Stimulus statistics change sounds from near-indiscriminable to hyperdiscriminable

Objects and events in the sensory environment are generally predictable, making most of the energy impinging upon sensory transducers redundant. Given this fact, efficient sensory systems should detect, extract, and exploit predictability in order to optimize sensitivity to less predictable inputs that are, by definition, more informative. Not only are perceptual systems sensitive to changes in physical stimulus properties, but growing evidence reveals sensitivity both to relative predictability of stimuli and to co-occurrence of stimulus attributes within stimuli. Recent results revealed that auditory perception rapidly reorganizes to efficiently capture covariance among stimulus attributes. Acoustic properties per se were perceptually abandoned, and sounds were instead processed relative to patterns of cooccurrence. Here, we show that listeners\u27 ability to distinguish sounds from one another is driven primarily by the extent to which they are consistent or inconsistent with patterns of covariation among stimulus attributes and, to a lesser extent, whether they are heard frequently or infrequently. When sounds were heard frequently and deviated minimally from the prevailing pattern of covariance among attributes, they were poorly discriminated from one another. In stark contrast, when sounds were heard rarely and markedly violated the pattern of covariance, they became hyperdiscriminable with discrimination performance beyond apparent limits of the auditory system. Plausible cortical candidates underlying these dramatic changes in perceptual organization are discussed. These findings support efficient coding of stimulus statistical structure as a model for both perceptual and neural organization. © 2016 Stilp, Kluender. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Efficient Coding and Statistically Optimal Weighting of Covariance among Acoustic Attributes in Novel Sounds

To the extent that sensorineural systems are efficient, redundancy should be extracted to optimize transmission of information, but perceptual evidence for this has been limited. Stilp and colleagues recently reported efficient coding of robust correlation (r = .97) among complex acoustic attributes (attack/decay, spectral shape) in novel sounds. Discrimination of sounds orthogonal to the correlation was initially inferior but later comparable to that of sounds obeying the correlation. These effects were attenuated for less-correlated stimuli (r = .54) for reasons that are unclear. Here, statistical properties of correlation among acoustic attributes essential for perceptual organization are investigated. Overall, simple strength of the principal correlation is inadequate to predict listener performance. Initial superiority of discrimination for statistically consistent sound pairs was relatively insensitive to decreased physical acoustic/psychoacoustic range of evidence supporting the correlation, and to more frequent presentations of the same orthogonal test pairs. However, increased range supporting an orthogonal dimension has substantial effects upon perceptual organization. Connectionist simulations and Eigenvalues from closed-form calculations of principal components analysis (PCA) reveal that perceptual organization is near-optimally weighted to shared versus unshared covariance in experienced sound distributions. Implications of reduced perceptual dimensionality for speech perception and plausible neural substrates are discussed

Real-Time Contrast Enhancement to Improve Speech Recognition

An algorithm that operates in real-time to enhance the salient features of speech is described and its efficacy is evaluated. The Contrast Enhancement (CE) algorithm implements dynamic compressive gain and lateral inhibitory sidebands across channels in a modified winner-take-all circuit, which together produce a form of suppression that sharpens the dynamic spectrum. Normal-hearing listeners identified spectrally smeared consonants (VCVs) and vowels (hVds) in quiet and in noise. Consonant and vowel identification, especially in noise, were improved by the processing. The amount of improvement did not depend on the degree of spectral smearing or talker characteristics. For consonants, when results were analyzed according to phonetic feature, the most consistent improvement was for place of articulation. This is encouraging for hearing aid applications because confusions between consonants differing in place are a persistent problem for listeners with sensorineural hearing loss

PCA network architecture.

<p>Two input units (one corresponding to AD, one to SS) are fully connected to two output units via feed-forward excitatory weights (solid arrows) without any hidden layer or bias. The first output unit projects inhibitory weights (dashed lines) back to the inputs, effectively removing the principal component from the inputs and leaving the second output to encode remaining (orthogonal) covariance. Euclidean distances among output patterns were calculated after each epoch.</p

Correlation coefficients (<i>r</i>), first and second Eigenvalues (λ₁, λ₂), covariance between AD and SS (σ_AD,SS), and effect sizes (Consistent versus Orthogonal discrimination in the first testing block, as measured by Cohen's <i>d</i>) for each experiment.

<p>Correlation Model indicates Eigenvalues calculated from the correlation matrix of the stimulus sets before the simulation, while Covariance Model indicates Eigenvalues calculated from the input covariance matrix before simulations. The order of experiments is intentionally transposed to highlight the robust negative correlation between the second Eigenvalue of the covariance matrix of the experimental stimuli with listener performance.</p

Stimulus matrix.

<p>Each circle represents one stimulus; different subsets from this matrix were presented in each experiment. Corner stimuli are replaced by spectrograms (500-ms abscissa, 10 kHz ordinate) to illustrate variation in Spectral Shape and Attack/Decay. Covariance between these properties occurs along either the Consistent statistical dimension (blue line) or the Orthogonal dimension (red line). Each experiment was counterbalanced such that half of listeners heard Consistent stimuli along the blue vector and Orthogonal stimuli along the red vector, while the other half heard Consistent stimuli along the red vector and Orthogonal stimuli along the blue vector.</p

Stimulus discriminability is modulated by statistical structure among acoustic properties.

<p>Figures plot mean accuracy for discriminating pairs of Consistent (blue) or Orthogonal sounds (red) as a function of testing block for each experiment. Insets depict stimulus matrices to indicate which stimuli were tested in each block of each experiment. Half of the participants in each experiment heard stimuli as depicted while the other half heard counterbalanced stimuli rotated 90°. Rows are arranged according to statistical properties of Orthogonal sounds (red text) indicating the extent to which they violated the prevailing pattern of covariance supported by the Consistent sounds, increasing progressively from Minimal Dissimilarity (top row; inferior discrimination) to Extreme Dissimilarity (bottom row; superior discrimination). Major columns indicate frequency of presentation for Consistent and Orthogonal sound pairs: equally often (left column) or Orthogonal sounds withheld until the third testing block (right column). Dashed lines represent baseline performance when acoustic dimensions shared zero redundancy (mean accuracy = 0.690 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161001#pone.0161001.ref024" target="_blank">24</a>]); significant improvement beyond baseline performance in Experiment 5 indicates hyperdiscriminability. Asterisks indicate statistically significant differences; *<i>P</i> < .05, **<i>P</i> < .01, ***<i>P</i> < .001. Error bars indicate standard error of the mean.</p