Looking at things this way deals with two things that have always bugged the old model.
One is sensitivity to timescale.
The other is computational tractability.
The timescale problem is that cause and effect may be separated by milliseconds (switching on a light bulb and experiencing illumination), minutes (having a drink and feeling tipsy) or even hours (eating something bad and getting food poisoning).
Looking backward, Dr.Namboodiri explains, permits investigation of an arbitrarily long list of possible causes.
Looking forward, without always knowing in advance how far to look, is much trickier.
This leads to the second problem.
Sensory experience is rich, and everything therein could potentially predict an outcome.
Making predictions based on every single possible cue would be somewhere between difficult and impossible.
It is far simpler, when a meaningful event happens, to look backwards through other potentially meaningful events for a cause.
In practice, however, it is hard to distinguish experimentally between the two models.
And that is especially true if you do not even bother to look - which, until now, people have not.
Dr. Jeong and Dr. Namboodiri have done so.
They devised and conducted 11 experiments involving mice, buzzers and drops of sugar solution that were designed specifically for the purpose.
During these they measured, in real time, the amount of dopamine being released by the nucleus accumbens, a region of the brain in which dopamine is implicated in learning and addiction.
All of the experiments came down in favour of the new model.
The 180° turnabout in thinking - from prospective to retrospective - that is implied by these experiments is causing quite a stir in the world of neuroscience.
It is "thought-provoking and represents a stimulating new direction," says Ilana Witten, a neuroscientist at Princeton University uninvolved with the paper.
More experiments will be needed to confirm the new findings.
But if confirmation comes, it will have ramifications beyond neuroscience.
It will suggest that the way AI works does not, as currently argued, have even a tenuous link with how brains operate, but was actually a lucky guess.
But it might also suggest better ways of doing AI.
Dr. Namboodiri thinks so, and is exploring the possibilities.
Evolution has had hundreds of millions of years to optimize the process of learning.
So learning from nature is rarely a bad idea.