The long range goal of our research goal has been to develop and test rigorous theories of visual processing that address important aspects of behavioral and neural performance. [I] During the previous grant period we developed a very efficient method (the descriptive function method) for measuring the detection, discrimination and identification performance of single neurons. Briefly, [I] the responses the a neuron are measured along various stimulus dimensions, (2) descriptive functions are fitted to the means and standard deviations of the responses, and then (3) the fitted functions are used to determine single neuron performance. The high efficiency of this method allows us to measure the discrimination (or identification) performance of large populations of neurons and hence compare population performance to behavioral performance. The descriptive function method will be used to determine population performance for a number of stimulus dimensions. [II] Recent work in the primary visual cortex has revealed two important non-linear mechanisms, contrast normalization and response expansion, which make critical contributions to the discrimination and identification performance of cortical neurons. We propose a series of experiments and simulations to characterize the spatial, temporal, and noise properties of these mechanisms. These studies will be important for understanding how the normalization and expansion mechanisms contribute to psychophysical performance, and how they are implemented in the neural circuity of the retina, LGN, and cortex. [III] Quantitative models of using spatiotemporal sinewave granting stimuli. Typically, the stimuli are presented for a fixed duration, in counterbalanced blocks, with careful control of fixation. To test and develop general theories of spatial vision it is important to begin bridging the gap between these carefully controlled stimulus presentation conditions and the more complex stimulus presentations which occur in the natural environment. We propose a series of experiments which will measure neural responses and behavioral discrimination performance for sinewave grating stimuli presented in a fashion which matches the sequence of fixations during saccadic inspection of complex natural images. These data will be compared with results from more conventional presentation methods and will be used to develop quantitative models of spatial visions that are appropriate for natural visual tasks.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY002688-24
Application #
6476294
Study Section
Visual Sciences B Study Section (VISB)
Program Officer
Wiggs, Cheri
Project Start
1981-12-01
Project End
2003-11-30
Budget Start
2001-12-01
Budget End
2002-11-30
Support Year
24
Fiscal Year
2002
Total Cost
$388,062
Indirect Cost
Name
University of Texas Austin
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
City
Austin
State
TX
Country
United States
Zip Code
78712
Benvenuti, Giacomo; Chen, Yuzhi; Ramakrishnan, Charu et al. (2018) Scale-Invariant Visual Capabilities Explained by Topographic Representations of Luminance and Texture in Primate V1. Neuron 100:1504-1512.e4
Paulun, Vivian C; Schütz, Alexander C; Michel, Melchi M et al. (2015) Visual search under scotopic lighting conditions. Vision Res 113:155-68
Bradley, Chris; Abrams, Jared; Geisler, Wilson S (2014) Retina-V1 model of detectability across the visual field. J Vis 14:
Michel, Melchi M; Chen, Yuzhi; Geisler, Wilson S et al. (2013) An illusion predicted by V1 population activity implicates cortical topography in shape perception. Nat Neurosci 16:1477-83
Geisler, Wilson S (2011) Contributions of ideal observer theory to vision research. Vision Res 51:771-81
Michel, Melchi; Geisler, Wilson S (2011) Intrinsic position uncertainty explains detection and localization performance in peripheral vision. J Vis 11:18
Najemnik, Jiri; Geisler, Wilson S (2009) Simple summation rule for optimal fixation selection in visual search. Vision Res 49:1286-94
Sit, Yiu Fai; Chen, Yuzhi; Geisler, Wilson S et al. (2009) Complex dynamics of V1 population responses explained by a simple gain-control model. Neuron 64:943-56
Najemnik, Jiri; Geisler, Wilson S (2008) Eye movement statistics in humans are consistent with an optimal search strategy. J Vis 8:4.1-14
Geisler, Wilson S (2008) Visual perception and the statistical properties of natural scenes. Annu Rev Psychol 59:167-92

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