Optimal sensory integration in spatial orientation
Maaike de Vrijer
PAC-meeting, September 17th 2009
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Sources of information
Vestibular system
Neck proprioceptors
Eyes
Tactile system
Blood pressure
A priori knowledge
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Optimal (Bayesian) observer theory
• Combination of two noisy signals leads to lower noise in final estimate
• A priori knowledge reduces noise in final estimate but may introduce bias
Sensor 1
Sensor 2
Combined
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An example
Perception of target location
Auditory Vision
Problem: sensory systems in spatial orientation cannot be easily isolated
Solution: use two spatial orientation tasks that rely on different combinations of the same signals
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Spatial orientation
Subjective body tilt (SBT)
Subjective visual vertical (SVV)
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Tilt angle [deg]
Resp
onse
err
or
[deg
]
SBT
SVV
Tilt angle [deg]
SBT and SVV performance
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Objectives:
To test whether optimal Bayesian observer theory applies to performance in the two tasks (SBT and SVV)
Can the theory explain why performance in body tilt perception (SBT) and visual verticality perception (SVV) differs?
Approach:
• Psychophysical SBT and SVV experiments • Sensory integration model
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Model
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Sensory integration model
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Experiments
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Set-up
Vestibular chair
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Experiments
SVV task:
• At 9 tilt angles between -120 and 120°• Measure of bias (systematic errors)
and variability (uncertainty of subject)
SBT task:
• Reference angles: 0 and 90° tilt, • Measure of bias (systematic errors)
and variability (uncertainty of subject)
“Judge orientation of line with respect to
gravity”
“Judge orientation of body with respect to 0
or 90°”
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Results
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Results single subject
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Results single subject
Tilt angleTilt angle
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Results of all subjects
SBT SVV
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Results of all subjects
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Fit results
Optimal parameter values :(averaged across subjects)
Body sensors:
σ=11°
Neck sensors:
σ=5°
Head sensors:
σ=1.9°+0.13∙|tilt|
Prior head-in-space:
σ=11°
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Summary
• Using a psychometric approach, we measured spatial orientation in two different tasks: body tilt perception (SBT) and subjective visual vertical (SVV) task
• Results showed that subjects made systematic SVV errors at tilt angles beyond ~60°. SBT performance was quite accurate but more variable than SVV performance
• These findings can be well explained within a Bayesian framework, based on the processing of noisy signals in a statistically optimal fashion.
This suggests that the neural computations underlying human spatial orientation are ‘Bayes’ optimal’
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Questions?
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Combined results
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SVV results (single subject) at all tilts
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Best-fit parameters
Model
parameters
MD SR FW JG Average
aHead [°/°] 0.18 ±
0.01 0.11 ± 0.01 0.17 ± 0.02 0.07 ± 0.02 0.13
bHead [°] 1.1 ± 0.4 1.3 ± 0.3 1.1 ± 0.5 4.1 ± 2.2 1.9
σPrior [°] 10.4 ± 0.9 9.0 ± 0.7 13.8 ± 1.3 11.6 ± 0.9 11.2
σBody [°] 10.9 ± 1.1 7.5 ± 0.5 9.1 ± 0.6 15.9 ± 2.4 10.8
σNeck [°] 3.3 ± 1.9 7.1 ± 1.8 5.9 ± 1.7 4.6 ± n/a* 5.2
AOCR [°] 25.3 ± 1.7 17.3 ± 1.8 16.5 ± 1.9 0.0 ± n/a* 14.8
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