Dr. Xingshan Li’s paper (Word knowledge influences character perception) published in Psychonomic Bulletin & Review, with Alexander Pollatsek as co-author, has been selected for the Best Article of 2011 for this journal. In recognition of the award, Dr. Li have received a commemorative paper weight and a monetary award. These have been distributed at the annual meeting in November in Seattle. The paper examined whether context information affects the activity of the nodes at the character level, which provides strong experimental evidence for the word recognition model (Interactive Activation Model). In the study, participants were asked to view two Chinese characters; one was intact, but the other (the target) was embedded in a rectangle of visual noise and increased in visibility over time. The two characters constituted a word in one condition but did not in the other condition. The task was to press a button to indicate whether the character in the noise was at the top or bottom of the rectangle. Response times were faster in the word condition than in the nonword condition. The results suggest that processing at the word level can feed back to fairly low level judgments such as where a character is. These results supported that identification of Chinese word is an interactive process.
The Psychonomic Society promotes the communication of scientific research in psychology and allied sciences. Its members are qualified to conduct and supervise scientific research, must hold the Ph. D. degree or equivalent, and must have published significant research other than the doctoral dissertation. The main function of the Society is to exchange information among scientists. The research field of the Psychonomic Society contains learning, memory, attention, motivation, perception, decision making, psycholinguistic, etc. Until now, it publishes six journals and annually hosts an international scientific meeting. Every year the Publication Committee of the Psychonomic Society asks each Editor of a Psychonomic journal to select a Best Article of the Year Award Winner.