Tag Archives: Brains

Separate Top-Down, Bottom-Up Brain Credit

Recently I decided to learn more about brain structure and organization, especially in humans. As modularity is a key concept in complex systems, a key question is: what organizing principles explain which parts are connected how strongly to which other parts? (Which in brains is closely related to which parts are physically close to which other parts.) Here are some things I’ve learned, most of which are well known, but one of which might be new.

One obvious modularity principle is functional relation: stuff related to achieving similar functions tends to be connected more to each other. For example, stuff dealing with vision tends to be near other stuff dealing with vision. But as large areas of the brain light up when we do most anything, this clearly isn’t the only organizing principle.

A second organizing principle seems clear: collect things at similar levels of abstraction. The rear parts of our brains tend to focus more on small near concrete details while the front parts of our brain tend to focus on big far abstractions. In between, the degree of abstraction tends to change gradually. This organizing principle is also important in recent deep learning methods, and it predicts the effects seen in construal level theory: when we think about one thing at a certain level of abstraction and distance, we tend to think of related things at similar levels of abstraction and distance. After all, it is easier for activity in one brain region to trigger activity in nearby regions. The trend to larger brains, culminating in humans, has been accompanied by a trend toward larger brain regions that focus on abstractions; we humans think more abstractly than do other animals.

A key fact about human brain organization is that the brain is split into two similar but weakly connected hemispheres. This is strange, as usually we’d think that, all else equal, for coordination purposes each brain module wants to be as close as possible to every other module. What organizing principle can explain this split?

There seems to be a lot of disagreement on how best to summarize how the hemispheres differ. Here are two summaries:

The left hemisphere deals with hard facts: abstractions, structure, discipline and rules, time sequences, mathematics, categorizing, logic and rationality and deductive reasoning, knowledge, details, definitions, planning and goals, words (written and spoken and heard), productivity and efficiency, science and technology, stability, extraversion, physical activity, and the right side of the body. … The right hemisphere specializes in … intuition, feelings and sensitivity, emotions, daydreaming and visualizing, creativity (including art and music), color, spatial awareness, first impressions, rhythm, spontaneity and impulsiveness, the physical senses, risk-taking, flexibility and variety, learning by experience, relationships, mysticism, play and sports, introversion, humor, motor skills, the left side of the body, and a holistic way of perception that recognizes patterns and similarities and then synthesizes those elements into new forms. (more)

The [left] is centered around action and is often the driving force behind risky behaviors. This hemisphere heavily relies upon emotional input leading it to make brash and uncalculated decisions. … The [right] … relies primarily on critical thinking and calculations to reach its decisions.[11] As such the conclusions reached by the [right] often result in avoidance of risk taking behaviors and overall inaction. … . In environments of scarcity, … taking risks is the foundational approach to survival. … However, in environments of abundance, as humans have observed, it is far more likely to die to damaging stimuli. … In areas of prosperity, … [right] domination is prevalent. … In areas of scarcity where cold and limited food are concerns [left] domination is prevalent. (more)

After reading a bit, I tentatively summarize the difference as: the right hemisphere tends to work bottom-up, while the left tends to work top-down. (In a certain sense of these terms.) Inference tends to be bottom-up, in that we aggregate many complex details into inferring fewer bigger things. For example, in a visual scene we start from a movie of pixels over time, and search for sets of possible objects and their motions that can make sense of this movie. In contrast, design tends to be top-down, in that to design a path to get us from here to there, we start with an abstract description of our goal, such as the start and end of our path, and then search for concrete details that can achieve that goal.

The right hemisphere tends to watch, mostly looking out to infer danger, while the left tends to initiate action, and thus must design actions. The right has a wide span of attention, watching the world looking out for surprises, most of which are bad, while the left has a narrow focus of attention, which supports taking purposive action, from which it expects good results. So the right hemisphere tends to do bottom-up processing, while the left does top-down processing.

In bottom-up processing, to explain one set of details one must consider many possible sets of abstractions, while in top-down processing, one set of goals gives rise to many possible specific details to achieve those goals. As a result, we should expect bottom-up work to need more resources at high abstraction levels, while top-down work needs more resources at detailed levels. And it fact, this is what we see in brain structure: the right hemisphere has a larger front abstract end, while the left hemisphere has a larger back concrete end. Our brains are “twisted” in this predicted way.

