Vision and Brain
How we perceive the world
About this book
''I loved this book. A highly readable and accessible introduction to vision, it is distinctive in its emphasis on the computational principles underlying our visual perception.''
From Dr J Read's book review in Perception journal, 2013. Download review (PDF, 97KB)
In this accessible and engaging introduction to modern vision science, Jim Stone uses visual illusions to explore how the brain sees the world.
Understanding vision, Stone argues, is not simply a question of knowing which neurons respond to particular visual features, but also requires a computational theory of vision.
Stone draws together results from David Marr's computational framework, Barlow's efficient coding hypothesis, Bayesian inference, Shannon's information theory, and signal processing to construct a coherent account of vision that explains not only how the brain is fooled by particular visual illusions, but also why any biological or computer vision system should be fooled by these illusions.
This short text includes chapters on
the eye and its evolution
how and why visual neurons from different species encode the retinal image in the same way
how information theory explains colour aftereffects
how different visual cues provide depth information
how the imperfect visual information received by the eye and brain can be rescued by Bayesian inference
how different brain regions process visual information
the bizarre perceptual consequences that result from damage to these brain regions.
The tutorial style emphasises key conceptual insights, rather than mathematical details, making the book accessible to the the non-scientist, and suitable for undergraduate or postgraduate study.
James V Stone is a Reader in the Psychology Department of the University of Sheffield. He is co-author (with John P. Frisby) of the widely used text Seeing: the Computational Approach to Biological Vision (second edition, MIT Press, 2010), and author of Independent Component Analysis: A tutorial introduction (MIT Press, 2004) and Bayes' Rule (2012).
Download Chapter 1 (PDF, 831KB)
The party trick
1. Vision: an overview
How we see: the brain as a detective
Illusions: how the brain fails?
Illusory lines: triangles and pandas
Recognizing objects: cubes, rings, and pianos
Perceiving three-dimensional shape: shading, craters, and faces
Shades of gray and grays of shade
Color and shade
Brains, vision, and bird flight
The evolution of eyes
Darwin's cold shudder
The simplest eyes
The simple eye
The pinhole camera
The human eye
An organ of imperfections
Not blinded by the light
What the eye does not tell the brain
3. The neuronal machinery of vision
Neurons and wineglasses
The cost of neuronal computation
The illusory vision of the horseshoe crab
Receptive fields and Mexican hats
The illusory vision of the Mexican hat
Receptive field size and spatial scale
Spatial frequency and Fourier analysis
Why have on-center and off-center cells?
Push-pull amplifiers in the brain?
Why does opponency yield linearity?
Evidence for push-pull processes
Logan's need to know
Receptive fields: what are they good for?
From Mexican hats to bells
The efficient coding hypothesis
4. The visual brain
From retina to visual cortex
From retina to LGN
Magno, parvo, and konio layers in the LGNs
From LGN to striate cortex
Temporal receptive fields
Maps in primary visual cortex
Pictures in the head?
The packing problem
Secondary visual cortex
A mill and a grand book
5. Depth: the rogue dimension
Space, the first frontier
Painting pictures on the retina
Pictorial cues to depth
Motion: what is it good for?
Now you see it...
Motion parallax and optic flow
The motion aftereffect
A neuronal model of the motion aftereffect
Structure from motion
How much structure from how much motion?
The correspondence problem
Shape from texture
Shape from shading
6. The perfect guessing machine
Perfectly ambiguous images
How probable is that image?
How probable is that shape?
Generalising Bayes' rule
Bayes' rule increases accuracy, on average
A prior for face convexity?
Evidence versus experience
Brains and Bayesian inference
Marr and Bayes
7. The Color of Information
Color and light
Light, cones, and rods
There's a hole in the sky where the light gets in
Big message, small wires
Navigating information theory, bit by bit
Bits, binary digits, and entropy
Photoreceptors as information channels
Bits and bins in the visual system
What a waste
Recoding and efficient coding
Ganglion cells as information channels
Principal component analysis
More pushing and pulling?
Are cone tuning curves optimal?
8. A hole in the head
Gedankenexperiment: not carving nature at her joints
Carving nature at her joints
Strategies for object and face recognition
Neuropsychology of object and face recognition
9. Brains, computation, and cupcakes
David Marr (Homo computatrix)