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Temporal weighting functions for interaural time and level differences. III. Temporal weighting for lateral position judgments.

ABSTRACT: Temporal variation in listeners' sensitivity to interaural time and level differences (ITD and ILD) was assessed using the temporal weighting function (TWF) paradigm [Stecker and Hafter (2002). J. Acoust. Soc. Am. 112, 1046-1057] in the context of sound-source lateralization. Brief Gabor click trains were presented over headphones with overall ITD and/or ILD ranging ±500 μs ITD and/or ±5 dB ILD across trials; values for individual clicks within each train varied by an additional ±100 μs or ±2 dB to allow TWF calculation by multiple regression. In separate conditions, TWFs were measured for (i) ITD alone, (ii) ILD alone, (iii) ITD and ILD covarying ("in agreement"), and (iv) ITD and ILD varying independently across clicks. Consistent with past studies that measured TWF for binaural discrimination, TWFs demonstrated high weight on the first click for stimuli with short interclick interval (ICI = 2 ms), but flatter weighting for longer ICI (5-10 ms). Some conditions additionally demonstrated greater weight for clicks near the offset than near the middle of the train [Stecker and Hafter (2009). J. Acoust. Soc. Am. 125, 3914-3924]. The latter result was observed only when stimuli carried ILD, and appeared more reliably for 5 ms than for 2 or 10 ms ICI.

SUBMITTER: Stecker GC

PROVIDER: S-EPMC3745506 | biostudies-other | 2013 Aug

REPOSITORIES: biostudies-other

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Project description:BackgroundA major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.MethodsThe three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.ResultsThe outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.ConclusionsWe developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.

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Temporal weighting functions for interaural time and level differences. III. Temporal weighting for lateral position judgments.

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