Authors: Peter W. Battaglia, Robert A. Jacobs, and Richard N. Aslin
COGS 272, Spring 2010
Instructor: Prof. Angela Yu
Presenter: Vikram Gupta
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Introduction
Background
Methods
Procedure
Results
Discussion
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Integration of multiple sensory and motor signals
 Sensory: binaural time, phase, intensity difference
 Motor: orientation of the head
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Typically, we receive consistent spatial cues
What if this is not true?
 Ex: Movie theater, television
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Visual capture
 Vision dominates over conflicting auditory cue.
 Ex: recalibration in juvenile owl
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Optimal?
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Winner Take All (ex. vision capture)
 Dominant signal exclusively decides
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Blend information from sensory sources
 Is blending statistically optimal?
 Example: Maximum Likelihood Estimate
▪ Assumption independent sensory signals, normal dist.
Impact of reliability on MLE estimate
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Is Normal distribution a good estimate of
neural coding of sensory input?
Does this integration always occur? Or are
there qualifying conditions?
Does it make sense to integrate if
• Lv* and La* are far apart?
• v and a are temporally separated?
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Ernst, 2006 (MLE integration for haptic and visual input
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Vision capture or MLE match empirical data?
Method summary:
 Noise is produced at 1 of 7 locations 1.50 apart
 Visual stimulus has noise at 5 levels
▪ 10%, 23%, 36%, 49%, 62%
 Single sensory modality trial (Audio / noisy Visual )
 MLE parameters  predict performance for
Audio + noisy Visual  compare with Empirical
data
S
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Single-modality
 Standard stimuli followed
by comparison
 Is C Left / Right of S?
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Bimodal
 Standard stimuli has Audio
and Visual apart from
center
 Audio and visual
Comparison stimuli are colocated.
 Only 1 subject aware of
spatial discrepancy in S
C
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Cumulative normal distribution fits to data
Mean and variance are used for MLE model
 Wv receives high value when visual noise is low
 Wa receives high value when visual noise is high
 rt = 1 comparison to the right of standard
 pt =
, probability of rt, given mean
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and variance
R = set of responses to the independent trials
Assuming normal distribution, MLE estimate
of mean and variance parameters
 µml = 1/T * (∑ rt)
σ2ml = 1/T * (rt - µml) 2
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Mean is calculated according to above
weighted average
Variance is smaller than either P(L|v) or P(L|a)
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MLE estimate for wv and wa are found by
maximizing RHS of (3) and using (6)
tau is scale parameter or slope
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Standard stimulus
 Visual -1.50
 Audio 1.50
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Point of Subjective Equality
 -1.10 for low visual noise
 0.10 for high noise
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Visual input dominates at
low noise
Equal weight at high noise
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MLE estimates for visual weight are significantly
lower than the empirical results.
A Bayesian model with a prior that reduces
variance in visual-only trials provides a good
regression fit for the data.
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For visual only trials, instead of using MLE for
mean and variance, we multiply the RHS
above with the probability of the occurrence
of the normal distribution
 mean is assumed to have a uniform distribution.
 variance is assumed to have inverse gamma
distribution with parameters biased for small
variance.
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Bayesian approach is a hybrid of MLE and
visual capture models.
How are variances encoded?
How are priors encoded?
How does temporal separation in cues impact
sensory integration?
Biological basis for Bayesian cue integration?