Thanks for the clarification! Perhaps I should follow Leahey and use “behavioralism” to focus on the methodological approach.

Literally all? Surely neuroscience has provided at least something here—the ring attractor for heading direction?

On the paper, this is exactly the point I was trying to get at (aside from the consciousness angle). Thanks for the pointer!

I’ll ignore the aim comment…

As I understand it, behaviorism, like cognitivism, is not a theory but a methodological and philosophical approach. Both approaches generate falsifiable theories.

As for the definition, perhaps mine was too generous. How would you define behaviorism?

But the moment we start taking our models too seriously, we fall into the trap Skinner warned us of: believing things we have no evidence for. I think that cognitive science has largely fallen into this trap (myself included), and that we'd benefit greatly from taking Skinner's ideas more seriously.

This is not to say cognitive models aren't valuable. Going full Hofstadter, the representations in a model are themselves representations: useful ways to understand behavior and brain. This deflates Skinner's critique of cognitivism. Representations aren't true or false; they don't need evidence.

This representation parameterization is useful because it (1) tends to generalize better and (2) is more interpretable. But this is just data-efficiency and aesthetics. These properties cannot provide *evidence* against behaviorism. And they provide weak (if any) evidence for the representation.

Good read. I'd go further and suggest that scientific evidence against behaviorism is impossible in principle. Behaviorism simply says that you can model behavior as p(behavior | experience, genes). Cognitivism just adds a latent state, r (representation):

p(b|e,g) = ∫ p(b|r) p(r|e,g) dr.

(1/4)

New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task

📢📢 Announcing this year's conference on the Mathematics of Neuroscience & AI (Rome, 9-12th June). We’ve got a stellar line-up and venue, and invite everyone to join:

www.neuromonster.org

You can read an un-copyedited version for free here: osf.io/preprints/ps...

Or you can get the real thing for $28 here:
press.princeton.edu/books/paperb...

Use discount code: P329

I'm most proud of Chapter 3, which provides a new formal definition of resource rationality: it's an interpolation between constrained optimization (bounded optimality) and cost-benefit tradeoffs (metalevel rationality, e.g. EVC).

The word "resource" turns out to be critical.

Book cover. A silhouette of a person's head filled with colorful geometric shapes—perhaps symbolizing cognitive resources or deployment thereof. The style is attractive and modern, if generic. text: The Rational Use of Cognitive Resources Falk Lieder, Frederick Callaway, Thomas L. Griffithts

I'm excited to announce that I had my first (co-authored) book published today! "The Rational Use of Cognitive Resources" with Falk Lieder and Tom Griffiths (@cocoscilab.bsky.social ). You can read it for free! (see thread)

Hybrid neural–cognitive models reveal how memory shapes human reward learning Nature Human Behaviour, Published online: 05 February 2026; doi:10.1038/s41562-025-02324-0Using artificial neural networks applied to human data, Eckstein et al. show that good models of reinforcement learning require memory components that track representations of the past.

Hybrid neural–cognitive models reveal how memory shapes human reward learning

Convincing evidence that people recall individual past experiences to inform decisions—specifically when an easier incremental learning strategy isn’t available. Also, a masterclass in experimental design.

With some trepidation, I'm putting this out into the world:
gershmanlab.com/textbook.html
It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class.

My hope is that this will be a living document, continuously improved as I get feedback.

Aha, clever. So I guess the behavioral signature would be an increased stopping probability specifically after extreme observations (relative to mean of past observations)

Very interesting result! Apologies if I missed this, but do you have a sense what the "raw" behavioral signature of ΔES is? In particular, how can we distinguish it from collapsing bound / urgency? @mmiedl.bsky.social

Writing is thinking

Outsourcing the entire task of writing to LLMs will deprive us of the essential creative task of interpreting our findings and generating a deeper theoretical understanding of the world.

How do we achieve few-shot generalization? New work led by @fabianrenz.bsky.social dives into the role of replay in learning and using structure to generalize reward. Dream team effort with Shany Grossman @nathanieldaw.bsky.social Peter Dayan & @doellerlab.bsky.social
www.biorxiv.org/content/10.6...

Now out in JEP: General, "How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently"

psycnet.apa.org/record/2026-...

Preprint with final version: osf.io/preprints/ps...

1/n

This feels to me like saying “how gas might viably work without the need for pressure and temperature.” Representations are just a way to describe neural activity, which is itself just a way to describe a bunch of particles moving around. The question is whether it’s a *useful* description.

FWIW I started unfollowing people who mostly post political content a few months ago and my feed is now mostly cool science. Complete control over what you see is a big benefit of bsky!

We're excited to announce that Cognitive Science at Dartmouth is recruiting PhD students to work collaboratively with me, Steven Frankland, and Fred Callaway. Come study the principles and mechanisms that enable us to understand, plan, and act in the world! Info: sites.dartmouth.edu/cogscigrad/

Yes! @upenn.edu declines signing The Compact. I'm proud of this decision.

That makes sense, thanks!

Against better judgment, I will ask a sincere question. Why is this best understood as trivializing rather than normalizing? I’m assuming it’s not literally the POS but instead using the term to describe common patterns of thought and behavior; is that right?

1st Sharp Lab preprint! 🚨 We tested how anxiety affects task generalization—not how people generalize threat stimuli, but how they reuse action-outcome structures when planning in new contexts.

Worry makes people avoid reusing actions that co-occurred w/ threat!
📄: osf.io/preprints/ps...

🧵 1/12

Forget modeling every belief and goal! What if we represented people as following simple scripts instead (i.e "cross the crosswalk")?

Our new paper shows AI which models others’ minds as Python code 💻 can quickly and accurately predict human behavior!

shorturl.at/siUYI%F0%9F%...

New in @pnas.org: doi.org/10.1073/pnas...

We study how humans explore a 61-state environment with a stochastic region that mimics a “noisy-TV.”

Results: Participants keep exploring the stochastic part even when it’s unhelpful, and novelty-seeking best explains this behavior.

#cogsci #neuroskyence

Summary of design and results from our three studies. (A: Design) Each study used a similar experimental design, measuring both positive and negative demand in an online experiment, with three commonly-used task types (dictator game, vignette, intervention). Our experiments had ns ≈ 250 per cell. (B: Results) Observed demand effects were statistically indistinguishable from zero. The plot shows means and 95% confidence intervals for standardized mean differences derived from frequentist analyses of each experiment and an inverse variance-weighted fixed-effect estimator pooling all experiments (solid bars). Prior measurements of experimenter demand from a previous dictator game experiment (de Quidt et al., 2018; standardized mean difference from regression coefficient) and a meta-analysis primarily including small-sample, in-person studies (Coles et al., 2025; Hedge’s g statistic) are also shown for comparison (striped bars). The main text includes Bayesian analyses that quantify our uncertainty.

We often hear from reviewers: "what about demand effects?" So we developed a method to eliminate them. Something weird happened during testing: We couldn’t detect demand effects in the first place! (1/8)