The adult cortex and cerebellum combined have on the order of 1e15 bits of synaptic capacity, compared to about 1e9 bits of genome capacity (only a tiny fraction of which can functionally code for brain wiring). Thus it is simply a physical fact that most cognitive complexity is learned, not innate—and the overwhelming weight of evidence from neuroscience supports the view that the human brain is a general learning machine. The genome mostly specifies the high level intra and inter module level connectivity patterns and motiffs, along with a library of important innate circuits, mostly in the brainstem.
The comparison between a modern deep learning system is especially relevant, because deep learning is reverse engineering the brain and modern DL systems are increasingly accurate - in some cases near perfect—models of neural activity.
The brainstem—which contains most of the complexity—is equivalent to the underlying operating system and software libraries, the basal ganglia and thalamus are equivalent to the control and routing code running on the CPU, and the cortex/cerebellum which implement the bulk of the learned computation are the equivalent of the various large matrix layers running on the GPU. A DL system has some module level innate connectivity specified by a few thousand to hundred thousand lines of code which defines the implicit initial connectivity between all neurons, equivalent to the genetically determined innate modular subdivisions and functional connectivity between different cortical/cerebellar/thalamar/BG circuit modules.
This is the framework in which all and any evidence from behavior genetics, psychology, etc must fit within.
Jacob—thanks for your comment. It offers an interesting hypothesis about some analogies between human brain systems and computer stuff.
Obviously, there’s not enough information in the human genome to specify every detail of every synaptic connection. Nobody is claiming that the genome codes for that level of detail. Just as nobody would claim that the genome specifies every position for every cell in a human heart, spine, liver, or lymphatic system.
I would strongly dispute that it’s the job of ‘behavior genetics, psychology, etc’ to fit their evidence into your framework. On the contrary, if your framework can’t handle the evidence for the heritability of every psychological trait ever studied that shows reliably measurable individual differences, then that’s a problem for your framework.
I will read your essay in more detail, but I don’t want to comment further until I do, so I’m sure that I understand your reasoning.
I would strongly dispute that it’s the job of ‘behavior genetics, psychology, etc’ to fit their evidence into your framework.
Not my framework, but that of modern neuroscience. Just as biology is constrained by chemistry which is constrained by physics.
I will read your essay in more detail, but I don’t want to comment further until I do, so I’m sure that I understand your reasoning.
I just reskimmed it and it’s not obviously out of date and still is a pretty good overview of the modern view of the brain from systems neuroscience which is mostly tabula rasa.
The whole debate of inherited traits is somewhat arbitrary based on what traits you consider.
Is the use of two spaces after a period an inheritable trait? Belief in marxism?
Genetics can only determine the high level hyperparameters and low frequency wiring of the brain—but that still allows a great deal to be inheritable, especially when one considers indirect correlations (eg basketball and height, curiosity drive and educational attainment, etc).
Jacob—I read your 2015 essay. It is interesting and makes some fruitful points.
I am puzzled, though, about when nervous systems are supposed to have evolved this ‘Universal Learning Machine’ (ULM) capability. Did ULMs emerge with the transition from invertebrates to vertebrates? From rat-like mammals to social primates? From apes with 400 cc brains to early humans with 1100 cc brains?
Presumably bumblebees (1 million neurons) don’t have ULM capabilities, but humans (80 billion neurons) allegedly do. Where is the threshold between them—given that bumblebees already have plenty of reinforcement learning capabilities?
I’m also puzzled about how the ULM perspective can accommodate individual differences, sex differences, mental disorders, hormonal influences on cognition and motivation, and all the other nitty-gritty wetware details that seem to get abstracted away.
For example, take sex differences in cognitive abilities, such as average male superiority on mental rotation tasks and female superiority on verbal fluency—are you really arguing that men and women have identical ULM capabilities in their neocortexes that are simply shaped differently by their information inputs? And it just happens that these can be influenced by manipulating sex hormone levels?
