Vision and Brain

How We Perceive The World

MIT Press, 2012

 

James V Stone

 

Preface

  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 

Conclusion

 

2 Eyes

  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 

The Retina

What the Eye Does not Tell the Brain 

    

3 The Neuronal Machinery of Vision

Neurons and Wineglasses 

Exponential Decay

Signal Boosters 

Synapses

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 

Simplifications

  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 

Simple Cells

Temporal Receptive Fields 

Maps in Primary Visual Cortex 

Hypercolumns

Pictures in the Head? 

The Packing Problem 

Secondary Visual Cortex 

Color Cortex

Motion Cortex

Losing Retinotopy

  Inferotemporal 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 ...

Staying Upright 

Motion Parallax and Optic Flow

  The Motion Aftereffect 

  A Neuronal Model of the Motion Aftereffect

Motion Blur 

Structure from Motion

How Much Structure from How Much Motion? 

Stereo Vision

Stereograms

The Correspondence Problem 

3-D Glasses 

Shape from Texture 

Shape from Shading 

Conclusion

  

6 The Perfect Guessing Machine

Perfectly Ambiguous Images

How Probable Is That Image? 

How Probable Is That Shape? 

           Generalizing Bayes' Rule

Bayes' Rule Increases Accuracy, on Average 

A Prior for Face Convexity?

Noisy Images 

Evidence versus Experience 

Bayesian Wars 

Brains and Bayesian Inference

Marr and Bayes 

Conclusion


7 The Color of Information 

Color and Light 

  Light, Cones, and Rods 

There's a Hole in the Sky Where the Light Gets In

    Information Theory

  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

Sum-Difference Recoding 

Recoding and Efficient Coding

Ganglion Cells as Information Channels 

Principal Component Analysis

Noise

More Pushing and Pulling? 

Color Aftereffects 

Are Cone Tuning Curves Optimal? 

Simplifications


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 

Conclusion


9 Brains, Computation, and Cupcakes 

David Marr ( Homo computatrix ) 

Conclusion


Further Reading 

 

References 

 

Index