Connectionists: Fwd: New on the VVTNS YouTube channel : Towards a general model of human reward-based learning | Maria Eckstein, Google Deepmind

[David Hansel]

[image: VVTNS.png]

>
> <https://84454458.streak-link.com/C-uuSj_sbJvSeZ9u1w21kx9L/https%3A%2F%2Fwww.wwtns.online>
>
https://www.wwtns.online
> <https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.online>
> - on twitter: wwtns at TheoreticalWide  and on YouTube
>

New on the VVTNS Website and YouTube Channel
https://www.wwtns.online
<https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.online>


Towards a general model of human reward-based learning

lecture delivered on May 20,  2026  by

Maria Eckstein

Google Deepmind

Abstract: Traditional work in the study of human reward-based learning
involves designing an experimental task---often inspired by Reinforcement
Learning (RL) theory---and fits a small set of computational models---often
inspired by RL algorithms---to that dataset. For example, researchers often
model human behavior on bandit tasks using variants of Q-learning. While
this approach has been highly productive, leading to landmark discoveries
such as the dopamine reward prediction error hypothesis, it also has
limitations. This talk focuses on the lack of generalizability of such
models: Even if they closely fit behavior on the original task, models
derived from the one-task-one-model paradigm usually predict behavior on
other tasks quite poorly. I argue that this lack of generalizability is a
fundamental problem for the cognitive sciences: we intuitively expect our
models to be robust to superficial task differences, such as variations in
the number of choice options, reward probabilities, or the exact kind of
non-stationarity. I will propose potential solutions to this problem along
two dimensions: the behavioral dataset and the computational model.
Regarding computational models, I will introduce work in which we moved
beyond the limitations of hand-crafted one-off models by employing
flexible, data-driven methods. These methods allowed us to compare classes
of models instead of individual model instances, allowing us to cover the
space of possible models more exhaustively, and innovate cognitive
mechanisms very efficiently. For the behavioral dataset, we move from using
single learning tasks to a comprehensive task space that encompasses most
existing paradigms in the literature, while closing the gaps between them
in a near-continuous fashion. Our results suggest that more general models
in conjunction with broader datasets can pave the road toward increasingly
general models of human reward-based learning and decision making, and a
persistent departure from many aspects of RL theory.
.

*About VVTNS : **VVTNS  is a weekly digital seminar on Zoom targeting the
theoretical neuroscience community. Launched as the World Wide Neuroscience
Seminar (WWTNS) in November 2020 and renamed in homage to Carl van
Vreeswijk in Memoriam (April 20, 2022), its aim is to be a platform to
exchange ideas among theoreticians, but not only. Speakers have the
occasion to talk about theoretical aspects of their work which cannot be
discussed in a setting where the majority of the audience consists of
experimentalists. The seminars  are 45 min long followed by a discussion
and are held on Wednesdays at 11 am ET. The talks are recorded with
authorization of the speaker and are available to everybody on our YouTube
channel.*



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