1.1 Summary & Table of Contents

This is the first of a series of eight blog posts, which I’ll be serializing over the next month or two. (Or email or DM me if you want to read the whole thing right now.) Here's an overview of the whole series, and then we’ll jump right into the first post!

1.1.1 Summary & Table of Contents—for the whole series

This is a rather ambitious series of blog posts, in that I’ll attempt to explain what's the deal with consciousness, free will, hypnotism, enlightenment, hallucinations, flow states, dissociation, akrasia, delusions, and more.

The starting point for this whole journey is very simple:

• The brain has a predictive (a.k.a. self-supervised) learning algorithm.
• This algorithm builds generative models (a.k.a. “intuitive models”) that can predict incoming data.
• It turns out that, in order to predict incoming data, the algorithm winds up not only building [...]

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Outline:

(00:05) 1.1 Summary and Table of Contents

(00:27) 1.1.1 Summary and Table of Contents—for the whole series

(04:50) 1.1.2 Summary and Table of Contents—for this first post in particular

(06:56) 1.2 Generative models and probabilistic inference

(07:02) 1.2.1 Example: bistable perception

(08:08) 1.2.2 Probabilistic inference

(09:59) 1.2.3 The thing you “experience” is the generative model (a.k.a. “intuitive model”)

(11:24) 1.2.4 Explanation of bistable perception

(12:19) 1.2.5 Teaser: Unusual states of consciousness as a version of bistable perception

(13:52) 1.3 Casting judgment upon intuitive models

(14:33) 1.3.1 “Is the intuitive model real, or is it fake?”

(15:22) 1.3.2 “Is the intuitive model veridical, or is it non-veridical?”

(18:08) 1.3.2.1 Non-veridical intuitive models are extremely common and unremarkable

(19:48) 1.3.2.2 …But of course it's good if you’re intellectually aware of how veridical your various intuitive models are

(21:32) 1.3.3 “Is the intuitive model healthy, or is it pathological?”

(23:23) 1.4 Why does the predictive learning algorithm build generative models / concepts related to what's happening in your own mind?

(26:37) 1.4.1 Further notes on the path from predictive learning algorithms to intuitive self-models

(28:56) 1.5 Appendix: Some terminology I’ll be using in this series

(29:18) Learning algorithms and trained models

(31:21) Concepts, models, thoughts, subagents

(33:53) 1.6 Appendix: How does this series fit into Philosophy Of Mind?

(34:00) 1.6.1 Introspective self-reports as a “straightforward” scientific question

(35:52) 1.6.2 Are explanations-of-self-reports a first step towards understanding the “true nature” of consciousness, free will, etc.?

(39:46) 1.6.3 Related work

(40:47) 1.7 Conclusion

The original text contained 7 footnotes which were omitted from this narration.

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First published:

Source:

Narrated by TYPE III AUDIO.

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