Are They Calling My Name? Attention Capture Is Reflected in the Neural Tracking of Attended and Ignored Speech

Neurophysiological Patterns of Attention and Distraction during Realistic Virtual-Reality Classroom Learning in Adults with and without ADHD

Many people, and particularly those diagnosed with ADHD, report difficulties maintaining attention and proneness to distraction during classroom learning. However, the behavioral, neural and physiological basis of attention in realistic learning contexts is not well understood, since current clinical and scientific tools used for evaluating and quantifying the constructs of "distractibility" and "inattention", are removed from the real-life experience in organic classrooms. Here we introduce a novel Virtual Reality (VR) platform for studying students' brain activity and physiological responses as they immerse in realistic frontal classroom learning. Using this approach, we studied whether adults with and without ADHD (N=49) exhibit differences in neurophysiological metrics associated with sustained attention, such as speech-tracking of the teacher's voice, power of alpha-oscillations and levels of arousal, as well as responses to potential disturbances by background sound-events in the classroom. Under these ecological conditions, we find that adults with ADHD exhibit higher auditory neural response to background sounds relative to their control-peers, which also contributed to explaining variance in the severity of ADHD symptoms, together with higher power of alpha-oscillations and more frequent gaze-shifts around the classroom. These results are in-line with higher sensitivity to irrelevant stimuli in the environment and increased mind-wandering/boredom. At the same time, both groups exhibited similar learning outcomes and showed similar neural tracking of the teacher's speech. This suggests that in this context, attention may not operate as a zero-sum game and that allocating some resources to irrelevant stimuli does not always detract from performing the task at hand. Given the dire need for more objective, dimensional and ecologically-valid measures of attention and its real-life deficits, this work provides new insights into the neurophysiological manifestations of attention and distraction experienced in real-life contexts, while challenging some prevalent notions regarding the nature of attentional challenges experienced by those with ADHD.

Neurophysiological Evidence for Semantic Processing of Irrelevant Speech and Own-Name Detection in a Virtual Café

The well-known "cocktail party effect" refers to incidental detection of salient words, such as one's own-name, in supposedly unattended speech. However, empirical investigation of the prevalence of this phenomenon and the underlying mechanisms has been limited to extremely artificial contexts and has yielded conflicting results. We introduce a novel empirical approach for revisiting this effect under highly ecological conditions, by immersing participants in a multisensory Virtual Café and using realistic stimuli and tasks. Participants (32 female, 18 male) listened to conversational speech from a character at their table, while a barista in the back of the café called out food orders. Unbeknownst to them, the barista sometimes called orders containing either their own-name or words that created semantic violations. We assessed the neurophysiological response-profile to these two probes in the task-irrelevant barista stream by measuring participants' brain activity (EEG), galvanic skin response and overt gaze-shifts.SIGNIFICANCE STATEMENT We found distinct neural and physiological responses to participants' own-name and semantic violations, indicating their incidental semantic processing despite being task-irrelevant. Interestingly, these responses were covert in nature and gaze-patterns were not associated with word-detection responses. This study emphasizes the nonexclusive nature of attention in multimodal ecological environments and demonstrates the brain's capacity to extract linguistic information from additional sources outside the primary focus of attention.

Investigating the attentional focus to workplace-related soundscapes in a complex audio-visual-motor task using EEG

Introduction

In demanding work situations (e.g., during a surgery), the processing of complex soundscapes varies over time and can be a burden for medical personnel. Here we study, using mobile electroencephalography (EEG), how humans process workplace-related soundscapes while performing a complex audio-visual-motor task (3D Tetris). Specifically, we wanted to know how the attentional focus changes the processing of the soundscape as a whole.

