Through echolocation, a bat can perceive not only the position of an object in the dark; it can also recognize its 3D structure. A tree, however, is a very complex object; it has thousands of reflective surfaces that result in a chaotic acoustic image of the tree. Technically, the acoustic image of an object is its impulse response (IR), i.e., the sum of the reflections recorded when the object is ensonified with an acoustic impulse. The extraction of the acoustic IR from the ultrasonic echo and the detailed IR analysis underlies the bats' extraordinary object-recognition capabilities. Here, a phantomobject playback experiment is developed to demonstrate that the bat Phyllostomus discolor can evaluate a statistical property of chaotic IRs, the IR roughness. The IRs of the phantom objects consisted of up to 4,000 stochastically distributed reflections. It is shown that P. discolor spontaneously classifies echoes generated with these IRs according to IR roughness. This capability enables the bats to evaluate complex natural textures, such as foliage types, in a meaningful manner. The present behavioral results and their simulations in a computer model of the bats' ascending auditory system indicate the involvement of modulation-sensitive neurons in echo analysis.T he neural interpretation of sensory input into an objectbased sensory scenery is a major focus in neuroscience. The echolocation of bats and dolphins is an ideal model system, because echolocating mammals have perfect control over their sensory data acquisition due to the active nature of echolocation. A useful analysis of the acoustic scenes, as they are represented in sequences of echoes, requires the identification of the acoustically complex objects surrounding the animals in their natural habitat. Many studies have provided insights into the extraordinary capabilities of echolocating animals in object recognition and classification (1-12).In their natural nocturnal habitat, bats are forced to orient in and navigate through a highly structured environment. How can echolocation serve these tasks? The echoes produced by potential landmarks for orientation, such as trees or bushes, are highly chaotic: the ultrasonic emission of a bat is reflected from a multitude of surfaces, the leaves, which are chaotically distributed in space and angle to the sound source and receiver. Thus, the echoes reflected from such an object will have a chaotic waveform and no systematic spectral interference pattern (Fig. 1). Moreover, the echoes are highly unstable over time, because they are susceptible to both changes of the bat's observation angle and, e.g., wind-induced movement of the object. Thus, a bat will rarely receive the same echo of an individual object twice.Until now, object recognition in echolocation has been studied only with deterministic echoes from small objects with very few reflections. The echoes from such objects can be evaluated according to their characteristic waveforms and͞or frequency patterns (2, 9, 13). However, these concepts appear insuffi...