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Alicia Chen
@aliciamchen.bsky.social
PhD student @ MIT Brain and Cognitive Sciences aliciamchen.github.io
72 followers61 following20 posts
ACaliciamchen.bsky.social

Ikr

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ACaliciamchen.bsky.social

Code, data, preregistrations available at osf.io/ubxjr/ Additional thanks to our editor and anonymous reviewers, for their thoughtful and detailed suggestions that improved the paper! (4/4)

A hierarchical Bayesian model of adaptive teaching
A hierarchical Bayesian model of adaptive teaching

Hosted on the Open Science Framework

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ACaliciamchen.bsky.social

We then test this model — which predicts that teachers and learners use rational inference to update their beliefs about each other in response to observed communication from their partner — in two behavioral experiments. (3/4)

Figure 1 in the paper. 
(A) depicts a teacher and learner interacting with each other. 
(B) and (C) depict the teacher and the learner agent, respectively.
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ACaliciamchen.bsky.social

We extend Bayesian models of pedagogy to account for how teachers and learners interact to resolve uncertainty at two levels: (1) the learner’s uncertainty about the target concept; and (2) the teacher and learner’s higher-order uncertainty about *what the other knows*. (2/4)

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ACaliciamchen.bsky.social

Our paper, "A hierarchical Bayesian model of adaptive teaching," is now out in Cognitive Science @cogscisociety.bsky.social@natvelali.bsky.social@rdhawkins.bsky.social@gershbrain.bsky.socialonlinelibrary.wiley.com/doi/10.1111/...

Title and abstract of paper. 

The abstract reads: 
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.
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Reposted by Alicia Chen
CScogscisociety.bsky.social
Reposted by Alicia Chen
MFmcxfrank.bsky.social

People are really good at creating conventions - new ways of talking - during dialogues. But what happens in larger groups? And what about when people can only respond using 😁?! New paper by Veronica Boyce, Robert Hawkins, Noah Goodman, and me, now out: www.pnas.org/doi/10.1073/...

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ACaliciamchen.bsky.social

🎉🎉🎉!!!

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Reposted by Alicia Chen
RSrebeccasaxe.bsky.social

New preprint. Feedback welcome! Example yesterday, in the wild. Me: "If I do a nice thing for my friend, we both expect they will later do a nice thing for me, but if I do a nice thing for you, then we both expect that I will keep doing that nice thing for you. Why?" Reply: "Cuz you're my Mum!"

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ACaliciamchen.bsky.social

More in the preprint! osf.io/preprints/ps...osf.io/ywbqu/ Feedback welcome!! :) (11/11)

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AC
Alicia Chen
@aliciamchen.bsky.social
PhD student @ MIT Brain and Cognitive Sciences aliciamchen.github.io
72 followers61 following20 posts