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Mind wandering is a phenomenon in which attention drifts away from the primary task to task-unrelated thoughts. Previous studies have used self-report methods to measure the frequency of mind wandering and its effects on task performance. Many of these studies have investigated mind wandering in simple perceptual and memory tasks, such as recognition memory, sustained attention, and choice reaction time tasks. Manipulations of task difficulty have revealed that mind wandering occurs more frequently in easy than in difficult conditions, but that it has a greater negative impact on performance in the difficult conditions. The goal of this study was to examine the relation between mind wandering and task difficulty in a high-level cognitive task, namely reading comprehension of standardized texts. We hypothesized that reading comprehension may yield a different relation between mind wandering and task difficulty than has been observed previously. Participants read easy or difficult versions of eight passages and then answered comprehension questions after reading each of the passages. Mind wandering was reported using the probe-caught method from several previous studies. In contrast to the previous results, but consistent with our hypothesis, mind wandering occurred more frequently when participants read difficult rather than easy texts. However, mind wandering had a more negative influence on comprehension for the difficult texts, which is consistent with the previous data. The results are interpreted from the perspectives of the executive-resources and control-failure theories of mind wandering, as well as with regard to situation models of text comprehension.

We investigated the linguistic features of temporal cohesion that distinguish variations in temporal coherence. In an analysis of 150 texts, experts rated temporal coherence on three continuous scale measures designed to capture unique representations of time. Coh-Metrix, a computational tool that assesses textual cohesion, correctly predicted the human ratings with five features of temporal cohesion. The correlations between predicted and actual scores were all statistically significant. In a complementary study, we explored the importance of temporal cohesion in characterizing genre. A discriminant function analysis, using Coh-Metrix temporal indices, successfully distinguished the genres of science, history, and narrative texts. The results suggested that history texts are more similar to narrative texts than to science texts in terms of temporal cohesion.

The PREG model of question asking assumes that questions emerge when there is cognitive disequilibrium, as in the case of contradictions, obstacles, and anomalies. Participants read illustrated texts about everyday devices (e.g., a cylinder lock) and then were placed in cognitive disequilibrium through a breakdown scenario (e.g., the key turns but the bolt does not move). The participants asked questions when given the breakdown scenario, and an eyetracker recorded their fixations. As was predicted, deep comprehenders asked better questions and fixated on device components that explained the malfunction. The eye fixations were examined before, during, and after the participants' questions in order to trace the occurrence and timing of convergence on faults, causal reasoning, and other cognitive processes.

Advances in computational linguistics and discourse processing have made it possible to automate many language- and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.

The authors describe a constructionist theory that accounts for the knowledge-based inferences that are constructed when readers comprehend narrative text. Readers potentially generate a rich variety of inferences when they construct a referential situation model of what the text is about. The proposed constructionist theory specifies that some, but not all, of this information is constructed under most conditions of comprehension. The distinctive assumptions of the constructionist theory embrace a principle of search (or effort) after meaning. According to this principle, readers attempt to construct a meaning representation that addresses the reader's goals, that is coherent at both local and global levels, and that explains why actions, events, and states are mentioned in the text. This study reviews empirical evidence that addresses this theory and contrasts it with alternative theoretical frameworks.

In three subject-paced experiments we evaluated reading patterns at the word, line, and sentence level for fast and slow readers. A moving-window method was used to collect word reading times for natural texts. At the word level, reading times of word N were influenced by features of word N-1 for fast readers but not for slow readers. The lag effect exhibited by fast readers indicates that they continue to process a word when it is no longer in view, thus limiting the notion of immediate processing. Contrary to our initial expectation that fast readers would process only a single new argument from a sentence, whereas slow readers would process several new arguments, we found that both reader groups adopted a many-argument strategy. However, fast and slow readers differed in terms of the text units (lines vs. sentences) defining the new-argument effects: Fast readers exhibited greater new-argument effects relative to lines, whereas slow readers exhibited greater new-argument effects relative to sentences. Specifically, slow readers integrated the new arguments primarily at the end of the sentence, whereas fast readers did so at line boundaries. These results are discussed in terms of a buffer-and-integrate model of reading comprehension.

In a subject-paced reading-time study, we examined the processing of new arguments at clause boundaries. Word reading times increased with the cumulative number of new-argument nouns at clause boundaries (as well as at sentence boundaries). New-argument nouns had a greater impact at clause boundaries than at nonboundary locations. In accordance with a buffer-integrate-purge model of reading (see Jarvella, 1979), the increase of reading times at boundaries was attributed to the integration of new information from the current sentence with prior information in the text representation. The increase at nonboundary locations was attributed to the growing load of buffering the new information. Reading times at clause boundaries were influenced to a greater extent by text-level integration than by such sentence-level processes as organization of words into clauses and linking of clauses within a sentence. The new contribution of this study was that it showed that clause boundaries provide an opportunity not only for sentence-level processes, but also for a text-level process, namely, the integration of text-new information with the growing text representation.