Past Events

International Lecture Series

February 21st, 2024, 4:15-5:45 pm: Antonia Bott on "Context effects on dynamic belief updating in psychosis and paranoia"

March 13th, 2024: 9:45-11 am: Peter Dayan on "Mindgames"

March 13th, 2024: 11:15 am-12:30 pm: Andreea Diaconescu on "Aberrant perception of environmental volatility in early psychosis"

June 26th, 2024, 4:15-5:45 pm: Philipp Sterzer on "Now you see it… now you don’t: Temporal fluctuations in perceptual inference and their role in psychosis"

July 17th, 2024, 4:15-5:45 pm: Klaas Enno Stephan on "Translational Neuromodeling, Computational Psychiatry & Computational Psychosomatics" (postponed from April 24th)

September 23rd, 2024, 1:00-2:30 pm: Joshua Gold on "Mechanisms of Adaptive Inference"

October 30th, 2024, 4:15-5:45 pm: Sören Krach on "Affected beliefs: Neurocomputational mechanisms and clinical implications

February 26th, 2025, 4:15-5:45 pm: Michael J. Frank on "Strategies for managing memory uncertainty to improve effect capacity in biological and artificial neural networks"

Abstract:

How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). I will present a neural network model of corticostriatal circuitry that can learn to reuse the same neural populations to store multiple items, leading to resourcelike constraints within a slot-like system, and inducing a tradeoff between quantity and precision of information. Such “chunking” strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. These simulations also suggest a computational rather than anatomical limit to WM capacity. As such I will also describe a new line of work linking mechanisms of WM gating in biological networks to those that can emerge in transformer neural networks underlying language models. Despite not having memory limits, we also find that storing and accessing multiple items requires an efficient gating policy, resembling the constraints found in frontostriatal models. When learned effectively, these gating strategies support enhanced generalization and increase the models' effective capacity to store and access multiple items in memory.