Not quite. Matlab recognizes some patterns of operations and for sufficiently large matrices automatically calls high performance multicore libraries that use knowledge of cache behaviour and vectorized hardware instructions to improve performance. These execute within one process.
Running larger sections independently requires starting multiple processes and transferring the data back and forth and analyzing what really needs to be sent to each process. The parallel computing toolbox does this work.
Because of the process overhead and the data transfer overhead and the loss of opportunity to take advantage of multiple CPU, it is very common for parfor to come out slower.