Category: Cognitive Science

Bereitschaftspotential and the Rehabilitation of Free Will

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The question of whether we, as people, have free will or not is both abstract and occasionally deeply relevant. We certainly act as if we have something like libertarian free will, and we have built entire systems of justice around this idea, where people are responsible for choices they make that result in harms to others. But that may be somewhat illusory for several reasons. First, if we take a hard deterministic view of the universe as a clockwork-like collection of physical interactions, our wills are just a mindless outcome of a calculation of sorts, driven by a wetware calculator with a state completely determined by molecular history. Second, there has been, until very recently, some experimental evidence that our decision-making occurs before we achieve a conscious realization of the decision itself.

But this latter claim appears to be without merit, as reported in this Atlantic article. Instead, what was previously believed to be signals of brain activity that were related to choice (Bereitschaftspotential) may just be associated with general waves of neural activity. The new experimental evidence puts the timing of action in line with conscious awareness of the decision. More experimental work is needed—as always—but the tentative result suggests a more tightly coupled pairing of conscious awareness with decision making.

Indeed, the results of this newer experimental result gets closer to my suggested model of how modular systems combined with perceptual and environmental uncertainty can combine to produce what is effectively free will (or at least a functional model for a compatibilist position). Jettisoning the Chaitin-Kolmogorov complexity part of that argument and just focusing on the minimal requirements for decision making in the face of uncertainty, we know we need a thresholding apparatus that fires various responses given a multivariate statistical topology.… Read the rest

Deep Learning with Quantum Decoherence

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Getting back to metaphors in science, Wojciech Zurek’s so-called Quantum Darwinism is in the news due to a series of experimental tests. In Quantum Darwinism (QD), the collapse of the wave function (more properly the “extinction” of states) is a result of decoherence from environmental entanglement. There is a kind of replication in QD, where pointer states are multiplied, and then a kind of environmental selection as well. There is no variation per se, however, though some might argue that the pointer states imprinted by the environment are variants of the originals. Still, it makes the metaphor a bit thin at the edges, but it is close enough for the core idea to fit most of the floor-plan of Darwinism. Indeed, some champion it as part of a more general model for everything. Even selection among viable multiverse bubbles has a similar feel to it: some survive while others perish.

I’ve been simultaneously studying quantum computing and complexity theories that are getting impressively well developed. Richard Cleve’s An Introduction to Quantum Complexity Theory and John Watrous’s Quantum Computational Complexity are notable in their bridging from traditional computational complexity to this newer world of quantum computing using qubits, wave functions, and even decoherence gates.

Decoherence sucks for quantum computing in general, but there may be a way to make use of it. For instance, an artificial neural network (ANN) also has some interesting Darwinian-like properties to it. The initial weight distribution in an ANN is typically a random real value. This is designed to simulate the relative strength of neural connections. Real neural connections are much more complex than this, doing interesting cyclic behavior, saturating and suppressing based on neurotransmitter availability, and so forth, but assuming just a straightforward pattern of connectivity has allowed for significant progress.… Read the rest

Bullshit, Metaphors, and Political Precision

Mark Davis2 Comments

Given this natural condition of uncertainty in the meaning of words, and their critical role in communication, to say the least, we can certainly expect that as we move away from the sciences towards other areas of human endeavor we have even greater vagueness in trying to express complex ideas. Politics is an easy example. America’s current American president is a babbling bullshitter, to use the explanatory framework of the essay, On Bullshit, and he is easy to characterize as an idiot, like when he conflates Western liberalism with something going on exclusively in modern California.

In this particular case, we have to track down what “liberal” means and meant at various times, then try to suss out how that meaning is working today. At one time, the term was simply expressive of freedom with minimal government interference. Libertarians still carry a version of that meaning forward, but liberalism also came to mean something akin to a political focus on government spending to right perceived economic and social disparities (to achieve “freedom from want and despair,” via FDR). And then it began to be used as a pejorative related to that same focus.

As linguist John McWhorter points out, abstract ideas—and perhaps especially political ones—are so freighted with their pragmatic and historical background that the best we can say is that we are actively working out what a given term means. McWhorter suggests that older terms like “socialist” are impossible to put to work effectively; a newer term like “progressive” is more desirable because it carries less baggage.

An even stronger case is made by George Lakoff where he claims central metaphors that look something like Freudian abstractions govern political perspectives.… Read the rest

Two Points on Penrose, and One On Motivated Reasoning

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Sir Roger Penrose is, without doubt, one of the most interesting polymaths of recent history. Even where I find his ideas fantastical, they are most definitely worth reading and understanding. Sean Carroll’s Mindscape podcast interview with Penrose from early January of this year is a treat.

I’ve previously discussed the Penrose-Hameroff conjectures concerning wave function collapse and their implication of quantum operations in the micro-tubule structure of the brain. I also used the conjecture in a short story. But the core driver for Penrose’s original conjecture, namely that algorithmic processes can’t explain human consciousness, has always been a claim in search of support. Equally difficult is pushing consciousness into the sphere of quantum phenomena that tend to show random, rather than directed, behavior. Randomness doesn’t clearly relate to the “hard problem” of consciousness that is about the experience of being conscious.

