motion percept quantified with laser method
  • Slide 14
  • Experiment Laser method v Screen and motorized laser on board of the chair Every NU and ND phase projection of moving laser dot Subject indicated with a toggle switch if the dot was moving too fast/slow in direction opposite to perceived selfmotion Matching velocity obtained using two methods: - 0-110 s: Adaptive staircase over runs - 110-180 s: Method of constant stimuli
  • Slide 15
  • Results I Verbal Estimates
  • Slide 16
  • Results I Verbal estimates Reported cone illusion latencies
  • Slide 17
  • Results I Verbal Estimates Estimated Radii
  • Slide 18
  • Results II Time course
  • Slide 19
  • Results II Staircase Data Staircase data from tilt series NU ND
  • Slide 20
  • Results II Staircase Data Staircase data from tilt series NU ND
  • Slide 21
  • Results II Staircase Data Staircase data from tilt series NU ND
  • Slide 22
  • Results II Staircase Data Staircase data from speed series NU ND
  • Slide 23
  • Results II Staircase Data Summary staircase data Stereotyped exponential decay to zero in 30-60 s in zero-tilt condition During OVAR short exponential decay followed by bifurcation into two opposite velocity levels Results in agreement with anecdotal reports Bifurcation depends on tilt angle Bifurcation depends on rotation speed
  • Slide 24
  • Results III Decomposition of response curves
  • Slide 25
  • Results III - Decomposition Decomposition of response curves Two processes (R & T) underlie self-motion perception. R follows the same time course in both phases (NU & ND) T has opposite sign in both phases Hence, matching velocity is: V NU = R +T V ND = R T Consequently: R = (V ND + V NU )/2 T = (V NU - V ND )/2 LED RED NU ND T R + T R +
  • Slide 26
  • Results III - Decomposition Decomposition data from tilt series R T
  • Slide 27
  • Results III - Decomposition Decomposition data from tilt series R T
  • Slide 28
  • Results III - Decomposition Decomposition data from tilt series R T
  • Slide 29
  • Results III - Decomposition Decomposition data from speed series R T
  • Slide 30
  • Results III - Decomposition Summary decomposition data R component shows exponential decay to zero independent of tilt angle and rotation speed T component starts at zero and gradually climbs to an asymptotic level. T component increase not always starts right after rotation onset Asympotic value of T component depends on tilt angle and rotation speed.
  • Slide 31
  • Results III - Decomposition Fit to decomposition data Rotation component: R(t) = A * exp(-t/T R ) Translation component: T(t) = 0if t < T T(t) = B * (1 exp((-t- T)/T T )if t > T
  • Slide 32
  • Results III - Decomposition Examples of fit
  • Slide 33
  • Results III - Decomposition Fit parameters show us: R component R(t) = A * exp(-t/T R ) T R is constant across experimental conditions Initial amplitude (A) of R component increases with increasing rotation speed T component T(t) = 0if t < T T(t) = B * (1 exp((-t- T)/T T ) Incorporating a delay ( T ) is essential Inter-subject differences for delay and T T Translation percept (B) increases both with tilt angle and rotation speed.
  • Slide 34
  • Results IV Constant stimuli data
  • Slide 35
  • Results IV Constant Stimuli Constant stimuli data from tilt series NU ND
  • Slide 36
  • Results IV Constant Stimuli Constant stimuli data from speed series NU ND
  • Slide 37
  • Results IV Constant Stimuli Summary constant stimuli data Observations from staircase data confirmed: -Increase of matching velocity with tilt angle -Increase of matching velocity with rotation speed Width of psychometric curve increases with rotation speed
  • Slide 38
  • Models
  • Slide 39
  • Model predictions Canal-otolith interactionFiltering 30 o /s and 15 o tilt
  • Slide 40
  • Models Model predictions 30 o /s and 15 o tilt Canal-Otolith
  • Slide 41
  • Models Model predictions 30 o /s and 15 o tilt Canal-Otolith Filter
  • Slide 42
  • Models Model predictions 30 o /s and 15 o tilt Data Canal-Otolith Filter Models cannot account for observed time course
  • Slide 43
  • Models Model predictions 15 o tilt Data Canal-Otolith Filter Models predict too large translations
  • Slide 44
  • Conclusions
  • Slide 45
  • We have developed a method that is able to capture the motion percepts that occur during OVAR Contemporary model hypotheses such as canal-otolith interaction and frequency segregation cannot explain our results Conclusions
  • Slide 46
  • The End
  • Slide 47
  • Canal-otolith interaction model - + + - + + + - + Body DynamicsSensory Dynamics S scc (s) S oto (s) S scc (s) S oto (s) - + + x g g a f e e f e a g g a a f oto scc oto k a k f k f k e f f g = (- x g)dt g = (- x g)dt Model of Body Dynamics Model of Sensory Dynamics Merfeld