Publications associées à Visual processing

Global information processing in feedforward deep networks

Michael Herzog, Ben Henrik Lönnqvist, Adrien Christophe Doerig, Alban Bornet

While deep neural networks are state-of-the-art models of many parts of the human visual system, here we show that they fail to process global information in a humanlike manner. First, using visual crowding as a probe into global visual information process ...

2022

Visual feature integration: how long can it last?

Michael Herzog, Leila Drissi Daoudi - Kleinbauer, Lukas Vogelsang

Spatiotemporal features are integrated along motion trajectories. For example, when a central line is followed by pairs of flanking lines, two motion streams diverging from the center are perceived. The central line is rendered invisible by the subsequent ...

SAGE Publishing2021

How crowding challenges (feedforward) convolutional neural networks

Michael Herzog, Ben Henrik Lönnqvist, Gregory Francis, Adrien Christophe Doerig, Alban Bornet, Lynn Schmittwilken

Are (feedforward) convolutional neural networks (CNNs) good models for the human visual system? Here, we used visual crowding as a well-controlled psychophysical test to probe CNNs. Visual crowding is a ubiquitous breakdown of object recognition in the hum ...

2021

Serial dependence does not originate from low-level visual processing

Michael Herzog, David Pascucci, Gizay Ceylan

Perception depends not only on the current sensory input but also on the preceding history of stimuli. In serial dependence (SD), for example, the orientation of a Gabor patch is mistakenly reported as more similar to previous trials than it actually is. T ...

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 ...

ASSOC RESEARCH VISION OPHTHALMOLOGY INC2021

The role of population receptive field size and recurrent processing in learning to “de-crowd”

Michael Herzog, Bogdan Draganski, Ayberk Ozkirli, Maya Anna Jastrzebowska

In crowding, the perception of a target is impeded by surrounding clutter. While traditional models are feedforward and local, there is increasing behavioral and neural evidence for a critical role of recurrent processing across the visual hierarchy in cro ...

SAGE Publishing2021

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Diego Ghezzi, Eleonora Borda, Vivien Gaillet, Elodie Geneviève Zollinger

Objective. Optic nerve's intraneural stimulation is an emerging neuroprosthetic approach to provide artificial vision to totally blind patients. An open question is the possibility to evoke individual non-overlapping phosphenes via selective intraneural op ...

IOP PUBLISHING LTD2021

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Visual processing

Global information processing in feedforward deep networks

Visual feature integration: how long can it last?

How crowding challenges (feedforward) convolutional neural networks

Serial dependence does not originate from low-level visual processing

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

Spatial and temporal integration of visual features

The role of population receptive field size and recurrent processing in learning to “de-crowd”

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

Graph Chatbot

Chattez avec Graph Search

Global information processing in feedforward deep networks

Visual feature integration: how long can it last?

Spatial and temporal integration of visual features

Serial dependence does not originate from low-level visual processing

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

A machine-learning algorithm correctly classifies cortical evoked potentials from both visual stimulation and electrical stimulation of the optic nerve

The role of population receptive field size and recurrent processing in learning to “de-crowd”

How crowding challenges (feedforward) convolutional neural networks