Breaking the boundaries: the power of ligatures in visual-word recognition

被引:0
作者
Fernandez-Lopez, Maria [1 ]
Perea, Manuel [1 ,2 ]
Marcet, Ana [3 ]
机构
[1] Univ Valencia, Dept Methodol Behav Sci & ERI Lect, Valencia, Spain
[2] Univ Antonio Nebrija, Ctr Res Cognit, Madrid, Spain
[3] Univ Valencia, Dept Language & Literature Teaching, Grp Invest Ensenanza Lenguas GIEL, Valencia, Spain
关键词
word recognition; lexical access; reading; lexical decision; visual similarity; SIMILARITY; PERCEPTION; FEEDBACK; PACKAGE; MODEL;
D O I
10.3389/fpsyg.2023.1166192
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
IntroductionCurrent neurobiological-inspired models of visual-word recognition propose that letter detectors in the word recognition system can tolerate some variations in the visual form of the letters. However, it is unclear whether this tolerance extends to novel ligatures, which combine two letters into a single glyph. MethodsTo investigate this, the present study utilized a masked priming experiment with a lexical decision task to examine whether primes containing novel ligatures are effective in activating their corresponding base word, relative to omitted-letter primes, in the initial stages of word processing. For each target word (e.g., VIRTUAL), were created an identity prime (virtual), a prime containing a novel ligature of two of the letters (e.g., virtual; "ir" in a single glyph), and an omitted-letter prime where one letter was removed (e.g., vrtual [omitted-vowel] in Experiment 1; vitual [omitted-consonant] in Experiment 2). ResultsResults showed that the presence of a novel ligature in the prime resulted in faster lexical decision times compared to a prime with an omitted vowel (Experiment 1), but not with an omitted consonant (Experiment 2). Furthermore, the performance with the primes containing the novel ligature was not different from that of the identity primes. DiscussionThese results suggest that the word recognition system can quickly enable separate letter detectors for novel ligatures. These findings have important implications for our understanding of the front-end of visual-word recognition.
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