Neural machinery for predicting the future
Friday, June 24th, 2011 by Antonio ManzaliniReading Roberto’s posts (on the incredible amount data around us) reminded me a paper (Predicting the future with social media) about how social media can be utilized to forecast future outcomes. While the study focused on the adaproblem of predicting box office revenues of movies by using the chatter from Twitter.com, the method can be extended to a large set of topics, ranging from the future rating of products and services of consumer interest to election outcomes. Tapping and elaborating information from social media can yield an extremely powerful and accurate indicator of future outcomes.
The brain rewarding system
This reminded me also another paper (Remembering the past to imagine the future: the prospective brain) arguing that, according to a growing number of recent neuroscience studies, imagining the future depends on much of the same neural machinery that is needed for remembering the past: the so called prospective brain. It is a crucial function of the brain; it uses stored information to imagine, simulate and predict possible future events. We know that for a living organism, preparing for the future is a vital task important for adaptation and survival: the anticipatory capacity is crucial for deciding between alternative courses of actions. For instance, one simple prediction made by the prospective brain is the probable time and magnitude of future rewarding events (a positive value that a living organism refers to an object, an act or an internal state. Physiological studies has recently complemented these arguments by identifying dopaminergic neurons whose fluctuating output signals changes or errors in the predictions of future salient and rewarding events. This is amazing. Once we understand deeper how rewards are processed by the brain, we may then even discover how reward information is used to produce behaviour that is directed towards rewarding goals.
The ultimate appears to be gaining a comprehensive understanding of the prospective neural machinery that deal with all aspects of reward that are relevant for explaining living organism goal-directed behaviour and adapatation. This will produce a wealth of important knowledge not only for making more accurate predictions of future events but also for designing self-adaptable future networks .
