In order to recognize a written word, the relative positions of its component letters must be encoded. Ultimately, this information must be precise enough to distinguish between anagrams such as causal and casual while retaining enough flexibility to recognize elehpant as elephant. The lexical decision experiments reported here used a more dramatic version of transposed letter priming than has previously been reported in order to identify the constraints on this flexibility. In light of the observed data, several current models of letter position coding were evaluated and suggestions for future models were proposed. The first goal of this research was to determine the degree of flexibility in word recognition in terms of how many transposed letters can be tolerated in the input. Reliable priming was observed throughout the experiments when as many as six of the eight letters had been transposed (most ps < .01). However, Experiments 5 and 6 identified the limit of this flexibility, in that fully transposed primes did not activate their target entries. The second goal was to identify letter position effects, or differences in the importance of various letter positions in lexical access. Experiments 1-4 supported Jordan et al.'s (2003) claim that the exterior letters of a word are the most crucial. Stronger priming was derived from primes with correctly placed exterior letters and transposed interior letters than from the reverse case. Support was also found for Inhoff et al.'s (2003) claim that a word's initial letters are more important to lexical access than later letters (Experiment 7). Overall, a trend of decreasing importance from left to right was observed, with the possible exception of the final letter. The observed data were compared to the predictions made by the BLIRNET model (Mozer, 1991), Grainger & van Heuven's (in press) open bigram coding scheme, the SOLAR model (Davis, 1999), and the SERIOL model (Whitney, 1999). This enabled us to identify particularly effective and problematic approaches to letter position coding. Finally, it is proposed that a visual word recognition system with two parallel, complementary processing streams best describes the data.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/280702 |
| Date | January 2004 |
| Creators | Guerrera, Christine |
| Contributors | Forster, Kenneth I. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Dissertation-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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