Original Research ARTICLE

Front. Hum. Neurosci., 27 May 2014 | doi: 10.3389/fnhum.2014.00327

Investigating bottom-up auditory attention

  • Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA

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.

Keywords: audition, attention, saliency, bottom-up, psychoacoustics

Citation: Kaya EM and Elhilali M (2014) Investigating bottom-up auditory attention. Front. Hum. Neurosci. 8:327. doi: 10.3389/fnhum.2014.00327

Received: 06 February 2014; Accepted: 01 May 2014;
Published online: 27 May 2014.

Edited by:

Silvio Ionta, University Hospital Center (CHUV) and University of Lausanne (UNIL), Switzerland

Reviewed by:

Gerwin Schalk, Wadsworth Center, USA
Hari M. Bharadwaj, Boston University, USA
Inyong Choi, Boston University, USA

Copyright © 2014 Kaya and Elhilali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mounya Elhilali, Department of Electrical and Computer Engineering, The Johns Hopkins University, 3400 N Charles St., Baltimore, MD 21218, USA e-mail: mounya@jhu.edu

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