Pelli Denis G, Burns Catherine W, Farell Bart, Moore-Page Deborah C
Institute for Sensory Research, Syracuse University, Syracuse, New York, NY 13210, USA.
Vision Res. 2006 Dec;46(28):4646-74. doi: 10.1016/j.visres.2006.04.023. Epub 2006 Jun 30.
Seeking to understand how people recognize objects, we have examined how they identify letters. We expected this 26-way classification of familiar forms to challenge the popular notion of independent feature detection ("probability summation"), but find instead that this theory parsimoniously accounts for our results. We measured the contrast required for identification of a letter briefly presented in visual noise. We tested a wide range of alphabets and scripts (English, Arabic, Armenian, Chinese, Devanagari, Hebrew, and several artificial ones), three- and five-letter words, and various type styles, sizes, contrasts, durations, and eccentricities, with observers ranging widely in age (3 to 68) and experience (none to fluent). Foreign alphabets are learned quickly. In just three thousand trials, new observers attain the same proficiency in letter identification as fluent readers. Surprisingly, despite this training, the observers-like clinical letter-by-letter readers-have the same meager memory span for random strings of these characters as observers seeing them for the first time. We compare performance across tasks and stimuli that vary in difficulty by pitting the human against the ideal observer, and expressing the results as efficiency. We find that efficiency for letter identification is independent of duration, overall contrast, and eccentricity, and only weakly dependent on size, suggesting that letters are identified by a similar computation across this wide range of viewing conditions. Efficiency is also independent of age and years of reading. However, efficiency does vary across alphabets and type styles, with more complex forms yielding lower efficiencies, as one might expect from Gestalt theories of perception. In fact, we find that efficiency is inversely proportional to perimetric complexity (perimeter squared over "ink" area) and nearly independent of everything else. This, and the surprisingly fixed ratio of detection and identification thresholds, indicate that identifying a letter is mediated by detection of about 7 visual features.
为了理解人们如何识别物体，我们研究了他们如何识别字母。我们预计这种对熟悉形式的26种分类方式会挑战流行的独立特征检测概念（“概率求和”），但相反地，我们发现这一理论能简洁地解释我们的结果。我们测量了在视觉噪声中短暂呈现的字母识别所需的对比度。我们测试了多种字母表和文字（英语、阿拉伯语、亚美尼亚语、中文、天城文、希伯来语以及几种人工文字）、三字母和五字母单词，以及各种字体、字号、对比度、持续时间和偏心度，观察者的年龄范围广泛（3岁至68岁）且经验各异（从无经验到流利阅读者）。外国字母表能很快被学会。在仅仅三千次试验中，新的观察者在字母识别方面就能达到与流利阅读者相同的熟练程度。令人惊讶的是，尽管有这种训练，这些观察者——就像临床逐字母阅读者一样——对于这些字符的随机字符串的记忆跨度与首次看到它们的观察者一样微薄。我们通过将人类与理想观察者进行对比，并将结果表示为效率，来比较不同难度的任务和刺激下的表现。我们发现字母识别的效率与持续时间、整体对比度和偏心度无关，仅微弱地依赖于字号，这表明在如此广泛的观看条件下，字母是通过类似的计算方式被识别的。效率也与年龄和阅读年限无关。然而，效率确实会因字母表和字体的不同而有所变化，正如格式塔感知理论所预期的那样，更复杂的形式效率更低。事实上，我们发现效率与周长复杂度（周长平方除以“墨水”面积）成反比，且几乎与其他所有因素无关。这一点，以及检测阈值和识别阈值令人惊讶的固定比例，表明识别一个字母是由大约7个视觉特征的检测所介导的。
