Computer vs. Human Information Processing: Differences and Insights

Modern AI and the human brain both harness the power of processing information, yet they do so in fundamentally different ways. The human brain is a sort of “biological computer,” collecting data about the world through our senses. Over the last few decades, researchers have applied the power of computation to understanding how our brains work, with the goal of gaining a better understanding of human intelligence and neurological disease. Artificial intelligence is transforming the lives of researchers and patients alike.

Network Neuroscience: Mapping the Brain

Network neuroscience is the study of the brain as a system of connections. Researchers use large datasets to study the structure and function of the brain. But the idea of the brain as a network has been around much longer. Nineteenth-century neuroscientist David Ferrier “almost defines brain networks” in a book he authored, says Cornelis Stam, a neuroscientist at Amsterdam University Medical Center. A neuroscience textbook may describe the brain as having separate regions controlling specific actions. Instead, they study the brain almost like a city transit map.

“Connectivity has become sort of a way that people interrogate human brains more and more these days,” says Sporns. Understanding these connections and networks can give researchers a better grasp of how the brain is so efficient. For example, a baby may only see a handful of human faces in its first few months of life and can learn to tell them apart. The human brain “is incredibly good at doing some things that computers have a very hard time of doing still,” Sporns says.

Human Brain vs. Computer: Key Differences in Information Processing

AI and the Human Brain: Contrasting Strengths

There are several theories for why AI can’t do what the human brain does, says physicist Dani Bassett of the University of Pennsylvania. AI systems can take a lesson from the human brain’s constraints. It’s small in volume and energy consumption, but still very powerful. “Where can we use simple models and have them informed by machine learning?” Bassett asks. “It's actually at that intersection that we'll be able to better understand the unexpected simplicities in complex systems,” Bassett says.

Brain Networks and Neurological Disorders

The models of brain networks scientists like Sporns and Bassett develop are not limited to understanding intelligence in the healthy brain. In conditions like Alzheimer’s disease and epilepsy, damage to brain networks can reroute neurological traffic to other major “hubs,” Stam proposes. “The whole hypothesis is that this overload, or this redirection, actually is the cause of a lot of problems,” Stam says. “If it lasts too long, then you get a cascade." Stam studies how this overload can be tracked and used as a biomarker, or measurable indication of disease. Human representations of brain networks are inherently imperfect, but they are constantly improving.

Network Control Theory and Future Directions

Bassett is forging new ideas in the study of network control theory. “You ask, what inputs have to be put into the brain in order to push it in a particular direction in some state space?” Bassett says. And in turn, advances in how we study the brain can feed back into better models of disease. They all contribute to the larger view given by network neuroscience.

“You need problems from neurology, but also hypotheses from neurobiology, and also the mathematical measures and tools, and then it all comes together,” Stam says. “I think it's highly interdisciplinary and integrative, but the funny thing is that it is not about one disease. It's not the trick that you apply to one disease, and then it stops.

Information Processing: A Historical Perspective

The development of the computer in the 1950s and 1960s had an important influence on psychology. The computer gave cognitive psychologists a metaphor, or analogy, to which they could compare human mental processing.

The Information Processing Model of Memory

The information processing model of memory consists of a series of stages, or boxes, representing stages of processing. This information can be used by other parts of the brain relating to mental activities such as memory, perception, and attention. When we selectively attend to one activity, we tend to ignore other stimulation.

Evaluative Points

However, there are a number of evaluative points to bear in mind when studying these models and the information processing approach in general.

1. There is evidence from dual-task experiments that parallel processing is possible.
2. Although these laboratory experiments are easy to interpret, the data may not apply to the real world outside the laboratory.
3. Expectation (top-down processing) often overrides information actually available in the stimulus (bottom-up), which we are, supposedly, attending to. These influences are known as “top-down” or “conceptually-driven” processes.