Eric Mandelbaum

@ericman.bsky.social

CUNY Prof of Philosophy & Psychology, Director of Cognitive Science, other stuff too

there was some discussion on here recently about the scientific legitimacy of cognitive dissonance research. as someone who has spent years investigating this literature, i wanted to make a thread to explain why pessimism is not justified by careful inspection of the evidence 1/

Experimental Philosophy@xphilosopher.bsky.social · 9mo ago

There’s growing evidence that something was going seriously wrong in the classic early work on cognitive dissonance Latest revelation: The story in When Prophecy Fails seems to have been fabricated in the most egregious way But this is not the only one… onlinelibrary.wiley.com/doi/abs/10.1...

Fantastic researcher, open-minded thinker, dedicated mentor, and one of the absolute nicest people in the field.

Mark Ho@markkho.bsky.social · 10mo ago

I'm recruiting grad students!! 🎓 The CoDec Lab @ NYU (codec-lab.github.io) is looking for PhD students (Fall 2026) interested in computational approaches to social cognition & problem solving 🧠 Applications through Psych (tinyurl.com/nyucp) are due Dec 1. Reach out with Qs & please repost! 🙏

It was so great! Really lucky to have been able to listen and learn from Barbara, Liuba, & Benedek. Some of the most insightful research happening today, from just the absolutely nicest, coolest people.

Barbara Pomiechowska@bpomie.bsky.social · 11mo ago

Heading for my last day of the ESPP in Warsaw. Amazing to see philosophers and psychologists in one room. It was a privilege to talk about how symbols and compositionality emerge in babies alongside @ljubapi.bsky.social and @benedek.bsky.social 💕 to @ericman.bsky.social for organizing the symposium!

A key takeaway from 20+ years of computational RL is: model-free=automatic, model-based=deliberate. My new paper w/ @benedek.bsky.social challenges this view, suggesting that MB algos are more ubiquitous, & automatic processing more sophisticated, than currently thought: www.pnas.org/doi/10.1073/...

Model-based algorithms shape automatic evaluative processing | PNAS

Computational theories of reinforcement learning suggest that two families of algorithm—model-based and model-free—tightly map onto the classic dis...

pnas.org