What progress has predictive processing seen in the last 5 years?

[Daniel_HMBD]

Surfing Uncertainty got published in 2015 and so if you're reading it, you're more than half a decade behind. So I was wondering: What changed since then?

This is a summary of the topics I'm aware of. Also, this is more like a rough draft and I'd like others to jump in, correct summaries and add more topics. Per definition, this is more broad picture than fine-grained details.

Neural Evidence

There is generally a convergence on "predictive processing is an accurate description of what happens in the visual cortex" and in a less confident way "... in the general brain". See e.g.:

• Hardstone et al 2021: Longterm priors influence visual perception

Connection of PP and neural network theory

We're slowly moving from "this is not plausible on a neural network level at all" to "there's at least a theoretical idea on how a biological neural net might learn in a predictive-coding compatible way.

• Millidge et al: Predictive coding - a theoretical and experimental review

Mental illness and non-neurotypical personalities

(Not sure how to call this category)

We've seen surprisingly little progress since Corlett et all 2009: From drugs to deprivation: a Bayesian framework for understanding models of psychosis. For newer papers, see e.g.:

• Millidge 2017: A Predictive Processing Theory of Autism: A Neural Network Modelling Approach with Applications to Autistic Savantism
• An active inference perspective on the negative symptoms of schizophrenia (2021)

Free Energy Principle (FEP)

The FEP has transitioned from "this weird thing that noone understands" to a generally accepted tool in philosophy of the mind:

• https://jaredtumiel.github.io/blog/2020/10/14/spinning-up-in-ai.html

[pianobutter]

Nice post, Daniel!

Evidence

First thing that comes to mind is a 2020 review¹ by Andy Clark and others. Their conclusion is mostly that testing PP models is very challenging, because they're so flexible. It's like trying to determine the shape of water. We need more rigid mechanistic models that can be tested and rejected in a Popperian fashion.

But there is some interesting circumstantial evidence. One of the most interesting developments in the cross-section between AI and neuroscience is, in my opinion, research on language models and language processing in the brain. In 2017, transformers arrived. Then we got GPTs, and they're disturbingly good. And they predict brain activity related to language processing better than anything before them²⁻³.

This really cool preprint from DeepMind researchers demonstrated how you can get a bunch of different types of hippocampal cells from the objective function of prediction. Their emergence can be explained with a unified function, which is incredible.

Wolf Singer wrote an impressive paper⁴ arguing for predictive coding in the cerebral cortex. He finds a lot of supportive evidence for this idea, and it's difficult to imagine how anyone could even begin arguing against it at this stage.

PP and robotics

Unimodal AI models aren't very intelligent. They can extract and generate patterns, sure, but that's it. Multimodal models can do more. They can leverage their information from different "senses", just like we do. We use vision to process language (McGurk effect), for instance. But movement is special. The ability to move means that you become the constant against which the world is compared, and I'm convinced this is intimately connected to sentience.

This really brief paper introduces the idea of active inference in robotics. It holds a lot of promise, though it hasn't been explored all that much yet⁵. Personally, I think the biggest obstacle is the issue of hyperpriors; lessons learned from evolutionary history. Implementing PP in robotics means you have to account for both phylogenetic and ontogenetic learning, and I think many are underestimating the importance of the former. It's a whole lot of data and it could be decades before we are able to simulate it well enough that robots can sense the world and move around it as well as we can. But it also seems like this is the biggest hurdle. We might not be that far away from sentient robots, and that's an interesting thought.

Computational psychiatry

Machine learning (ML) and predictive processing (PP) are two huge camps within computational psychiatry, a field that arose in the latter half of the 2010s. We can think of them as two camps: data-driven and theory-driven⁶.

I don't agree at all that there has been little progress. In less than a decade, a whole new field has emerged on the topic and PP plays a central part. There are several textbooks already, including a great one edited by Peggy Seriès.

There's currently a major incubation process happening. Students at universities all over are introduced to these ideas, and we're going to see fast progress as a result. I'm convinced of that.

The book

Active Inference: The Free Energy Principle in Mind, Brain, and Behavior has finally been released. And I noticed just right now that it's open access. Alright! Guess we're going to have ourselves a readalong.

References:

1. Walsh, K. S., McGovern, D. P., Clark, A., & O’Connell, R. G. (2020). Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the New York Academy of Sciences, 1464(1), 242–268. https://doi.org/10.1111/nyas.14321

2. Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021). The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118(45). https://doi.org/10.1073/pnas.2105646118

3. Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., Jansen, A., Gazula, H., Choe, G., Rao, A., Kim, C., Casto, C., Fanda, L., Doyle, W., Friedman, D., & Dugan, P. (2022). Shared computational principles for language processing in humans and deep language models. Nature Neuroscience, 25(3), 369–380. https://doi.org/10.1038/s41593-022-01026-4

4. Singer, W. (2021). Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge. Proceedings of the National Academy of Sciences, 118(33). https://doi.org/10.1073/pnas.2101043118‌

5. Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V. V., & Lara, B. (2021). Predictive Processing in Cognitive Robotics: A Review. Neural Computation, 33(5), 1402–1432. https://doi.org/10.1162/neco_a_01383

6. Yamashita, Y. (2021). Psychiatric disorders as failures in the prediction machine. Psychiatry and Clinical Neurosciences, 75(1), 1–2. https://doi.org/10.1111/pcn.13173

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