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Perceptual Learning in Motion Processing

Perceptual Learning in Motion Processing
运动处理中的感知学习
批准号:
9905194
负责人:
Takeo Watanabe
金额:
$0.0万
依托单位国家:
美国
项目类别:
Continuing grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-08-01 至 2002-07-31

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中文摘要
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英文摘要
Visual perception is thought of as a passive and simple event. This view may stand on the assumption that visual information goes from the retina to higher-level areas in the brain only in a one-way direction. Processing that flows in this direction is called "bottom-up processing." However, recent anatomical and physiological findings indicate that there are massive feedback pathways in the brain along which information is carried from these higher-level brain areas back to low-level areas, including those involved in processing visual information. Processing that occurs along these pathways is called "top-down processing." Unfortunately, it is poorly known what role top-down processing play in visual information processing in the brain. In the meantime, recent physiological studies have pointed out that even the parts of the adult brain which process primitive visual information have much higher plasticity than had been previously thought. The behavioral manifestation of this plasticity is that very primitive visual functions such as discrimination of motion direction and orientation of lines can be improved as a result of repetitive trials. Thus, an interesting question arises. Does top-down processing influence perceptual learning in the early stages of motion processing? If so, what is the role of top-down processing? The proposed study will examine the nature and role of top-down processing and its interaction with bottom-up processing during perceptual learning. A series of experiments will be conducted using several human subjects. They will watch a stimulus called random-dot cinematogram which consists of small dots moving randomly within a certain range of directions. In each stimulus, a global flow is perceived to move in one direction just like a flow of a river. This indicates that there are at least two stages in early motion processing; the first, local motion stage and the second, global motion stages. We will first examine the extent to which top-down signals from the stage of global motion processing affect lower stages. The results of preliminary experiments suggest that during the learning of global motion lower stages may be strongly affected by top-down signals. We will conduct experiments to examine this more systematically. Second, we will examine the role of the interaction between top-down and bottom-up processing in perceptual learning. Essential to our experiments is a "bottom-up-absent" random-dot cinematogram which does not include any dots moving in directions close to the global motion direction such that activation of the intended global motion direction can not be attributed to bottom-up signals. Using this bottom-up-absent stimulus, we will examine location specificity as well as the stages involved in learning and compare the results with those using the conventional "bottom-up-present" stimulus. A difference in results between the two types of stimuli would suggest roles for bottom-up activation and the interaction between bottom-up and top-down processing.
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NSF-BSF: Reactivation and sleep in visual and motor skill acquisition: learning beyond training
  • 批准号:
    2241417
  • 项目类别:
    Standard Grant
  • 资助金额:
    $57.34万
  • 财政年份:
    2023
  • 负责人:
    Takeo Watanabe
  • 依托单位:
The Neural Basis and Mechanisms of Task-Irrelevant Perceptual Learning
  • 批准号:
    0549036
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2006
  • 负责人:
    Takeo Watanabe
  • 依托单位:
Multiple stages of motion processing
  • 批准号:
    0345746
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $35.0万
  • 财政年份:
    2004
  • 负责人:
    Takeo Watanabe
  • 依托单位:
Roles of Attention in Motion Processing
  • 批准号:
    9631573
  • 项目类别:
    Continuing grant
  • 资助金额:
    $0.0万
  • 财政年份:
    1996
  • 负责人:
    Takeo Watanabe
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位:
煤矿安全人机混合群智感知任务的约束动态多目标Q-learning进化分配
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    吉建娇
  • 依托单位:
基于领弹失效考量的智能弹药编队短时在线Q-learning协同控制机理
  • 批准号:
    62003314
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2020
  • 负责人:
    沈剑
  • 依托单位: