Our main strategy for developing theories of visual processing has been to work out, in quantitative detail, the constraints on visual performance imposed by anatomical and physiological mechanisms, and to carry out psychophysical experiments to determine what aspects of human performance might be accounted for by these mechanisms. The present proposal is to continue applying this research strategy to the study of spatial, contrast, shape and position discrimination. Three major projects are proposed. The first major project will be a quantitative analysis of the mean response and noise characteristics of a large population of neurons in primary visual cortex of cat and monkey. One of the main hypotheses that will be tested is that the variance of a cortical neuron's response is proportional to its mean response independent of the stimulus conditions that produce that mean response. We also use signal-detection methods to compute the discrimination and identification performance of cortical neurons along a wide range of stimulus dimensions including contrast, spatial frequency, temporal frequency, position/phase, and orientation. The second major project will involve the psychophysical investigation of three aspects of pattern detection and discrimination that have received relatively little attention in the past, but are crucial for developing general accounts of discrimination performance. One set of experiments will be directed at investigating the information pooling rules applied by the visual system in pattern detection and discrimination. Specifically, we propose several experiments to measure cycle summation rules for compound grating patterns. A second set of experiments will be directed at extending our efforts to understand how light/dark adaptation mechanisms contribute to pattern detection performance. Flashed-background increment-threshold functions will be measured for sinewave grating targets during early and long-term dark adaptation and during early light adaptation. A third set of experiments will be psychophysical tests of the hypothesis that the response variance of cortical neurons is proportional to the mean response (which seems to hold in primary visual cortex). The third major project will be directed at quantitative modeling of pattern detection and discrimination performance. The models we plan to investigate are unique in that they explicitly include (a) optical/receptor factors, (b) retinal light/dark adaptation factors, and (c) many of physiological properties observed in neurons in primary visual cortex.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
2R01EY002688-17
Application #
2158513
Study Section
Visual Sciences B Study Section (VISB)
Project Start
1981-12-01
Project End
1998-11-30
Budget Start
1994-12-01
Budget End
1995-11-30
Support Year
17
Fiscal Year
1995
Total Cost
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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