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

What is the long‐term trajectory of semantic memory deficits in patients who have suffered structural brain damage? Memory is, per definition, a changing faculty. The traditional view is that after an initial recovery period, the mature human brain has little capacity to repair or reorganize. More recently, it has been suggested that the central nervous system may be more plastic with the ability to change in neural structure, connectivity, and function. The latter observations are, however, largely based on normal learning in healthy subjects. Here, we report a patient who suffered bilateral ventro‐medial damage after presumed herpes encephalitis in 1971. He was seen regularly in the eighties, and we recently had the opportunity to re‐assess his semantic memory deficits. On semantic category fluency, he showed a very clear category‐specific deficit performing better that control data on non‐living categories and significantly worse on living items. Recent testing showed that his impairments have remained unchanged for more than 40 years. We suggest cautiousness when extrapolating the concept of brain plasticity, as observed during normal learning, to plasticity in the context of structural brain damage.

Abstract

Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests 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 indicate that with an increase in network depth, there is an increase in the distinction between object- and background information. For more shallow networks, results indicated a benefit of training on segmented objects. Overall, these results indicate that, de facto, scene segmentation can be performed by a network of sufficient depth. We conclude that the human brain could perform scene segmentation in the context of object identification without an explicit mechanism, by selecting or “binding” features that belong to the object and ignoring other features, in a manner similar to a very deep convolutional neural network.

Abstract

A fundamental component of interacting with our environment is gathering and interpretation of sensory information. When investigating how perceptual information influences decision-making, most researchers have relied on manipulated or unnatural information as perceptual input, resulting in findings that may not generalize to real-world scenes. Unlike simplified, artificial stimuli, real-world scenes contain low-level regularities that are informative about the structural complexity, which the brain could exploit. In this study, participants performed an animal detection task on low, medium or high complexity scenes as determined by two biologically plausible natural scene statistics, contrast energy (CE) or spatial coherence (SC). In experiment 1, stimuli were sampled such that CE and SC both influenced scene complexity. Diffusion modelling showed that the speed of information processing was affected by low-level scene complexity. Experiment 2a/b refined these observations by showing how isolated manipulation of SC resulted in weaker but comparable effects, with an additional change in response boundary, whereas manipulation of only CE had no effect. Overall, performance was best for scenes with intermediate complexity. Our systematic definition quantifies how natural scene complexity interacts with decision-making. We speculate that CE and SC serve as an indication to adjust perceptual decision-making based on the complexity of the input.

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.