Cortical network underlying audiovisual semantic integration and modulation of attention: An fMRI and graph-based study

Cognitive neural responses in the semantic comprehension of sound symbolic words and pseudowords

Introduction

Sound symbolism is the phenomenon of sounds having non-arbitrary meaning, and it has been demonstrated that pseudowords with sound symbolic elements have similar meaning to lexical words. It is unclear how the impression given by the sound symbolic elements is semantically processed, in contrast to lexical words with definite meanings. In event-related potential (ERP) studies, phonological mapping negativity (PMN) and N400 are often used as measures of phonological and semantic processing, respectively. Therefore, in this study, we analyze PMN and N400 to clarify the differences between existing sound symbolic words (onomatopoeia or ideophones) and pseudowords in terms of semantic and phonological processing.

Methods

An existing sound symbolic word and pseudowords were presented as an auditory stimulus in combination with a picture of an event, and PMN and N400 were measured while the subjects determined whether the sound stimuli and pictures match or mismatch.

Results

In both the existing word and pseudoword tasks, the amplitude of PMN and N400 increased when the picture of an event and the speech sound did not match. Additionally, compared to the existing words, the pseudowords elicited a greater amplitude for PMN and N400. In addition, PMN latency was delayed in the mismatch condition relative to the match condition for both existing sound symbolic words and pseudowords.

Discussion

We concluded that established sound symbolic words and sound symbolic pseudowords undergo similar semantic processing. This finding suggests that sound symbolism pseudowords are not judged on a simple impression level (e.g., spiky/round) or activated by other words with similar spellings (phonological structures) in the lexicon, but are judged on a similar contextual basis as actual words.

HC-Net: A hybrid convolutional network for non-human primate brain extraction

Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.

Semantically congruent audiovisual integration with modal-based attention accelerates auditory short-term memory retrieval

Evidence has shown that multisensory integration benefits to unisensory perception performance are asymmetric and that auditory perception performance can receive more multisensory benefits, especially when the attention focus is directed toward a task-irrelevant visual stimulus. At present, whether the benefits of semantically (in)congruent multisensory integration with modal-based attention for subsequent unisensory short-term memory (STM) retrieval are also asymmetric remains unclear. Using a delayed matching-to-sample paradigm, the present study investigated this issue by manipulating the attention focus during multisensory memory encoding. The results revealed that both visual and auditory STM retrieval reaction times were faster under semantically congruent multisensory conditions than under unisensory memory encoding conditions. We suggest that coherent multisensory representation formation might be optimized by restricted multisensory encoding and can be rapidly triggered by subsequent unisensory memory retrieval demands. Crucially, auditory STM retrieval is exclusively accelerated by semantically congruent multisensory memory encoding, indicating that the less effective sensory modality of memory retrieval relies more on the coherent prior formation of a multisensory representation optimized by modal-based attention.

Semantically Congruent Bimodal Presentation with Divided-Modality Attention Accelerates Unisensory Working Memory Retrieval

Although previous studies have shown that semantic multisensory integration can be differentially modulated by attention focus, it remains unclear whether attentionally mediated multisensory perceptual facilitation could impact further cognitive performance. Using a delayed matching-to-sample paradigm, the present study investigated the effect of semantically congruent bimodal presentation on subsequent unisensory working memory (WM) performance by manipulating attention focus. The results showed that unisensory WM retrieval was faster in the semantically congruent condition than in the incongruent multisensory encoding condition. However, such a result was only found in the divided-modality attention condition. This result indicates that a robust multisensory representation was constructed during semantically congruent multisensory encoding with divided-modality attention; this representation then accelerated unisensory WM performance, especially auditory WM retrieval. Additionally, an overall faster unisensory WM retrieval was observed under the modality-specific selective attention condition compared with the divided-modality condition, indicating that the division of attention to address two modalities demanded more central executive resources to encode and integrate crossmodal information and to maintain a constructed multisensory representation, leaving few resources for WM retrieval. Additionally, the present finding may support the amodal view that WM has an amodal central storage component that is used to maintain modal-based attention-optimized multisensory representations.

Impact of multisensory learning on perceptual and lexical processing of unisensory Morse code

Multisensory learning profits from stimulus congruency at different levels of processing. In the current study, we sought to investigate whether multisensory learning can potentially be based on high-level feature congruency (same meaning) without perceptual congruency (same time) and how this relates to changes in brain function and behaviour. 50 subjects learned to decode Morse code (MC) either in unisensory or different multisensory manners. During unisensory learning, the MC was trained as sequences of auditory trains. For low-level congruent (perceptual) multisensory learning, MC was applied as tactile stimulation to the left hand simultaneously to the auditory stimulation. In contrast, high-level congruent multisensory learning involved auditory training, followed by the production of MC sequences requiring motor actions and thereby excludes perceptual congruency. After learning, group differences were observed within three distinct brain regions while processing unisensory (auditory) MC. Both types of multisensory learning were associated with increased activation in the right inferior frontal gyrus. Multisensory low-level learning elicited additional activation in the somatosensory cortex, while multisensory high-level learners showed a reduced activation in the inferior parietal lobule, which is relevant for decoding MC. Furthermore, differences in brain function associated with multisensory learning was related to behavioural reaction times for both multisensory learning groups. Overall, our data support the idea that multisensory learning is potentially based on high-level features without perceptual congruency. Furthermore, learning of multisensory associations involves neural representations of stimulus features involved in learning, but also share common brain activation (i.e. the right IFG), which seems to serve as a site of multisensory integration.