by Li Yang Ku (Gooly)
How our brain handles visual input is a myth. When Hubel and Wiesel discovered the Gabor filter like neuron in cat’s V1 area, several feed forward model theories appear. These models view our brain as a hierarchical classifier that extracts features layer by layer. Poggio’s papers “A feedforward architecture accounts for rapid categorization” and “Hierarchical models of object recognition in cortex” are good examples. These kind of structure are called discriminative models. Although this new type of model helped the community leap forward one step, it doesn’t solve the problem. Part of the reason is that there are ambiguities if you are only viewing part of the image locally and a feed-forward only structure can’t achieve global consistency.
Therefore the idea that some kind of feedback model has to exist gradually emerged. Some of the early works in the computer science community had first came up with models that rely on feedback, such as Gefforey Hinton’s Boltzman Machine invented back in the 80’s which developed into the so called deep learning around late 2000. However it was only around early 2000 had David Mumford clearly addressed the importance of feedback in the paper “Hierarchical Bayesian inference in the visual cortex“. Around the same time Wu and others had also combined feedback and feedforward models successfully on textures in the paper “Visual learning by integrating descriptive and generative methods“. Since then the computer vision community have partly embraced the idea that the brain is more like a generative model which in addition to categorizing inputs is capable of generating images. An example of human having generative skills will be drawing images out of imagination.
Slightly before David Mumford addresses the importance of the generative model. Lamme in the neuroscience community also started a series of research on the recurrent process in the vision system. His paper “The distinct modes of vision offered by feedforward and recurrent processing” published in 2000 addressed why recurrent (feedback) processing might be associated with conscious vision (recognizing object). While in the same year the paper “Competition for consciousness among visual events: the psychophysics of reentrant visual processes.” published in the field of psychology also addressed the reentrant (feedback) visual process and proposed a model where conscious vision is associated with the reentrant visual process.
While both the neuroscience and psychology field have research results that suggests a brain model that is composed of feedforward and feedback processing where the feedback mechanism is associated with conscious vision, a recent paper “Detecting meaning in RSVP at 13 ms per picture” shows that human is able to recognize high level concept of an image within 13 ms, a very short gap that won’t allow the brain to do a complete reentrant (feedback) visual process. This conflicting result could suggest that conscious vision is not the result of feedback processing or there are still missing pieces that we haven’t discover. This kind of reminds me one of Jeff Hawkins’ brain theory, which he said that solving the mystery of consciousness is like figuring out the world is round not flat, it’s easy to understand but hard to accept, and he believes that consciousness does not reside in one part of the brain but is simply the combination of all firing neuron from top to bottom.