Neural decoding can be conceptualized as the problem of mapping brain responses back to sensory stimuli via a feature space. We introduce (i) a novel experimental paradigm that uses well-controlled yet highly naturalistic stimuli with a priori known …
Visual representations can be generated via feedforward or feedback processes. The extent to which these processes result in overlapping representations remains unclear. Previous work has shown that imagined stimuli elicit similar representations as …
Eye movements can have serious confounding effects in cognitive neuroscience experiments. Therefore, participants are commonly asked to fixate. Regardless, participants will make so-called fixational eye movements under attempted fixation, which are …
Mental imagery and visual perception rely on similar neural mechanisms, but the function of this overlap remains unclear. One idea is that imagery can influence perception. Previous research has shown that imagining a stimulus prior to binocular …
Population receptive field (pRF) mapping is an important asset for cognitive neuroscience. The pRF model is used for estimating retinotopy, defining functional localizers and to study a vast amount of cognitive tasks. In a classic pRF, the cartesian …
Visual and auditory representations in the human brain have been studied with encoding, decoding and reconstruction models. Representations from convolutional neural networks have been used as explanatory models for these stimulus-induced …
Amodal completion is the phenomenon of perceiving completed objects even though physically they are partially occluded. In this review, we provide an extensive overview of the results obtained from a variety of neuroimaging studies on the neural …
For decades, the extent to which visual imagery relies on the same neural mechanisms as visual perception has been a topic of debate. Here, we review recent neuroimaging studies comparing these two forms of visual experience. Their results suggest …
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of …
Visual perception and imagery rely on similar representations in the visual cortex. During perception, visual activity is characterized by distinct processing stages, but the temporal dynamics underlying imagery remain unclear. Here, we investigated …