Valence in overview
Adam: What is emotional valence (as opposed to valence in chemistry)?
Mike: Put simply, emotional valence is how pleasant or unpleasant something is. A somewhat weird fact about our universe is that some conscious experiences do seem to feel better than others.
Adam: What makes things feel the way they do? What makes some things feel better than others?
Mike: This sounds like it should be a simple question, but neuroscience just don’t know. It knows a lot of random facts about what kinds of experiences, and what kinds of brain activation patterns, feel good, and which feel bad, but it doesn’t have anything close to a general theory here.
..the way affective neuroscience talks about this puzzle sometimes sort of covers this mystery up, without solving it.
And the way affective neuroscience talks about this puzzle sometimes sort of covers this mystery up, without solving it. For instance, we know that certain regions of the brain, like the nucleus accumbens and ventral pallidum, seem to be important for pleasure, so we call them “pleasure centers”. But we don’t know what
makes something a pleasure center. We don’t even know how common painkillers like
acetaminophen (paracetamol) work! Which is kind of surprising.
In contrast, the hypothesis about valence I put forth in Principia Qualia would explain pleasure centers and acetaminophen and many other things in a unified, simple way.
Adam: How does the hypothesis about valence work?
Mike: My core hypothesis is that symmetry in the mathematical representation of an experience corresponds to how pleasant or unpleasant that experience is. I see this as an identity relationship which is ‘True with a capital T’, not merely a correlation. (Credit also goes to Andres Gomez Emilsson & Randal Koene for helping explore this idea.)
What makes this hypothesis interesting is that
(1) On a theoretical level, it could unify all existing valence research, from Berridge’s work on hedonic hotspots, to Friston & Seth’s work on predictive coding, to Schmidhuber’s idea of a compression drive;
(2) It could finally explain how the brain’s so-called “pleasure centers” work– they function to tune the brain toward more symmetrical states!
(3) It implies lots and lots of weird, bold, *testable* hypotheses. For instance, we know that painkillers like acetaminophen, and anti-depressants like SSRIs, actually blunt both negative *and* positive affect, but we’ve never figured out how. Perhaps they do so by introducing a certain type of stochastic noise into acute & long-term activity patterns, respectively, which disrupts both symmetry (pleasure) and anti-symmetry (pain).
Adam: What kinds of tests would validate or dis-confirm your hypothesis? How could it be falsified and/or justified by weight of induction?
Mike: So this depends on the details of how activity in the brain generates the mind. But I offer some falsifiable predictions in PQ (Principia Qualia):
- If we control for degree of consciousness, more pleasant brain states should be more compressible;
- Direct, low-power stimulation (TMS) in harmonious patterns (e.g. 2hz+4hz+6hz+8hz…160hz) should feel remarkably more pleasant than stimulation with similar-yet-dissonant patterns (2.01hz+3.99hz+6.15hz…).
Those are some ‘obvious’ ways to test this. But my hypothesis also implies odd things such as that chronic tinnitus (ringing in the ears) should product affective blunting (lessened ability to feel strong valence).
Note: see https://qualiacomputing.com/2017/06/18/quantifying-bliss-talk-summary/ and http://opentheory.net/2018/08/a-future-for-neuroscience/ for a more up-to-date take on this.
Adam: Why is valence research important?
Mike Johnson: Put simply, valence research is important because valence is important. David Chalmers famously coined “The Hard Problem of Consciousness”, or why we’re conscious at all, and “The Easy Problem of Consciousness”, or how the brain processes information. I think valence research should be called “The Important Problem of Consciousness”. When you’re in a conscious moment, the most important thing to you is how pleasant or unpleasant it feels.
That’s the philosophical angle. We can also take the moral perspective, and add up all the human and non-human animal suffering in the world. If we knew what suffering was, we could presumably use this knowledge to more effectively reduce it and make the world a kinder place.
We can also take the economic perspective, and add up all the person-years, capacity to contribute, and quality of life lost to Depression and chronic pain. A good theory of valence should allow us to create much better treatments for these things. And probably make some money while doing it.
Finally, a question I’ve been wondering for a while now is whether having a good theory of qualia could help with AI safety and existential risk. I think it probably can, by helping us see and avoid certain failure-modes.
