Abstract
Purpose. Strokes in Chinese characters can sometimes be grouped into identifiable stroke patterns. Are Chinese characters recognized holistically, by strokes or stroke patterns? Here we address this question by studying recognition efficiency for Chinese characters of different overall complexities, number of strokes or number of stroke patterns. Methods. Three normally sighted young adults participated in each of the three experiments. Stimuli were Chinese characters categorized into three groups, four characters each, according to (1) perimetric complexity, (2) number of strokes, and (3) number of stroke patterns in Experiments 1, 2 and 3 respectively. Perimetric complexity was defined as perimeter2/ ‘ink’ area. Average complexities for the three groups in Experiment 1 were 198.9, 272.1 and 335.5. In Experiments 2 and 3, perimetric complexity was controlled across the three groups. Observers performed a 4AFC task with the character presented in uniform grey background or Gaussian noise (sigma = 75% of background luminance) for 200ms. Contrast thresholds for 62.5% accuracy were measured by method of constant stimuli with five RMS contrast levels. Human performance was compared to an ideal observer model to calculate efficiency. Results. Average recognition efficiencies were 39.99%, 8.09% and 2.95% for low, medium and high complexities; 1.64%, 1.71% and 1.35% for 11–12 strokes, 14–15 strokes, and 17–19 strokes; 1.14%, 1.24% and 1.01% for 2-pattern, 3-pattern, and 4-pattern groups respectively. Recognition efficiency of Chinese characters decreased as a function of characters' complexity, not of their number of strokes or stroke patterns. Conclusion. Chinese character recognition efficiency was limited only by characters' overall complexity, but not by number of strokes or stroke patterns. The results suggested strokes or stroke patterns were not the component features of Chinese characters. Previous findings supporting strokes or stroke patterns as featural components in Chinese characters may be confounded by characters' overall complexity.