A few of us have started a Decision Theory journal club where we plan on reading papers from a variety of fields that examine how decisions are made. We have people from neuroscience, economics, and cognitive science participating (so far), including people participating through Google+ hangouts!, which will hopefully make lead to some productive discussions. I’m a couple papers behind, but I hope to post summaries of what we have been reading.
Our first paper follows an idea that is common in the psychological literature concerning how someone gains evidence, the noisy evidence accumulator (diffusion to a boundary). Let’s say you hear a loud noise and have to decide whether to look to your left or your right. If the noise is almost directly behind you, it can be difficult to tell which way to look. Both of your ears are going to be hearing something loud, and as the sound waves crash about the room it will make the sound even noisier: sometimes one ear will be louder than the other. But one ear is usually louder than the other and when you’ve received enough evidence that one ear is hearing something louder than the other, your head will swivel and your decision is made.
We can do essentially the same thing with rats. They can be put into a chamber where clicks will randomly come from speakers to their left and to their right, and if they turn in the direction with the most clicks, they get a reward. Rats are fairly good at this – as are humans. One interesting difference, though, is that when humans are certain, they will always go in the direction of the most clicks. Rats, on the other hand, peak out at about 90% certainty; I guess they don’t trust the experiment as much as people do and want to explore their environment more!
But we’re interested in how this decision is made, so we can go back to our noisy evidence accumulator and see if that can explain how well the decision is made. We can also through in all sorts of options: is the memory of the rats a bit forgetful? Is there all sorts of internal noise in the brain? Is there noise in the environment? And so on. It turns out that the headline of the paper tells it all: rats and humans are optimal evidence accumulators. There is no internal noise. There is no forgetting. Every bit of evidence that is given to the animals is in there, waiting to be used.
Fortunately, results from a different paper can explain to us what might be happening. There are direct connections between the cortical auditory neurons and neurons in striatum – an area of the brain that receives dopamine and is involved in selecting the best action to take. Activating these auditory neurons signals the striatum and makes the animal more likely to go in whichever direction the experimenter wants. Inhibiting these neurons has the opposite effect. It’s quite possible that the auditory input is interacting with this dopamine system to keep track of where an animal wants to go – and what decision it wants to make.
As for the optimality of the animals, well, that’s at least the headline, and it would be great if that were always true. In actuality, there’s a large population of rats which show sub-optimal evidence accumulators. Although they don’t discuss this in the paper, to me this is the most exciting result (although the lack of neural noise ranks up there, too. Our brains are machines.). Of course you’d expect that evolution would evolve animals that, well, make good decisions. So why are there any animals that do show significant neural noise? Why is there such large variability in forgetfulness? Although the majority of animals are almost perfect, many are not. Hopefully in the future, we will be able to explain why it’s good to not always be perfect.
Brunton, B., Botvinick, M., & Brody, C. (2013). Rats and Humans Can Optimally Accumulate Evidence for Decision-Making Science, 340 (6128), 95-98 DOI: 10.1126/science.1233912
Znamenskiy, P., & Zador, A. (2013). Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination Nature, 497 (7450), 482-485 DOI: 10.1038/nature12077