Algorithm 1. Adapting the reward threshold.

1:	θ ← 4.5	▷ Initialize the reward threshold.
2:	h[1: 10] ← [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]	▷ Initialize the recent reward history.
3:	for each second’s vocalization do
4:	if S > θ then	▷ If salience is high, reward.
5:	r ← 1
6:	else
7:	r ← 0
8:	h[11] ← r	▷ Update the recent reward history.
9:	h ← h[2: 11]
10:	if ${\sum }_{n=1}^{10}h\left[n\right]\ge 3$ then	▷ If the recent reward rate is 30% or higher
11:	θ ← θ + .1	▷ increase the reward threshold
12:	h[1: 10] ← [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]	▷ and reset the recent reward history.