dae houlihan

@dae@mastodon.online
486 Followers
209 Following
177 Posts
computational cognitive science ~ emotion and social cognition ~ postdoc at Dartmouth, PhD from MIT. #searchable
websitehttp://daeh.info
twitterhttps://twitter.com/DaeHoulihan
githubhttps://github.com/daeh
OpenCheckhttps://opencheck.is/person/Sean_Dae_Houlihan

@elduvelle @NicoleCRust @tdverstynen I wrote an app for this (just needs a browser, and not even an internet connection if you download the html file). Just stick this on a laptop that faces them. It works surprisingly well at keeping people on time (and easily configurable).

http://neural-reckoning.org/conference-timer.html

Workshop timer

I'm hiring a postdoc! If you'd like to work on a research project that fits into either of these two research areas (https://lindsay-lab.github.io/research/) then send a CV, half page project proposal & contact info for 3 references to grace.lindsay@nyu.edu with subject "Postdoc Application"
#neuroscience
Lindsay Lab - Research

Artificial neural networks applied to psychology, neuroscience, and climate change

New paper! How do our expectations come to affect our perceptions? New work with the inimitable Mariam Aly (@mariam), Sam Feng, Nick Turk-Browne, @ptoncompmemlab, & Jon Cohen, now out in CABN: https://rdcu.be/deySH. Details in ๐Ÿงต (1/n)
Associative memory retrieval modulates upcoming perceptual decisions

Enormous gratitude to my coauthors @rebecca_saxe, Josh Tenenbaum, @max, Luke Hewitt. And also to the reviewers who helped improve the work.

๐Ÿ“„ paper: https://daeh.info/pubs/houlihan2023computedappraisals.pdf

โš™๏ธ code: https://github.com/daeh/computed-appraisals

23/

This work has wholly transformed how I think about modeling the mind.

It has also been an ideal collaboration in that it has built on, and extended, prior work from all of the coauthors to do something we all imagined was possible. 22/

And both lesions impair the model's ability to update emotion predictions for specific players based on personalizing prior information.
21/

But the rich model structure is necessary to capture human social cognition, even in this simple game.

Lesioning inverse planning, or lesioning social preferences, impairs the capture of specific emotions.
20/

Finally, we compare our model to simpler alternatives.

The Golden Balls game is highly constrained (two people, binary choices, a pot size).

And this model is elaborate... Counterfactuals over recursive inverse inferences of social preferences, etc.
19/

The model predicted how personalizing information would bias observers' emotion predictions. Eg, because the software engineer was inferred to care more about not being taken advantage of, the model predicted that observers would expect him to experience more envy, which they did.
18/
Since the model is Bayesian, it depends on priors. This offers a way to test if the model responds to prior information like humans. We gave observers brief personalizing descriptions of specific players. One group rated the players' motivations, another group rated emotions.
17/