Our long-term goal is to understand how the human brain recognizes objects. This 3-year project will characterize the computational kernel (computation that is applied independently to many parts of the image data) that is isolated by crowding experiments. We present the discovery that recognition of simple objects is performed by recognition units implementing the same computation at every eccentricity. These units are dense in the fovea and thus hard to isolate there, but they are sparse in the periphery, and easily isolated. Our fMRI & psychophysics pilot data show that each of these units, at every eccentricity, has a circular receptive field with a radius of 2.61.5 mm (meanSD) in human cortical area hV4. Because of cortical magnification, that 2.6 mm corresponds to a tiny 0.05 deg in the fovea, but grows linearly with eccentricity, to a comfortable 3 deg at 10 deg eccentricity. We test this idea by pursuing its implications physiologically (Aim 1), clinically (Aim 2), and psychophysically and computationally (Aim 3).
Aim 1. Better noninvasive measures for the health and development of visual cortex are needed. Conservation of crowding distance (in mm) in a particular cortical area (hV4) would validate crowding distance as a quick, noninvasive measure of that area's condition.
Aim 2. Huge public interventions seek to help dyslexic children read faster and identify amblyopic children sooner. It would be valuable to know whether crowding contributes to reading problems and provides a basis for effective screening for dyslexia and amblyopia, as it can be measured before children learn to read.
Aim 3. Documenting conservation of efficiency gives evidence that the same universal computation recognizes objects at every eccentricity. We are testing the first computational model of object recognition that accounts for many human characteristics of simple-object recognition. The new work extends to effect of receptive field size and learning.
(relevance to public health) This proposal is a collaboration between a psychophysicist, expert on human object recognition, a computer scientist, expert on machine learning for object recognition by computers, and a brain imager, expert on brain mapping, to discover to what extent computer models of object recognition and the brain can account for key properties of human performance. Our first aim tracks the development of crowding in normal and amblyopic children, in collaboration with experts in optometry, reading, and development. Advances in this area could shed light on the problems of people with impaired object recognition, including amblyopia and dyslexia, with a potential for development of early pre-literate screening tests for amblyopia and risk of dyslexia.
|Majaj, Najib J; Pelli, Denis G (2018) Deep learning-Using machine learning to study biological vision. J Vis 18:2|
|Benson, Noah C; Winawer, Jonathan (2018) Bayesian analysis of retinotopic maps. Elife 7:|