Associate Professor John Perrone
The School of Psychology, The University of Waikato
Research overview
My research
fits within the general area of visual perception and visual neuroscience. My
PhD thesis focused on visual slant perception and I developed a model of human
slant misperception [1,2]. As a Postdoctoral Fellow at NASA
Ames Research Center (U.S.A), I applied my surface slant model to the problem of
understanding the visual information used by pilots during the approach and
landing phase of flight. I was able to successfully account for some of the
landing approach errors made by pilots during night-landings [3].
During the
period at NASA Ames Research Center and Stanford University (U.S.A), I shifted
my research focus from ‘static’ perception to the perception of visual motion.
At the time, the Vision
group at NASA Ames was one of the strongest internationally in terms of
motion perception research and it particularly excelled in the development of
computer models of visual motion perception. I was able to learn these ‘new’
motion modeling techniques from the NASA researchers and soon published the
first physiologically plausible model of human self-motion estimation
[4]. In collaboration with Dr Lee Stone, the model was
extended [5] to include the role of eye-movements. Knowing how
humans and animals use vision to navigate through the world (self-motion) is an
important topic within psychology and in the general field of neuroscience. It
also has many important practical applications (e.g., robotics, aerospace and
driving research). An
advantage of our self-motion estimation model over competing models is that it
is closely tied to the known properties of neurons in the primate visual cortex
(V1, primary visual cortex; MT, Middle Temporal area; MST, Medial Superior
Temporal area).
After the
publication of our self-motion estimation model, there was a lot of debate as to
whether or not neurons in a particular area of the brain (MST) could do what we
had proposed. We carried out tests of our model and verified that the neuron
properties were consistent with the model mechanisms [6].
There was
still some debate however about the properties of neurons in another area of the
brain (MT) that we had also incorporated into our self-motion estimation model.
In collaboration with Dr. Alex Thiele (then at the Salk Institute for Biological
Studies, U.S.A.), we showed for the first time that MT neurons are speed tuned
[7]. Prior to the publication of our results, there was an
open question as to whether or not MT neurons in the brain were truly speed
tuned (i.e., do they respond selectively to a particular rate of image motion
independently of the spatial structure of the stimulus?)
I then went on
to develop a model of how neurons within area MT could develop speed tuning [8,
9, 10]. This model of the V1-MT stage has
an advantage over many other competing models because it can be tested with
moving pictures and stimuli that exactly match those used in primate striate and
extra-striate cell recordings. It also forms the last phase of my overall plan
to develop a general model of visual motion processing in the brain. When
completed it will be a very powerful tool for understanding many aspects of
motion perception.
One of the
first applications for the model will be to apply it to the age-old question of
why the world appears still when we move our eyes. This is the topic of a
recently funded (2006) Royal Society of New
Zealand Marsden grant application: ‘Vector addition in the brain: Why the
world stays still when we move our eyes’ with
Professor
Rich Krauzlis (the Salk Institute for Biological Studies, U.S.A.) as co-PI.
The Marsden
project will make use of our previous experience with motion models and
eye-movement research to understand how humans manage to maintain the perception
of a stable visual world despite the fact that they constantly move their eyes.
During these eye movements, the resulting retinal image motion is ambiguous
because it could represent movement of the world, movement of the observer or
combinations of both. Despite this ambiguous input, our brains somehow manage to
solve this ‘eye rotation problem’ and correctly construct the perception of a
stable world. We have discovered a mechanism (a type of vector addition) that
our visual system could potentially use to cancel the effect of eye movements.
Using a combination of computer modelling and psychophysical methods, we aim to
find evidence for this cancellation mechanism which, if verified, would lead to
a significant breakthrough into the long standing question of why eye movements
do not cause apparent movement of the world. A solution to the problem would
also help provide fundamental insights into the way our brains work and how the
brain combines visual and motor signals. This knowledge could also be used for
designing better artificial-vision systems for robots.
References:
1. Perrone, J.A. (1982). Slant underestimation: A general model.
Perception, 11, 641-654.
2. Perrone, J.A. (1980). Slant underestimation: A model based on the
size of the viewing aperture. Perception, 9, 258-302.
3.
Perrone, J.A. (1984). Visual slant misperception and the "Black-Hole" landing
situation. Aviation; Space, and Environmental Medicine, 55, 1020-5.
4. Perrone, J.A. (1992). Model for the computation of self-motion in
biological systems. Journal of the Optical Society of America A, 9,
177-194.
5. Perrone, J.A. & Stone, L.S. (1994) A model of self-motion estimation
within primate extrastriate visual cortex. Vision Research, 34,
2917-2938.
6. Perrone, J.A. & Stone, L.S. (1998) Emulating the visual receptive field
properties of MST neurons with a template model of heading estimation. The
Journal of Neuroscience, 18, 5958-5975.
7. Perrone, J. A. &
Thiele, A. (2001). Speed skills: measuring the visual speed analyzing properties
of primate MT neurons. Nature Neuroscience, 4(5), 526-532.
8. Perrone, J. A.,
& Thiele, A. (2002). A model of speed tuning in MT neurons. Vision Research,
42, 1035-1051.
9. Perrone, J. A.
(2004). A visual motion sensor based on the properties of V1 and MT neurons.
Vision Research, 44, 1733-55.
10.
Perrone, J.A. (2006). A single mechanism can explain the speed tuning properties
of MT and V1 complex neurons. The Journal of Neuroscience, 26,
11987-11991.
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