How are the weights of a single layer perceptron updated given the misclassification of a point?

Here is some data separated by decision line

The equation of decision line/boundary is $$x_1 -3x_2 + 3 = 0$$. Therefore $$w_1 = 1, w_2 = -3$$ and, if $$w_0 = -1$$, then the bias $$\theta = -3$$.

Now, I have a question which states that the point $$P = (2, 2)$$ is being misclassified, and I have to explain why the perceptron algorithm will change weights upon hitting this point.

I really don't know how to do it. I also need to calculate the weights, but I don't know how to do it. Have I calculated the new weights correctly? Should I calculated them as follows?

$$w_{new} = w_{old} + error \times x = [1, -3] + (1-0) \times [2, 2] = [3, -1]$$

Therefore, the new equation of the decision boundary is $$3x_1 - x_2 + 3 = 0$$, where $$w_1 = 3, w_2 = -1$$, and $$\theta= -3$$. Is this correct?