The two a priori conditions can also be viewed as 4 possible worlds by branching on the outcome of the (unobserved because it hasn't happened yet or, if you prefer, after it happens but before you observe it) coin toss:

    P(a)P(heads|a)     (a, heads)
    P(a)P(tails|a)     (a, tails)
    P(b)P(heads|b)     (b, heads)
    P(b)P(tails|b)     (b, tails)

When the coin toss is observed, you are removing the possible worlds in which the coin toss did not have the observed outcome. The renormalization step at that point will yield the same posterior probabilities as Bayes' rule.

In your example, we are eliminating the possible worlds where the flip is heads, leaving a total remaining probability that is precisely p(-1).