“Conscious realism”: a new way to think about reality (or the lack thereof?)


Interesting interview in the Atlantic with cognitive scientist Donald D. Hoffman:

The Case Against Reality

“I call it conscious realism: Objective reality is just conscious agents, just points of view. Interestingly, I can take two conscious agents and have them interact, and the mathematical structure of that interaction also satisfies the definition of a conscious agent. This mathematics is telling me something. I can take two minds, and they can generate a new, unified single mind. Here’s a concrete example. We have two hemispheres in our brain. But when you do a split-brain operation, a complete transection of the corpus callosum, you get clear evidence of two separate consciousnesses. Before that slicing happened, it seemed there was a single unified consciousness. So it’s not implausible that there is a single conscious agent. And yet it’s also the case that there are two conscious agents there, and you can see that when they’re split. I didn’t expect that, the mathematics forced me to recognize this. It suggests that I can take separate observers, put them together and create new observers, and keep doing this ad infinitum. It’s conscious agents all the way down.”


Here’s the striking thing about that. I can pull the W out of the model and stick a conscious agent in its place and get a circuit of conscious agents. In fact, you can have whole networks of arbitrary complexity. And that’s the world.


“As a conscious realist, I am postulating conscious experiences as ontological primitives, the most basic ingredients of the world. I’m claiming that experiences are the real coin of the realm. The experiences of everyday life—my real feeling of a headache, my real taste of chocolate—that really is the ultimate nature of reality.”

I don’t agree with everything in the article (especially the quantum stuff) but I think many people interested in consciousness and metaphysics will find plenty of food for thought here:

The Case Against Reality

Also, the “conscious agents all the way down” is the exact position I was criticizing in a recent 3QD essay:

3quarksdaily: Persons all the way down: On viewing the scientific conception of the self from the inside out

The diagram above is from a science fiction story I was working on, back when I was a callow youth. It closely related to the idea of a network of conscious agents. Here’s another ‘version’ of it.


Not sure why I made it look so morbid. 🙂

Perception is a creative act: On the connection between creativity and pattern recognition

An answer I wrote to the Quora question Does the human brain work solely by pattern recognition?:

Great question! Broadly speaking, the brain does two things: it processes ‘inputs’ from the world and from the body, and generates ‘outputs’ to the muscles and internal organs.

Pattern recognition shows up most clearly during the processing of inputs. Recognition allows us to navigate the world, seeking beneficial/pleasurable experiences and avoiding harmful/negative experiences.* So pattern recognition must also be supplemented by associative learning: humans and animals must learn how patterns relate to each other, and to their positive and negative consequences.

And patterns must not simply be recognized: they must also be categorized. We are bombarded by patterns all the time. The only way to make sense of them is to categorize them into classes that can all be treated similarly. We have one big category for ‘snake’, even though the sensory patterns produced by specific snakes can be quite different. Pattern recognition and classification are closely intertwined, so in what follows I’m really talking about both.

Creativity does have a connection with pattern recognition. One of the most complex and fascinating manifestations of pattern recognition is the process of analogy and metaphor. People often draw analogies between seemingly disparate topics: this requires creative use of the faculty of pattern recognition. Flexible intelligence depends on the ability to recognize patterns of similarity between phenomena. This is a particularly useful skill for scientists, teachers, artists, writers, poets and public thinkers, but it shows up all over the place. Many internet memes, for example, involve drawing analogies: seeing the structural connections between unrelated things.

One of my favourites is a meme on twitter called #sameguy. It started as a game of uploading pictures of two celebrities that resemble each other, followed by the hashtag #sameguy. But it evolved to include abstract ideas and phenomena that are the “same” in some respect. Making cultural metaphors like this requires creativity, as does understanding them. One has to free one’s mind of literal-mindedness in order to temporarily ignore the ever-present differences between things and focus on the similarities.

Here’s a blog that collects #sameguy submissions: Same Guy

On twitter you sometimes come across more imaginative, analogical #sameguy posts: #sameguy – Twitter Search

The topic of metaphor and analogy is one of the most fascinating aspects of intelligence, in my opinion. I think it’s far more important that coming up with theories about ‘consciousness’. 🙂 Check out this answer:

Why are metaphors and allusions used while writing?
(This Quora answer is a cross-post of a blog post I wrote: Metaphor: the Alchemy of Thought)

In one sense metaphor and analogy are central to scientific research. I’ve written about this here:

What are some of the most important problems in computational neuroscience?

