Abstract
Bats use echolocation through flexible active sensing via ultrasounds to identify environments suitable for their habitat and foraging. Mimicking the sensing strategies of bats for echolocation, this study examined how humans acquire new acoustic-sensing abilities, and proposes effective strategies for humans. A target geometry identification experiment-involving 15 sighted people without experience of echolocation-was conducted using two targets with different geometries, based on a new sensing system. Broadband frequency-modulated pulses with short inter-pulse intervals (16 ms) were used as a synthetic echolocation signal. Such pulses mimic buzz signals emitted by bats for echolocation prior to capturing their prey. The study participants emitted the signal from a loudspeaker by tapping on Android devices. Because the signal included high-frequency signals up to 41 kHz, the emitted signal and echoes from a stationary or rotating target were recorded using a 1/7-scaled miniature dummy head. Binaural sounds, whose pitch was down-converted, were presented through headphones. This way, time-varying echo information was made available as an acoustic cue for target geometry identification under a rotating condition, as opposed to a stationary one. In both trials, with (i.e., training trials) and without (i.e., test trials) answer feedback immediately after the participants answered, the participants identified the geometries under the rotating condition. Majority of the participants reported using time-varying patterns in terms of echo intensity, timbre, and/or pitch under the rotating condition. The results suggest that using time-varying patterns in echo intensity, timbre, and/or pitch enables humans to identify target geometries. However, performance significantly differed by condition (i.e., stationary vs. rotating) only in the test trials. This difference suggests that time-varying echo information is effective for identifying target geometry through human echolocation especially when echolocators are unable to obtain answer feedback during sensing.
Highlights
Echolocating bats recognize their environments by emitting ultrasounds and listening to echoes from objects in a process called echolocation
The generalized linear mixed models (GLMMs) evaluations indicate that the participants in the test trials were more likely to identify the geometries correctly than chance performance under the rotating condition (Fig 5B, middle right panel; β = 0.750 ± 0.189, z = 3.964, p < 0.001) but not under the stationary condition (Fig 5B, top right panel; β = 0.076 ± 0.183, z = 0.413, p = 0.680)
The GLMM evaluation indicates that the participants in the test trials were more likely to identify the geometries correctly under the rotating condition than under the stationary condition (Fig 5B, bottom right panel; β = 0.700 ± 0.193, z = 3.619, p < 0.001)
Summary
Echolocating bats recognize their environments by emitting ultrasounds and listening to echoes from objects in a process called echolocation. Bats change the acoustic features of echolocation signals (e.g., frequency, duration, and sound pressure) and strategies for signal emissions (e.g., timing, rate, and direction) according to their flight environment and purpose [3,4,5,6,7,8,9]. Such flexible acoustic sensing using echolocation signals enables advanced sonar behaviors among animals [10, 11]. Evaluating and modeling sensing strategies are scientifically interesting and useful for instructing and guiding new users of human echolocation
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