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Paul Bays

@bayslab.org

140 followers 74 following 48 posts

Computational cognitive scientist, Professor at University of Cambridge.

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Paul Bays @bayslab.org · Aug 1

New work on sensory prediction resolves some long-standing puzzles about attenuation of self-touch journals.physiology.org/doi/full/10....

Divisive attenuation based on noisy sensorimotor predictions accounts for excess variability in self-touch | Journal of Neurophysiology | American Physiological Society

When one part of the body exerts force on another part, the resulting tactile sensation is perceived as weaker than when the same force is applied by an external agent. This phenomenon has been studie...

journals.physiology.org

Paul Bays @bayslab.org · Jan 29

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 www.nature.com/articles/s44...

Weight illusions explained by efficient coding based on correlated natural statistics - Communications Psychology

Weight illusions reflect the efficient coding of everyday experiences with objects. Bayesian models that account for the resulting differences in discriminability predict the size-weight and material-...

Paul Bays @bayslab.org · Nov 21

We have an opening for a postdoc to work on a collaborative project with Máté Lengyel (UCambridge Engineering) combining machine learning methods with human experiments on visual perception and memory www.jobs.cam.ac.uk/job/49299/

Paul Bays @bayslab.org · Nov 19

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

Paul Bays @bayslab.org · Nov 19

Nice commentary from @salinas_urgent and @mangosheikh_B on our new eLife paper

Paul Bays @bayslab.org · Nov 19

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

Paul Bays @bayslab.org · Nov 19

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

Paul Bays @bayslab.org · Nov 19

A critique of the psychological similarity account of working memory errors: work with @ivntmc now out in JEP:LMC

Paul Bays @bayslab.org · Nov 19

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

Paul Bays @bayslab.org · Nov 19

New in Psych Review with Sebastian Schneegans & Jess McMaster: comparing the roles of time and space in binding features in working memory

Paul Bays @bayslab.org · Nov 19

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

Paul Bays @bayslab.org · Nov 19

Views of an object before and after a saccade may be combined even if you are aware the object has changed - new with Garry Kong, @DAagtenMurphy and Jess McMaster.

Paul Bays @bayslab.org · Nov 19

New in JOV: the ability to combine visual evidence across gaze fixations depends on a limited but flexible memory resource

Paul Bays @bayslab.org · Nov 19

New paper on the consequences of stroke for recall precision and binding in visual WM, a collaboration with Roy Kessels and colleagues at the Donders (@DondersInst)

Paul Bays @bayslab.org · Nov 19

Our new Analogue Report Toolbox can be downloaded at . It implements in MATLAB a range of methods we use in the Bays lab for analyzing and modelling behavioural responses on VWM recall tasks, including...

Paul Bays @bayslab.org · Nov 19

Our new PNAS paper reveals how the main competing models of working memory limits can all be interpreted in terms of sampling. A number of surprises, including that item limits don't require discrete representations.

Paul Bays @bayslab.org · Nov 19

New with @robthedatafiend in Psych Review: for every visual feature dimension there is a consistent upper bound on the "s.d." you can obtain by fitting errors with a normal+uniform mixture...and it just might be telling us something about neural tuning!

Paul Bays @bayslab.org · Nov 19

"Stochastic sampling provides a unifying account of working memory limits" - our new preprint:

Paul Bays @bayslab.org · Nov 19

A short note on BioRxiv on the relationship between the psychophysical scaling account of working memory by @timothyfbrady @markSchurgin and population coding models of the same:

Paul Bays @bayslab.org · Nov 19

Asymmetric competition in visual working memory: storing orientations doesn't affect memory for facial expressions, but storing expressions degrades orientation recall - new paper with Viljami Salmela on WM at different levels of the visual hierarchy:

Paul Bays @bayslab.org · Nov 19

An independent store in working memory for the locations of visual objects in relation to one another: provides a separate source of information for recalling locations and doesn't tap absolute (egocentric) WM resources - work with @DAagtenMurphy: PDF here

Paul Bays @bayslab.org · Nov 19

We have an opening for a graduate Research Assistant to join @BaysLab. More info here:

Paul Bays @bayslab.org · Nov 19

A Journal of Neuroscience "journal club" about our work on drift in working memory representations, by Ben Cuthbert & Dominic Standage

Paul Bays @bayslab.org · Nov 19

Our new review article identifies three distinct functions of transsaccadic memory

Paul Bays @bayslab.org · Nov 19

Another postdoc opening in @BaysLab: we're looking for someone with background in experimental study of eye movements to investigate visual evidence accumulation across gaze fixations. Apply here: