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UID:20260425T024656EDT-3307X1N8w1@132.216.98.100
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DESCRIPTION:Learning --- and Learning to Learn\n\nBy Nathaniel Daw\n\nProfe
 ssor Princeton University\n\nDate: Wednesday\, April 29\, 2026\n	Time: 10:0
 0 a.m. to 1:00 p.m.\n	Location: Online\n\nAttend online\n\nView poster\n\n
 \nAbstract\n\nClassic work on learning from rewards has emphasized a simpl
 e error-driven mechanism for incremental averaging\, with accompanying beh
 avioral evidence\, neural correlates\, and connections to normative ideas 
 from statistical machine learning. This talk reviews theory and evidence a
 bout how such trial-by-trial learning rules are themselves adaptively shap
 ed by more abstract learning that adapts them to individual situations. I 
 discuss how our conceptions of such 'learning to learn' or 'metalearning' 
 have grown in scope and ambition: from adjusting hyperparameters of a sing
 le baseline learning rule\; to judiciously trading off several different c
 andidate learning rules according to context\; to discovering new abstract
 ions or learning rules optimized to a task or environment. This work in bi
 ological learning parallels a similar expansion of perspective in machine 
 learning\, most recently in 'in-context learning' in large language models
 . Neurally\, this research implies a shift from textbook synaptic plastici
 ty rules for incremental learning toward more flexible accounts whereby 'i
 nner-loop' learning concerns representations maintained in recurrent activ
 ity states (e.g.\, in prefrontal cortex)\, whose dynamics and implied 'lea
 rning rules' are themselves shaped by plasticity. Recent behavioral and ne
 ural evidence from humans and animals has begun to shed light on these two
  mechanisms.\n
DTSTART:20260429T140000Z
DTEND:20260429T170000Z
SUMMARY:MCCHE Precision Convergence Webinar Series with Nathaniel Daw
URL:/dobson/channels/event/mcche-precision-convergence
 -webinar-series-nathaniel-daw-372712
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