About
16
Publications
2,344
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
161
Citations
Introduction
I am currently working as a PhD student in the Vision and Computational Cognition lab at the Max Planck Institute for Human Cognitive and Brain Sciences. Here, I am studying the functional topography of object dimensions in the human brain. To that end, I combine computational models of object recognition with large-scale fMRI data to connect neural, behavioral, and model representations of objects.
Current institution
Additional affiliations
April 2018 - present
April 2016 - April 2017
April 2017 - August 2017
Publications
Publications (16)
Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioural goals, suggesting basic limitations of this category-centric framework. To address these limitat...
Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioral goals, suggesting basic limitations of this category-centric framework. To address these limitati...
Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalogra...
The primary somatosensory cortex (SI) contains fine-grained tactile representations of the body, arranged in an orderly fashion. Using ultra-high resolution fMRI data to describe such detailed individual topographic maps or to detect group differences is challenging, because group alignment often does not preserve the high spatial detail of the dat...
Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale datasets comprising functional MRI, magnetoencephalographic recordings, and 4.70 million similarity judgments in...
A detailed understanding of visual object representations in brain and behavior is fundamentally limited by the number of stimuli that can be presented in any one experiment. Ideally, the space of objects should be sampled in a representative manner, with (1) maximal breadth of the stimulus material and (2) minimal bias in the object categories. Su...
Musical excerpts have been shown to have the capacity to prime the processing of target words and vice versa, strongly suggesting that music can convey concepts. However, to date no study has investigated an influence of musical semantics on novel word acquisition, thus corroborating evidence for a similarity of underlying semantic processing of mu...
Objectives: When physical exercise is systematically coupled to music production, exercisers experience improvements in mood, reductions in perceived effort, and enhanced muscular efficiency. The physiology underlying these positive effects remains unknown. Here we approached the investigation of how such musical agency may stimulate the release of...
Contextual cueing can be enhanced by reward. However, there is a debate if reward is associated with the repeated target–distractor configurations or with the repeated target locations that occur in both repeated and new displays. Based on neuroimaging evidence, we hypothesized that reward becomes associated with the target location only in new dis...
Questions
Questions (2)
Dear community,
I'm looking into ways how to do an a-priori power analysis for an fMRI experiment where the main analysis will be a representational similarity analysis (RSA).
The experiment will present the same stimuli in two successive fMRI-sessions (with a behavioral training in between). For each fMRI session, I plan to do a model-based RSA on brain responses elicited by the stimuli. Voxel restriction will be done with a searchlight procedure. The most interesting outcome will be the difference in these results between the two training sessions, as estimated with a GLM contrast.
I think this is not uncommon as i found other experiments adopting a similar analysis procedure. I found no clue however on how to estimate the necessary sample size to achieve a certain statistical power (say 80%).
Since this is a bit of a frankenstein made from other common statistical approaches, I'm not sure if the general logic of fMRI-power analysis applies here.
Has anybody experience in this area or can point me to literature that contemplates this issue?
Thanks,
Oliver
For a vision fMRI experiment, we want to use short video stimuli (somewhere around 5 seconds) presenting rotating objects. The videos should not contain any clutter but only the object itself in front of a uniform background. Hence naturalistic stimuli are not wished for.
Does anybody know any established datasets that fit the description?
Thanks!
