Dataset Information

Developmental Differences in Filtering Auditory and Visual Distractors During Visual Selective Attention.

ABSTRACT: The current experiment examined changes in visual selective attention in young children, older children, young adults, and older adults while participants were instructed to ignore auditory and visual distractors. The aims of the study were to: (a) determine if the Perceptual Load Hypothesis (PLH) (distraction greater under low perceptual load) could predict which irrelevant stimuli would disrupt visual selective attention, and (b) if auditory to visual shifts found in modality dominance research could be extended to selective attention tasks. Overall, distractibility decreased with age, with incompatible distractors having larger costs in young and older children than adults. In regard to accuracy, visual distractibility did not differ across age nor load, whereas, auditory interference was more pronounced early in development and correlated with age. Auditory and visual distractors also slowed down responses in young and older children more than adults. Finally, the PLH did not predict performance. Rather, children often showed the opposite pattern, with visual distractors having a greater cost in the high load condition (older children) and auditory distractors having a greater cost in the high load condition (young children). These findings are consistent with research examining the development of modality dominance and shed light on changes in multisensory processing and selective attention across the lifespan.

SUBMITTER: Robinson CW

PROVIDER: S-EPMC6304370 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Publications

Developmental Differences in Filtering Auditory and Visual Distractors During Visual Selective Attention.

Robinson Christopher W CW Hawthorn Andrew M AM Rahman Arisha N AN

Frontiers in psychology 20181217

The current experiment examined changes in visual selective attention in young children, older children, young adults, and older adults while participants were instructed to ignore auditory and visual distractors. The aims of the study were to: (a) determine if the Perceptual Load Hypothesis (PLH) (distraction greater under low perceptual load) could predict which irrelevant stimuli would disrupt visual selective attention, and (b) if auditory to visual shifts found in modality dominance researc ...[more]

PMID: 30618983

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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

Developmental Differences in Filtering Auditory and Visual Distractors During Visual Selective Attention.

Publications

Developmental Differences in Filtering Auditory and Visual Distractors During Visual Selective Attention.

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