Size does not matter: size-invariant echo-acoustic object classification

Abstract

Echolocating bats can not only extract spatial information from the auditory analysis of their ultrasonic emissions, they can also discriminate, classify and identify the three-dimensional shape of objects reflecting their emissions. Effective object recognition requires the segregation of size and shape information. Previous studies have shown that, like in visual object recognition, bats can transfer an echo-acoustic object discrimination task to objects of different size and that they spontaneously classify scaled versions of virtual echo-acoustic objects according to trained virtual-object standards. The current study aims to bridge the gap between these previous findings using a different class of real objects and a classification-instead of a discrimination paradigm. Echolocating bats (Phyllostomus discolor) were trained to classify an object as either a sphere or an hour-glass shaped object. The bats spontaneously generalised this classification to objects of the same shape. The generalisation cannot be explained based on similarities of the power spectra or temporal structures of the echo-acoustic object images and thus require dedicated neural mechanisms dealing with size-invariant echo-acoustic object analysis. Control experiments with human listeners classifying the echo-acoustic images of the objects confirm the universal validity of auditory size invariance. The current data thus corroborate and extend previous psychophysical evidence for sonar auditory-object normalisation and suggest that the underlying auditory mechanisms following the initial neural extraction of the echo-acoustic images in echolocating bats may be very similar in bats and humans.