501 citations · 645 across the 13 of their papers we have counts for
13 papers
The Acquisition of Physical Knowledge in Generative Neural Networks
Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz
As children grow older, they develop an intuitive understanding of the physical processes around them. Their physical understanding develops in stages, moving along developmental t…
Turning large language models into cognitive models
Marcel Binz, Eric Schulz
Large language models are powerful systems that excel at many tasks, ranging from translation to mathematical reasoning. Yet, at the same time, these models often show unhuman-like…
Evaluating alignment between humans and neural network representations in image-based learning tasks
Can Demircan, Tankred Saanum, Leonardo Pettini +5
Humans represent scenes and objects in rich feature spaces, carrying information that allows us to generalise about category memberships and abstract functions with few examples. W…
Reinforcement Learning with Simple Sequence Priors
Tankred Saanum, Noémi Éltető, Peter Dayan +2
Everything else being equal, simpler models should be preferred over more complex ones. In reinforcement learning (RL), simplicity is typically quantified on an action-by-action ba…
Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata +3
Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonst…
Playing repeated games with Large Language Models
Elif Akata, Lion Schulz, Julian Coda-Forno +3
LLMs are increasingly used in applications where they interact with humans and other agents. We propose to use behavioural game theory to study LLM's cooperation and coordination b…
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto +2
In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different…
Learning Parsimonious Dynamics for Generalization in Reinforcement Learning
Tankred Saanum, Eric Schulz
Humans are skillful navigators: We aptly maneuver through new places, realize when we are back at a location we have seen before, and can even conceive of shortcuts that go through…
Stochastic Gradient Descent Captures How Children Learn About Physics
Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz
As children grow older, they develop an intuitive understanding of the physical processes around them. They move along developmental trajectories, which have been mapped out extens…
Using cognitive psychology to understand GPT-3
Marcel Binz, Eric Schulz
We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and ca…
Modeling Human Exploration Through Resource-Rational Reinforcement Learning
Marcel Binz, Eric Schulz
Equipping artificial agents with useful exploration mechanisms remains a challenge to this day. Humans, on the other hand, seem to manage the trade-off between exploration and expl…
Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation
Eric Schulz, Maarten Speekenbrink, Björn Meder
How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation as a computational solution to this problem. As a first proof of conce…
Better safe than sorry: Risky function exploitation through safe optimization
Eric Schulz, Quentin J. M. Huys, Dominik R. Bach +2
Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations…