June 1, 2017
In his 1890 opus, The
Principles of Psychology, William James invoked Romeo and Juliet to
illustrate what makes conscious beings so different from the particles that
make them up.
“Romeo wants Juliet as the filings want the magnet;
and if no obstacles intervene he moves towards her by as straight a line as
they,” James wrote. “But Romeo and Juliet, if a wall be built between them, do
not remain idiotically pressing their faces against its opposite sides like the
magnet and the filings. … Romeo soon finds a circuitous way, by scaling the
wall or otherwise, of touching Juliet’s lips directly.”
Erik Hoel, a 29-year-old theoretical
neuroscientist and writer, quoted the passage in a
recent essay in which he laid out his new mathematical
explanation of how consciousness and agency arise. The existence of agents —
beings with intentions and goal-oriented behavior — has long seemed profoundly
at odds with the reductionist assumption that all behavior arises from
mechanistic interactions between particles. Agency doesn’t exist among the
atoms, and so reductionism suggests agents don’t exist at all: that Romeo’s
desires and psychological states are not the real causes of his actions, but
merely approximate the unknowably complicated causes and effects between the
atoms in his brain and surroundings.
Hoel’s theory, called “causal emergence,” roundly
rejects this reductionist assumption.
“Causal emergence is a way of claiming that your
agent description is really real,” said Hoel, a postdoctoral researcher at
Columbia University who first proposed the idea with Larissa
Albantakis and Giulio
Tononi of the University of Wisconsin, Madison. “If you just say
something like, ‘Oh, my atoms made me do it’ — well, that might not be true. And it might be provably not true.”
Erik Hoel, a theoretical neuroscientist at Columbia University.
Julia Buntaine
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Using the mathematical
language of information theory, Hoel and his collaborators claim to show that
new causes — things that produce effects — can emerge at macroscopic scales.
They say coarse-grained macroscopic states of a physical system (such as the
psychological state of a brain) can have more causal power over the system’s
future than a more detailed, fine-grained description of the system possibly
could. Macroscopic states, such as desires or beliefs, “are not just
shorthand for the real causes,” explained Simon
DeDeo, an information theorist and cognitive scientist at Carnegie Mellon
University and the Santa Fe Institute who is not involved in the work, “but
it’s actually a description of the real causes, and a more fine-grained
description would actually miss those causes.”
“To me, that seems like the right way to talk about
it,” DeDeo said, “because we do want to attribute causal properties to
higher-order events [and] things like mental states.”
Hoel and collaborators have been developing the
mathematics behind their idea since 2013. In a
May paper in the journal Entropy, Hoel placed causal emergence
on a firmer theoretical footing by showing that macro scales gain causal power
in exactly the same way, mathematically, that error-correcting codes increase the amount
of information that can be sent over information channels. Just as codes reduce
noise (and thus uncertainty) in transmitted data — Claude Shannon’s 1948
insight that formed the bedrock of information theory — Hoel claims that macro
states also reduce noise and uncertainty in a system’s causal structure,
strengthening causal relationships and making the system’s behavior more deterministic.
“I think it’s very significant,” George
Ellis, a South African cosmologist who has also written
about top-down causation in nature, said of Hoel’s new paper. Ellis
thinks causal emergence could account for many emergent phenomena such as superconductivityand topological phases of
matter. Collective systems like bird flocks and superorganisms — and even
simple structures like crystals and waves — might also exhibit causal
emergence, researchers said.
The work on causal emergence is not yet widely
known among physicists, who for centuries have taken a reductionist view of
nature and largely avoided further philosophical thinking on the matter. But at
the interfaces between physics, biology, information theory and philosophy,
where puzzles crop up, the new ideas have generated excitement. Their ultimate
usefulness in explaining the world and its mysteries — including consciousness,
other kinds of emergence, and the relationships between the micro and macro
levels of reality — will come down to whether Hoel has nailed the notoriously
tricky notion of causation: Namely, what’s a cause? “If you brought 20
practicing scientists into a room and asked what causation was, they would all
disagree,” DeDeo said. “We get mixed up about it.”
