Catch the ball: shifting from text to visual/motor thinking for complex problems

We are shifting our thinking style to accommodate a much more complex and interdependent world than we have faced in the past. Consider:

• In 2014, Google purchased Deep Mind, formerly a UK-based startup with a focus on “Neural Turing Machines“.       Deep Mind’s unique approach: to use video game play as a learning environment, using reinforcement learning.
• Along similar lines, New York’s Quest to Learn school uses a curriculum based on games as a pedagogical model, with a “dynamic, challenge-based curriculum with content-rich questing to learn at its core.” A core element of the school’s curriculum: learning for complexity and systems thinking.
• Josh Kerbel, Chief Analytic Methodologist at the Defense Intelligence Agency, says that cutting up complex problems into pieces without a necessary re-synthesis phase can “profoundly distort” the government’s understanding of major issues.

• There is substantial growth in mentions of the phrase “unintended consequences” in books, reflecting an understanding that a lack of “connected” thinking is leading to problems:

• In the last two decades, the field of artificial intelligence has experienced a profound shift, from a focus on symbolic processing as the foundation of intelligence, to subsymbolic modalities, as are modeled by neural networks. As deep learning has taken off, in particular, interest in logical reasoning is declining:

• Data visualization is increasing in importance:

The common thread here: a shift from auditory/sequential to visual/spatial thinking. Throw a ball at your friend, and the math he does to catch it happens in a part of the brain also responsible for spatial reasoning, the parietal lobe. The same goes for gamers. The parietal lobe is also in charge of visual orientation, perception, and recognition, and when damaged, can hinder your ability to think about mathematics.

Think about if we could use the motor/visual intelligence we use in simple motor tasks like this one to solve complex global problems, instead of being limited by the buggy “alpha software” of purely linguistic analysis.

In particular, language processing is generally accepted to happen primarily in Broca’s area, which is smaller and evolutionarily newer.   Indeed, many of the systematic cognitive biases (ways in which humans think irrationally) can be attributed to using linguistic thinking where another modality would be superior. Broca’s, then is small, “alpha software”, and still has a few bugs.

The bottom line:

From text to pictures.  From pictures to data visualization. From data visualization to interactive decision/systems exploration.

We are in the midst of an important shift.  The sooner, the better.