But take the idea that since mathematicians can prove things that are blocked by Gödel incompleteness, our brains must be different from Turing machines or collections of them. Our brains are likely messy and not theorem proving machines per se, despite operating according to logico-causal processes. Indeed, throw in an active analog to biological evolution based on variation-and-retention of ideas and insights that might actually have a bit of pseudo-randomness associated with it, and there is no reason to doubt that we are capable of the kind of system transcendence that Penrose is looking for.

Note that this doesn’t in any way impact the other horn of Penrose-Hameroff concerning the measurement problem in quantum theory, but there is no reason to suspect that quantum collapse is necessary for consciousness. It might flow the other way, though, and Penrose has created the Penrose Institute to look experimentally for evidence about these effects.… Read the rest

Theoretical Reorganization

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Sean Carroll of Caltech takes on the philosophy of science in his paper, Beyond Falsifiability: Normal Science in a Multiverse, as part of a larger conversation on modern theoretical physics and experimental methods. Carroll breaks down the problems of Popper’s falsification criterion and arrives at a more pedestrian Bayesian formulation for how to view science. Theories arise, theories get their priors amplified or deflated, that prior support changes due to—often for Carroll—coherence reasons with other theories and considerations and, in the best case, the posterior support improves with better experimental data.

Continuing with the previous posts’ work on expanding Bayes via AIT considerations, the non-continuous changes to a group of scientific theories that arrive with new theories or data require some better model than just adjusting priors. How exactly does coherence play a part in theory formation? If we treat each theory as a binary string that encodes a Turing machine, then the best theory, inductively, is the shortest machine that accepts the data. But we know that there is no machine that can compute that shortest machine, so there needs to be an algorithm that searches through the state space to try to locate the minimal machine. Meanwhile, the data may be varying and the machine may need to incorporate other machines that help improve the coverage of the original machine or are driven by other factors, as Carroll points out:

We use our taste, lessons from experience, and what we know about the rest of physics to help guide us in hopefully productive directions.

The search algorithm is clearly not just brute force in examining every micro variation in the consequences of changing bits in the machine. Instead, large reusable blocks of subroutines get reparameterized or reused with variation.… Read the rest

Free Will and Algorithmic Information Theory (Part II)

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Bad monkey

So we get some mild form of source determinism out of Algorithmic Information Complexity (AIC), but we haven’t addressed the form of free will that deals with moral culpability at all. That free will requires that we, as moral agents, are capable of making choices that have moral consequences. Another way of saying it is that given the same circumstances we could have done otherwise. After all, all we have is a series of if/then statements that must be implemented in wetware and they still respond to known stimuli in deterministic ways. Just responding in model-predictable ways to new stimuli doesn’t amount directly to making choices.

Let’s expand the problem a bit, however. Instead of a lock-and-key recognition of integer “foodstuffs” we have uncertain patterns of foodstuffs and fallible recognition systems. Suddenly we have a probability problem with P(food|n) [or even P(food|q(n)) where q is some perception function] governed by Bayesian statistics. Clearly we expect evolution to optimize towards better models, though we know that all kinds of historical and physical contingencies may derail perfect optimization. Still, if we did have perfect optimization, we know what that would look like for certain types of statistical patterns.

What is an optimal induction machine? AIC and variants have been used to define that machine. First, we have Solomonoff induction from around 1960. But we also have Jorma Rissanen’s Minimum Description Length (MDL) theory from 1978 that casts the problem more in terms of continuous distributions. Variants are available, too, from Minimum Message Length, to Akaike’s Information Criterion (AIC, confusingly again), Bayesian Information Criterion (BIC), and on to Structural Risk Minimization via Vapnik-Chervonenkis learning theory.

All of these theories involve some kind of trade-off between model parameters, the relative complexity of model parameters, and the success of the model on the trained exemplars.… Read the rest

Free Will and Algorithmic Information Theory

Mark Davis1 Comment

I was recently looking for examples of applications of algorithmic information theory, also commonly called algorithmic information complexity (AIC). After all, for a theory to be sound is one thing, but when it is sound and valuable it moves to another level. So, first, let’s review the broad outline of AIC. AIC begins with the problem of randomness, specifically random strings of 0s and 1s. We can readily see that given any sort of encoding in any base, strings of characters can be reduced to a binary sequence. Likewise integers.

Now, AIC states that there are often many Turing machines that could generate a given string and, since we can represent those machines also as a bit sequence, there is at least one machine that has the shortest bit sequence while still producing the target string. In fact, if the shortest machine is as long or a bit longer (given some machine encoding requirements), then the string is said to be AIC random. In other words, no compression of the string is possible.

Moreover, we can generalize this generator machine idea to claim that given some set of strings that represent the data of a given phenomena (let’s say natural occurrences), the smallest generator machine that covers all the data is a “theoretical model” of the data and the underlying phenomena. An interesting outcome of this theory is that it can be shown that there is, in fact, no algorithm (or meta-machine) that can find the smallest generator for any given sequence. This is related to Turing Incompleteness.

