Abstract
The ability of “looking into the future”—namely, the capacity of anticipating future states of the environment or of the body—represents a fundamental function of human (and animal) brains. A goalkeeper who tries to guess the ball's direction; a chess player who attempts to anticipate the opponent's next move; or a man-in-love who tries to calculate what are the chances of her saying yes—in all these cases, people are simulating possible future states of the world, in order to maximize the success of their decisions or actions. Research in neuroscience is showing that our ability to predict the behavior of physical or social phenomena is largely dependent on the brain's ability to integrate current and past information to generate (probabilistic) simulations of the future. But could predictive processing be augmented using advanced technologies? In this contribution, we discuss how computational technologies may be used to support, facilitate or enhance the prediction of future events, by considering exemplificative scenarios across different domains, from simpler sensorimotor decisions to more complex cognitive tasks. We also examine the key scientific and technical challenges that must be faced to turn this vision into reality.
Highlights
Modern cognitive neuroscience describes the brain as a predictive device, not a stimulus-response system
A key challenge of predictive technologies is how to represent the information about an upcoming event
An augmented reality system allows superimposing digital information on the physical environment in real-time using a smartphone or head-mounted see-through displays coupled with a wearable computer
Summary
Modern cognitive neuroscience describes the brain as a predictive device, not a stimulus-response system In this “predictive brain” perspective, the brain continuously predicts environmental dynamics and anticipates action effects, and this permits animals to be “ahead of time” when it takes decisions, rather than just react to what it currently senses (Pezzulo, 2008; Bar, 2009; Friston, 2010). The most comprehensive attempt to describe formally the “predictive brain” perspective is the free energy principle developed by Friston and collaborators (Friston, 2010; Pezzulo et al, 2015) In this perspective, the brain is a statistical machine that learns a so-called generative model of external dynamics (especially how the environment changes as a function of the agent’s actions) and uses it for continuous prediction. We consider this and other specific examples of predictive technologies that—we will argue—might be soon within our reach
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