Adam: How can understanding valence could help make future AIs safer? (How to help define how the AI should approach making us happy?, and in terms of a reinforcement mechanism for AI?)
Mike: Last year, I noted a few ways a better understanding of valence could help make future AIs safer on my blog. I’d point out a few notions in particular though:
- If we understand how to measure valence, we could use this as part of a “sanity check” for AI behavior. If some proposed action would cause lots of suffering, maybe the AI shouldn’t do it.
- Understanding consciousness & valence seem important for treating an AI humanely. We don’t want to inadvertently torture AIs- but how would we know?
- Understanding consciousness & valence seems critically important for “raising the sanity waterline” on metaphysics. Right now, you can ask 10 AGI researchers about what consciousness is, or what has consciousness, or what level of abstraction to define value, and you’ll get at least 10 different answers. This is absolutely a recipe for trouble. But I think this is an avoidable mess if we get serious about understanding this stuff.
Adam: Why the information theoretical approach?
Mike: The way I would put it, there are two kinds of knowledge about valence: (1) how pain & pleasure work in the human brain, and (2) universal principles which apply to all conscious systems, whether they’re humans, dogs, dinosaurs, aliens, or conscious AIs.
It’s counter-intuitive, but I think these more general principles might be a lot easier to figure out than the human-specific stuff. Brains are complicated, but it could be that the laws of the universe, or regularities, which govern consciousness are pretty simple. That’s certainly been the case when we look at physics. For instance, my iPhone’s processor is super-complicated, but it runs on electricity, which itself actually obeys very simple & elegant laws.
Elsewhere I’ve argued that:
>Anything piped through the complexity of the brain will look complex, regardless of how simple or complex it starts out as. Similarly, anything will look irreducibly complex if we’re looking at it from the wrong level of abstraction.
Adam: What do you think of Thomas A. Bass’s view of ITheory – he thinks that (at least in many cases) it has not been easy to turn data into knowledge. That there is a pathological attraction to information which is making us ‘sick’ – he calls it Information Pathology. If his view offers any useful insights to you concerning avoiding ‘Information Pathology’ – what would they be?
Mike: Right, I would agree with Bass that we’re swimming in neuroscience data, but it’s not magically turning into knowledge. There was a recent paper called “Could a neuroscientist understand a microprocessor?” which asked if the standard suite of neuroscience methods could successfully reverse-engineer the 6502 microprocessor used in the Atari 2600 and NES. This should be easier than reverse-engineering a brain, since it’s a lot smaller and simpler, and since they were analyzing it in software they had all the data they could ever ask for, but it turned out that the methods they were using couldn’t cut it. Which really begs the question of whether these methods can make progress on reverse-engineering actual brains. As the paper puts it, neuroscience thinks it’s data-limited, but it’s actually theory-limited.
The first takeaway from this is that even in the age of “big data” we still need theories, not just data. We still need people trying to guess Nature’s structure and figuring out what data to even gather. Relatedly, I would say that in our age of “Big Science” relatively few people are willing or able to be sufficiently bold to tackle these big questions. Academic promotions & grants don’t particularly reward risk-taking.
Adam: Information Theory frameworks – what is your “Eight Problems” framework and how does it contrast with Giulio Tononi’s Integrated Information Theory (IIT)? How might IIT help address valence in a principled manner? What is lacking IIT – and how does your ‘Eight Problems’ framework address this?
Mike: IIT is great, but it’s incomplete. I think of it as *half* a theory of consciousness. My “Eight Problems for a new science of consciousness” framework describes what a “full stack” approach would look like, what IIT will have to do in order to become a full theory.
The biggest two problems IIT faces is that (1) it’s not compatible with physics, so we can’t actually apply it to any real physical systems, and (2) it says almost nothing about what its output means. Both of these are big problems! But IIT is also the best and only game in town in terms of quantitative theories of consciousness.
Principia Qualia aims to help fix IIT, and also to build a bridge between IIT and valence research. If IIT is right, and we can quantify conscious experiences, then how pleasant or unpleasant this experience is should be encoded into its corresponding mathematical object.
Adam: What are the three principles for a mathematical derivation of valence?
