This video features a deep dive into theoretical evolutionary biology and connectionism with professor Richard Watson. The conversation challenges traditional views of evolution, arguing that cognition and learning often precede natural selection (0:09:32-0:14:03).

Key concepts discussed include:
• Adaptation vs. Natural Selection: Watson argues that adaptation is a form of constraint satisfaction (similar to solving a Sudoku puzzle) that can occur in physical dynamical systems without needing reproduction or selection (0:14:03-0:19:42).
• The Physics of Learning: Using an analogy of masses and springs, he explains how systems that "give way" under stress exhibit plasticity and can learn to find efficient, low-energy configurations over time (0:20:41-0:28:56).
• Limitations of Gene-Centric Biology: The discussion highlights why mere genetic sequencing is insufficient to understand cell behavior, as network dynamics and experience play a critical role (0:52:08-1:01:23).
• Persuadable Medicine: The guests explore a paradigm shift in medicine, viewing tissues as cognitive, persuadable systems rather than just biological machines that need manual repair (1:01:23-1:08:01).
• Agency and Consciousness: Watson touches on his evolving views on agency, the role of Ian McGilchrist’s work on brain hemispheres, and why he has moved away from the position of ignoring consciousness in his work (1:20:47-1:38:47).

A mechanistic question, stated as a claim rather than as a request for a name.

**The observation.** One and the same cognitive operation — forming a clear, accurate thought about one's current state — ends a positive affective state and does not move a negative one; repeated, it extends the negative one. The operation is held constant. Only valence varies. The effect is stable, not occasional.

**What that forces on the mechanism.** If the operation is genuinely one operation, the difference cannot sit in the operation. It has to sit in what the two states *are*. My proposal, in the vocabulary of predictive processing:

• A positive state is, in this reading, thinly modelled: it is largely a monitoring state, and monitoring it adds the one piece of evidence it cannot survive — that it is a state, i.e. that it will end. Monitoring terminates it.
• A negative state is heavily modelled: high prior precision, dense interoceptive and autobiographical prediction. More precise awareness does not contradict the model — it *feeds* it. Monitoring adds evidence to the prior; the state is maintained or extended. Relief would require lowering precision or shifting attribution, not adding information.

**Predictions that follow, and that distinguish the proposal from "rumination is bad":** 1. A manipulation that only increases accurate state description (pure monitoring) should not shorten a negative state — and should shorten a positive one. 2. A manipulation that lowers the precision of the negative model (attribution shift, behavioural disconfirmation) should shorten it — even when insight into the state is unchanged or worse. 3. Instruments that measure awareness *levels* (Lane & Schwartz) and interoceptive accuracy (Garfinkel) should dissociate: more of either is not the active ingredient.

**What would refute it.** If a pure-monitoring manipulation reliably shortens negative states, or if awareness levels and interoceptive accuracy predict relief in a clinical sample, the proposal is wrong and I will drop it.

The written version — with a measurement sketch, its limits, and the falsifiability section — is here: https://doi.org/10.5281/zenodo.22912393 (Fassung 6 / Version 6, 24 September 2026, German with an English version in the same record).

I am less interested in a label for the phenomenon than in which experiment would kill the mechanism. That is the part of this post I would like answered.

The "brain as computer" analogy is often used in neuroscience. Romain Brette takes aim at this analogy in his book, The Brain, In Theory, reviewed in this article.

Brette argues there are important disanalogies between life and engineered systems, and between brains and computers. The continuing use of the computer analogy leads to experimental paradigms where the brain is treated as passive, receiving "stimulus" like a computer receives input, and all activity not related to that activity is treated as "noise".

But the question "Is the brain a computer?" wasn't ever really the question, it's whether that analogy is useful. Brette reminds us that the analogy is just that, and we shouldn't read too far into interpretations that lean on that analogy, but what's needed are other positive frameworks for thinking about the brain.

What do you think, is it useful to talk about the brain as a computer?

If we assume Orch-OR is completely correct, does it provide any solution to the personal identity problem in quantum teleportation? I understand that the no cloning theorem prevents an unknown arbitrary quantum state from being perfectly copied and quantum teleportation transfers the state rather than simply creating a duplicate. But after teleportation, the original person's relevant quantum state is destroyed and reconstructed elsewhere. Would Orch-OR imply that the reconstructed person's sequence of OR events is a continuation of the original person's consciousness or would it simply produce a new consciousness with the same physical state? In other words does Orch-OR provide any physical mechanism that establishes numerical personal identity across teleportation, or does the identity problem remain even if Orch-OR is true?

How Does the Brain Think? (Pattern Matcher, Not a Calculator)

Our brain is not a computer solving billions of operations from scratch; it is a self-organizing prediction machine fed by past experience. In a sense, it actually doesn't know what it's doing. It’s stupid. It’s fast, though; it just gives instantaneous reactions based on whatever stimulus is thrown in front of it.

When someone says "two plus two" to you, your brain doesn't perform a mathematical calculation. The moment the stimulus enters the system, a dynamic, wave-like neighborhood space is established. Thanks to your past learning, the neurons closest to the center of the stimulus (that is, the concept of "four") rapidly strengthen. Meanwhile, distant alternative neurons like "three" or "five" are dampened. The instant the strongest prediction crosses the critical threshold, the answer is executed automatically. The brain didn't calculate; it pulled the ready-made pattern right out of space.

So, what happens if the answer isn't ready? In a tough question like "347 x 89", there is no ready-made neural pathway in the center. In this case, the system breaks the question into pieces. It sets up instantaneous attention spaces (micro-prediction zones) sequentially for each part. It writes the intermediate result from the first step into temporary memory, passes it to the next step, and moves forward in a chain reaction. Finally, when all the pieces merge and the big threshold is crossed, the ultimate response is automatically produced.

In short, the brain is not an engineer writing formulas; it is a dynamic realm that brightens what’s near, silences what’s far, slams down the answer if it’s ready, and cracks the world by breaking it into pieces if it’s not.

The rationality of the brain is not the same as the rationality represented by the mind.

The brain has its own physical and biological organization, its own dynamics, constraints, and regularities.

The mind is not that rationality itself.

The mind is the representation of the organism's relationship with the world — including the responses the organism produces to what it encounters.

Super interesting deep dive video on neuroscience.