In the current study, we investigated bilingual error detection by measuring the repair rate of language intrusions (i.e., involuntary production of nontarget language words) that arose while bilinguals produced sentences in a language switching context. This allowed us to compare two prominent accounts of error detection in a bilingual setting. According to the conflict monitoring account, error detection is initiated by interference. Since language switching increases bilingual language interference, error detection should be better in switch relative to repetition trials. According to the perceptual loop theory, error detection is based on language comprehension. Since language switching is known to impair language comprehension, it follows that error detection should be worse in switch relative to repetition trials. The results showed that the repair rate of language intrusions was higher in switch than repetition trials, thus providing evidence that bilingual language interference instigates error detection, in line with the conflict monitoring account.

Manual error annotation of learner corpora is time-consuming and error-prone, whereas existing automatic techniques cannot reliably detect and correct all types of error. This paper shows that the two methods can successfully complement each other: automatic detection and partial correction of trivial errors relieves the human annotator from the laborious task of incessantly marking up oft-committed mistakes and enables him or her to focus on errors which cannot or cannot yet be handled mechanically, thus enabling more consistent annotation with considerably less manual time and effort expended.

Learner corpora, electronic collections of spoken or written data from foreign language learners, offer unparalleled access to many hitherto uncovered aspects of learner language, particularly in their error-tagged format. This article aims to demonstrate the role that the learner corpus can play in CALL, particularly when used in conjunction with web-based interfaces which provide flexible access to error-tagged corpora that have been enhanced with simple NLP techniques such as POS-tagging or lemmatization and linked to a wide range of learner and task variables such as mother tongue background or activity type. This new resource is of interest to three main types of users: teachers wishing to prepare pedagogical materials that target learners' attested difficulties; learners themselves for editing or language awareness purposes and NLP researchers, for whom it serves as a benchmark for testing automatic error detection systems.

Unawareness of deficit is a common feature of degenerative dementia. The present study explored awareness and correction of naturalistic action errors in 54 dementia participants and 10 healthy controls while they performed a series of everyday tasks, such as toast preparation and gift-wrapping. Awareness for everyday task performance and cognitive functioning was also assessed with questionnaire discrepancy scores, and a neuropsychological test protocol was administered. Dementia participants were aware of and corrected a significantly smaller proportion of errors compared to controls (z = 4.59, p < 001). Awareness and correction of action errors was not significantly correlated with the number of naturalistic errors committed, questionnaire discrepancy scores, or neuropsychological test data. Within-group analyses showed awareness differed across error types, such that participants were aware of a greater proportion of substitution and sequence errors compared to omissions, perseverations, and action addition (i.e., utilization behavior) errors (z ≤ −3.2,p≤.002 for all analyses). Taken together these data suggest that error awareness and correction during the course of action is not related to error production or awareness measured via questionnaire discrepancy scores. Rather, direct assessment of error detection and correction may provide novel information about behavioral monitoring that can not be extrapolated from measures of dementia severity or traditional neuropsychological assessment.

The nature of error detection as manifested by the error-related negativity was examined in both a Sternberg memory search task and a visual search task. Both tasks were performed in conditions with consistent or varied stimulus–response mapping and loads of three or six letters. After subjects were trained extensively in all conditions, they performed the tasks throughout the night without sleeping. The data suggest that the effectiveness of error detection decreases over time because of a decrease in the quality of perceptual processing. Error detection also suffers when performance requires more search-related resources. In both cases, the representation of the correct response is compromised. These results indicate that error detection depends on the same perceptual and cognitive processes that are required for correct performance.