Time sharing 

In the real world, we also perform a number of tasks concurrently. We attend to a task and then if another task also needs our attention, we switch to it, attend to it for a while, and then go back to the first task. Let's look at a real-world example of how an office receptionist deals with their tasks. 

When you visit any office, there is usually a receptionist who receives you and asks for your business. Say that, just as they are asking about who you need to meet, the office buzzer rings. They take the call, say "hello," speak for a while, and then ask you to wait for a second. Once the call is finished, they resume talking to you. These actions are shown in the following diagram:

The receptionist is sharing her time among all the parties interested in talking to her. She is working in a way so that everyone gets a slice of her time. 

Now, keeping the toll plaza and the receptionist in mind, replace the toll operators with a CPU core and the cars with tasks, and you get a fairly good mental model of today's concurrent hardware. If we increase the number of toll operators from three to six, we will increase the number of cars getting serviced in parallel (at the exact same time) to six. A pleasant side effect is that the queued cars will also spread out, and every car will get serviced faster. The same holds true when we execute a concurrent program. Hence, things are faster overall.

Just as the receptionist is doing multiple things at the same time, such as time sharing between the visitors, a CPU shares time with processes (running programs). This is how concurrency gets supported on a single CPU.