Dimension-based Attention in Learning and Understanding Spoken Language Frederic K Dick (
[email protected]) Birkbeck/UCL Centre for NeuroImaging, 26 Bedford Way, London WC1H 0DS Lori L Holt (
[email protected]) Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue Pittsburgh, PA 15213 Howard Nusbaum (
[email protected]) Department of Psychology, The University of Chicago, 5848 South University Avenue Chicago, Illinois 60637 Neeraj Sharma (
[email protected]) Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, 560 012 Barbara Shinn-Cunningham (
[email protected]) Department of Biomedical Engineering, Boston University, 610 Commonwealth Ave. Boston, MA 02215 Thus, a basic problem is discovering the acoustic Keywords: attention; learning; computational modeling; speech communication. dimensions that allow for speech decoding. In particular, the listener must track parameters of speech-relevant acoustic Background dimensions, so that the information they provide for speech decoding can be appropriately weighted. This information The acoustic world is variable and messy. Signals that includes 1) a dimension's validity (how strong is the mapping allow listeners to identify and/or disambiguate important between variability across an auditory dimension, and sound auditory objects (an unusual rustle of leaves in a darkening identity or category?); 2) its availability (how often is that forest, your winning bingo number) will likely share many dimension encountered); 3) its redundancy (how much acoustic characteristics with other sounds in the environment. informational overlap is there between this dimension and What is more, the most diagnostic auditory dimensions for others?); 4) its salience (to what degree do the basic acoustics detecting the ominously rustling leaves or categorizing a of the dimension attract exogenous attention); and 5) its cost crucial syllable may be obscured, distorted, or missing.