On Decision and Confidence

We make many decisions every day, consciously as well as unconsciously. The term “decisions” here are not just about the high-level processes that govern how we think and asses events and observations, it is also the low-level ones that control perception and movement.  For example, have you ever wondered how our body can move so smoothly ? This is definitely not an easy task if you ask any roboticists who are struggling to implement the human gaits on robots. Some scientists believe that our brain makes reliable, quick-fire predictions about the result of every movement we make, which results in a efficient sequence of actions that we call “walking”.

Confidence holds a crucial role in this process. How confident we feel about our choices will influence our behavior. If we did not have an accurate mechanism for confidence that is usually right, we would have difficulties in correcting decisions.

Important as it is, the way it works remains an unsolved riddle. The classical approach assumes that the brain takes shortcuts when processing information: it make approximations rather than uses precise statistical calculations. However, in a very recent paper, Adam Kepecs, professor of neuroscience at Cold Spring Harbor Laboratory has concluded that the subjective feeling of confidence stems from objective statistical calculations in the brain.

 

To determine whether the brains use objective calculations to compute the level of confidence, Kepecs created a video game to compare human and computer performance. Human volunteers would listen to streams of clicking sounds and determine which clicks were faster. Participants rated confidence in each choice on a scale of one (a random guess) to five (high confidence). What Kepecs and his colleagues found was that human responses were similar to statistical calculations. The brain produces feelings of con- fidence that inform decisions the same way statistics pulls patterns out of noisy data.

Figure 2
The human feeling of confience follows statistical predictions in a perceptual decision task. Source: Aam Kepecs et al.

To further examine his model, Kepecs organised another experiment in which participants answered questions comparing the populations of various countries. Unlike the perceptual test, this one had the added complexity of each participant’s individual knowledge base. Even human foibles, such as being overconfident in the face of hard choices with poor data or under-confident when facing easy choices, were consistent with Kepecs’s model.

This is not the first time a scientist suggest that our brains relies more on a statistical model than a heuristic one.  In many perception tasks, it was showed that people tend to make estimates in a way that fits with Bayesian probability framework. There’s also evidence that the brain makes internal predictions and updates them in a Bayesian manner. When we read a book or listen to someone talking, for example, our brain is not simply receiving information, it is constantly analyzing this stream of data and predicting what it expects to read or hear. These predictions strongly influence what we actually read or hear. More general, we can argue that our perception of the world is in fact a reconstruction made by the brain: we don’t (or can’t ?) see the world as it is, but we see it the way our brain is expecting it.

To maintain a level of homogeneity between the real world and the “reconstructed” reality, the brain is constantly revises its predictions based on what information comes next. Making predictions and re-evaluation them seems to be a universal feature of the brain. At all times our brain is weighing its inputs and comparing them with internal predictions in order to make sense of the world.

So far we have seen some arguments support the  (Bayesian) statistical paradigm. However, the scientists from the “anti-Bayesian” camp have provided a number of strong counter-arguments, especially when it comes to high-level decision making. It is fairly easy to come up with probability puzzles that should yield to Bayesian methods, but that regularly leave many people flummoxed. For instance, many people will say that if we toss a series of coins, getting all heads or all tails is less likely than getting any “seemingly random” sequence, for example, tails–tails–heads–tails–heads. It is not: as the coin tosses are independent, there is no reason to expect one sequence is more likely than another. There’s considerable evidence like the coin tosses experiment above which shows that most people are basically non-Bayesian when performing high-level, logical reasoning.

All in all, we are dealing with the most complicated thing in the known universe, and all the discovery up to know about our brain just scratch the surface. A lot of work still need to be done in order to truly understand how we think.

In conclusion, I believe that Bayesian paradigm, with its quirks and imperfections, represent a potential approach that can eventually help us see the complete picture of our brain.

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