Since this came up in our book discussion or Range yesterday, something relevant from this article. It’s interesting how the salience network mediates between and integrates two normally one on, one off networks. And how it is the connections between networks that seems to do the trick akin to the book’s description of how those with range make analogous connections between ideas and domains.
“Three of these distinct brain networks — the default mode, the executive control network and the salience network — have been identified by Dr Beaty and colleagues as being associated with creativity.
“The default mode network is activated when people are relaxed and their mind is wandering to different topics or experiences, associated with remembering past experiences, thinking about possible future experience and daydreaming.
“The executive control network comes into play when you need to pay close attention and focus on something in the environment. It comes online when we have to focus our attention and cognitive resources on more demanding tasks that require us to hone our attention and manage multiple things in our mind at one time, directing the content of our thoughts.
“The salience network plays a significant role in detecting and filtering important — or salient — information. It’s called salience because it helps us to pick up on salient information in the environment or internally. Interestingly, the default mode and the executive control networks don’t typically work together — when one network is activated, the other tends to be deactivated. One thing that we think the salience network might be doing is switching between an idea-generation mode, which is more of a default process, and the idea-evaluation mode, which is more of a control way of thinking. […] More creative people tended to have more network connections.”
Jordan Hall of the Neurohacker Collective on decentralized collective intelligence. Sounds a lot how our group works, our collaborations creating something greater than our individual contributions, even though the latter are part and parcel of the process. What happens when we node thyself.
In his new book, Range: Why Generalists Triumph in a Specialized World,David J. Epstein investigates the significant advantages of generalized cognitive skills for success in a complex world. We’ve heard and read many praises for narrow expertise in both humans and AIs (Watson, Alpha Go, etc.). In both humans and AIs, however, narrow+deep expertise does not translate to adaptiveness when reality presents novel challenges, as it does constantly.
As you ingest this highly readable, non-technical book, please add your observations to the comments below.
Cahill’s response prompted an interview by Medium Neuroscience writer Meghan Daum.
Scientific findings have a way of upsetting apple carts, especially when we consider our oft-demonstrated human capacity to bend science to advantage some power-coveting groups over others.
Valid research amply shows there are real differences in male and female neuroanatomy and functions. Honest science must follow the evidence where it leads. Clearly, any discovered differences cannot be allowed to justify unequal social or economic opportunities or treatment. Cahill compares the situation to genetics. That people differ genetically in a vast number of ways cannot be taken as cause to misstate scientific findings or preclude further learning about genetics.
There are times and circumstances in which certain research approaches must be blocked for humane or other reasons but that is a different argument than denying the findings of a body of research because they are uncomfortable or inconvenient.
If you are familiar with complex systems theorist Dr. Stuart Kauffman’s ideas you know he covers a broad range of disciplines and concepts, many in considerable depth, and with a keen eye for isomorphic and integrative principles. If you peruse some of his writings and other communications, please share with us how you see Kauffman’s ideas informing our focal interests: brain, mind, intelligence (organic and inorganic), and self-aware consciousness.
Do you find Kauffman’s ideas well supported by empirical research? Which are more scientific and which, if any, more philosophical? What intrigues, provokes, or inspires you? Do any of his perspectives or claims help you better orient or understand your own interests in our focal topics?
Following are a few reference links to get the conversation going. Please add your own in the comments to this post. If you are a member and have a lot to say on a related topic, please create a new post, tag it with ‘Stuart Kauffman,’ and create a link to your post in the comments to this post.
Team Human by Douglas Rushkoff investigates the impacts of current and emerging technologies and digital culture on individuals and groups and seeks ways to evade or extract ourselves from their corrosive effects.
After you read the book, please post your thoughts as comments to this post or, if you prefer, as new posts. There are interviews and other resources about the book online. Feel free to recommend in the comments those you find meaningful. Also, the audiobook is available through the Albuquerque Public Library but may have a long wait queue (I’m aiming for a record number of ‘q’s in this sentence).
Please use the tag and/or category ‘Rushkoff’ in your new posts. Use any other tags or categories you want. To access categories and tags while composing a post, click ‘Document’ at the top of the options area on the right side of the editing page.
Any comments you add to this post should inherit the post’s categories and tags. Add any additional ones as you like.
Last, this site includes a book reviews app for registered site members. To use it, log in and select Review under the New menu.
From this piece located at the publications page of the International Computer Science Institute. “Mathematical models help describe reality, but only by ignoring its inherent integrity.” Computers work on binary logic and the world is full of ‘noise.’ Hence computers, and mathematical models for that matter, can only approximate reality by eliminating that noise.
“Can a bunch of bits represent reality exactly, in a way that can be controlled and predicted indefinitely? The answer is no, because nature is inherently chaotic, while a bunch of bits representing a program can never be so, by definition.”
Which leads us to ask: “Are our mathematical models just a desperate, failed attempt to de-noise an otherwise very confusing, extremely blurred reality?”
So yes, math and computers are quite useful as long as we keep the above in mind instead of assuming they reveal reality as it is. And as long as we also search for that noisy humanity in the spaces between binary logic, which will never be revealed by math or computers alone.
“In this episode of Tech Effects, we explore the impact of music on the brain and body. From listening to music to performing it, WIRED’s Peter Rubin looks at how music can change our moods, why we get the chills, and how it can actually change pathways in our brains.”
For me the most interesting part was later in the video (10:20), how when we improvise we shut down the pre-frontal planning part of the brain and ‘just go with the flow,’ which is our most creative and innovation moments. This though does depend on having used the pre-frontal cortex in learning the techniques of music to get them so ingrained in memory that we are then free to play with what we’ve programmed.
Ideally, automation would yield a Star Trek reality of increasing leisure and quality of choice and experience. Why isn’t this our experience? An article on Medium offers insight into why this is not occurring on any significant scale.
Evolved behavioral strategies explained by the prisoner’s dilemma damn the majority of humans to a constant doubling down. We exchange the ‘leisure dividend’ (free time) granted by automation for opportunities to outcompete others.
Apparently, the sort of reciprocal social learning that could lead us to make healthy choices with our leisure opportunities depends on us and our competitors being able to mutually track our outcomes across consecutive iterations of the ‘game’. That ‘traceability’ quickly breaks down with the complexity inherent in vast numbers of competitors. When we conclude that any viable competitor may use her leisure dividend to further optimize her competitive position, rather than to pause to enjoy her life, we tend to do the same. Each assumes the other will sprint ahead and so chooses to sprint ahead. Both forfeit the opportunity to savor the leisure dividend.
The prisoner’s dilemma shows that we (most humans) would rather be in a grueling neck-and-neck race toward an invisible, receding finish line than permit the possibility a competitor may increase her lead.
Any strategy that’s so endemic must have evolutionary roots. Thoughts?