Visual object recognition is central to our behavior, and knowledge of the underlying brain mechanisms is critical to understanding human visual perception and memory. The key problem is creation of selectivity for object identity that tolerates changes in an object's retinal image, such as changes in position and size. The primate brain appears to construct this selectivity in the ventral visual stream because neuronal responses in the highest area of that stream--the anterior inferotemporal cortex (AIT)--show shape selectivity that can tolerate position and size changes. Yet, we do not understand these key neuronal properties--reports of AIT tolerance are limited and inconsistent, and recent studies show that it can be very restricted. Thus, the goals of this proposal are an understanding of key factors likely to determine AIT position and size tolerance, and to determine if AIT tolerance can explain behavioral tolerance.
Our first aim i s to systematically determine the position and size tolerance of AIT neuronal shape selectivity for a range of object sets and object training histories. We will establish the relationship of selectivity and AIT position and size tolerance, the interaction of AIT position and size tolerance, and the effect of object-specific training on these relationships. These data will establish neuronal tolerance at the highest level of the primate visual system and provide a much-needed foundation for further study. The mechanisms that might underlie position and size tolerance fall into two broad classes: (1) automatic generalization; and (2) tolerance learned by experiencing objects across changes in position and size.
Our second aim i s to determine if position- or size-specific object experience have substantial effects on the position or size tolerance of AIT shape selectivity. Because this has not been examined, any result would be extremely informative in constraining mechanisms and guiding future studies. Although it is thought that AIT tolerance underlies behavioral tolerance, this has not been systematically examined.
Our third aim i s to determine if the position and size tolerance of object identification can be explained by the tolerance of AIT neuronal shape selectivity. This is a vital to understanding the link between high-level, ventral stream neuronal responses and visual object identification.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY014970-04
Application #
7198019
Study Section
Central Visual Processing Study Section (CVP)
Program Officer
Oberdorfer, Michael
Project Start
2004-04-01
Project End
2009-03-31
Budget Start
2007-04-01
Budget End
2008-03-31
Support Year
4
Fiscal Year
2007
Total Cost
$304,244
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
Rajalingham, Rishi; Issa, Elias B; Bashivan, Pouya et al. (2018) Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. J Neurosci 38:7255-7269
Hong, Ha; Yamins, Daniel L K; Majaj, Najib J et al. (2016) Explicit information for category-orthogonal object properties increases along the ventral stream. Nat Neurosci 19:613-22
Aparicio, Paul L; Issa, Elias B; DiCarlo, James J (2016) Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex. J Neurosci 36:12729-12745
Rajalingham, Rishi; Schmidt, Kailyn; DiCarlo, James J (2015) Comparison of Object Recognition Behavior in Human and Monkey. J Neurosci 35:12127-36
Afraz, Arash; Boyden, Edward S; DiCarlo, James J (2015) Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination. Proc Natl Acad Sci U S A 112:6730-5
Majaj, Najib J; Hong, Ha; Solomon, Ethan A et al. (2015) Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance. J Neurosci 35:13402-18
Cadieu, Charles F; Hong, Ha; Yamins, Daniel L K et al. (2014) Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput Biol 10:e1003963
Yamins, Daniel L K; Hong, Ha; Cadieu, Charles F et al. (2014) Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc Natl Acad Sci U S A 111:8619-24
Baldassi, Carlo; Alemi-Neissi, Alireza; Pagan, Marino et al. (2013) Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLoS Comput Biol 9:e1003167
Issa, Elias B; Papanastassiou, Alex M; DiCarlo, James J (2013) Large-scale, high-resolution neurophysiological maps underlying FMRI of macaque temporal lobe. J Neurosci 33:15207-19

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