For Interdisciplinary Research!

Our sample was a bit small, so we ran MANY tests to ensure that our findings were solid (validations, cross-validation, permutations, step-wise regressor removals…). Our results survived all of these tests! Most importantly, ALL the networks identified by our analysis were STORAGE networks.

With the model results in hand, we went back to the brain data, and examined which patterns of brain connectivity at rest best account for individual differences in the speed of forgetting. A machine-learning technique identified only 8 connectivity patterns between 9 networks.

I’ll spare you the details but suffice to say that the model explains the behavioral data surprisingly well: it predicts almost every response and response time of every participant. And it does so by fitting ONE parameter only, the speed at which information is lost from memory

Memory involves brain activity at rest (especially in the DMN), while “storage” and “retrieval” involve different brain networks. So, we can test our hypotheses using individual differences: Does more forgetting show different resting-state brain activity in the storage or in the retrieval regions?

Ladies and gentlemen, I would like to share my greatest scientific contribution at the 32nd ACT-R workshop, captured for posterity by Zoom's voice-to-speech feature:

Ha ha ha of course! This is what I was watching last time Chantel was away!

Holy cow I thought "this can't be true" but instead

May I suggest this Canadian horror classic?

#StandUpForScience in Seattle today ❤️

Rubio is exactly Tom Hagen in this scene of Succession

Reminds me of this

Strong Chernobyl vibes from RFK

This vibe

Together with Christian Lebiere , Robert Thomson, Patrick Rice, Briana Smith , and Don Morrison , we have done a deep dive and outlined how the different ACT-R equations and assumptions match to know brain processes and circuits. 2/4

That is so cool 😃 I asked because we do have horses at home --- here they are!