2 for further discussion.) However, we must also note that even tasks that should be less onerous than reading (e.g., x-string scanning) can lead to longer reading times ( Rayner & Fischer, 1996). Second, our framework predicted that effects of proofreading for nonwords should not show up exclusively in late measures, since proofreading for nonwords should emphasize word identification processes, which must occur upon first encountering a word. Consistent with this prediction, in Experiment 1 we found effects of task on early measures including fixation probability, first fixation duration,

single fixation duration, and gaze duration; and interactions of task with word frequency on single-fixation duration and gaze duration. Third, our framework predicted that predictability effects should be magnified more selleck compound in proofreading for wrong words than in proofreading for nonwords, since proofreading for wrong words emphasizes processes that intrinsically implicate the degree of fit between a word and the rest of the sentence, (e.g., word-context validation and integration), but proofreading for nonwords does not. Indeed,

whereas when proofreading for nonwords (Experiment 1) the task (reading vs. proofreading) never interacted with predictability, when proofreading for wrong words (Experiment 2) task and predictability interacted in regressions into and total time on the target word. With respect to interpretation of Kaakinen and Hyönä’s previous results on proofreading, our new results overall favor our Rucaparib framework’s task-sensitive word processing account, in which component sub-processes of reading are differentially modulated by change of task, over the more cautious reader

account, in which proofreading simply involves processing words to a higher degree of confidence. In the more cautious reading account, sensitivity to each word property that we manipulated (frequency and predictability) should be affected similarly by both types of proofreading—frequency and predictability effects would have been magnified across the board. Instead, we see different effects on predictability in proofreading for nonwords vs. proofreading for wrong words, consistent with our framework. The other major results Lepirudin in our data, though not directly predicted by our framework, can be readily understood within it. First, Experiment 1 affirms Kaakinen and Hyönä’s (2010) original result that frequency effects are larger in proofreading for nonwords, showing that the pattern they found in Finnish also holds in English. Experiment 2 extended this result to the case of proofreading for spelling errors that produce real words. These results were supported by interactions between frequency effects and task (in both early and late reading measures) for error-free trials. Importantly, effects of word frequency were modulated differently in the two proofreading tasks.