How do we identify an object from the features that we detect? Understanding how the brain recognizes objects might give insight into how the brain solves problems in general. The object recognition problem has withstood a century of attempts, but we bring new tools - fruits of the last grant period - that allow us to sketch the outlines of a solution. Four approaches, all new and different, converge on one answer.
AIM 1. Use crowding, along with other manipulations, to characterize three parallel processes in reading by normal and dyslexic readers.
AIM 2. Count features by probability summation. Extending traditional probability summation from explaining just detection to also explain object identification, we acquire a new tool, allowing us to count the number of features the observer must detect in order to identify.
AIM 3. Capture the observer's classification algorithm by computer modeling of the observer's responses to thousands of letters in white noise. We use statistical learning theory to build a classifier that accounts for human performance. The observer classifies each of several thousand images of a letter in noise as """"""""a"""""""", """"""""b"""""""", or """"""""c"""""""", etc. These classifications are data that can tell us what the observer is doing. We use a powerful statistical learning algorithm to create a simple classifier that best models human performance.
AIM 4. fMRI: Where in the brain are letters identified? Correlate the activation of the """"""""letter"""""""" area in the left fusiform gyrus, and elsewhere, with two psychophysically-discovered signatures of letter identification: fast learning and channel frequency. Thus techniques from cognition, perception, statistical learning theory, and physiology together will reveal what is computed where, in the brain, when an observer identifies an object. ? ?

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
2R01EY004432-20A2
Application #
7036051
Study Section
Central Visual Processing Study Section (CVP)
Program Officer
Oberdorfer, Michael
Project Start
1982-07-01
Project End
2011-01-31
Budget Start
2006-02-01
Budget End
2007-01-31
Support Year
20
Fiscal Year
2006
Total Cost
$333,160
Indirect Cost
Name
New York University
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
041968306
City
New York
State
NY
Country
United States
Zip Code
10012
Rosen, Sarah; Pelli, Denis G (2015) Crowding by a repeating pattern. J Vis 15:10
Song, Shuang; Levi, Dennis M; Pelli, Denis G (2014) A double dissociation of the acuity and crowding limits to letter identification, and the promise of improved visual screening. J Vis 14:3
Rosen, Sarah; Chakravarthi, Ramakrishna; Pelli, Denis G (2014) The Bouma law of crowding, revised: critical spacing is equal across parts, not objects. J Vis 14:10
Pelli, Denis G; Cavanagh, Patrick (2013) Object recognition: visual crowding from a distance. Curr Biol 23:R478-9
Suchow, Jordan W; Pelli, Denis G (2013) Learning to detect and combine the features of an object. Proc Natl Acad Sci U S A 110:785-90
Pelli, Denis G; Bex, Peter (2013) Measuring contrast sensitivity. Vision Res 90:10-4
Dubois, Matthieu; Poeppel, David; Pelli, Denis G (2013) Seeing and hearing a word: combining eye and ear is more efficient than combining the parts of a word. PLoS One 8:e64803
Freeman, Jeremy; Chakravarthi, Ramakrishna; Pelli, Denis G (2012) Substitution and pooling in crowding. Atten Percept Psychophys 74:379-96
Chakravarthi, Ramakrishna; Pelli, Denis G (2011) The same binding in contour integration and crowding. J Vis 11:
Chakravarthi, Ramakrishna; Cavanagh, Patrick (2009) Bilateral field advantage in visual crowding. Vision Res 49:1638-46

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