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The brain first perceives changes in visual input (local motion) in the primary visual cortex. The local motion signals are then integrated in the later visual processing stages and interpreted as global motion in the higher-level processes. But when subjects in a recent experiment using moving dots were asked to detect global motion (the overall direction of the dots moving together), the results show that their learning relied on more local motion processes (the movement of dots in small areas) than global motion areas.

show that the improvement in detection of global motion is not due to learning of the global motion but to learning of local motion of the moving dots in the test.

Miller and Cohen propose an Integrative Theory of Prefrontal Cortex Function. The two theorize that “cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represents goals and means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task” (Miller & Cohen, 2001). Essentially the two theorize that the prefrontal cortex guides the inputs and connections which allows for cognitive control of our actions.

Their discovery reveals a critical period during which a LIM homeodomain transcription factor known as Lhx2 decides over the progenitors' regional destiny: Once the window of opportunity closes, their fate is sealed.

In 2007, Jung and Richard Haier, now professor emeritus of psychology at the University of California, Irvine, developed the first comprehensive theory drawn from neuroimaging of how the brain gives rise to intelligence.

Applying existing theories of how information flows in the brain, Jung and Haier hypothesized that neural signals travel from nodes near the back of the brain, where sensory data is collected and synthesized, to those in the frontal lobes, which are responsible for decision making and planning. The connections between these nodes, they argued, are just as critical as the nodes themselves.

One advantage of nanoscale electrodes is that they can register and stimulate the tiniest components of the brain.

Over the past decade, neuroscientists have found that real brains seem to work in this way. In perception and learning experiments, for example, people tend to make estimates - of the location or speed of a moving object, say - in a way that fits with Bayesian probability theory. There's also evidence that the brain makes internal predictions and updates them in a Bayesian manner. When you listen to someone talking, for example, your brain isn't simply receiving information, it also predicts what it expects to hear and constantly revises its predictions based on what information comes next. These predictions strongly influence what you actually hear, allowing you, for instance, to make sense of distorted or partially obscured speech.

In fact, making predictions and re-evaluating them seems to be a universal feature of the brain. At all times your brain is weighing its inputs and comparing them with internal predictions in order to make sense of the world.

as providing the most promising attempt at a unified theory of brain functions

Through a Darwinian process, selecting from the competing models the one best supported by the evidence, a basis for action is chosen.

Even if these simple AIs could modify their own utility functions, why would they? Nothing outside the utility function has the power to generate base “motivation”. I’m not sure why thIs Is hard to understand. It’s not going to magically change when AIs become smarter.

But they represent a new forefront in the field of artificial intelligence. Today’s AI doesn’t try to re-create the brain. Instead, it uses machine learning, massive data sets, sophisticated sensors, and clever algorithms to master discrete tasks.