A Theory of Cause
In a fatal drunk driving accident, what’s the cause
of death? Doctors name a ruptured organ, while a psychologist blames impaired
decision-making abilities and a sociologist points to permissive attitudes
toward alcohol. Biologists, chemists and physicists, in turn, see ever more
elemental causes. “Famously, Aristotle had a half-dozen notions of causes,”
DeDeo said. “We as scientists have rejected all of them except things being in
literal contact, touching and pushing.”
The true causes, to a physicist, are the
fundamental forces acting between particles; all effects ripple out from there.
Indeed, these forces, when they can be isolated, appear perfectly deterministic
and reliable — physicists can predict with high precision the outcomes of
particle collisions at the Large Hadron Collider, for instance. In this view,
causes and effects become hard to predict from first principles only when there
are too many variables to track.
It’s a bit of a bold thing
to do to talk about the mathematics of causation.
Simon DeDeo
Furthermore, philosophers have argued that causal
power existing at two scales at once would be twice what the world needs; to
avoid double-counting, the “exclusion argument” says all causal power must
originate at the micro level. But it’s almost always easier to discuss causes
and effects in terms of macroscopic entities. When we look for the cause of a
fatal car crash, or Romeo’s decision to start climbing, “it doesn’t seem right
to go all the way down to microscopic scales of neurons firing,” DeDeo said.
“That’s where Erik [Hoel] is jumping in. It’s a bit of a bold thing to do to
talk about the mathematics of causation.”
Friendly and large-limbed, Hoel grew up reading
books at Jabberwocky, his family’s bookstore in Newburyport, Massachusetts. He
studied creative writing as an undergraduate and planned to become a writer.
(He still writes fiction and has started a
novel.) But he was also drawn to the question of consciousness — what it is,
and why and how we have it — because he saw it as an immature scientific
subject that allowed for creativity. For graduate school, he went to Madison,
Wisconsin, to work with Tononi — the only person at the time, in Hoel’s view,
who had a truly scientific theory of consciousness.
Tononi conceives of consciousness as information:
bits that are encoded not in the states of individual neurons, but in the
complex networking of neurons, which link together in the brain into larger and
larger ensembles. Tononi argues that this special “integrated information”
corresponds to the unified, integrated state that we experience as subjective
awareness. Integrated information theory has gained prominence in the last few
years, even as debates have ensued about whether it is an accurate and
sufficient proxy for consciousness. But when Hoel first got to Madison in 2010,
only the two of them were working on it there.
Giulio Tononi, a neuroscientist and psychiatrist at the University of Wisconsin, Madison, best known for his research on sleep and consciousness. John Maniaci/UW Health
Tononi tasked Hoel with exploring the general
mathematical relationship between scales and information. The scientists later
focused on how the amount of integrated information in a neural network changes
as you move up the hierarchy of spatiotemporal scales, looking at links between
larger and larger groups of neurons. They hoped to figure out which ensemble
size might be associated with maximum integrated information — and thus,
possibly, with conscious thoughts and decisions. Hoel taught himself
information theory and plunged into the philosophical debates around
consciousness, reductionism and causation.
Hoel soon saw that understanding how consciousness
emerges at macro scales would require a way of quantifying the causal power of
brain states. He realized, he said, that “the best measure of causation is in
bits.” He also read the works of the computer scientist and philosopher Judea
Pearl, who developed a logical language for studying causal relationships in
the 1990s called causal calculus. With Albantakis and Tononi, Hoel formalized a
measure of causal power called “effective information,” which indicates how
effectively a particular state influences the future state of a system.
(Effective information can be used to help calculate integrated information,
but it is simpler and more general and, as a measure of causal power, does not
rely on Tononi’s other ideas about consciousness.)
