Publications

Abstract

Frequency tagging has been successfully used to investigate selective stimulus processing in electroencephalography (EEG) or magnetoencephalography (MEG) studies. Recently, new projectors have been developed that allow for frequency tagging at higher frequencies (>60 Hz). This technique, rapid invisible frequency tagging (RIFT), provides two crucial advantages over low-frequency tagging as (i) it leaves low-frequency oscillations unperturbed, and thus open for investigation, and ii) it can render the tagging invisible, resulting in more naturalistic paradigms and a lack of participant awareness. The development of this technique has far-reaching implications as oscillations involved in cognitive processes can be investigated, and potentially manipulated, in a more naturalistic manner.

Abstract

Feedforward deep convolutional neural networks (DCNNs) are matching and even surpassing human performance on object recognition. This performance suggests that activation of a loose collection of image
features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Recent findings in humans however, suggest that while feedforward activity may suffice for
sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to
performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects
and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicated less distinction between object- and background features for more shallow networks. For those networks, we observed a benefit of training on segmented objects (as compared to unsegmented objects). Overall, deeper networks trained on natural
(unsegmented) scenes seem to perform implicit 'segmentation' of the objects from their background, possibly by improved selection of relevant features.

Abstract

Blindsight refers to the observation of residual visual abilities in the hemianopic field of patients without a functional V1. Given the within- and between-subject variability in the preserved abilities and the phenomenal experience of blindsight patients, the fine-grained description of the phenomenon is still debated. Here we tested a patient with established “perceptual” and “attentional” blindsight (c.f. Danckert and Rossetti, 2005). Using a pointing paradigm patient MS, who suffers from a complete left homonymous hemianopia, showed clear above chance manual localisation of ‘unseen’ targets. In addition, target presentations in his blind field led MS, on occasion, to spontaneous responses towards his sighted field. Structural and functional magnetic resonance imaging was conducted to evaluate the magnitude of V1 damage. Results revealed the presence of a calcarine sulcus in both hemispheres, yet his right V1 is reduced, structurally disconnected and shows no fMRI response to visual stimuli. Thus, visual stimulation of his blind field can lead to “action blindsight” and spontaneous antipointing, in absence of a functional right V1. With respect to the antipointing, we suggest that MS may have registered the stimulation and subsequently presumes it must have been in his intact half field.