Auditory attention—focusing the searchlight on sound

The Journal of the Acoustical Society of America, 2013

Frontiers in Psychology, 2012

Auditory perception and cognition entails both low-level and high-level processes, which are likely to interact with each other to create our rich conscious experience of soundscapes. Recent research that we review has revealed numerous influences of high-level factors, such as attention, intention, and prior experience, on conscious auditory perception. And recently, studies have shown that auditory scene analysis tasks can exhibit multistability in a manner very similar to ambiguous visual stimuli, presenting a unique opportunity to study neural correlates of auditory awareness and the extent to which mechanisms of perception are shared across sensory modalities. Research has also led to a growing number of techniques through which auditory perception can be manipulated and even completely suppressed. Such findings have important consequences for our understanding of the mechanisms of perception and also should allow scientists to precisely distinguish the influences of different higher-level influences.

Classically, attentional selectivity has been conceptualized as a passive by-product of capacity limits on stimulus processing. Here, we examine the role of more active cognitive control processes in attentional selectivity, focusing on how distraction from task-irrelevant sound is modulated by levels of task engagement in a visually presented short-term memory task. Task engagement was varied by manipulating the load involved in the encoding of the (visually presented) to-be-remembered items. Using a list of Navon letters (where a large letter is composed of smaller, different-identity letters), participants were oriented to attend and serially recall the list of large letters (low encoding load) or to attend and serially recall the list of small letters (high encoding load). Attentional capture by a single deviant noise burst within a task-irrelevant tone sequence (the deviation effect) was eliminated under high encoding load (Experiment 1). However, distraction from a continuously changing sequence of tones (the changing-state effect) was immune to the influence of load (Experiment 2). This dissociation in the amenability of the deviation effect and the changing-state effect to cognitive control supports a duplex-mechanism over a unitary-mechanism account of auditory distraction in which the deviation effect is due to attentional capture whereas the changing-state effect reflects direct interference between the processing of the sound and processes involved in the focal task. That the changing-state effect survives high encoding load also goes against an alternative explanation of the attenuation of the deviation effect under high load in terms of the depletion of a limited perceptual resource that would result in diminished auditory processing.

Frontiers in Human Neuroscience, 2014

Bottom-up attention is a sensory-driven selection mechanism that directs perception toward a subset of the stimulus that is considered salient, or attention-grabbing. Most studies of bottom-up auditory attention have adapted frameworks similar to visual attention models whereby local or global "contrast" is a central concept in defining salient elements in a scene. In the current study, we take a more fundamental approach to modeling auditory attention; providing the first examination of the space of auditory saliency spanning pitch, intensity and timbre; and shedding light on complex interactions among these features. Informed by psychoacoustic results, we develop a computational model of auditory saliency implementing a novel attentional framework, guided by processes hypothesized to take place in the auditory pathway. In particular, the model tests the hypothesis that perception tracks the evolution of sound events in a multidimensional feature space, and flags any deviation from background statistics as salient. Predictions from the model corroborate the relationship between bottom-up auditory attention and statistical inference, and argues for a potential role of predictive coding as mechanism for saliency detection in acoustic scenes.

PLoS ONE, 2014

Participants were requested to respond to a sequence of visual targets while listening to a well-known lullaby. One of the notes in the lullaby was occasionally exchanged with a pattern deviant. Experiment 1 found that deviants capture attention as a function of the pitch difference between the deviant and the replaced/expected tone. However, when the pitch difference between the expected tone and the deviant tone is held constant, a violation to the direction-of-pitch change across tones can also capture attention (Experiment 2). Moreover, in more complex auditory environments, wherein it is difficult to build a coherent neural model of the sound environment from which expectations are formed, deviations can capture attention but it appears to matter less whether this is a violation from a specific stimulus or a violation of the current direction-of-change (Experiment 3). The results support the expectation violation account of auditory distraction and suggest that there are at least two different expectations that can be violated: One appears to be bound to a specific stimulus and the other would seem to be bound to a more global cross-stimulus rule such as the direction-of-change based on a sequence of preceding sound events. Factors like base-rate probability of tones within the sound environment might become the driving mechanism of attentional capture-rather than violated expectations-in complex sound environments.

Frontiers in …, 2000

Introduction 3. Psychological models of auditory selective attention 4. ERPs and physiological models of auditory selective attention 4.1. The "gain" theory of attention 4.2. The "attentional trace" model of attention 4.3. Electrophysiological evidence for complex physiological processes 4.3.1. Processing of relevant inputs 4.3.2. Active rejection of irrelevant inputs 5. A peripheral filter mechanism of attention? 6. Functional brain imaging studies of auditory attention 6.1. Localization of the brain structures involved in auditory attention 6.2. Active rejection of unattended stimuli 7. Conclusion and perspectives: An adaptive filtering model of selective attention 8. Acknowledgments 9. References

Frontiers in Neuroscience, 2016

Frontiers in Neuroscience, 2022

Listening in noisy or complex sound environments is difficult for individuals with normal hearing and can be a debilitating impairment for those with hearing loss. Extracting meaningful information from a complex acoustic environment requires the ability to accurately encode specific sound features under highly variable listening conditions and segregate distinct sound streams from multiple overlapping sources. The auditory system employs a variety of mechanisms to achieve this auditory scene analysis. First, neurons across levels of the auditory system exhibit compensatory adaptations to their gain and dynamic range in response to prevailing sound stimulus statistics in the environment. These adaptations allow for robust representations of sound features that are to a large degree invariant to the level of background noise. Second, listeners can selectively attend to a desired sound target in an environment with multiple sound sources. This selective auditory attention is another f...

International Journal of Psychological Studies, 2015

Audiovisual integration interacts with attentional mechanisms. Additionally, salient auditory stimuli automatically draw attention to an audiovisual event, while spatial attention can modulate audiovisual integration. Attention induced by auditory inputs (sound-driven attention) facilitates visual perception. Similarly, visual attention improves performance on a visual task. However, the difference between attention driven by auditory and visual cues is not clear. When visual attention facilitates visual perception, there is a trade-off between spatial and temporal resolution. In contrast, audition has superior temporal resolution to vision. In the present study, we investigated the difference between auditory and visual cue-driven attention with respect to this trade-off. The results indicated that visual cueing increased spatial resolution but decreased temporal resolution. On the other hand, auditory cueing affected the efficiency of visual processing (i.e., response time) for temporal gap detection. These findings suggest that auditory cueing capitalizes on resources available for visual processing. In contrast, visual cueing may increase activation of the spatial channel instead of inhibiting the temporal channel, as proposed in previous study. Overall, there appear to be clear differences between mechanisms involved in auditory and visual cues-driven attention.