TRANSMISSION OF INFORMATION IN VISUAL SYSTEM

手头的结果告诉我们，字母识别是通过特征来调节的 (Pelli，Burns，Farell和Moore，1996)。这些功能的带宽有限， 具有与空间频率频道相同的调谐(所罗门和佩利， 1994年)，而且它们很简单(Pelli等人，1966)。这与以下情况一致 一种流行的观点，即特征就像是以中心为导向--围绕着接受 场或小波。我们建议： 使用空间和空间频率中的关键频段掩码来 识别与视觉检测和识别有关的功能 使用候选人特征对字母标识进行建模 使用人工字母和传统字母进行表演。 对这种方法的成功充满信心的一个原因是，我们有 已经成功地模拟了单词识别性能，采用 字母作为候选特征，表明人类表现接近 但永远不会超过一个原本理想的观察者的表现， 它的决定建立在独立的后一种身份的基础上。获取要素 小于一个字母将使上限更低 一项更严格的测试。超过这一界限将证明 人的表现受独立决策影响的猜想 这一组特征。 其他三个项目将使用字母和噪音来： 弱视和弱视患者视力低下的经络特征 中心凹和周边正常；(试点数据显示，我们的最后一个通道 假说可以解释字母敏锐度。) 测量外围的等效噪声，并将其与 基于神经节细胞噪声的生理测量的预测； 使对比反应的fMRI测量与我们的测量平行 提高了字母识别的效率。

Results in hand tell us that letter identification is mediated by features (Pelli, Burns, Farell, and Moore, 1996). The features are bandlimited, with the same tuning as a spatial-frequency channel (Solomon and Pelli, 1994), and they are simple (Pelli, et al., 1966). This is consistent with the popular idea that features are like oriented center-surround receptive fields, or wavelets. We propose to: to use critical band masking in space and spatial frequency to identify the features that mediate visual detection and identification to use the candidate features to model letter identification performance with artificial and traditional alphabets. One reason for confidence in the success of this approach is that we have already succeeded in modeling word identification performance, taking letters as candidate features, showing that human performance approaches but never exceeds the performance of an otherwise-ideal observer that must base its decisions on independent latter identifications. Taking features to be smaller than a letter will bring the upper bound still lower making an even more stringent test. Exceeding that bound would disprove the conjecture that human performance is mediated by independent decisions on that set of features. Three other projects will use letters and noise to: characterize the channel mediating the poor acuity in amblyopic and normal fovea and periphery; (Pilot data indicate that our last-channel hypothesis may explain letter acuity.) measure equivalent noise in the periphery and compare it with predictions based on physiological measurements of ganglion cell noise; make fMRI measurements of contrast response paralleling our measure of efficiency of letter identification.