Final answer:
A bigram detector in the feature network model explains why low-quality words are recognized more easily than low-quality non-words, due to familiar letter pairs providing sufficient cues for word identification.
Step-by-step explanation:
In the context of reading and word recognition, the feature network model is a psychological model that describes how people can recognize letters and words. A bigram detector is a hypothetical mechanism within this model that responds to specific pairs of letters (i.e., bigrams), which is above the level of individual feature detectors for letters.
The correct answer to the student's question is 'a. the fact that low-quality words are recognized more easily than low-quality non-words'. This can be explained by the presence of bigram detectors because recognizable word bigrams can provide enough information to identify a word, even if the individual letters are not clearly presented. This contrasts with non-words, which might not contain familiar bigrams and therefore are harder to recognize.
Frequency effects refer to the phenomenon where words that occur more frequently in a language are recognized more quickly and easily; and priming effects describe the process by which exposure to a stimulus influences the response to a later stimulus. These phenomena can often be explained without the need to invoke bigram detectors, relying primarily on word-level processing.