
A motley crew of science nerds trying to understand the inner workings and limits of human memory. Plus some Bayesian stuff...and 🧁. Director: @jeremyRmanning
Hanover, NH
Joined May 2016
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We also did some digging to get at the specific networks that seem to exhibit this mode switching process most strongly (DMN!) or weakly (primary sensory cortex!) and also used some cool @neurosynth analyses to help us interpret the results.
When we are engrossed in a challenging or highly engaging task, our brain switches to a mode that prioritizes its ability to distinguish one moment’s activity patterns from other moments’ patterns.
Our study’s core finding is that the brain reconfigures itself from moment to moment to maintain a tradeoff between computational complexity and robustness to noise, according to ongoing cognitive demands.
For context, let's appreciate for a moment that our brains are responsible for every human thought, story, relationship, artwork, and scientific discovery EVER. Meanwhile, they're packed into a volume of under 1.5L and they run on less power than a standard incandescent 💡!
Chatify can help with explaining code, debugging, brainstorming project ideas, test your understanding, and more.
If you've installed/loaded the extension, your notebook now has AI superpowers 🦸🦹🤖!
You can add the "%%explain" magic command to the top of any code cell to interact with Chatify:
We found that *covert* changes to the focus of category- and location-based volitional attention affect memory encoding at different timescales and likely through different mechanisms!
MAJOR ANNOUNCEMENT!!
Effective immediately, all lab activities will now be performed by LLMs fine-tuned on our papers. Lab members will serve only as a physical interface to the external world.
Wanna participate in the upcoming (free!) spring 2023 offering of Intro to Programming for Psych Scientists?
Check out our course materials (github.com/ContextLab/cs-for…) and join our Discord workspace to participate in discussions, ask questions, etc.: discord.gg/AdDgJd7cbM
We did some interesting exploratory things too, using a combination of @neurosynth and ChatGPT to help understand what different patterns we found might “mean” from a functional perspective.






