Should normalization be included in cross-correlation-based models of binaural processing?

Abstract

It is generally accepted that cross-correlation-based models of binaural information processing provide excellent qualitative and quantitative accounts of data obtained in a wide variety of experimental contexts. These contexts include binaural detection, lateralization, localization, and the perception of pitch mediated by strictly binaural cues. The purpose of this research was to investigate the application of ‘‘normalization’’ as part of the computation of indices of interaural correlation. Such indices have often been utilized to account for binaural detection data but the normalization, to our knowledge, has neither been explicitly studied nor evaluated on its own merits. Normalization ensures that the value of correlation obtained with identical inputs will be +1 (or for polarity-opposite inputs, −1). Nevertheless, normalization, as a stage of processing, is not overtly included in modern cross-correlation-function-based models. In this presentation logical arguments and new data regarding the need to include normalization within cross-correlation-based models of binaural detection/discrimination are provided. The new data were obtained while roving the overall levels of the stimuli in a binaural detection task. The data indicate that, to account for binaural detection, an extremely rapid and accurate stage of normalization must be included in cross-correlation-based models. [Work supported by NIH-DC-02103 and NIH-DC-00234.]