The researchers showed that in simple models of
neural networks, the amount of effective information increases as you
coarse-grain over the neurons in the network — that is, treat groups of them as
single units. The possible states of these interlinked units form a causal structure,
where transitions between states can be mathematically modeled using so-called
Markov chains. At a certain macroscopic scale, effective information peaks:
This is the scale at which states of the system have the most causal power,
predicting future states in the most reliable, effective manner. Coarse-grain
further, and you start to lose important details about the system’s causal
structure. Tononi and colleagues hypothesize that the scale of peak causation
should correspond, in the brain, to the scale of conscious decisions; based on
brain imaging studies, Albantakis guesses that this might happen at the scale
of neuronal microcolumns, which consist of around 100 neurons.
Causation is what you need
to give structure to the universe.
...Larissa Albantakis, a theoretical neuroscientist at the University of Wisconsin, Madison. Sophia Loschky
Causal emergence is possible, Hoel explained,
because of the randomness and redundancy that plagues the base scale of
neurons. As a simple example, he said to imagine a network consisting of two
groups of 10 neurons each. Each neuron in group A is linked to several neurons
in group B, and when a neuron in group A fires, it usually causes one of the B
neurons to fire as well. Exactly which linked neuron fires is unpredictable.
If, say, the state of group A is {1,0,0,1,1,1,0,1,1,0}, where 1s and 0s represent
neurons that do and don’t fire, respectively, the resulting state of group B
can have myriad possible combinations of 1s and 0s. On average, six neurons in
group B will fire, but which six is nearly random; the micro state is
hopelessly indeterministic. Now, imagine that we coarse-grain over the system,
so that this time, we group all the A neurons together and simply count the
total number that fire. The state of group A is {6}. This state is highly
likely to lead to the state of group B also being {6}. The macro state is more
reliable and effective; calculations show it has more effective information.
A real-world example cements the point. “Our life
is very noisy,” Hoel said. “If you just give me your atomic state, it may be
totally impossible to guess where your future [atomic] state will be in 12
hours. Try running that forward; there’s going to be so much noise, you’d have
no idea. Now give a psychological description, or a physiological one: Where
are you going to be in 12 hours?” he said (it was mid-day). “You’re going to be
asleep — easy. So these higher-level relationships are the things that seem
reliable. That would be a super simple example of causal emergence.”
For any given system, effective information peaks
at the scale with the largest and most reliable causal structure. In addition
to conscious agents, Hoel says this might pick out the natural scales of rocks,
tsunamis, planets and all other objects that we normally notice in the world.
“And the reason why we’re tuned into them evolutionarily [might be] because
they are reliable and effective, but that also means they are causally
emergent,” Hoel said.
Brain-imaging experiments are being planned in
Madison and New York, where Hoel has joined the lab of the Columbia
neuroscientist Rafael Yuste. Both groups will examine
the brains of model organisms to try to home in on the spatiotemporal scales
that have the most causal control over the future. Brain activity at these
scales should most reliably predict future activity. As Hoel put it, “Where
does the causal structure of the brain pop out?” If the data support their
hypothesis, they’ll see the results as evidence of a more general fact of
nature. “Agency or consciousness is where this idea becomes most obvious,” said
William Marshall, a postdoctoral researcher in the Wisconsin group. “But if we
do find that causal emergence is happening, the reductionist assumption would
have to be re-evaluated, and that would have to be applied broadly.”
New Philosophical Thinking
Sara Walker, a physicist and
astrobiologist at Arizona State University who studies the origins of life,
hopes measures like effective information and integrated information will help
define what she sees as the gray scale leading between nonlife and life (with
viruses and cell cycles somewhere in the gray area). Walker has been
collaborating with Tononi’s team on studies of real and artificial cell cycles,
with preliminary indications that integrated information might correlate with
being alive.
RELATED:
In other recent work, the Madison group has
developed a way of measuring causal emergence called “black-boxing” that they
say works well for something like a single neuron. A neuron isn’t simply the
average of its component atoms and so isn’t amenable to coarse-graining. Black-boxing
is like putting a box around a neuron and measuring the box’s overall inputs
and outputs, instead of assuming anything about its inner workings.
“Black-boxing is the truly general form of causal emergence and is especially
important for biological and engineering systems,” Tononi said in an email.