Method

Participants played a game of 3D Tetris in which they had to use both hands to control falling blocks. At the same time, participants listened to a complex soundscape, similar to what is found in an operating room (i.e., the sound of machinery, people talking in the background, alarm sounds, and instructions). In this within-subject design, participants had to react to instructions (e.g., "place the next block in the upper left corner") and to sounds depending on the experimental condition, either to a specific alarm sound originating from a fixed location or to a beep sound that originated from varying locations. Attention to the alarm reflected a narrow attentional focus, as it was easy to detect and most of the soundscape could be ignored. Attention to the beep reflected a wide attentional focus, as it required the participants to monitor multiple different sound streams.

Results and discussion

Results show the robustness of the N1 and P3 event related potential response during this dynamic task with a complex auditory soundscape. Furthermore, we used temporal response functions to study auditory processing to the whole soundscape. This work is a step toward studying workplace-related sound processing in the operating room using mobile EEG.

"Unattended, distracting or irrelevant": Theoretical implications of terminological choices in auditory selective attention research

For seventy years, auditory selective attention research has focused on studying the cognitive mechanisms of prioritizing the processing a 'main' task-relevant stimulus, in the presence of 'other' stimuli. However, a closer look at this body of literature reveals deep empirical inconsistencies and theoretical confusion regarding the extent to which this 'other' stimulus is processed. We argue that many key debates regarding attention arise, at least in part, from inappropriate terminological choices for experimental variables that may not accurately map onto the cognitive constructs they are meant to describe. Here we critically review the more common or disruptive terminological ambiguities, differentiate between methodology-based and theory-derived terms, and unpack the theoretical assumptions underlying different terminological choices. Particularly, we offer an in-depth analysis of the terms 'unattended' and 'distractor' and demonstrate how their use can lead to conflicting theoretical inferences. We also offer a framework for thinking about terminology in a more productive and precise way, in hope of fostering more productive debates and promoting more nuanced and accurate cognitive models of selective attention.

An ecological investigation of the capacity to follow simultaneous speech and preferential detection of ones’ own name

Many situations require focusing attention on one speaker, while monitoring the environment for potentially important information. Some have proposed that dividing attention among 2 speakers involves behavioral trade-offs, due to limited cognitive resources. However the severity of these trade-offs, particularly under ecologically-valid circumstances, is not well understood. We investigated the capacity to process simultaneous speech using a dual-task paradigm simulating task-demands and stimuli encountered in real-life. Participants listened to conversational narratives (Narrative Stream) and monitored a stream of announcements (Barista Stream), to detect when their order was called. We measured participants’ performance, neural activity, and skin conductance as they engaged in this dual-task. Participants achieved extremely high dual-task accuracy, with no apparent behavioral trade-offs. Moreover, robust neural and physiological responses were observed for target-stimuli in the Barista Stream, alongside significant neural speech-tracking of the Narrative Stream. These results suggest that humans have substantial capacity to process simultaneous speech and do not suffer from insufficient processing resources, at least for this highly ecological task-combination and level of perceptual load. Results also confirmed the ecological validity of the advantage for detecting ones’ own name at the behavioral, neural, and physiological level, highlighting the contribution of personal relevance when processing simultaneous speech.

Ear-EEG Measures of Auditory Attention to Continuous Speech

Auditory attention is an important cognitive function used to separate relevant from irrelevant auditory information. However, most findings on attentional selection have been obtained in highly controlled laboratory settings using bulky recording setups and unnaturalistic stimuli. Recent advances in electroencephalography (EEG) facilitate the measurement of brain activity outside the laboratory, and around-the-ear sensors such as the cEEGrid promise unobtrusive acquisition. In parallel, methods such as speech envelope tracking, intersubject correlations and spectral entropy measures emerged which allow us to study attentional effects in the neural processing of natural, continuous auditory scenes. In the current study, we investigated whether these three attentional measures can be reliably obtained when using around-the-ear EEG. To this end, we analyzed the cEEGrid data of 36 participants who attended to one of two simultaneously presented speech streams. Speech envelope tracking results confirmed a reliable identification of the attended speaker from cEEGrid data. The accuracies in identifying the attended speaker increased when fitting the classification model to the individual. Artifact correction of the cEEGrid data with artifact subspace reconstruction did not increase the classification accuracy. Intersubject correlations were higher for those participants attending to the same speech stream than for those attending to different speech streams, replicating previously obtained results with high-density cap-EEG. We also found that spectral entropy decreased over time, possibly reflecting the decrease in the listener's level of attention. Overall, these results support the idea of using ear-EEG measurements to unobtrusively monitor auditory attention to continuous speech. This knowledge may help to develop assistive devices that support listeners separating relevant from irrelevant information in complex auditory environments.

