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