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CATEGORIES:Conferences/Seminars/Lectures
DESCRIPTION:Speaker: Yael Niv\, Princeton University\n\nAbstract: No two ev
 ents are alike. But still\, we learn\, which means that we implicitly decid
 e what events are similar enough that experience with one can inform us abo
 ut what to do in another. Starting from early work by Sam Gershman\, we hav
 e suggested that this relies on parsing of incoming information into “clust
 ers” according to inferred hidden (latent) causes. In this talk\, I will pr
 esent this theory and illustrate its breadth in explaining human learning. 
 I will then discuss the relevance of latent cause inference to understandin
 g mental health conditions and their treatment.\n\nResearch in the Niv lab 
 focuses on the neural and computational processes underlying reinforcement 
 learning and decision-making. We study the ongoing day-to-day processes by 
 which animals and humans learn from trial and error\, without explicit inst
 ructions\, to predict future events and to act upon the environment so as t
 o maximize reward and minimize punishment. In particular\, we are intereste
 d in how attention and memory processes interact with reinforcement learnin
 g to create representations that allow us to learn to solve new tasks so ef
 ficiently. \n\nOur emphasis is on model-based experimentation: we use compu
 tational models to define precise hypotheses about data\, to design experim
 ents\, and to analyze results. In particular\, we are interested in normati
 ve explanations of behavior: models that offer a principled understanding o
 f why our brain mechanisms use the computational algorithms that they do\, 
 and in what sense\, if at all\, these are optimal. In our hands\, the main 
 goal of computational models is not to simulate the system\, but rather to 
 understand what high-level computations is that system realizing\, and what
  functionality do these computations fulfill. \n\nA new focus of the lab is
  computational cognitive neuropsychiatry. Here our aim is to use the comput
 ational toolkit that we have developed for quantifying dynamical behavioral
  processes in order to better diagnose\, understand\, and treat psychiatric
  illnesses such as depression\, OCD\, schizophrenia and addiction. This wor
 k is done under the auspices of the new Rutgers-Princeton Center for Comput
 ational Cognitive Neuropsychiatry.
DTEND:20240206T223000Z
DTSTAMP:20260305T113627Z
DTSTART:20240206T210000Z
GEO:42.362302;-71.091766
LOCATION:Singleton Auditorium\, 46-3002
SEQUENCE:0
SUMMARY:Quest | CBMM Seminar Series: Latent cause inference and mental heal
 th
UID:tag:localist.com\,2008:EventInstance_45401976917198
URL:https://calendar.mit.edu/event/quest-cbmm-seminar-series-latent-cause-i
 nference-and-mental-health
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