Or, take the fact that some people start developing symptoms of schizophrenia—such as paranoid thoughts and auditory hallucinations—in their mid-20s. Sometimes this dramatic change in neocortical activity is triggered by over-use of amphetamines; sometimes it’s triggered by a relationship breakup; often it reflects a heritable family propensity towards schizotypy. Would you characterize the onset of schizophrenia as just the ULM getting some bad information inputs?
I am puzzled, though, about when nervous systems are supposed to have evolved this ‘Universal Learning Machine’ (ULM) capability.
The core architecture of brainstem, basal ganglia, thalamus and pallium/cortex is at least 500 million years old.
Where is the threshold between them
You are looking for some binary threshold which simply does not exist, the dominance of intra over inter lifetime learning is continuous and depends on brain size * lifespan or cumulative optimization power.
Likewise one could ask: What is the threshold between between Alexnet and VIT L/14@336px?
are you really arguing that men and women have identical ULM capabilities in their neocortexes that are simply shaped differently by their information inputs?
What would make you suspect I would argue that?
From the ULM post, the core hypothesis:
The universal learning hypothesis proposes that all significant mental algorithms are learned; nothing is innate except for the learning and reward machinery itself (which is somewhat complicated, involving a number of systems and mechanisms), the initial rough architecture (equivalent to a prior over mindspace), and a small library of simple innate circuits (analogous to the operating system layer in a computer). In this view the mind (software) is distinct from the brain (hardware). The mind is a complex software system built out of a general learning mechanism.
Significant mental algorithms are things like adding numbers, forming sentences, recognizing attractive vs unattractive mates, bipedal walking, courtship strategies, etc—essentially almost everything that infants can’t do at birth, which is nearly everything for humans.
Both the hardware and initial rough architecture—the architectural prior—are innate, which is where you see the genetic differences between individuals, families, races, sexes, species, etc.
And it just happens that these can be influenced by manipulating sex hormone levels?
“it just happens” is massively underselling how much effect the sex hormones have on which genetics activate, isn’t it? somewhere on here someone did an analysis of how genetically-defined mouse neurons train the recognition of mouse squeaks, or something like that, which would be a wonderful bridge between your field and practical brain understanding.
(slightly off topic question I’ve been wanting to ask—are you familiar with evolutionary game theory?)
My phrasing was slightly tongue-in-cheek; I agree that sex hormones, hormone receptors in the brain, and the genomic regulatory elements that they activate, have pervasive effects on brain development and psychological sex differences.
Off topic: yes, I’m familiar with evolutionary game theory; I was senior research fellow in an evolutionary game theory center at University College London 1996 − 2000, and game theory strongly influenced my thinking about sexual selection and social signaling.
The adult cortex and cerebellum combined have on the order of 1e15 bits of synaptic capacity, compared to about 1e9 bits of genome capacity (only a tiny fraction of which can functionally code for brain wiring). Thus it is simply a physical fact that most cognitive complexity is learned, not innate—and the overwhelming weight of evidence from neuroscience supports the view that the human brain is a general learning machine. The genome mostly specifies the high level intra and inter module level connectivity patterns and motiffs, along with a library of important innate circuits, mostly in the brainstem.
The comparison between a modern deep learning system is especially relevant, because deep learning is reverse engineering the brain and modern DL systems are increasingly accurate - in some cases near perfect—models of neural activity.
The brainstem—which contains most of the complexity—is equivalent to the underlying operating system and software libraries, the basal ganglia and thalamus are equivalent to the control and routing code running on the CPU, and the cortex/cerebellum which implement the bulk of the learned computation are the equivalent of the various large matrix layers running on the GPU. A DL system has some module level innate connectivity specified by a few thousand to hundred thousand lines of code which defines the implicit initial connectivity between all neurons, equivalent to the genetically determined innate modular subdivisions and functional connectivity between different cortical/cerebellar/thalamar/BG circuit modules.
This is the framework in which all and any evidence from behavior genetics, psychology, etc must fit within.
Jacob—thanks for your comment. It offers an interesting hypothesis about some analogies between human brain systems and computer stuff.
Obviously, there’s not enough information in the human genome to specify every detail of every synaptic connection. Nobody is claiming that the genome codes for that level of detail. Just as nobody would claim that the genome specifies every position for every cell in a human heart, spine, liver, or lymphatic system.
