Publications

Abstract

*All authors contributed equally to this work
Meaningful gestures enhance degraded speech comprehension in neurotypical adults, but it is unknown whether this is the case for neurodivergent populations, such as autistic individuals. Previous research demonstrated atypical multisensory and speech-gesture integration in autistic individuals, suggesting that integrating speech and gestures may be more challenging and less beneficial for speech comprehension in adverse listening conditions in comparison to neurotypicals. Conversely, autistic individuals could also benefit from additional cues to comprehend speech in noise, as they encounter difficulties in filtering relevant information from noise. We here investigated whether gestural enhancement of degraded speech comprehension differs for neurotypical (n = 40, mean age = 24.1) compared to autistic (n = 40, mean age = 26.8) adults. Participants watched videos of an actress uttering a Dutch action verb in clear or degraded speech accompanied with or without a gesture, and completed a free-recall task. Gestural enhancement was observed for both autistic and neurotypical individuals, and did not differ between groups. In contrast to previous literature, our results demonstrate that autistic individuals do benefit from gestures during degraded speech comprehension, similar to neurotypicals. These findings provide relevant insights to improve communication practices with autistic individuals and to develop new interventions for speech comprehension.

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.

Abstract

Selective brain responses to objects arise within a few hundreds of milliseconds of neural processing, suggesting that visual object recognition is mediated by rapid feed-forward activations. Yet disruption of neural responses in early visual cortex beyond feed-forward processing stages affects object recognition performance. Here, we unite these discrepant findings by reporting that object recognition involves enhanced feedback activity (recurrent processing within early visual cortex) when target objects are embedded in natural scenes that are characterized by high complexity. Human participants performed an animal target detection task on natural scenes with low, medium or high complexity as determined by a computational model of low-level contrast statistics. Three converging lines of evidence indicate that feedback was selectively enhanced for high complexity scenes. First, functional magnetic resonance imaging (fMRI) activity in early visual cortex (V1) was enhanced for target objects in scenes with high, but not low or medium complexity. Second, event-related potentials (ERPs) evoked by target objects were selectively enhanced at feedback stages of visual processing (from ~220 ms onwards) for high complexity scenes only. Third, behavioral performance for high complexity scenes deteriorated when participants were pressed for time and thus less able to incorporate the feedback activity. Modeling of the reaction time distributions using drift diffusion revealed that object information accumulated more slowly for high complexity scenes, with evidence accumulation being coupled to trial-to-trial variation in the EEG feedback response. Together, these results suggest that while feed-forward activity may suffice to recognize isolated objects, the brain employs recurrent processing more adaptively in naturalistic settings, using minimal feedback for simple scenes and increasing feedback for complex scenes.