58 citations · 64 across the 6 of their papers we have counts for
7 papers
Meta-learning as a principle for human-like visual representations
Can Demircan, Marcel Binz, Alireza Modirshanechi +1
The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual representations with unprecedented…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization
Alireza Modirshanechi, Benjamin Eysenbach, Peter Dayan +1
Unsupervised pretraining has driven empirical advances in goal-conditioned reinforcement learning (GCRL), but its theoretical foundations remain poorly understood. In particular, a…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
A taxonomy of surprise definitions
Alireza Modirshanechi, Johanni Brea, Wulfram Gerstner
Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on th…
Fitting summary statistics of neural data with a differentiable spiking network simulator
Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi +2
Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose par…
Learning in Volatile Environments with the Bayes Factor Surprise
Vasiliki Liakoni, Alireza Modirshanechi, Wulfram Gerstner +1
Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarc…