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Project Summary/Abstract Visual systems must be matched (via evolution and learning over the lifespan) to the natural tasks organisms perform to survive and reproduce. Thus, it is of fundamental importance to analyze visual systems with respect to natural tasks and to the statistical properties of natural stimuli relevant to performing those tasks. In our lab we call this "natural systems analysis." This novel approach to vision science is composed of several steps: (1) identify natural tasks, (2) measure the natural scene statistics relevant for those tasks, (3) determine how to optimally use those statistics to perform the tasks, given appropriate biological constraints, and (4) use the first three steps to formulate principled hypotheses which are tested and refined in behavioral or physiological experiments. Using a unique suite of measurement devices, computational tools, and psychophysical paradigms developed in our laboratory, we propose to tackle (within the framework of natural systems analysis) four fundamental and interrelated classes of visual tasks: (1) image interpolation, where the goal is to estimate missing retinal image information due to occluding surfaces, normal cone sampling, or abnormal cone loss, (2) estimation of object (surface) boundary locations and which side of the boundary is in the foreground (nearer), (3) defocus estimation, where the goal is to estimate the magnitude and sign of image defocus in local regions of the retinal image, and (4) contrast and sharpness estimation in the visual periphery, where the ganglion cells severely filter and down sample the retinal image. The overall aim of tackling tasks (1) and (2) is to build an integrated picture of how the visual system identifies occlusion regions and interpolates behind them under natural conditions. The overall aim of tackling tasks (3) and (4) is to understand how the visual system estimates defocus and contrast in the fovea and periphery, which are relevant to the first two tasks. Many of the proposed studies will be the first to precisely characterize the statistical constraints in natural images underlying the visual system's ability to perform these tasks accurately. Many of the proposed studies will also be the first to measure performance in these fundamental tasks using natural stimuli. Promising pilot data has been obtained in many of the proposed studies.
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An optical-genetic toolbox for reading and writing neural population codes in functional maps
  • 批准号:
    9355717
  • 项目类别:
  • 资助金额:
    $57.83万
  • 财政年份:
    2016
  • 负责人:
    WILSON S GEISLER
  • 依托单位:
Visual Search and Detection in Natural Scenes
  • 批准号:
    9331655
  • 项目类别:
  • 资助金额:
    $38.57万
  • 财政年份:
    2015
  • 负责人:
    WILSON S GEISLER
  • 依托单位:
Visual Search and Detection in Natural Scenes
  • 批准号:
    10264825
  • 项目类别:
  • 资助金额:
    $45.51万
  • 财政年份:
    2015
  • 负责人:
    WILSON S GEISLER
  • 依托单位:
Visual Search and Detection in Natural Scenes
  • 批准号:
    9130183
  • 项目类别:
  • 资助金额:
    $38.57万
  • 财政年份:
    2015
  • 负责人:
    WILSON S GEISLER
  • 依托单位:
国内基金
海外基金
Behavioral Insights on Cooperation in Social Dilemmas
  • 批准号:
    --
  • 项目类别:
    外国优秀青年学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    LIEN,Jaimie Wei-Hung
  • 依托单位: