#Bayesplaining

A slide titled “Summary Hallucination-Like Perception (HALIP)” with four bullet points on the right: “HALIP increases after hallucinogenic manipulations in mice,” “HALIP correlates with hallucinations in humans,” “Striatal dopamine causally mediates HALIP,” and “Expectations link striatal dopamine and HALIP.” On the left are four icons illustrating each point: a mouse hearing a sound, a human wearing headphones, a dopamine D2 receptor graphic, and a schematic of Bayesian inference with overlapping prior and likelihood curves. A large magenta arrow points toward the Bayesian schematic.

I somewhere heard the term #Bayesplaining 😂

I confess: guilty as charged. Exhibit A: my slide.

Enough #Bayesplaining. It's time to start "deriving."

We're unifying physics and neuroscience from first principles (yes, really).

Come argue with me tomorrow @ 11 AM.

@neuripsconf.bsky.social
🧠🤖🧠📈

But free energy (F) minimization is not enough.

We need specific "prescriptions" to guide top-down algorithm development. Otherwise, we risk falling into this trap:

P.S. I recently learned this is called #Bayesplaining 🙂

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