PINTS is an annual event aimed to bring together cognitive, computational, and systems neuroscience researchers from the Paris region.

This year's symposium will feature keynote and invited lectures by renowned speakers, with a primary focus on contributed talks from the community. We encourage everyone to submit an abstract for a talk and/or poster at PINTS. Submissions are welcome from all career stages, and you are welcome to recycle abstracts from previous conferences (e.g. SFN or COSYNE).

A fundamental question in neuroscience is to understand how coordinated neural activity and structured circuitry in the brain are responsible for generating behavior. Decades of experimental and theoretical work have demonstrated that correlated neural activity has a strong impact on population coding by reshaping neural representations of external stimuli. More recently, large-scale recordings have provided insight as to the dynamic mechanisms by which neural populations perform computations, but the link to their circuitry remains unclear.

A workshop organized by the ENS and the University of Hyderabad (India).

The information we encounter on a daily basis involves both objective facts about the world and people’s subjective opinions. This distinction is also reflected in language: Words that express opinions (e.g. fun, amazing) differ from words conveying more objective facts (e.g. wooden, Bostonian): Subjective adjectives are perspective-sensitive and reflect someone’s opinion/attitude.

Publication bias can distort meta-analytic results, sometimes justifying considerable skepticism toward meta-analyses. This talk will discuss recently developed statistical sensitivity analyses for publication bias, which enable statements such as: “For publication bias to shift the observed point estimate to the null, ‘significant’ results would need to be at least 10-fold more likely to be published than negative or ‘non-significant’ results” or “no amount of publication bias could explain away the average effect.” The methods are based on inverse-probability weighted estimators and

Psychologists and computer scientists have very different views of the mind. Psychologists tell us that humans are error-prone, using simple heuristics that result in systematic biases. Computer scientists view human intelligence as aspirational, trying to capture it in artificial intelligence systems. How can we reconcile these two perspectives? In this talk, I will argue that we can do so by reconsidering how we think about rational action.

Abstract coming soon.

The DEC organizes a monthly colloquium with guests from the international scientific community. 

2022-2023 PRE-PROGRAM

Humans are not the only species that learns from others, but only humans learn and communicate in rich, diverse social contexts, and build repertoires of abstract, structured knowledge. What makes human social learning so distinctive, powerful, and smart?  In this talk, I argue that social learning is inferential at its core (inferential social learning); rather than copying what others do or trusting what others say, humans learn from others by drawing rich inferences from others’ behaviors, and help others learn by generating evidence tailored to others’ goals and knowledge states.