Abstract
Marsolek et al. (2006) have differentiated antipriming effects from priming effects, by adopting a novel priming paradigm comprised of four phases that include a baseline measurement. The general concept of antipriming supports the overlapping representation theory of knowledge. This study extended examination of the Marsolek et al. (2006) paradigm by investigating antipriming and priming effects in a series of Chinese character identification tasks. Results showed that identification accuracy of old characters was significantly higher than baseline measurements (i.e., the priming effect), while identification accuracy of novel characters was significantly lower than baseline measurements (i.e., the antipriming effect). This study demonstrates for the first time the effect of visual antipriming in Chinese character identification. It further provides new evidence for the overlapping representation theory of knowledge, and supports generalizability of the phenomenon to Chinese characters.
Highlights
There has been controversy in the literature as to whether knowledge representations are stored in a discrete or overlapping way
The results of post-hoc analyses using the least significant difference (LSD) criterion indicated that the identification accuracy of antiprimed characters (0.86) was significantly lower than baseline (0.92; p < 0.001); the identification accuracy of primed characters (0.97) was significantly higher than baseline (p < 0.001)
These results indicate support for the visual antipriming effect in Chinese character identification
Summary
There has been controversy in the literature as to whether knowledge representations are stored in a discrete or overlapping way. Previous studies in the area have found that different objects (e.g., house, shoes, chairs) activate the same area of the human ventral temporal cortex (Ishai et al, 1999; Haxby et al, 2001), and that their representations undergo dynamic learning changes after initial usage (Sigala and Logothetis, 2002; Marsolek, 2003). This overlapping representation is helpful in improving knowledge storage efficiency, and in recognizing new information (McClelland and Rumelhart, 1985). The same neuron can respond to different classes of objects depending on their visual similarity (Riesenhuber and Poggio, 2002)
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