Neuro-Nonsense
The Folly in Trying to Find the Location of Behavior
A Biological Basis for Learning and Memory
Neuroscience is the science that tries to understand the nervous system; that highly complex system of neurons and signals permeating the human body. This includes the brain, spinal cord, and an intricate network of nerves. The nervous system is the major control system our bodies use to coordinate our physical and behavioural attributes. By transforming sensory information into usable signals, nervous systems enable bodies to respond to changes in their environment.
Neuroscience looks to benefit from advances in everything from molecular biology to computational science, motivating the effort to explain human behaviors in terms of how the brain is structured. Neuroscience inspects how our brains have evolved physically, hoping to demonstrate how evolved structure leads to both normal and pathological behaviors.
Neuroscience is touted as our window into the biological basis of learning and memory. It looks to bring a kind of rigor to our understanding of brains; something more tangible than the etherial models of psychology. There is an apparent concreteness to the explanations of neuroscience because they rest on physicality.
Neuroscience has a number of tools at its disposal. Researchers can investigate patterns of neuronal connectivity using molecular biology and genetics to track changes, attempting to correlate those changes to various biological functions.
Beyond the usual quest to understand nature, neuroscience may also contribute to the treatment and diagnosis of brain disorders. A better understanding of the brain could shed light on things that affect our lives. Neuroscience is one of those research areas that appears worthwhile for both scientific and ethical reasons.
Where Differences Come From
People can't help but be interested in what makes us unique. We are bombarded with messages of who comes from where, and how cultures do things differently. On its face, there isn't much wrong with this. After all, differences make us uncommon and interesting. There also seems to be some utility here. Understanding what makes us different might lead to a better society, since knowledge about why people are different could lead to useful interventions.
But understanding what actually makes us different is difficult. First, so-called differences are ultimately just mental labels. Nature is under no obligation to adhere to the demarcations we create in our minds. We envision borders between things to make it easier to comprehend and navigate our world. Second whatever actual differences exist become increasing difficult to ascertain under complexity. While it’s easy to notice when things look different, this isn’t the same as knowing how structure leads to function. The former is interesting but not necessarily consequential. The latter requires uncovering causes, connecting the appearance of something to the behaviors we observe. This is the notion of locality, where we attempt to identify the specific regions responsible for something we observe more generally.
Neuroscience makes its way into these “difference discussions” because many of the discrepancies in people’s behavior must stem ultimately from our brains. If we could find structural differences in the brain then these might explain some of the behavioural differences we see across individuals. In extreme cases, like patients with epilepsy, such identification might dramatically improve someone’s quality of life, since to know a cause is to conceive of an action that might change outcomes.
One way neuroscience chases local causes is by looking for structural differences using functional magnetic resonance imaging (fMRI). fMRI scans reveal areas of the brain that light up when people experience emotions or conduct some mental process. Fear, anticipation, happiness and reasoning are all mapped in the brain by correlating these behaviors to specific places in the brain.
When causal explanations are given they present science with targets for future intervention. If these targets are correct then treatments can target the problem area and potentially improve outcomes. But if the targets are wrong then at best they waste people’s time and money, and at worst they heavily discriminate against, or even damage, parts of the population for bogus reasons.
The Myth of Root Causes
Finding root causes defines much of science, since scientists are largely concerned with uncovering the “why” behind what we observe. Explanations are the grand purpose of today’s science. With an explanation in hand we have a theory about how something supposedly works.
But while peeling back the layers of various phenomena can show us its parts, such reverse engineering cannot tell us how nontrivial outputs are arrived at. Structural differences may play a role, but there is no way to know what that role is, because the notion of “role” is misplaced under complexity. A role relates to an individual thing’s function. It is some specific activity intended towards a specific goal. But that’s not how complexity works. Specific things don’t produce specific outputs, because the outputs we observe and measure in complex situations are arrived at in aggregate.
It is intellectually dishonest to conduct science under the premise that root causes can be uncovered under complexity. Chasing the root cause of a complex situation will always look like a wild goose chase because there’s nothing to chase. It is not a matter of peeling back more layers, it’s a matter of reasoning under an incorrect paradigm. Pieces interact in fantastically intricate ways to produce what we observe in complex phenomena. Outputs are achieved in a multiplicative fashion, not some simplistic sum of inputs.
If we look at the history of neuroscience’s attempt to attribute observed behaviors to specific regions of the brain we see the unsurprising jumping-around of observations. One study points to memory in the hippocampus, only to be revised when some memories are “found” to reside outside this region. Extraction of some piece of the brain stops seizures, but also renders other cognitive abilities lifeless. Every attempt to isolate the cause is met with some other region of the brain that was critically involved in achieving the overall function.
The correct scientific stance on anything complex is that what we observe is arrived at via the entire system. While such truth goes against much of the convenient reductionism at the heart of today’s science, it’s still truth. Complex systems call upon all the pieces of a system in order to produce what we observe. It is a concerted effort. Structural differences in the brain, as with any physical system, undoubtedly produce different information. But those differences are used to achieve what’s needed in aggregate, not to play a specific role. The human need to create mental demarcations can corrupt any science that looks to understand complexity if such misplaced concreteness is taken too literally.