Why would it make sense to separate bottom-up from top-down thinking? A key problem in the design of intelligent systems is that of how to distribute reward or credit. And a common solution to this problem is to create a standard of good in one part of the system, today often called a “cost function” in AI circles, and then reward or credit other parts of the system for getting closer to achieving that standard. In inference, the standard is typically some form of statistical fit: how well a model of the world predicts the data that one sees. In design, the standard is more naturally centered on goals: how well does a plan achieve its goals?

Top-down and bottom-up styles of processing seem to me to use incompatible systems of credit assignment. That is, it seems hard to design a system that simultaneously credits abstract world scenarios for predicting details seen, while also rewarding details chosen for achieving abstract goals. Credit assignment systems work better when they have a single common direction in which credit flows. One can allow multiple design goals at a similar high level of abstraction, as then the design process can give credit for synergy, and search for details that satisfy all the goals. And one can allow multiple sources of detail, like sight and sound, and combine their statistical credit to infer which objects are moving how. But it seems hard to combine the two systems of credit.

And so that is my proposal for a third organizing principle of brains: separate bottom-up from top-down systems of credit assignment. I haven’t heard anyone else say this, though I wouldn’t be surprised if someone has said it before.

Added 1Sep: The main risk of mixing credit directions is creating self-supporting credit cycles not well connected to real needs. This may be why the connections between the two hemispheres are mostly inhibitory, reducing activity.

GD Star Rating
loading...
Tagged as: ,

General Evolvable Brains

Human brains today can do a remarkably wide range of tasks. Our mental capacities seem much more “general” than that of all the artificial systems we’ve ever created. Those who are trying to improve such systems have long wondered: what is the secret of human general intelligence? In this post I want to consider we can learn about this from fact that the brain evolved. How would an evolved brain be general?

A key problem faced by single-celled organisms is how to make all of their materials and processes out of the available sources of energy and materials. They do this mostly via metabolism, which is mostly a set of enzymes that encourage particular reactions converting some materials into others. Together with cell-wall containers to keep those enzymes close to each other. Some organisms are more general than others, in that they can do this key task in a wider range of environments.

Most single-celled organisms use an especially evolvable metabolism design space. That is, their basic overall metabolism system seems especially well-suited to finding innovations and adaptations mostly via blind random search, in a way that avoids getting stuck in local maxima. As I explained in a recent post, natural metabolisms are evolvable in part because they have genotypes that are highly redundant relative to phenotypes: many sets of enzymes can map any given set of inputs into any given set of outputs. And this redundancy requires a substantial overcapacity; the metabolism needs to contain many more enzymes than are strictly needed to create any given mapping.

The main way that such organisms are general is that they have metabolisms with a large library of enzymes. Not just a large library of genes that could code for enzymes if turned on, but an actual large set of enzymes usually created. They make many more enzymes than they actually need in each particular environment where they find themselves. This comes at a great cost; making all those enzymes and driving their reactions doesn’t come cheap.

A relevant analogous toy problem is that of logic gates mapping input signals onto output signals:

[In] a computer logic gate toy problem, … there are four input lines, four output lines, and sixteen binary logic gates between. The genotype specifies the type of each gate and the set of wires connecting all these things, while the phenotype is the mapping between input and output gates. … All mappings between four inputs and four outputs can be produced using only four internal gates; sixteen gates is a factor of four more than needed. But in the case of four gates the set of genotypes is not big enough compared to the set of phenotypes to allow easy evolution. For [evolvable] innovation, sixteen gates is enough, but four gates is not. (more)

Note that evolution doesn’t always use such highly evolvable design spaces. For example, our skeletal structure doesn’t have lots of extra bones sitting around ready to be swapped into new roles in new environments. In such cases, evolution chose not to pay large extra costs for generality and evolvability, because the environment seemed predictable enough to stay close to a good enough design. As a result, innovation and adaptation of skeletal structure is much slower and more painful, and could fail badly in novel enough environments.

Now let’s consider brains. It may be that for some tasks, evolution found such an effective structure that it chose to commit to that structure, betting that its solution was stable and reliable enough across future environments to let it forgoe the big extra costs of more general and evolvable designs. But if we are looking to explain a surprising generality, flexibility, and rapid evolution in human brains, it makes sense to consider the possibility that human brain design took a different path, one more like that of single-celled metabolism.