I would strongly dispute that it’s the job of ‘behavior genetics, psychology, etc’ to fit their evidence into your framework. On the contrary, if your framework can’t handle the evidence for the heritability of every psychological trait ever studied that shows reliably measurable individual differences, then that’s a problem for your framework.
I will read your essay in more detail, but I don’t want to comment further until I do, so I’m sure that I understand your reasoning.
Not my framework, but that of modern neuroscience. Just as biology is constrained by chemistry which is constrained by physics.
I just reskimmed it and it’s not obviously out of date and still is a pretty good overview of the modern view of the brain from systems neuroscience which is mostly tabula rasa.
The whole debate of inherited traits is somewhat arbitrary based on what traits you consider.
Is the use of two spaces after a period an inheritable trait? Belief in marxism?
Genetics can only determine the high level hyperparameters and low frequency wiring of the brain—but that still allows a great deal to be inheritable, especially when one considers indirect correlations (eg basketball and height, curiosity drive and educational attainment, etc).
Jacob—I read your 2015 essay. It is interesting and makes some fruitful points.
I am puzzled, though, about when nervous systems are supposed to have evolved this ‘Universal Learning Machine’ (ULM) capability. Did ULMs emerge with the transition from invertebrates to vertebrates? From rat-like mammals to social primates? From apes with 400 cc brains to early humans with 1100 cc brains?
Presumably bumblebees (1 million neurons) don’t have ULM capabilities, but humans (80 billion neurons) allegedly do. Where is the threshold between them—given that bumblebees already have plenty of reinforcement learning capabilities?
I’m also puzzled about how the ULM perspective can accommodate individual differences, sex differences, mental disorders, hormonal influences on cognition and motivation, and all the other nitty-gritty wetware details that seem to get abstracted away.
For example, take sex differences in cognitive abilities, such as average male superiority on mental rotation tasks and female superiority on verbal fluency—are you really arguing that men and women have identical ULM capabilities in their neocortexes that are simply shaped differently by their information inputs? And it just happens that these can be influenced by manipulating sex hormone levels?
Or, take the fact that some people start developing symptoms of schizophrenia—such as paranoid thoughts and auditory hallucinations—in their mid-20s. Sometimes this dramatic change in neocortical activity is triggered by over-use of amphetamines; sometimes it’s triggered by a relationship breakup; often it reflects a heritable family propensity towards schizotypy. Would you characterize the onset of schizophrenia as just the ULM getting some bad information inputs?
The core architecture of brainstem, basal ganglia, thalamus and pallium/cortex is at least 500 million years old.
You are looking for some binary threshold which simply does not exist, the dominance of intra over inter lifetime learning is continuous and depends on brain size * lifespan or cumulative optimization power.
Likewise one could ask: What is the threshold between between Alexnet and VIT L/14@336px?
What would make you suspect I would argue that?
From the ULM post, the core hypothesis:
Significant mental algorithms are things like adding numbers, forming sentences, recognizing attractive vs unattractive mates, bipedal walking, courtship strategies, etc—essentially almost everything that infants can’t do at birth, which is nearly everything for humans.
Both the hardware and initial rough architecture—the architectural prior—are innate, which is where you see the genetic differences between individuals, families, races, sexes, species, etc.
“it just happens” is massively underselling how much effect the sex hormones have on which genetics activate, isn’t it? somewhere on here someone did an analysis of how genetically-defined mouse neurons train the recognition of mouse squeaks, or something like that, which would be a wonderful bridge between your field and practical brain understanding.
(slightly off topic question I’ve been wanting to ask—are you familiar with evolutionary game theory?)
My phrasing was slightly tongue-in-cheek; I agree that sex hormones, hormone receptors in the brain, and the genomic regulatory elements that they activate, have pervasive effects on brain development and psychological sex differences.
Off topic: yes, I’m familiar with evolutionary game theory; I was senior research fellow in an evolutionary game theory center at University College London 1996 − 2000, and game theory strongly influenced my thinking about sexual selection and social signaling.