Computational and Perceptual Characterization of Auditory Attention

Humans are remarkably capable at making sense of a busy acoustic environment in real-time, despite the constant cacophony of sounds reaching our ears. Attention is a key component of the system that parses sensory input, allocating limited neural resources to elements with highest informational value to drive cognition and behavior. The focus of this thesis is the perceptual, neural, and computational characterization of auditory attention. Pioneering studies exploring human attention to natural scenes came from the visual domain, spawning a number of hypotheses on how attention operates among the visual pathway, as well as a considerable amount of computational work that attempt to model human perception. Comparatively, our understanding of auditory attention is yet very elementary, particularly pertaining to attention automatically drawn to salient sounds in the environment, such as a loud explosion. In this work, we explore how human perception is affected by the saliency of sound, characterized across a variety of acoustic features, such as pitch, loudness, and timbre. Insight from psychoacoustical data is complemented with neural measures of attention recorded directly from the brain using electroencephalography (EEG). A computational model of attention is presented, tracking the statistical regularities of incoming sound among a high-dimensional feature space to build predictions of future feature values. The model determines salient time points that will attract attention by comparing its predictions to the observed sound features. The high degree of agreement between the model and human experimental data suggests predictive coding as a potential mechanism of attention in the auditory pathway. We investigate different modes of volitional attention to natural acoustic scenes with a "cocktail-party" simulation. We argue that the auditory system can direct attention in at least three unique ways (globally, based on features, and based on objects) and that perception can be altered depending on how attention is deployed. Further, we illustrate how the saliency of sound affects the various modes of attention. The results of this work improve our understanding of auditory attention, highlighting the temporally evolving nature of sound as a significant distinction between audition and vision, with a focus on using natural scenes that engage the full capability of human attention

Neural Circuit Mechanisms Of Stimulus Selection Underlying Spatial Attention

Humans and animals routinely encounter competing pieces of information in their environments, and must continually select the most salient in order to survive and behave adaptively. Here, using computational modeling, extracellular neural recordings, and focal, reversible silencing of neurons in the midbrain of barn owls, we uncovered how two essential computations underlying competitive selection are implemented in the brain: a) the ability to select the most salient stimulus among all pairs of stimulus locations, and b) the ability to signal the most salient stimulus categorically. We first discovered that a key inhibitory nucleus in the midbrain attention network, called isthmi pars magnocellularis (Imc), encodes visual space with receptive fields that have multiple excitatory hotspots (‘‘lobes’’). Such (previously unknown) multilobed encoding of visual space is necessitated for selection at all location-pairs in the face of scarcity of Imc neurons. Although distributed seemingly randomly, the RF lobe-locations are optimized across the high-firing Imc neurons, allowing them to combinatorially solve selection across space. This combinatorially optimized inhibition strategy minimizes metabolic and wiring costs. Next, we discovered that a ‘donut-like’ inhibitory mechanism in which each competing option suppresses all options except itself is highly effective at generating categorical responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization used commonly in decision-making models. We demonstrated experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is required for categorical signaling by it. Moreover, the pattern of inhibition in the midbrain forms an exquisitely structured ‘multi-holed’ donut consistent with this network’s combinatorial inhibitory function (computation 1). Our work demonstrates that the vertebrate midbrain uses seemingly carefully optimized structural and functional strategies to solve challenging computational problems underlying stimulus selection and spatial attention at all location pairs. The neural motifs discovered here represent circuit-based solutions that are generalizable to other brain areas, other forms of behavior (such as decision-making, action selection) as well as for the design of artificial systems (such as robotics, self-driving cars) that rely on the selection of one among many options