Cognitive (neuro)scientist at @Inserm and @Cognition_ENS, interested in how we make decisions (and why we make avoidable errors) in the face of uncertainty.
In doi.org/10.1016/j.neur…, using a similar categorization task in humans, fitted lapse rates are only about 1-2% and typically lose against lapse-free accounts. And rightly so, if subjects have understood that task rules *never* change (which mice haven’t). [2/9]
Oct 23, 2018 • 15 tweets • 5 min read
All models are wrong, sure, but how wrong? Check out my BBS commentary (goo.gl/TBf1H7) to the target piece by @DobyRahnev and @rndenison (goo.gl/mZPYRh). I argue that the self-consistency of modeled decisions should be considered a relevant metric. 1/15
Relative model comparison is most often used to identify a better model across a limited set of candidates. But how good, in an absolute sense, are the models being tested? The upper bound on model evidence is usually unknown, making this burning question hard to answer. 2/15
Oct 11, 2018 • 16 tweets • 8 min read
Learn more about our new study, “Computational noise in reward-guided learning drives behavioral variability in volatile environments” (goo.gl/BtNAXR), with @FindlingCharles, @vasilisa_skv and @StePalminteri! #tweetstorm#preprint 1/16
When tracking the value of actions in volatile environments, humans make seemingly irrational decisions which fail to maximize expected value. Existing theories attribute these ‘non-greedy’ decisions to information seeking – a.k.a., the exploration-exploitation trade-off. 2/16
