Three experiments are reported investigating the role of letter order in orthographic subset priming (e. g., grdn-GARDEN) using both the conventional masked priming technique as well as the sandwich priming technique ...
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Three experiments are reported investigating the role of letter order in orthographic subset priming (e. g., grdn-GARDEN) using both the conventional masked priming technique as well as the sandwich priming technique in a lexical decision task. In all three experiments, subset primes produced priming with the effect being considerably larger when sandwich priming was used. More importantly, there was very little difference in the degree of priming produced by subset primes with transposed (i.e., gdrn) vs. nontransposed (grdn) internal letters. The priming effects with transposed letter subset primes contradict Peressotti and Grainger's claim that letter order must be maintained in order to produce subset priming effects (i.e., their "relative position priming constraint").
Recently, an important aspect of human visual word recognition has been characterized. The letter position is encoded in our brain using an explicit representation of order based on letter pairs: the openbigramcoding...
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ISBN:
(纸本)9783319076959;9783319076942
Recently, an important aspect of human visual word recognition has been characterized. The letter position is encoded in our brain using an explicit representation of order based on letter pairs: the openbigramcoding [15]. We hypothesize that spelling has evolved in order to minimize reading errors. Therefore, word recognition using bigrams - instead of letters- should be more efficient. First, we study the influence of the size of the neighborhood, which defines the number of bigrams per word, on the performance of the matching between bigrams and word. Our tests are conducted against one of the best recognition solutions used today by the industry, which matches letters to words. Secondly, we build a cortical map representation of the words in the bigram space - which implies numerous experiments in order to achieve a satisfactory projection. Third, we develop an ultra-fast version of the self-organizing map in order to achieve learning in minutes instead of months.
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