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

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

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent processing remain unclear. In this paper, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Though ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain electroencephalography (EEG) activity within a visual masking paradigm. Sixty-two humans and fifty artificial agents (10 ResNet models of depths - 4, 6, 10, 18 and 34) completed an object categorization task. We show that deeper networks (ResNet-10, 18 and 34) explained more variance in brain activity compared to shallower networks (ResNet-4 and 6). Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at ∼98ms (from stimulus onset). These early differences indicated that EEG activity reflected ‘pure’ feedforward signals only briefly (up to ∼98ms). After ∼98ms, deeper networks showed a significant increase in explained variance which peaks at ∼200ms, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans.