Lecture notes

Bayesian models of perception AND how autism influences vision- PLUS bonus extra reading and critical analysis of papers on these topics

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Module
Active Vision

Institution
Cardiff University (CF)

Book
Vision Science

My favourite yet one of the most complicated lectures of the series. This lecture explores Bayesian models of perception. It also explores the incredibly fascinating phenomenon of why Autistic people do not seem to experience visual illusions in the same way that neurotypical people do. Papers by P...

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Uploaded on April 13, 2018
Number of pages 12
Written in 2016/2017
Type Lecture notes
Professor(s) Dr tom freeman
Contains 4

perception
autism
perception in autism
visual perception
bayes
bayes theorem
bayesian models
illusions
filhene illusion
limitations
eyes
retinal
retina
retinal signals
bayesian models of perception
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Lecture 4
Pursuit gain = proportion. E.g. a pursuit gain of 1 means perfect pursuit, 0 means
eyes are not moving at all. >1 means eye is moving faster than target, <1 means
eye is moving slower than target.
Psychological gain = estimate of how fast the target is going, separate from
pursuit gain.

Bayesian models of perception
Signals measure speeds, one measuring a retinal stimulus
and one measuring the eye movements (ER) and then
there are estimates.
The signals are wrong, they have different gains. Eye
movement, pursuit, is not always accurate. Retinal slip
occurs and saccades are used to adapt to this.
By why do we have measurement errors?
Bayesian’s claim that the errors are at the estimates
rather than the signals.

‘Imprecise-signals’ model
Bayesian model claims:
“hard claim” or first claim: sensory evidence is accurate,
the early visual signals, are as accurate as each other. The
difference between them is the difference in precision of
each of them, and there is some ambiguity.
Precision: things are variable. Neurons are variable,
fluctuate. But the world is not. Interpret an invariable
stimulus in the “face” of variability.
Von Helm Hotlz: must be that the brain is having to draw inferences about what
is going on in the world based on variable activity.
Perception is a best guess based on this and prior expectations about the state of
the world. A model is needed to help interpret the activity.
Gives a balance between the fluctuation and prior
expectations.

Sensory evidence is also ambiguous because of various
geometric facts. The short line on the back of the eye
could correspond to any line out in the “real world”.
Don’t know which line it corresponds to – do not see
infinite options, just see one thing. Gives a constant
percept and sometimes gets it wrong.

Two options: in or out. Instead of random – use prior
expectation of light coming from above, so the discs with
shading at the bottom must be sticking out and those
with shading at the top must be concave. So this is an
example of drawing inferences based on an expectation
of something that’s going on in the world.

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Lecture notes

Bayesian models of perception AND how autism influences vision- PLUS bonus extra reading and critical analysis of papers on these topics

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