Many different invertebrate and vertebrate species use acoustic communication
for pair formation. In the cricket Gryllus bimaculatus, females recognize
their species-specific calling song and localize singing males by positive
phonotaxis. The song pattern of males has a clear structure consisting of
brief and regular pulses that are grouped into repetitive chirps. Information
is thus present on a short and a long time scale. Here, we ask which
structural features of the song critically determine the phonotactic
performance. To this end we employed artificial neural networks to analyze a
large body of behavioral data that measured females’ phonotactic behavior
under systematic variation of artificially generated song patterns. In a first
step we used four non-redundant descriptive temporal features to predict the
female response. The model prediction showed a high correlation with the
experimental results. We used this behavioral model to explore the integration
of the two different time scales. Our result suggested that only an attractive
pulse structure in combination with an attractive chirp structure reliably
induced phonotactic behavior to signals. In a further step we investigated all
feature sets, each one consisting of a different combination of eight proposed
temporal features. We identified feature sets of size two, three, and four
that achieve highest prediction power by using the pulse period from the short
time scale plus additional information from the long time scale