In terms of applications, Gregory Chaitin, who is one of the originators of the core ideas of AIC, has proposed that the theory sheds light on questions of meta-mathematics and specifically that it demonstrates that mathematics is a quasi-empirical pursuit capable of producing new methods rather than being idealistically derived from analytic first-principles.… Read the rest

Hypersensitive Conspiracy Disorder

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I was once cornered in a bar in Suva, Fiji by an Indian man who wanted to unburden himself and complain a bit. He was convinced that the United States had orchestrated the coups of 1987 in which the ethnically Fijian-dominated military took control of the country. The theory went like this: ethnic Indians had too much power for the Americans to bear as we were losing Subic Bay as a deep water naval base in the South Pacific. Suva was the best, nearest alternative but the Indians, with their cultural and political ties to New Delhi, were too socialist for the Americans. Hence the easy solution was to replace the elected government with a more pro-American authoritarian regime. Yet another Cold War dirty tricks effort, like Mossaddegh or Allende, far enough away that the American people just shrugged our collective shoulders. My drinking friend’s core evidence was an alleged sighting of Oliver North by someone, sometime, chatting with government officials. Ollie was the 4D chess grandmaster of the late 80s.

It didn’t work out that way, of course, and the coups continued into the 2000s. More amazing still was that the Berlin Wall came down within weeks of that bar meetup and the entire engagement model for world orders slid into a brief decade of deconstruction and confusion. Even the economic dominance of Japan ebbed and dissipated around the same time.

But our collective penchant for conspiracy theories never waned. And with the growth of the internet and then social media, the speed and ease of disseminating fringe and conspiratorial ideas has only increased. In the past week there were a number of news articles about the role of conspiracy theories, from a so-called “QAnon” advocate meeting with Trump to manipulation of the government by Israel’s Black Cube group.… Read the rest

Running, Ancient Roman Science, Arizona Dive Bars, and Lightning Machine Learning

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I just returned from running in Chiricahua National Monument, Sedona, Painted Desert, and Petrified Forest National Park, taking advantage of the late spring before the heat becomes too intense. Even so, though I got to Massai Point in Chiricahua through 90+ degree canyons and had around a liter of water left, I still had to slow down and walk out after running short of liquid nourishment two-thirds down. There is an eerie, uncertain nausea that hits when hydration runs low under high stress. Cliffs and steep ravines take on a wolfish quality. The mind works to control feet against stumbling and the lips get serrated edges of parched skin that bite off without relieving the dryness.

I would remember that days later as I prepped to overnight with a wilderness permit in Petrified Forest only to discover that my Osprey Exos pack frame had somehow been bent, likely due to excessive manhandling by airport checked baggage weeks earlier. I considered my options and drove eighty miles to Flagstaff to replace the pack, then back again.

I arrived in time to join Dr. Richard Carrier in an unexpected dive bar in Holbrook, Arizona as the sunlight turned to amber and a platoon of Navajo pool sharks descended on the place for billiards and beers. I had read that Dr. Carrier would be stopping there and it was convenient to my next excursion, so I picked up signed copies of his new book, The Scientist in the Early Roman Empire, as well as his classic, On the Historicity of Jesus, that remains part of the controversial samizdat of so-called “Jesus mythicism.”

If there is a distinguishing characteristic of OHJ it is the application of Bayesian Theory to the problems of historical method.… Read the rest

Black and Gray Boxes with Autonomous Meta-Cognition

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Vijay Pande of VC Andreessen Horowitz (who passed on my startups twice but, hey, it’s just business!) has a relevant article in New York Times concerning fears of the “black box” of deep learning and related methods: is the lack of explainability and limited capacity for interrogation of the underlying decision making a deal-breaker for applications to critical areas like medical diagnosis or parole decisions? His point is simple, and related to the previous post’s suggestion of the potential limitations of our capacity to truly understand many aspects of human cognition. Even the doctor may only be able to point to a nebulous collection of clinical experiences when it comes to certain observational aspects of their jobs, like in reading images for indicators of cancer. At least the algorithm has been trained on a significantly larger collection of data than the doctor could ever encounter in a professional lifetime.

So the human is almost as much a black box (maybe a gray box?) as the algorithm. One difference that needs to be considered, however, is that the deep learning algorithm might make unexpected errors when confronted with unexpected inputs. The classic example from the early history of artificial neural networks involved a DARPA test of detecting military tanks in photographs. The apocryphal to legendary formulation of the story is that there was a difference in the cloud cover between the tank images and the non-tank images. The end result was that the system performed spectacularly on the training and test data sets but then failed miserably on new data that lacked the cloud cover factor. I recalled this slightly differently recently and substituted film grain for the cloudiness. In any case, it became a discussion point about the limits of data-driven learning that showed how radically incorrect solutions could be created without careful understanding of how the systems work.… Read the rest