Find information about my current and past projects below.
Where applicable, collaborators leading those projects are highlighted.
My peer-reviewed and preprint publications can be found on Google Scholar.
All my project reports and presentations are hosted at figshare.


Iterative category inference in recurrent neural networks

With: Adrien Doerig, Tim Kietzmann
Summary: Can we see signs of iterative category inference in RNNs? What kind of shape/texture/semantic space does the inference adhere to? Does the network play 20 questions with the image? What operations in the RNN lead to these processes?
Publications: CCN'23 paper

Characterising catastrophic forgetting and finding solutions

With: Daniel Anthes, Tim Kietzmann, Peter K├Ânig
Summary: What aspects of training neural networks continually leads to catastrophic forgetting? Can we find simple solutions, either bio-inspired or executable without much overhead, to workaround those aspects?
Publications: CCN'23 paper

Brain reading with a Transformer

With: Victoria Bosch, Tim Kietzmann, et al.
Summary: Using fMRI responses to natural scenes to condition the sentence generation in a Transformer, we study the neural underpinnings of scene semantics (objects and their relationships) encoded in natural language.

Shape/texture bias in minds and machines

With: Zejin Lu, Tim Kietzmann, Radoslaw Cichy
Summary: Building off Geirhos et al. 2018, where CNNs were shown to be texture-biased as compared to humans, we redefine the shape bias metric and assess the influence of recurrent processing and developmental trajectories on the shape bias in RNNs and humans.

Assessing the emergence of an attention schema in object tracking

With: Lotta Piefke, Adrien Doerig, Tim Kietzmann
Summary: In tracking an object through clutter with signal-boosting spatial attention, does a reinforcement learning agent learn to rely on an explicit encoding of the attention state - the attention schema? Related to Wilterson et al. PNAS 2021.


Perception of rare inverted letters among upright ones

With: Jochem Koopmans, Genevieve Quek, Marius Peelen
Summary: In a Sperling-like task where the letters are mostly upright, there is a general tendency to report occasionally-present and absent inverted letters as upright to the same extent. Previously reported expectation-driven illusions might be post-perceptual.
Comments: Jochem defended his masters' thesis.


Task-dependent characteristics of neural multi-object processing

With: Lu-Chun Yeh, Marius Peelen
Summary: The association between the neural processing of multi-object displays and the representations of those objects presented in isolation is task-dependent: same/different judgement relates to earlier, and object search to later stages in MEG/fMRI signals.
Comments: Preprint

Size-dependence of object search templates in natural scenes

With: Surya Gayet, Marius Peelen, et al.
Summary: Object size varies with the location of the object in scenes. During search for an object, in addition to the object's identity, the attentional template contains information its size, entangled with its identity, which is inferred from its location in the scene.
Comments: Preprint


Statistical learning of distractor co-occurrences facilitates visual search

With: Genevieve Quek, Marius Peelen
Summary: Efficient visual search relies on the co-occurrence statistics of distractor shapes. Increased search efficiency among co-occurring distractors is probably driven by faster and/or more accurate rejection of a distractor's partner as a possible target.
Publication: JOV'22 paper
Comments: JOV paper in brief

Bodies as features in visual search

With: Marius Peelen
Summary: Are high-level visual features prioritised, via feature-based attention, spatially-globally? We found attentional gain modulation of the fMRI representations of body silhouettes, presented in task-irrelevant locations, in high-level visual cortex.
Publication: NeuroImage'22 paper
Comments: NeuroImage paper in brief, Code + Data


Recurrent operations in neural networks trained to recognise objects

With: Giacomo Aldegheri, Tim Kietzmann
Summary: In a recurrent neural network trained for object categorization, the recurrent flow carries category-orthogonal object feature (e.g. object location) information, which is used, iteratively, to constrain the subsequent inferences about the object's category.
Publication: SVRHM'21 paper
Comments: SVRHM paper in brief


The function of early task-based modulations in object detection

With: Giacomo Aldegheri, Marcel van Gerven, Marius Peelen
Summary: Task-based modulation of early visual processing in neural networks alleviates subsequent capacity limits caused by task and neural constraints. Bias/gain modulation of neural activations can be linked to tapping into a superposition of networks. Optimised neural modulations are not feature-similarity gain modulations.
Publications: CCN'18 paper, CCN'19 paper

The influence of scene information on object processing

With: Ilze Thoonen, Sjoerd Meijer, Marius Peelen
Summary: Task-irrelevant scene information biases categorization response towards co-varying objects (e.g. cars on roads). However, no evidence is found, across 4 experiments, for task-irrelevant scene information boosting the sensitivity of detecting co-varying objects. Further experimentation is required for validating these observations.
Comments: Summary presentation

The nature of the animacy organization in human ventral temporal cortex

With: Daria Proklova, Daniel Kaiser, Marius Peelen
Summary: The animacy organisation in the ventral temporal cortex is not fully driven by visual feature differences (modelled with a CNN). It also depends on non-visual (inferred) factors such as agency (quantified through a behavioural task).
Publications: eLife'19 paper, Masters Thesis
Comments: Masters thesis in brief, eLife paper in brief


Reverse dictionary using a word-definition based graph search

With: Varad Choudhari
Summary: A method to process any forward word dictionary to build a reverse dictionary, using a n-hop reverse search on a graph, through word definitions. Performs as well as the state-of-the-art on a 3k lexicon. Doesn't scale well to 80k.
Publication: COLING'16 Paper
Comments: COLING paper in brief


A Spiking Neural Network as a Quadcopter Flight Controller

With: Sukanya Patil, Bipin Rajendran
Summary: a. Model-based control system for quadcopters towards velocity-waypoint navigation.
b. Modular SNNs for real-time arithmetic operations, using plastic synapses. SNNs are hard to tame!
Publications: IJCNN'15 paper, B.Tech. Thesis
Comments: Thesis rumination, IJCNN paper in brief