Walker is also a fan of Hoel’s new work tracing
effective information and causal emergence to the foundations of information
theory and Shannon’s noisy-channel theorem. “We’re in such deep conceptual territory
it’s not really clear which direction to go,” she said, “so I think any
bifurcations in this general area are good and constructive.”
Robert Bishop, a philosopher and
physicist at Wheaton College, said, “My take on EI” —effective information —
“is that it can be a useful measure of emergence but likely isn’t the only
one.” Hoel’s measure has the charm of being simple, reflecting only reliability
and the number of causal relationships, but according to Bishop, it could be
one of several proxies for causation that apply in different situations.
Hoel’s ideas do not impress Scott
Aaronson, a theoretical computer scientist at the University of Texas, Austin.
He says causal emergence isn’t radical in its basic premise. After reading
Hoel’s recent essay for the Foundational Questions Institute, “Agent Above,
Atom Below” (the one that featured Romeo and Juliet), Aaronson said, “It was
hard for me to find anything in the essay that the world’s most orthodox
reductionist would disagree with. Yes, of course you want to pass to higher
abstraction layers in order to make predictions, and to tell causal stories
that are predictively useful — and the essay explains some of the reasons why.”
It didn’t seem so obvious to others, given how the
exclusion argument has stymied efforts to get a handle on higher-level
causation. Hoel says his arguments go further than Aaronson acknowledges in
showing that “higher scales have provably more information and causal influence
than their underlying ones. It’s the ‘provably’ part that’s hard and is
directly opposite to most reductionist thinking.”
Moreover, causal emergence isn’t merely a claim
about our descriptions or “causal stories” about the world, as Aaronson
suggests. Hoel and his collaborators aim to show that higher-level causes — as
well as agents and other macroscopic things — ontologically exist. The
distinction relates to one that the philosopher David Chalmers makes about
consciousness: There’s the “easy problem” of how neural circuitry gives rise to
complex behaviors, and the “hard problem,” which asks, essentially, what
distinguishes conscious beings from lifeless automatons. “Is EI measuring
causal power of the kind that we feel that we have in action, the kind that we
want our conscious experiences or selves to have?” said Hedda Hassel Mørch, a
philosopher at New York University and a protégé of Chalmers’. She says it’s
possible that effective information could “track real ontological emergence,
but this requires some new philosophical thinking about the nature of laws,
powers and how they relate.”
The criticism that hits Hoel and Albantakis the
hardest is one physicists sometimes make upon hearing the idea: They assert
that noise, the driving force behind causal emergence, doesn’t really exist;
noise is just what physicists call all the stuff that their models leave out.
“It’s a typical physics point of view,” Albantakis said, that if you knew the
exact microscopic state of the entire universe, “then I can predict what
happens until the end of time, and there is no reason to talk about something
like cause-effect power.”
One rejoinder is that perfect knowledge of the
universe isn’t possible, even in principle. But even if the universe could be
thought of as a single unit evolving autonomously, this picture wouldn’t be
informative. “What is left out there is to identify entities — things that
exist,” Albantakis said. Causation “is really the measure or quantity that is
necessary to identify where in this whole state of the universe do I have
groups of elements that make up entities? … Causation is what you need to give
structure to the universe.” Treating causes as real is a necessary tool for
making sense of the world.
Maybe we sort of knew all along, as Aaronson
contends, that higher scales wrest the controls from lower scales. But if these
scientists are right, then causal emergence might be how that works,
mathematically. “It’s like we cracked the door open,” Hoel said. “And actually
proving that that door is a little bit open is very important. Because anyone
can hand-wave and say, yeah, probably, maybe, and so on. But now you can say,
‘Here’s a system [that has these higher-level causal events]; prove me wrong on
it.’”
Correction: This article
was revised on June 1, 2017, to clarify that Erik Hoel was not Giulio Tononi’s
first collaborator on integrated information theory and that Tononi, Hoel and
Larissa Albantakis formalized, but did not devise, the measure used to assess
causal emergence.
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SOURCE: https://www.quantamagazine.org/a-theory-of-reality-as-more-than-the-sum-of-its-parts-20170601/
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