The Effect of Word Predictability on Phonological Activation in Cantonese Reading: A Study of Eye-Fixations and Pupillary Response.

This study aimed to investigate the effects of contextual predictability on orthographic and phonological activation during Chinese sentence reading by Cantonese-speaking readers using the error disruption paradigm. Participants' eye fixations and pupil sizes were recorded while they silently read Chinese sentences containing homophonic, orthographic, and unrelated errors. Sentences had varying amounts of contextual information leading up to target words such that some targets were more predictable than others. Results of the fixation time analysis indicated that orthographic effects were significant in first fixation and gaze duration, while phonological effects emerged later in total reading time. However, interactions between predictability and the homophonic condition were found in gaze duration. These results suggest that, while Cantonese readers activate word meanings primarily through orthography in early processing, early phonological activation can occur when facilitated by semantics in high-constraint sentence contexts. Analysis of pupillary response measurements revealed that participants' pupil sizes became larger when they read words containing orthographic errors, suggesting that orthographic error recovery processes significantly increase cognitive load.

Momentary lapse of control: A cognitive continuum approach to understanding and mitigating perseveration in human error.

Everyday complex and stressful real-life situations can overwhelm the human brain to an extent that the person is no longer able to accurately evaluate the situation and persists in irrational actions or strategies. Safety analyses reveal that such perseverative behavior is exhibited by operators in many critical domains, which can lead to potentially fatal incidents. There are neuroimaging evidences of changes in healthy brain functioning when engaged in non-adaptive behaviors that are akin to executive deficits such as perseveration shown in patients with brain lesion. In this respect, we suggest a cognitive continuum whereby stressors can render the healthy brain temporarily impaired. We show that the dorsolateral prefrontal cortex is a key structure for executive and attentional control whereby any transient (stressors, neurostimulation) or permanent (lesion) impairment compromises adaptive behavior. Using this neuropsychological insight, we discuss solutions involving training, neurostimulation, and the design of cognitive countermeasures for mitigating perseveration.

Disruption in neural phase synchrony is related to identification of inattentional deafness in real-world setting.

Individuals often have reduced ability to hear alarms in real world situations (e.g., anesthesia monitoring, flying airplanes) when attention is focused on another task, sometimes with devastating consequences. This phenomenon is called inattentional deafness and usually occurs under critical high workload conditions. It is difficult to simulate the critical nature of these tasks in the laboratory. In this study, dry electroencephalography is used to investigate inattentional deafness in real flight while piloting an airplane. The pilots participating in the experiment responded to audio alarms while experiencing critical high workload situations. It was found that missed relative to detected alarms were marked by reduced stimulus evoked phase synchrony in theta and alpha frequencies (6-14 Hz) from 120 to 230 ms poststimulus onset. Correlation of alarm detection performance with intertrial coherence measures of neural phase synchrony showed different frequency and time ranges for detected and missed alarms. These results are consistent with selective attentional processes actively disrupting oscillatory coherence in sensory networks not involved with the primary task (piloting in this case) under critical high load conditions. This hypothesis is corroborated by analyses of flight parameters showing greater maneuvering associated with difficult phases of flight occurring during missed alarms. Our results suggest modulation of neural oscillation is a general mechanism of attention utilizing enhancement of phase synchrony to sharpen alarm perception during successful divided attention, and disruption of phase synchrony in brain networks when attentional demands of the primary task are great, such as in the case of inattentional deafness.

Neural signature of inattentional deafness.

Inattentional deafness is the failure to hear otherwise audible sounds (usually alarms) that may occur under high workload conditions. One potential cause for its occurrence could be an attentional bottleneck that occurs when task demands are high, resulting in lack of resources for processing of additional tasks. In this fMRI experiment, we explore the brain regions active during the occurrence of inattentional deafness using a difficult perceptual-motor task in which the participants fly through a simulated Red Bull air race course and at the same time push a button on the joystick to the presence of audio alarms. Participants were instructed to focus on the difficult piloting task and to press the button on the joystick quickly when they noticed an audio alarm. The fMRI results revealed that audio misses relative to hits had significantly greater activity in the right inferior frontal gyrus IFG and the superior medial frontal cortex. Consistent with an attentional bottleneck, activity in these regions was also present for poor flying performance (contrast of gates missed versus gates passed for the flying task). A psychophysiological interaction analysis from the IFG identified reduced effective connectivity to auditory processing regions in the right superior temporal gyrus for missed audio alarms relative to audio alarms that were heard. This study identifies a neural signature of inattentional deafness in an ecologically valid situation by directly measuring differences in brain activity and effective connectivity between audio alarms that were not heard compared to those that were heard. Hum Brain Mapp 38:5440-5455, 2017. © 2017 Wiley Periodicals, Inc.

Characterization of mind wandering using fNIRS.

Assessing whether someone is attending to a task has become important for educational and professional applications. Such attentional drifts are usually termed mind wandering (MW). The purpose of the current study is to test to what extent a recent neural imaging modality can be used to detect MW episodes. Functional near infrared spectroscopy is a non-invasive neuroimaging technique that has never been used so far to measure MW. We used the Sustained Attention to Response Task (SART) to assess when subjects attention leaves a primary task. Sixteen-channel fNIRS data were collected over frontal cortices. We observed significant activations over the medial prefrontal cortex (mPFC) during MW, a brain region associated with the default mode network (DMN). fNIRS data were used to classify MW data above chance level. In line with previous brain-imaging studies, our results confirm the ability of fNIRS to detect Default Network activations in the context of MW.

Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.

Real-time state estimation in a flight simulator using fNIRS.

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.

Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals.

Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.