Publications by Adrien Christophe Doerig

Why computational complexity may set impenetrable barriers for epistemic reductionism

Michael Herzog, Christian Sachse, Adrien Christophe Doerig

According to physicalism, everything is physical or metaphysically connected to the physical. If physicalism were true, it seems that we should - in principle - be able to reduce the descriptions and explanations of special sciences to physical ones, for e ...

Springer2023

Fixing the problems of deep neural networks will require better training data and learning algorithms

Martin Schrimpf, Adrien Christophe Doerig, Matthias Bethge, Jianghao Liu, Kuntal Ghosh

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs ar ...

Cambridge Univ Press2023

First-person experience cannot rescue causal structure theories from the unfolding argument

Michael Herzog, Adrien Christophe Doerig, Aaron Schurger

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the cri ...

Elsevier2022

Dissecting (un)crowding

Michael Herzog, Adrien Christophe Doerig, Alban Bornet, Oh-Hyeon Choung

In crowding, perception of a target deteriorates in the presence of nearby flankers. Surprisingly, perception can be rescued from crowding if additional flankers are added (uncrowding). Uncrowding is a major challenge for all classic models of crowding and ...

2021

A new empirical challenge for local theories of consciousness

Adrien Christophe Doerig

Local theories of consciousness state that one is conscious of a feature if it is adequately represented and processed in sensory brain areas, given some background conditions. We challenge the core prediction of local theories based on long-lasting postdi ...

2021

A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities

Michael Herzog, Adrien Christophe Doerig, Alban Bornet

Deep neural networks (DNNs) have revolutionized computer science and are now widely used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as neuroscientific models of the human visual system; the debate centers on to what ...

2021

Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing

Michael Herzog, Adrien Christophe Doerig, Mauro Manassi, Alban Bornet, Oh-Hyeon Choung

In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is poo ...

2021

Shrinking Bouma's window: How to model crowding in dense displays

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements ...

2021

Response to commentaries on 'hard criteria for empirical theories of consciousness'

Michael Herzog, Adrien Christophe Doerig, Aaron Schurger

In consciousness research, we have a very large number of theories, which exceeds by far the number of theories in other fields. We recently presented a set of criteria for evaluating and comparing theories of consciousness, and then applied the criteria t ...

2021

Crowding reveals fundamental differences in local vs. global processing in humans and machines

Michael Herzog, Aurélien Bornet, Adrien Christophe Doerig, Oh-Hyeon Choung

Feedforward Convolutional Neural Networks (ffCNNs) have become state-of-the-art models both in computer vision and neuroscience. However, human-like performance of ffCNNs does not necessarily imply human-like computations. Previous studies have suggested t ...

2020