That is, one straightforward way to design a general evolvable brain is to use a extra large toolbox of mental modules that can be connected together in many different ways. While each tool might be a carefully constructed jewel, the whole set of tools would have less of an overall structure. Like a pile of logical gates that can be connected many ways, or metabolism sub-networks that can be connected together into many networks. In this case, the secret to general evolvable intelligence would be less in the particular tools and more in having an extra large set of tools, plus some simple general ways to search in the space of tool combinations. A tool set so large that the brain can do most tasks in a great many different ways.

Much of the search for brain innovations and adaptations would then be a search in the space of ways to connect these tools together. Some aspects of this search could happen over evolutionary timescales, some could happen over the lifetime of particular brains, and some could happen on the timescale of cultural evolution, once that got started.

On the timescale of an individual brain lifetime, a search for tool combinations would start with brains that are highly connected, and then prune long term connections as particular desired paths between tools are found. As one learned how to do a task better, one would activate smaller brain volumes. When some brain parts were damaged, brains would often be able to find other combinations of the remaining tools to achieve similar functions. Even losing a whole half of a brain might not greatly reduce performance. And these are all in fact common patterns for human brains.

Yes, something important happened early in human history. Some key event changed the growth rate of human abilities, though not immediate ability levels, and it did this without much changing brain modules and structures, which remain quite close to those of other primates. Plausibly, we had finally collected enough hard-wired tools, or refined them well enough, to let us start to reliably copy each others’ behaviors. And that allowed cultural evolution, a much-faster-than-evolutionary search in the space of practices. Such practices included choices of which combinations of brain modules to activate in which contexts.

What can this view say about the future of brains? On ems, it suggests that human brains have a lot of extra capacity. We can probably go far in taking an em that can do a job task and throwing away brain modules not needed for that task. At some point cutting hurts performance too much, but for many job tasks you might cut 50% to 90% before then.

Regarding other artificial intelligence, it suggests that if we still have a lot to learn via substantially random search, with no grand theory to integrate it all, then we’ll have to focus on collecting more better tools. Machines would gradually get better as we collect more tools. There may be thresholds where you need enough tools to do a certain jobs well, and while most tools would make only small contributions, perhaps there are a few bigger tools that matter more. So key thresholds would come from the existence of key jobs, and from the lumpiness of tools. We should expect progress to be relatively continuous, except perhaps due to the discovery of especially  lumpy tools, or to passing thresholds that enable key jobs to be done.

GD Star Rating
loading...
Tagged as: ,

Personality Is Overt

If the human mind is split to parts that manage overt appearances, and parts that manage covert strategies, which parts do you think more control our personalities? Yup, personalities are closer to overt appearances:

By using composite images rendered from three dimensional (3D) scans of women scoring high and low on health and personality dimensions, we aimed to examine the separate contributions of facial shape, skin texture and viewing angle to the detection of these traits, while controlling for crucial posture variables. After controlling for such cues, participants were able to identify Agreeableness, Neuroticism, and Physical Health. … Information allowing accurate personality identification is largely lateralized to the right side of the face. (more)

Chimpanzees, other primates, and humans produce asymmetrical facial expressions with greater [emotional] expression on the left side of the face (right hemisphere of the brain). (more)

In most animals, left brains tend to manage and initiate actions within the current mode, while right brains watch in the background for patterns and reasons to veto current actions and switch modes. In humans, it seems the current-action-sequencer brain half was recruited to focus more on managing overt rule-following language, decisions, and actions, ready to explain away any apparent rule-violations. The less-introspectively-accessible pattern-recognizing background-watcher brain half, in contrast, was apparently recruited to focus on harder-to-testify-on-and-so-more-easily-covert meaning, opinion, and communication, including art and music. (more)

GD Star Rating
loading...
Tagged as: , ,

Whence Better Brains?

The cover story of the July Scientific American is on brain physics. It persuades me that raw brain hardware was more important than I’d thought in our history.  Here is my current best guess on brain history.

Across diverse species we see strong convergence in brain organization, especially conditional on brain size. Species differ more in their brain hardware components, and their energy sources. For example, primates have innovative cell designs allowing higher neuron density. Given access to such cells, primates could afford to evolve bigger brains, and then bigger pair-bond-based social groups.

Humans found a way to use big primate brains to support big-group far-traveling long-life versions which could access richer energy sources, which in turn supported large energy-hungry brains. Humans found a way to use those huge old social brains to support robust accumulation of culture, which is our main advantage over other primates. This was probably supported by only minor changes in brain organization.

