Decision-making, in most cases, is not a straightforward process. Facing contradictory facts and opinions, people often struggle to decide on the best way forward when a complicated problem needs to be solved. In human society, we eventually rely on two methods of decision-making: we can follow the will of the majority, or we can delegate the task of decision-making to a leader thus leaving all deliberations (and responsibilities) to this single person.
Neuroscientists ask: How does this works on the level of brain cells? If your brain has to make a decision, how do your individual neurons come to a unified conclusion? Do they use the “majority mechanism”? Or maybe there are some neurons whose “opinion” dominates the others?
The latter approach is known as a “grandmother theory”. Grandmother analogy was suggested in the 1960s to propagate the idea that the final decision about a single event may be taken by one dominant neuron. However, scientists also suggested that the process might be more “democratic” and involve the contribution of multiple neurons in generating the final outcome. This process is sometimes described as neuronal “crowdsourcing”. The most recent data points to the possibility that both processes play a role in the human decision-making process.
Coming to any decision is a complex mental process involving the weighing and choosing from a number of options, with each option having various characteristics. Decision-making is not a reflex action. When we make a decision there are some expected or foreseen consequences. And it is also important to note that in making any decision, we use the information that has been accumulated in our brain prior to the time of making that decision. Finally, every decision made involves risk, as there is a degree of uncertainty around making decisions, and we are aware that many of these decisions can have long-term consequences. Although we know that decision-making plays a central part in our mental development, very little is known about how it occurs at the neuronal level.
Decision-making events are far more common than we tend to think. It is not simply deciding the way to take while driving, or what food to have for a lunch. Decision-making is involved even in the easiest of tasks. What seems a simple task at first may have quite a complex mechanism underlying it. Just take any simple conversation as an example. Most words used in a conversation can have multiple meanings, and decisions regarding their appropriate use have to be taken at very high speed. These can be called momentous decisions: they do not require a high level of thinking and are often based on prior experience. At the neuronal level, such decisions are often taken by small groups of neurons or can even be taken by a single dominant neuron.
But not all decisions are momentous. Some decisions require a high level of thinking and calculation, and they require taking the long-term outcome of the decision into consideration. For long-term decision-making, we voluntarily focus on various information sources, and then we decide what is and is not relevant in achieving our long-term goals. Moreover, our decisions continually change according to changes in our environment.
Recent studies on macaque monkeys have shown that a large part of the brain is involved in collecting information in the initial steps of decision-making. After a certain time period, when enough information is processed, the cells reach a threshold where a decision has to be made. It is at this point, it seems, that all cells start to shift in favor of one single criterion and the decision is made. It appears that when the task is complex and there are too many choices, crowdsourcing works best. Lots of information is analyzed before all of the neurons come to a single decision, and this decision is communicated to each other.
I’m not trying to say that quick decision-making cannot be done with crowdsourcing. In fact, many decisions would likely make use of more distant parts of the brain and multiple neurons. We are often faced with decisions that are complex yet have to be taken quickly. Quick decisions have their own benefits and risks. There are higher chances of error, yet, in many cases, making a decision quickly is imperative. It is quite possible that the brain has some kind of mechanism to minimize this rate of error for quick and decentralized decision-making. The process of crowdsourcing and decentralized decision-making has been studied in detail in colonies of ants, where many decisions are taken by the crowd, though it often comes at the cost of time.
Whether decision-making is prompt or delayed, a factor that seems to be of great influence is the expected reward or outcome. It is quite possible that certain areas of the brain may be dedicated to it. Thus playing the role of influencers, be it the decision of some small group of neurons or crowdsourcing.
Whatever way the decision-making is done, what seems to be evident from animal studies is that it has two phases. The first phase is information gathering, and the second phase consists of coming to a consensus and making the final decision. To a degree that depends on the situation, this final step may be influenced by more important neurons. Unsurprising considering the complexity of the mechanisms involved, the ‘details’ of this process are cloudy at best.
References
Binder, M.D., Hirokawa, N., Windhorst, U. (Eds.), 2009. Grandmother Neuron, in: Encyclopedia of Neuroscience. Springer Berlin Heidelberg, pp. 1766–1766. doi: 10.1007/978-3-540-29678-2_2077.
Bowers, J.S., 2011. What is a Grandmother Cell? And How Would You Know if You Found One? Connect Sci 23, 91–95. doi: 10.1080/09540091.2011.568608.
How neurons use crowdsourcing to make decisions [WWW Document], n.d. . ScienceDaily. URL https://www.sciencedaily.com/releases/2017/06/170606135736.htm
Huk, A.C., Shadlen, M.N., 2005. Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. J. Neurosci. Off. J. Soc. Neurosci. 25, 10420–10436. doi: 10.1523/JNEUROSCI.4684-04.2005.
Marshall, J.A.R., Dornhaus, A., Franks, N.R., Kovacs, T., 2006. Noise, cost and speed-accuracy trade-offs: decision-making in a decentralized system. J. R. Soc. Interface 3, 243–254. doi: 10.1098/rsif.2005.0075.
Michigan State University, n.d. Is crowdsourcing the future of scientific research? [WWW Document]. MSUToday. URL http://msutoday.msu.edu/news/2014/is-crowdsourcing-the-future-of-scientific-research/
Roesch, M.R., Calu, D.J., Schoenbaum, G., 2007. Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards. Nat. Neurosci. 10, 1615–1624. doi: 10.1038/nn2013.
Wang, X.-J., 2008. Decision Making in Recurrent Neuronal Circuits. Neuron 60, 215–234. doi: 10.1016/j.neuron.2008.09.034.
Source: Brain Blogger