Nature Doesn’t Localize Behavior
A common way to model complex systems is by using networks. Networks are just a bunch of nodes (vertices) connected by lines (edges). Networks can be analyzed for their behavior and properties, providing insights into complex systems. The human brain can be modeled as a complex network since the overall structure of any brain is a network of neurons connected by synapses, with neurotransmitters moving across these bridges.
A common behavior seen in complex networks are power law relationships. This is when a quantity varies as a power of another. Think of doubling the length of a side of a square; the area ends up being multiplied by a factor of four. Many real-world phenomena appear to obey power laws, including (unsurprisingly) the activity patterns of neurons. Power laws are characterized by a number of properties. One property is scale invariance, whereby some feature of the object does not change despite altering the scale by which we observe it. Many geological phenomena possess scale invariance. The size distributions of rock fragments, volcanic eruptions and earthquakes all show this property.
Scale invariance is a type of universality. Universality occurs when the properties of a system are independent of the details of the system, leading to many different physical systems exhibiting the same behavior. For example, the behavior of water and CO₂ are almost identical near their boiling points, despite being entirely different substances. Universality upends the notion of locality because universal properties are agnostic to the underling physicality of the system. For something to be locally important means that one part of a system that exists at a specific scale is producing what we observe. But if the properties that matter are agnostic to scale and physicality then attributing overall behavior to a region isn’t reality, it’s fiction.
Take as an example, influencers in social networks. These are individuals whom, for whatever reason, become popular hubs of network activity. Influencers have the most followers, and quite literally influence the overall behavior of the network. When an influencer says something many people will be exposed to that message, and apply more weight to it compared to some random Joe.
But hubs don’t exist without the rest of the population. A hub is a location where we see the most intense activity, but that activity is arrived at via a multi-way street. An influencer is only defined by virtue of being embedded inside a network of countless individuals. Without that context there is no hub. An influencer has nothing to influence without a crowd. But it’s more profound than that. The information that makes an influencer what they are fully depends on the flow of information from the rest of the network. The influencer is not really a concentrated region more causally connected to the behavior of the network; that’s just a convenient way for our minds to think of them. An influencer’s informational output materializes out of the multiplicative interaction between many individuals.
An influencer is not a “target.” It is too interconnected and codependent to be thought of as such. Wiping out a terrorist cell or tyrannical leader will undoubtedly impede terror attacks, but only momentarily. What makes complex networks so resilient is their distributed nature. There are 2 things that happen to complex systems in response to intervention; they either self-heal and adapt, or they collapse. If we collapse a terror network, we’re not merely wiping out terrorist cells, we’re wiping out food distribution, education and potentially an entire economy. If we extract a region of the brain to stop seizures, we’ll be stopping a lot more than just someone’s seizures. There is no such thing as targeting and/or extracting some isolated portion of a network and deterministically controlling the outcome.
It is only the human need to create contrived labels that we assign a location to what we observe. No different than the false narrative that “geniuses” are responsible for human progress. The appearance of major parts of activity does not equate to the source of behavior we observe. Regions are just names we assign to locations that appear to concentrate activity.
The behaviour we notice depends not on some unique local structure, but instead on the way information works across the entire network. Nature does not localize its behavior because the very purpose of a network is leverage long-range correlations to establish what’s needed.
A Better Neuroscience
There are a lot of good things to study in neuroscience. The brain is obviously a worthwhile test subject, perhaps the most important one. If someone suffers from some extreme condition, such as a debilitating mental illness, then perhaps an intervention is worthwhile.
But research that runs counter to how complex systems function helps nobody. The chasing of fMRI hotspots will forever be in vain because that’s not how complexity works. It is not some error or lack of sophistication in measurement that produces the mercurial firing patterns in the human brain; it’s the natural and fully expected behavior inside a fantastically intricate network.
Trying to find the location of behavior is a remnant of outdated science. Neuroscience needs to detach itself from the reductionism that plagues so much of today’s complex sciences. Neuroscience is not physics. The brain cannot be understood by reverse engineering it down to components with specific roles.
A new generation of neuroscientists need to reset the bogus aspects of their field. They need to re-found their vocation, absent the false premise that complex behavior stems from regions. They need to embrace the fundamental opacity that precludes the notion that specific structures produce specific behaviors, focusing instead on universal properties that all complex systems share.
Neuroscience is just another example of what degrades so much of today’s scientific enterprise. Today’s paradigm can’t help but try to reverse engineer the phenomena we observe. Since the so-called “enlightenment” humanity has oriented its scientific efforts around extraction and isolation, leading to targets and subsequent intervention. It worked well for the simple systems studied in physics and the machines created during the industrial revolution; it does not work for the overwhelming number of phenomena that define our real, complex world.
