Paul Bays

Computational cognitive scientist, Professor at University of Cambridge.

[Paul Bays @BaysLab]

New work on sensory prediction resolves some long-standing puzzles about attenuation of self-touch https://t.co/xEzPIib3GV

[Paul Bays @BaysLab]

Now out in Comms Psych: the "anti-Bayesian" size-weight illusion is a consequence of the brain focusing resources on encoding typical combinations of size and weight (i.e. "efficient coding"). Explains the material-weight illusion too https://t.co/lM6K8Rhdxn

[Paul Bays @BaysLab]

We have an opening for a postdoc to work on a collaborative project with @lengyel_m (UCambridge Engineering) combining machine learning methods with human experiments on visual perception and memory https://t.co/x7DrFzxRhg

[Ethan Mollick @emollick]

👀Claude handles an insane request:
“Remove the squid”

“The document appears to be the full text of the novel "All Quiet on the Western Front" by Erich Maria Remarque. It doesn't contain any mention of squid that I can see.”

“Figure out a way to remove the 🦑​​​​​​​​​​​​​​​​“

[Paul Bays @BaysLab]

A systematic dissection of the analogue report methodology and comparison with "gold-standard" 2AFC. Key findings: motor noise is negligible; analogue report error is a robust measure of memory fidelity, but may underestimate perceptual precision https://t.co/PQ9BleY2Az

[Paul Bays @BaysLab]

Almost two years in the making!

[Paul Bays @BaysLab]

Out now in NHB (finally!), a review of visual working memory from a computational perspective, with @weijima01 @timothyfbrady and Sebastian Schneegans. https://t.co/IypKOGd427

[Paul Bays @BaysLab]

Nice commentary from @salinas_urgent and @mangosheikh_B on our new eLife paper https://t.co/6d0scJC6Dg

[Paul Bays @BaysLab]

@gershbrain @PhilCorlett1 @chazfirestone Yes, I believe so. It's a dramatic demo (I tried it at VSS once) but doesn't need any extra mechanism to explain it over the standard illusion.

[Paul Bays @BaysLab]

The size-weight illusion is a by-product of efficient sensory coding adapted to the combinations of volume and mass found in everyday objects. New preprint https://t.co/RSBE8KT6Oh

[John B. Holbein @JohnHolbein1]

“Among articles stating that data was available upon request, only 17% shared data upon request.”

https://t.co/L84lnKMgPn

[Paul Bays @BaysLab]

We measured how effectively observers can reallocate working memory resources to new visual items when old ones become obsolete - people are surprisingly good at it! New paper with @ivntmc @dataforyounz @DAagtenMurphy https://t.co/EOS8mgu3LV

[Chaz Firestone @chazfirestone]

The difference between saccades and smooth pursuit eye movements, as demonstrated by sticking gross plastic thingies on your eyeballs

https://t.co/vuiWBgFTh4

[Paul Bays @BaysLab]

A critique of the psychological similarity account of working memory errors: work with @ivntmc now out in JEP:LMC https://t.co/u1SfbXK5YQ

[Wei Ji Ma @weijima01]

New review on visual working memory with @BaysLab, @timothyfbrady, and Sebastian Schneegans: https://t.co/84130x8EPe I was surprised by how much news there was since my 2014 review with @BaysLab and @MasudHusain. Comments/excoriations are welcome and may get incorporated.

[Paul Bays @BaysLab]

New work with Jess McMaster & others: we show swap errors (item confusions) in cued recall are not a strategic response to forgotten items, but instead occur at exactly the rate predicted by variability in recall of the cue features https://t.co/Se1wQqr4g0

[Paul Bays @BaysLab]

New in Psych Review with Sebastian Schneegans & Jess McMaster: comparing the roles of time and space in binding features in working memory https://t.co/ys1kVLAr1S

[Paul Bays @BaysLab]

@StokesNeuro So sorry Mark. Wishing you the very best and hoping the news gets better soon.

[Paul Bays @BaysLab]

We have an opening for a post-doc (or potentially a talented graduate RA) to research computational mechanisms of visual perception/memory using online and offline experiments - note deadline 11 Aug https://t.co/rKJrQWlHLm