What can we learn from learning models about sensitivity to letter-order in visual word recognition?

• We examined letter-position coding and transposed-letter effects in a connectionist model.
• We used a fundementalist strategy producing a simple and readily-extendable model.
• We show how noise and the statistics of the language determine transposed-letter effects.
• Our model generated novel predictions for targeted empirical research.
• This demonstrates a key strength of learning models for studying visual word recognition.

Recent research on the effects of letter transposition in Indo-European Languages has shown that readers are surprisingly tolerant of these manipulations in a range of tasks. This evidence has motivated the development of new computational models of reading that regard flexibility in positional coding to be a core and universal principle of the reading process. Here we argue that such approach does not capture cross-linguistic differences in transposed-letter effects, nor does it explain them. To address this issue, we investigated how a simple domain-general connectionist architecture performs in tasks such as letter-transposition and letter substitution when it had learned to process words in the context of different linguistic environments. The results show that in spite of the neurobiological noise involved in registering letter-position in all languages, flexibility and inflexibility in coding letter order is also shaped by the statistical orthographic properties of words in a language, such as the relative prevalence of anagrams. Our learning model also generated novel predictions for targeted empirical research, demonstrating a clear advantage of learning models for studying visual word recognition.