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?

Task-dependent characteristics of multi-object processing

With: Lu-Chun Yeh, Marius Peelen
Summary: The association between the neural processing of multi-object displays and the (independent) representations of those objects presented in isolation is task-dependent: in terms of unique associations with spatiotemporal stages of independent representations, same/different judgement - earlier, and object search - later stages.
Comments: Paper being written.


Size-dependence of object search templates in natural scenes

With: Surya Gayet, Marius Peelen
Summary: Object size varies with the location of the object in scenes. These variations get reflected in the search for that object in a given set of locations in a scene: our search is better for a smaller object further out in the scene as compared to closer in the scene.

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. This suggests that previously reported expectation-driven illusions might be post-perceptual in nature.


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