Figure 1.
Normalized spectra (means and SD) of four of the bats used in the experiments.
Each spectrum was normalized to have a maximum of 1. Note the overall similar shape and the high variability.
Table 1.
Basic Call Parameters.
Figure 2.
The correct decision percentage is presented as a function of the day of training for each bat. Training was stopped once a bat performed 75% or more of the trials for three consecutive days.
Table 2.
Bat Performance.
Table 3.
The Performance of Linear and Non-linear SVM Classifiers.
Figure 3.
Bats mean performance as a function of the non-linear classifiers' metric – the distance to the hyperplane.
The performance of each bat was normalized to a maximum of 1 for the distance class with the highest performance. The distance classes are organized in increasing distances from the hyperplane - i.e., 4 is the class farthest from the hyperplane (easiest to classify), while 1 is the closest (most difficult to classify). The positive correlation implies that the model behaves similarly to the bat.
Figure 4.
Normalized PSDs (mean and SEM) of the calls of bat 1 (black) and bat 3 (blue).
(A) The mean of all calls; (B) The mean of calls misclassified by the bats; (C) The mean of the 15 calls closest to the hyperplane; (D) The mean of the 15 calls farthest from the hyperplane. (E) Difference between the mean PSDs of bat 1 and bat 3 for the four groups of calls shown in A–D. (F) Linear correlation coefficients (a measure of similarity) between the curves presented in Figure 3E representing the difference between the average PSDs (Figure 3A–D).
Figure 5.
Testing the prototype hypothesis.
(A) The mean normalized performance of the bats as a function of the sum of prototype distances. The performance of each bat was normalized to a maximum of 1, for the distance class with the highest performance. The distance used was the sum of Euclidian distances from the pair of calls to the means of the classes. The distance classes are organized according to the distances from the prototype: 4 is the farthest class from the prototype, while 1 is the closest. In contrast to the distances from the SVM hyperplane, for the prototype classifier far means far from the prototype and therefore difficult to classify. We thus expected to find a negative correlation between performance and distance, which is what happened. (B) The similarity between the test call pairs of bat 1 and bat 3 and the mean difference between spectrograms. X axis depicts the distance between the calls according to the SVM metric. The strong positive correlation (linear coefficient C = ∼0.6) implies that the pairs that are more similar to the mean are considered easier to classify by the model.
Table 4.
The Performance of a Prototype Classifier.