Frequency Effects on Spelling Error Recognition: An ERP Study

Spelling errors are ubiquitous in all writing systems. Most studies exploring spelling errors focused on the phonological plausibility of errors. However, unlike typical pseudohomophones, spelling errors occur in naturally produced written language with variable frequencies. We investigated the time course of recognition of the most frequent orthographic errors in Russian (error in an unstressed vowel at the root) and the effect of word frequency on this process. During ERP recording, 26 native Russian speakers silently read high-frequency correctly spelled words, low-frequency correctly spelled words, high-frequency words with errors, and low-frequency words with errors. The amplitude of P200 was more positive for correctly spelled words than for misspelled words and did not depend on the frequency of the words. Word frequency affected spelling recognition in the later stages of word processing (350-700 ms): high-frequency misspelled words elicited a greater P300 than high-frequency correctly spelled words, and low-frequency misspelled words elicited a greater N400 than low-frequency correctly spelled words. We observe spelling effects in the same time window for both the P300 and N400, which may reflect temporal overlap between mainly categorization processes based on orthographic properties for high-frequency words and phonological processes for low-frequency words. We concluded that two independent pathways can be active simultaneously during spelling recognition: one reflects mainly orthographic processing of high-frequency words and the other is the phonological processing of low-frequency words. Our findings suggest that these pathways are associated with different ERP components. Therefore, our results complement existing reading models and demonstrate that the neuronal underpinnings of spelling error recognition during reading depend on word frequency.

Anger, race, and the neurocognition of threat: attention, inhibition, and error processing during a weapon identification task

This study measured event-related brain potentials (ERPs) to test competing hypotheses regarding the effects of anger and race on early visual processing (N1, P2, and N2) and error recognition (ERN and Pe) during a sequentially primed weapon identification task. The first hypothesis was that anger would impair weapon identification in a biased manner by increasing attention and vigilance to, and decreasing recognition and inhibition of weapon identification errors following, task-irrelevant Black (compared to White) faces. Our competing hypothesis was that anger would facilitate weapon identification by directing attention toward task-relevant stimuli (i.e., objects) and away from task-irrelevant stimuli (i.e., race), and increasing recognition and inhibition of biased errors. Results partially supported the second hypothesis, in that anger increased early attention to faces but minimized attentional processing of race, and did not affect error recognition. Specifically, angry (vs. neutral) participants showed increased N1 to both Black and White faces, ablated P2 race effects, and topographically restricted N2 race effects. Additionally, ERN amplitude was unaffected by emotion, race, or object type. However, Pe amplitude was affected by object type (but not emotion or race), such that Pe amplitude was larger after the misidentification of harmless objects as weapons. Finally, anger slowed overall task performance, especially the correct identification of harmless objects, but did not impact task accuracy. Task performance speed and accuracy were unaffected by the race of the face prime. Implications are discussed.

Reduced Error Recognition Explains Post-Error Slowing Differences among Children with Attention Deficit Hyperactivity Disorder

Objective: Youth with attention deficit hyperactivity disorder (ADHD) often show reduced post-error slowing (PES) compared to typically developing controls. This finding has been interpreted as evidence that children with ADHD have error recognition and adaptive control impairments. However, several studies report mixed results regarding PES differences in ADHD, and among healthy controls, there is considerable debate about the cognitive-behavioral origin of PES. Methods: We tested competing hypotheses aimed at clarifying whether reduced PES in children with ADHD is due to impaired error detection, deficits in adaptive control, and/or attention orienting to novelty. Children aged 7–11 years with a diagnosis of ADHD (n = 74) and controls (n = 30) completed four laboratory-based computer tasks with variable cognitive loads and error types. Results: ADHD diagnosis was associated with shorter PES only on a task with high cognitive load and low error-cuing, consistent with impaired error recognition. In contrast, there was no evidence of impaired adaptive control or heightened novelty orienting among children with ADHD. Conclusions: The cognitive-behavioral origin of PES is multifactorial, but reduced PES among children with an ADHD diagnosis is due to impaired error recognition during cognitively demanding tasks. Behavioral interventions that scaffold error recognition may facilitate improved performance among children with ADHD.

