Our framework made no SB431542 clinical trial direct predictions regarding this result, but it follows naturally from consideration of what information sources are required to detect each type of error. As discussed in Section 1.3.1, nonword spelling errors may be more easily detectable based on surface features (e.g., trcak violates rules of English orthography while trial does not). Identifying a nonword error requires only successful wordhood assessment—which can be done without regard for context but which context may nevertheless be helpful for—while identifying a wrong word error requires successful word-context validation. Thus, more information sources support nonword identification

than support wrong word identification. In this vein, the question naturally arises to what GSI-IX solubility dmso extent readers were using orthographic or phonological well-formedness to identify nonwords, as opposed to a full check against the lexicon or against context. To investigate this question, we coded each error item in Experiment 1 as being

either pronounceable or unpronounceable in English. Even though approximately half of the words were pronounceable and half were not, this distinction did not affect detection accuracy (88% vs. 89%; z < 1, p > .94). These data suggest that subjects were primarily assessing wordhood through a full check against the lexicon or against context, rather than purely checking surface features such as pronounceability. As mentioned above, though, the errors in Experiment 1 were easier to detect than those in Experiment 2, suggesting that

the need to integrate the word with the sentence context in order to identify whether it is an Edoxaban error was likely what made the proofreading task in Experiment 2 more difficult. The results of our study, combined with the experiments discussed in the introduction (Section 1), suggest that word and sentence processing during reading is highly adaptive and responsive to task demands. That is, our subjects’ proofreading performance involved not just a more cautious version of normal reading, but rather a qualitative readjustment of different component sub-processes of overall reading so as to efficiently achieve high accuracy in identifying errors. We saw that the size of the frequency effect increased when proofreading for any type of spelling error, reflecting the fact that word frequency is useful for detecting violations of word status (i.e., nonwords do not have a detectable word frequency), which might be a first step in checking for spelling errors. Likewise, when the relationship between words was crucial to identify spelling errors (in Experiment 2), we saw that the magnitude of the predictability effect increased, as well.