While the brains of smarter humans today may use a better set of long term connections, probably most of their advantage comes from using more energy-intensive brain hardware. So it probably wasn’t until our recent cheap energy era that high IQ humans gained large advantages. The tendency 0f smarter humans to choose lower fertility lowers their advantage today.

Many quotes from that article: Continue reading "Whence Better Brains?" »

GD Star Rating
loading...
Tagged as: , , ,

Two-Faced Brains

Although human language allowed egalitarian rules whose uniform enforcement would have greatly reduced the advantages to big-brain conniving, humans had the biggest brains of all to unequally evade such rules. (more)

As with most lying or self-deception, homo hypocritus faces a serious implementation problem: how to keep the lies it tells separate from the “real” beliefs on which it acts. Since brains tend to be liberal with interconnections, there is a real risk of cross-talk between contradictory sets of opinions; lies may infect beliefs, and beliefs may infect lies.

I’ve previously discussed one solution: have the different sets of opinions apply to different topics. For example, hold socially-acceptable opinions on far topics, where the personal consequences of actions tend to be smaller, and keep more realistic opinions on near topics, where such consequences tend to be larger. Yes there’s a risk others may notice that you change opinions without good reason as items move from near to far or far to near, but that may be a relatively small price to pay.

A different solution is to have two distinct processing centers, each highly-connected internally, but with only modest between-center connections. One center would manage a coherent set of lies, while the other managed a coherent set of true beliefs. And in fact real brains have exactly this architecture! Left and right brains are highly connected internally, but only modestly connected to each other. Does the left brain manage a coherent set of overt opinions, while the right brain manages a coherent set of covert opinions? Consider:

  1. In all vertebrates left brains tend to control routine behavior (e.g. feeding) while right brains tend to respond to unusual events (e.g. fight/flight).
  2. Left brains tend to initiate actions, via positive feelings, while right brains tend to inhibit actions, via negative feelings.
  3. Compared to other primates, left vs. right human brains differ a lot more in function.
  4. The left human brain manages language’s literal quotably-overt syntax, vocabulary, and semantics, while the right brain handles language’s less-socially-verifiable tone, accent, metaphor, allegory, and ambiguity.
  5. Split brain patients show that left brains are adept at making up respectable explanations for arbitrary right brain behavior.
  6. Right brains tend to be used more in crafting lies, and they can read subtle emotion clues better.
  7. Left brain damage tends to distort behavior in more obvious and understandable ways.
  8. Left brains emphasize decision-making, fact retrieval, numbers, and careful sequenced acts like throwing, while right brains emphasize art, music, spatial manipulation, and recognizing of shapes, patterns, and faces.

It seems that in most animals, left brains tend to manage and initiate actions within the current mode, while right brains watch in the background for patterns and reasons to veto current actions and switch modes. In humans, it seems the current-action-sequencer brain half was recruited to focus more on managing overt rule-following language, decisions, and actions, ready to explain away any apparent rule-violations. The less-introspectively-accessible pattern-recognizing background-watcher brain half, in contrast, was apparently recruited to focus on harder-to-testify-on-and-so-more-easily-covert meaning, opinion, and communication, including art and music.

I’m not saying that overt vs. covert human beliefs map exactly to human left vs. right brains, any more than socially-useful vs. action-practical beliefs map exactly onto far vs. near beliefs. I’m just suggesting that human brain design took pre-existing animal brain structures, such as near vs. far modes and left vs. right brain splits, and recruited them to the task of managing the uniquely human task of hypocrisy: simultaneously espousing and evading rules. In particular, the left-right brain split become an important tool for minimizing undesirable leakage between the overt rule-following images we present to others, and the cover rule-evading actions and communication which better achieve our real ends.

More quotes:

The left hemisphere is specialized not only for the actual production of speech sounds but also for the imposition of syntactic structure on speech and for much of what is called semantics – comprehension of meaning.  The right hemisphere , on the other hand, doesn’t govern spoken words but seems to be concerned with more subtle aspects of language such as nuances of metaphor, allegory and ambiguity. (Ramachandran, quoted in TMHH p56)

No other [vertebrate] species consistently prefers the same hand for certain skilled actions. … The human brain is distinguished from the brains of the great apes by an extraordinary extent of lateralization of function. (more)

GD Star Rating
loading...
Tagged as: , ,