@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

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

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 🧵 (1/n)

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

📄 paper: https://daeh.info/pubs/houlihan2023computedappraisals.pdf

⚙️ code: https://github.com/daeh/computed-appraisals

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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.
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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.
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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.
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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.
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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.
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The learned functions reflect the sophistication of observers' latent Theory of Mind reasoning. The computed appraisal variables (and the learned emotion concepts) are interpretable owing to the cognitive structure of the generative model.
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This yields a "computational appraisal theory."

In effect, we learn the computational structure of people's intuitive theory of emotion, directly from observers' emotion judgments.
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Since the joint distribution of computed appraisals is designed to recapitulate observers' latent reasoning about players' minds, we can constrain the function learning using the generative structure of the model.
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Module 3 learns "emotion concepts" — functions that translate computed appraisals to continuous intensities of 20 emotions.
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We compute subjective utilities, prediction errors, and multiple types of counterfactuals over players' inferred preferences and beliefs. This yields a rich space of "computed appraisals".
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Module 2 computes players' appraisals — how observers think that a player will evaluate the outcome of the game, based on the player's mental contents

We extend the idea of "inverse planning" to generate probabilistic representations of players' reactions to new world states.
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We compare the model's inverse inference to an independent group of observers, who made similar inferences about players' mental contents. Eg players who cooperate are inferred to be less greedy, to desire a prosocial reputation, and to expect their opponents to also cooperate.
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We describe the model in 3 Modules.

Module 1 recursively inverts increasingly complex generative models of players' behavior. This enables the model to infer what unobserved mental contents were likely to have motivated a player's observed action.
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Building a formal model that can reason about players' emotional reactions based on an event description requires rich computational structure. We integrate ideas from inverse planning, behavioral economics, (reverse-) appraisal theory, and constructivist theories of emotion.
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Observers were given descriptions of people playing a public prisoner’s dilemma and predicted the people's emotional reactions to the outcome of the game. The rules were based on the British TV game show, Golden Balls.
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