Algorithmic intuition for logarithmic complexity
The $\Theta(\log n)$ complexity is usually connected with subdivision. When using lists as an example, imagine a list whose elements are sorted. You can search in this list in $\mathcal{O}(\log n)$ time - you do not actually need to look at each element because of the sorted nature of the list. 

If you look at the element in the middle of the list and compare it to the element you search for, you can immediately say whether it lies in the left or right half of the array. Then you can just take this one half and repeat the procedure until you find it or reach a list with 1 item which you trivially compare.

You can see that the list effectively halves each step. That means if you get a list of length $32$, the maximum steps you need to reach one-item list is $5$. If you have a list of $128 = 2^7$ items, you need only $7$ steps, for a list of $1024 = 2^{10}$ you need only $10$ steps etc. 

As you can see, the exponent $n$ in $2^n$ always shows the number of steps necessary. Logarithm is used to "extract" exactly this exponent number, for example $\log_2 2^{10} = 10$. It also generalizes to list lengths that are not powers of two long.