Chris Jungerius
@ChrisJungerius
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Computational Neuroscience Postdoc @ Cambridge University. Studying causal inference and transsaccadic integration of visual information. He/him.
Joined November 2010
RT @SolomonKurz: New #rstats blog up!. This is the first in a new series discussing causal inference with experime….
solomonkurz.netlify.app
Causal inference with the GLMM, Part 1
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RT @rlmcelreath: How can we reform science? I have some ideas. But I am not sure you’ll like them, because they don’t promise much. https:/….
elevanth.org
What hope is there for science reform, if we can't agree on what to reform? Right now, principles are more important than practices.
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RT @WMSymposium: Can you believe that WMS2025 (July 8-11) is less than a week away? If you are interested in Working Memory Research, WMS20….
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RT @WMSymposium: We are excited to announce that @ChrisJungerius .is joining the team of organizers of WMS 2025! With him joining, the prep….
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Exciting updates! .I defended my PhD under Heleen Slagter in January—'Learning What Matters: How to Pay Attention in a Volatile World'—and today, I’m starting as a Research Associate in the @BaysLab at Cambridge. Looking forward to new challenges ahead!
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RT @BaiweiLiu: Interested in how the internal and external processes work together to support efficient behaviour?! Check our latest findin….
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RT @johnmark_taylor: A vision science classic from Li Zhaoping that I somehow overlooked till now: feeding the top images into your left/ri….
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RT @EikoFried: “We conclude that relying solely on traditional interaction terms.in linear models can be misleading.”.
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This is amazing work. Highly recommend checking out the preprint linked in the article - the discussion in particular is a well-written, sassy takedown of the whole field.
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RT @Afinetheorem: The hardest thing to explain to non-academics is how different technical rigor is across fields. Here's (yet another) art….
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RT @Dom_Makowski: I procrastinated again by starting a tutorial on "cognitive models" (stats models that fit well reaction time data) in #J….
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Check out the full preprint for all the details on our methodology, results, and interpretations!.With @HeleenASlagter and @freekvanede .#CognitiveScience #Attention #WorkingMemory #Research.🧵 (7/7).
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Our findings contribute to the understanding that internal and external attention are qualitatively different. This opens up new questions about how we manage and prioritize information internally versus externally. 🧵 (6/7).
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What does this mean? It suggests that internal attention (within working memory) and external attention (to perception) involve different selection mechanisms. They might be operating to solve different goals. 🧵 (5/7).
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Across all experiments, we found no robust evidence that statistical learning can bias the prioritization of contents in working memory. 🧵 (4/7).
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To find out, we ran multiple experiments. Participants performed visual working memory tasks where the relevance of items was implicitly predicted by:.- The combination of two items.- An implicit retro-cue.- Implicit temporal cueing.🧵 (3/7).
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It’s well-known that statistical learning can shape how we direct our attention to external stimuli in our environment. But can it do the same for how we focus on information in our working memory?.🧵 (2/7).
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🚨 New preprint! 🚨.Excited to share the last preprint of my PhD with @HeleenASlagter and @freekvanede. We explored if statistical learning biases internal attention in visual working memory. The results were surprising!. 🧵 (1/7).
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One more example of why we should scrap the teaching of ANOVA and its battery of assumption-checks from the stats curriculum. Teach a student linear regression and you feed them for a lifetime.
You might have 99 problems, but heteroskedasticity is not one of them. An update from Data Q&A:.
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