Giuseppe Castegnetti (@gcastegnetti) 's Twitter Profile
Giuseppe Castegnetti

@gcastegnetti

Neuroscientist, former member of @bendemartino lab at UCL and @bachlab_cog at UZH.

ID: 381089745

calendar_today27-09-2011 18:37:18

67 Tweet

106 Takipçi

597 Takip Edilen

bachlab (@bachlab_cog) 's Twitter Profile Photo

1/2 New from the lab by Karita Ojala: a review of 10 types of conditioned responses that are used to measure fear conditioning. Relation to amygdala-based learning, computational learning models, measurability?authors.elsevier.com/a/1b3wHY3M3Q80r

Maurizio Cecconi (@drmcecconi) 's Twitter Profile Photo

Our last #COVID19 patient has just left and we are closing our last COVID19 ICU. Thank you to my team, to our patients and to their families, to my hospital colleagues, to the ICU network. And don't worry everyone, we will be here and ready, but for today: thank you and stay safe

Katrin Preller (@katrinpreller) 's Twitter Profile Photo

#Psychedelic drugs: neurobiology and potential for treatment of psychiatric disorders. Take a look at our new review paper: nature.com/articles/s4158…

Simon Kuestenmacher (@simongerman600) 's Twitter Profile Photo

Resharing this series of paths created by 800 unmanned bicycles being pushed until they fall over. Science is beautiful. Source: buff.ly/2n3Q7mx

Resharing this series of paths created by 800 unmanned bicycles being pushed until they fall over. Science is beautiful. Source: buff.ly/2n3Q7mx
bachlab (@bachlab_cog) 's Twitter Profile Photo

New from the lab by Yanfang Xia 夏艳芳 Filip Melinscak in Behav Res Methods: Saccadic scan path length/speed during CS as indicator of human fear conditioning. Effect size (Hedge's g) 0.4-0.6. Underlying mechanism: more and longer fixation on screen centre during CS+. doi.org/10.3758/s13428…

New from the lab by <a href="/SerenXia/">Yanfang Xia 夏艳芳</a> <a href="/fmelinscak/">Filip Melinscak</a> in Behav Res Methods: Saccadic scan path length/speed during CS as indicator of human fear conditioning. Effect size (Hedge's g) 0.4-0.6. Underlying mechanism: more and longer fixation on screen centre during CS+.  doi.org/10.3758/s13428…
bachlab (@bachlab_cog) 's Twitter Profile Photo

1/ New from the lab in Nature Human Behaviour, with Filip Melinscak, @smfleming and Manuel Völkle: how to optimise measurement of experimental dependent variables across fields of psychology. nature.com/articles/s4156…

Giuseppe Castegnetti (@gcastegnetti) 's Twitter Profile Photo

Very happy to share our new modelling paper with bachlab and Dan Bush, in which we propose a possible mechanism for how anxiolytic medications can reduce fear/anxiety (spoiler: by disrupting threat <-> location associations) ⚡️⚡️⚡️

bachlab (@bachlab_cog) 's Twitter Profile Photo

Today is our official start at the new Hertz Chair for Artificial Intelligence and Neuroscience Rheinische Friedrich-Wilhelms-Universität Bonn. Looking forward to working with brilliant colleagues, and very honoured by this warm welcome: uni-bonn.de/en/news/062-20…

bachlab (@bachlab_cog) 's Twitter Profile Photo

New behavioural methods paper by brilliant Jelena Wehrli with Yanfang Xia 夏艳芳 and Samuel Gerster: best way to measure long-interval (15 s) human trace fear conditioning (pupil dilation and SCR), and its retention after 7 days (startle eye-blink). onlinelibrary.wiley.com/doi/10.1111/ps…

Stefano Palminteri (@stepalminteri.bsky.social) (@stepalminteri) 's Twitter Profile Photo

everybody: Where are you from in Italy? me: Sicily almost everybody: Ah, Sicily = Mafia! 30 years after prosecutor Borsellino's killing the time is ripe to explain to my international friends why this “joke” is not funny (even if I understand why: so no hard feelings!) 1/n

everybody: Where are you from in Italy? 
me: Sicily
almost everybody: Ah, Sicily = Mafia!

30 years after prosecutor Borsellino's killing the time is ripe to explain to my international friends why this “joke” is not funny 

(even if I understand why: so no hard feelings!)

1/n
Patrick Cannon (@pw_cannon) 's Twitter Profile Photo

Is model misspecification the biggest challenge for modern SBI algorithms? In our new work, we show that many neural SBI techniques can fail dramatically when simulators are even slightly misspecified. For details, see arxiv.org/abs/2209.01845