@article{oai:nagoya.repo.nii.ac.jp:00001628,
 author = {川上, 正浩 and Kawakami, Masahiro},
 journal = {名古屋大学大学院教育発達科学研究科紀要. 心理発達科学},
 month = {Dec},
 note = {Most models on visual word recognition assume that not only the word visually presented but also other words that are orthographically similar to that word, that is, its orthographic neighbors are also activated. Coltheart, Davelaar, Jonasson, and Besner (1977) defined the neighbor of a letter string as any word that can be generated by replacing a single letter from the base string. This study presents tables of the numbers of neighbors for Japanese 3-letter, 4-letter, and 5-letter katakana words. In this study, orthographic neighbors for katakana words were defined as words that can be constructed by changing one katakana character of the target item preserving letter positions. So orthographic neighbors of a katakana word are also katakana words. Neighbors were split into subgroups and sub-neighborhood sizes were counted within each subgroup. The purpose of this study was as follows. 1. To report neighborhood size when vocabulary was split by familiarity. 2. To examine the stability of neighborhood size when vocabulary was split by familiarity. 3. To report detailed neighborhood size database for controlling cognitive psychological experiment with Japanese katakana words. The word sample on which the tables were based was selected from NTT database (PSYLEX) reported by Amano & Kondo (2000). PSYLEX reports familiarity of words (subjective evaluation) and words were split into 6 subgroups with their familiarity. The result showed that the neighborhood size counted with split vocabulary correlate the neighborhood size counted with full vocabulary when N of the subgroup is enough large. And tables reported here may be employed to provide normative data for experimental studies in word recognition using 3-letter, 4-letter, and 5-letter Japanese katakana words., 国立情報学研究所で電子化したコンテンツを使用している。},
 pages = {375--406},
 title = {カタカナ表記語の正書法的類似語数表 : 親密度ごとの検討},
 volume = {47},
 year = {2000}
}