Data Analytics Software for Automatic Detection of Anomalies in Well Testing

This paper will present a software that was developed to diagnose well test data. The software monitors the data, and through a series of algorithms alarms the user in case of discrepancies. This allows the user to investigate possible source of errors and correct them in real time. Several datasets from previous operations were analyzed and the basic physics governing how a certain datum depends on others were laid out. All the well test data traditionally acquired were put on a matrix, showing the dependencies between each datum and other physical properties that are available - either measured or modelled. Acceptable fluctuations in acquired data were also identified for use as tolerance limits. The software scans through the data as it is acquired and raises an alarm when the identified dependencies are broken. The software also identified which parameter is most likely causing the error. The software was built based on previous well test data and reports. Subsequently, two field trials were conducted to fine tune the algorithms and allowable data fluctuations. The process of validating the software consisted of: (1) Identifying flagged errors that should have not been flagged (dependencies set too tight); (2) identifying errors that should have been flagged and were not (dependencies set too loose); (3) improving the user interface for ease of use. The results were positive, with several improvements in the error recognition and several discrepancies flagged that would not have been caught by the naked eye. The user interface was also improved, allowing the user to clear error messages and provide input to improve the algorithm. The field trial also demonstrated that the methodology is scalable to other data acquisition plans and to more advanced analytics. The algorithms are simple, allowing the software to be implemented in all operations. More advanced algorithms are likely to depend on job specific data and parameters. Traditional data acquisition systems used during well test only present the data. Alarms trigger the user's attention only when certain defined operability limits are about to be reached. Being able to confirm that the data is cohesive during the well test prevents a loss of confidence in the results and painful post processing exercises. Moreover, given the algorithms used are based on simple physics, it is easy to deploy the software in any operation.

Video-assisted cardiopulmonary resuscitation: Does the camera perspective matter? A randomized, controlled simulation trial

Background Dispatcher assistance can help to save lives during layperson cardiopulmonary resuscitation during cardiac arrest. The aim of this study was to investigate the influence of different camera positions on the evaluation of cardiopulmonary resuscitation performance during video-assisted cardiopulmonary resuscitation. Methods For this randomized, controlled simulation trial, seven video sequences of cardiopulmonary resuscitation performance were recorded from three different camera positions: side, foot and head position. Video sequences showed either correct cardiopulmonary resuscitation performance or one of the six typical errors: low and high compression rate, superficial and increased compression depth, wrong hand position or incomplete release. Video sequences with different cardiopulmonary resuscitation performances and camera positions were randomly combined such that each evaluator was presented seven individual combinations of cardiopulmonary resuscitation and camera position and evaluated each cardiopulmonary resuscitation performance once. A total of 46 paramedics and 47 emergency physicians evaluated seven video sequences of cardiopulmonary resuscitation performance from different camera positions. The primary hypothesis was that there are differences in accuracy of correct assessment/error recognition depending on camera perspective. Generalized linear multi-level analyses assuming a binomial distribution and a logit link were employed to account for the dependency between each evaluator's seven ratings. Results Of 651 video sequences, cardiopulmonary resuscitation performance was evaluable in 96.8% and correctly evaluated in 74.5% over all camera positions. Cardiopulmonary resuscitation performance was classified correctly from a side perspective in 81.3%, from a foot perspective in 68.8% and from a head perspective in 73.6%, revealing a significant difference in error recognition depending on the camera perspective ( p = .01). Correct cardiopulmonary resuscitation was mistakenly evaluated to be false in 46.2% over all perspectives. Conclusions Participants were able to recognize significantly more mistakes when the camera was located on the opposite side of the cardiopulmonary resuscitation provider. Foot position should be avoided in order to enable the dispatcher the best possible view to evaluating cardiopulmonary resuscitation quality.

Intelligent recognition of dance training movements based on machine learning and embedded system

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.