Mike: First, a few words about the larger context. Probably the most important question in consciousness research is whether consciousness is real, like an electromagnetic field is real, or an inherently complex, irreducible linguistic artifact, like “justice” or “life”. If consciousness is real, then there’s interesting stuff to discover about it, like there was interesting stuff to discover about quantum mechanics and gravity. But if consciousness isn’t real, then any attempt to ‘discover’ knowledge about it will fail, just like attempts to draw a crisp definition for ‘life’ (elan vital) failed.
If consciousness is real, then there’s a hidden cache of predictive knowledge waiting to be discovered. If consciousness isn’t real, then the harder we try to find patterns, the more elusive they’ll be- basically, we’ll just be talking in circles. David Chalmers refers to a similar distinction with his “Type-A vs Type-B Materialism”.
I’m a strong believer in consciousness realism, as are my research collaborators. The cool thing here is, if we assume that consciousness is real, a lot of things follow from this– like my “Eight Problems” framework. Throw in a couple more fairly modest assumptions, and we can start building a real science of qualia.
Anyway, the formal principles are the following:
- Consciousness can be quantified. (More formally, that for any conscious experience, there exists a mathematical object isomorphic to it.)
- There is some order, some rhyme & reason & elegance, to consciousness. (More formally, the state space of consciousness has a rich set of mathematical structures.)
- Valence is real. (More formally, valence is an ordered property of conscious systems.)
Basically, they combine to say: this thing we call ‘valence’ could have a relatively simple mathematical representation. Figuring out valence might not take an AGI several million years. Instead, it could be almost embarrassingly easy.
Adam: Does Qualia Structuralism, Valence Structuralism and Valence Realism relate to the philosophy of physics principles of realism and structuralism? If so, is there an equivalent ontic Qualia Structuralism and Valence Structuralism?….
Mike: “Structuralism” is many things to many contexts. I use it in a specifically mathematical way, to denote that the state space of qualia quite likely embodies many mathematical structures, or properties (such as being a metric space).
Re: your question about ontics, I tend to take the empirical route and evaluate claims based on their predictions whenever possible. I don’t think predictions change if we assume realism vs structuralism in physics, so maybe it doesn’t matter. But I can get back to you on this. 🙂
Adam: What about the Qualia Research Institute I’ve also recently heard about :D! It seems both you (Mike) and Andrés Gómez Emilson are doing some interesting work there
Mike: We know very little about consciousness. This is a problem, for various and increasing reasons– it’s upstream of a lot of futurist-related topics.
But nobody seems to know quite where to start unraveling this mystery. The way we talk about consciousness is stuck in “alchemy mode”– we catch glimpses of interesting patterns, but it’s unclear how to systematize this into a unified framework. How to turn ‘consciousness alchemy’ into ‘consciousness chemistry’, so to speak.
Qualia Research Institute is a research collective which is working on building a new “science of qualia”. Basically, we think our “full-stack” approach cuts through all the confusion around this topic and can generate hypotheses which are novel, falsifiable, and useful.
Right now, we’re small (myself, Andres, and a few others behind the scenes) but I’m proud of what we’ve accomplished so far, and we’ve got more exciting things in the pipeline. 🙂
Mike Johnson is a philosopher living in the Bay Area, writing about mind, complexity theory, and formalization. He is Co-founder of the Qualia Research Institute. Much of Mike’s research and writings can be found at the Open Theory website.
‘Principia Qualia’ is Mike’s magnum opus – a blueprint for building a new Science of Qualia. Click here for the full version, or here for an executive summary.
If you like Mike’s work, consider helping fund it at Patreon.
Joscha Bach, Ph.D. is an AI researcher who worked and published about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He earned his Ph.D. in cognitive science from the University of Osnabrück, Germany, and has built computational models of motivated decision making, perception, categorization, and concept-formation. He is especially interested in the philosophy of AI and in the augmentation of the human mind.
Joscha Bach, Ph.D. is an AI researcher who worked and published about cognitive architectures, mental representation, emotion, social modeling, and multi-agent systems. He earned his Ph.D. in cognitive science from the University of Osnabrück, Germany, and has built computational models of motivated decision making, perception, categorization, and concept-formation. He is especially interested in the philosophy of AI and in the augmentation of the human mind.