Dataset Information

A common source of attention for auditory and visual tracking.

ABSTRACT: Tasks that require tracking visual information reveal the severe limitations of our capacity to attend to multiple objects that vary in time and space. Although these limitations have been extensively characterized in the visual domain, very little is known about tracking information in other sensory domains. Does tracking auditory information exhibit characteristics similar to those of tracking visual information, and to what extent do these two tracking tasks draw on the same attention resources? We addressed these questions by asking participants to perform either single or dual tracking tasks from the same (visual-visual) or different (visual-auditory) perceptual modalities, with the difficulty of the tracking tasks being manipulated across trials. The results revealed that performing two concurrent tracking tasks, whether they were in the same or different modalities, affected tracking performance as compared to performing each task alone (concurrence costs). Moreover, increasing task difficulty also led to increased costs in both the single-task and dual-task conditions (load-dependent costs). The comparison of concurrence costs between visual-visual and visual-auditory dual-task performance revealed slightly greater interference when two visual tracking tasks were paired. Interestingly, however, increasing task difficulty led to equivalent costs for visual-visual and visual-auditory pairings. We concluded that visual and auditory tracking draw largely, though not exclusively, on common central attentional resources.

SUBMITTER: Fougnie D

PROVIDER: S-EPMC6061001 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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A common source of attention for auditory and visual tracking.

Fougnie Daryl D Cockhren Jurnell J Marois René R

Attention, perception & psychophysics 20180801 6

Tasks that require tracking visual information reveal the severe limitations of our capacity to attend to multiple objects that vary in time and space. Although these limitations have been extensively characterized in the visual domain, very little is known about tracking information in other sensory domains. Does tracking auditory information exhibit characteristics similar to those of tracking visual information, and to what extent do these two tracking tasks draw on the same attention resourc ...[more]

PMID: 29717471

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Project description:Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ?1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.

Dataset Information

A common source of attention for auditory and visual tracking.

Publications

A common source of attention for auditory and visual tracking.

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