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets

With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS), and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed a stable lag and a small jitter (< 3 ms) indicating a high temporal precision of the system. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

OBJECTIVE

Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention.

APPROACH

Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied EEG-based auditory attention decoding (AAD) model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events.

MAIN RESULTS

The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5-seconds and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features.

CONCLUSION

Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.

Behavioral Account of Attended Stream Enhances Neural Tracking

During the past decade, several studies have identified electroencephalographic (EEG) correlates of selective auditory attention to speech. In these studies, typically, listeners are instructed to focus on one of two concurrent speech streams (the "target"), while ignoring the other (the "masker"). EEG signals are recorded while participants are performing this task, and subsequently analyzed to recover the attended stream. An assumption often made in these studies is that the participant's attention can remain focused on the target throughout the test. To check this assumption, and assess when a participant's attention in a concurrent speech listening task was directed toward the target, the masker, or neither, we designed a behavioral listen-then-recall task (the Long-SWoRD test). After listening to two simultaneous short stories, participants had to identify keywords from the target story, randomly interspersed among words from the masker story and words from neither story, on a computer screen. To modulate task difficulty, and hence, the likelihood of attentional switches, masker stories were originally uttered by the same talker as the target stories. The masker voice parameters were then manipulated to parametrically control the similarity of the two streams, from clearly dissimilar to almost identical. While participants listened to the stories, EEG signals were measured and subsequently, analyzed using a temporal response function (TRF) model to reconstruct the speech stimuli. Responses in the behavioral recall task were used to infer, retrospectively, when attention was directed toward the target, the masker, or neither. During the model-training phase, the results of these behavioral-data-driven inferences were used as inputs to the model in addition to the EEG signals, to determine if this additional information would improve stimulus reconstruction accuracy, relative to performance of models trained under the assumption that the listener's attention was unwaveringly focused on the target. Results from 21 participants show that information regarding the actual - as opposed to, assumed - attentional focus can be used advantageously during model training, to enhance subsequent (test phase) accuracy of auditory stimulus-reconstruction based on EEG signals. This is the case, especially, in challenging listening situations, where the participants' attention is less likely to remain focused entirely on the target talker. In situations where the two competing voices are clearly distinct and easily separated perceptually, the assumption that listeners are able to stay focused on the target is reasonable. The behavioral recall protocol introduced here provides experimenters with a means to behaviorally track fluctuations in auditory selective attention, including, in combined behavioral/neurophysiological studies.

EEG-Based Intersubject Correlations Reflect Selective Attention in a Competing Speaker Scenario

Several solutions have been proposed to study the relationship between ongoing brain activity and natural sensory stimuli, such as running speech. Computing the intersubject correlation (ISC) has been proposed as one possible approach. Previous evidence suggests that ISCs between the participants' electroencephalogram (EEG) may be modulated by attention. The current study addressed this question in a competing-speaker paradigm, where participants (N = 41) had to attend to one of two concurrently presented speech streams. ISCs between participants' EEG were higher for participants attending to the same story compared to participants attending to different stories. Furthermore, we found that ISCs between individual and group data predicted whether an individual attended to the left or right speech stream. Interestingly, the magnitude of the shared neural response with others attending to the same story was related to the individual neural representation of the attended and ignored speech envelope. Overall, our findings indicate that ISC differences reflect the magnitude of selective attentional engagement to speech.