Science: the Quest for Symmetry

This essay is tangentially related to the topic of creativity and patterns:

From Cell Membranes to Computational Aesthetics: On the Importance of Boundaries in Life and Art

* The brain’s outputs — commands to muscles and glands — are closely  linked with pattern recognition too. What you choose to do depends on  what you can do given your intentions, circumstances, and bodily  configuration. The state that you and the universe happen to be in  constrains what you can do, and so it is useful for the brain to  recognize and categorize the state in order to mediate decision-making,  or even non-conscious behavior.When you’re walking on a busy street, you rapidly process pathways that are available to you. even if you stumble, you can quickly and unconsciously act to minimize damage to yourself and others. Abilities of this sort suggest that pattern recognition is not purely a way to create am ‘image’ of the world, but also a central part of our ability to navigate it.

Does the human brain work solely by pattern recognition?

From Cell Membranes to Computational Aesthetics: On the Importance of Boundaries in Life and Art

My next 3QD column is out. I speculate about the role of boundaries in life and aesthetic experience. (Dopamine cells make a cameo appearance too.)

This image is a taster:

If you want to know what this diagram might mean, check out the article:
From Cell Membranes to Computational Aesthetics: On the Importance of Boundaries in Life and Art

What is a biological model? Here’s a useful categorization system for people interested in neuroscience, cognitive science, and biology

I found an excellent classification of models in a paper on neurogenesis: Using theoretical models to analyse neural development.

I think this should be illuminating for anyone interested in theoretical, mathematical and/or computational approaches in neuroscience, cognitive science, and biology.

There are several ways in which models of biological processes can be classified. 

Formal or informal models

Informal models are expressed in words or diagrams, whereas formal models — which this Review is concerned with — are described in mathematical equations or computer instructions. Using formal language forces a model to be precise and self-consistent. The process of constructing a formal model can therefore identify inconsistencies, hidden assumptions and missing pieces of experimental data. Formal models allow us to deduce the consequences of the postulated interactions among the components of a given system, and thus to test the plausibility of hypothetical mechanisms. Models can generate new hypotheses and make testable predictions, thereby guiding further experimental research. Equally importantly, models can explain and integrate existing data.

 Phenomenological or mechanistic models 

Most formal models lie on a continuum between two extreme categories: phenomenological and mechanistic. A phenomenological model attempts to replicate the experimental data without requiring the variables, parameters and mathematical relationships in the model to have any direct correspondence in the underlying biology. In a mechanistic model, the mathematical equations directly represent biological elements and their actions. Solving the equations then shows how the system behaves. We understand which processes in the model are mechanistically responsible for the observed behaviour, the variables and parameters have a direct biological meaning and the model lends itself better to testing hypotheses and making predictions. Although mechanistic models are often considered superior, both types of model can be informative. For example, a phenomenological model can be useful as a forerunner to a more mechanistic model in which the variables are given explicit biological interpretations. This is particularly important considering that a complete mechanistic model may be difficult to construct because of the great amount of information it should incorporate. Mechanistic models therefore often focus on exploring the consequences of a selected set of processes, or try to capture the essential aspects of the mechanisms, with a more abstract reference to underlying biological processes. 

Top-down or bottom-up models 

Formal models can be constructed using a top-down or a bottom-up approach. In a top-down approach, a model is created that contains the elements and interactions that enable it to have specific behaviours or properties. In a bottom-up approach, instead of starting with a pre-described, desired behaviour, the properties that arise from the interactions among the elements of the model are investigated. Although it is a strategy and not a type of model, the top-down approach resembles phenomenological modelling because it is generally easier to generate the desired behaviour without all of the elements of the model having a clear biological interpretation. Conversely, the bottom-up approach is related to mechanistic modelling, as it is usual to start with model elements that have a biological meaning. Both approaches have their strengths and weaknesses.

(I removed citation numbers for clarity.)

One point might be relevant here: a model is neither true nor false — ideally it’s an internally consistent mini-world. A theory is the assertion that a model corresponds with reality.

The Pentagon of Neuroscience — An Infographic/Listicle for Understanding the Neuroculture

Click here to go straight to the infographic. It should open in Firefox and Chrome.

brainzNeuroscience has hit the big time. Every day, popular newspapers, websites and blogs offer up a heady stew of brain-related self-help (neuro-snake oil?) and gee wiz science reporting (neuro-wow?). Some scientists and journalists — perhaps caught up in the neuro-fervor — throw caution to the wind, promising imminent brain-based answers to the kinds of questions that probably predate civilization itself: What is the nature of mind? Why do we feel the way we do? Does each person have a fundamental essence? How can we avoid pain and suffering, and discover joy, creativity, and interpersonal harmony?

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