Memory influences visual cognition across multiple functional states of interactive cortical dynamics

The Brain's Sensitivity to Real-world Statistical Regularity Does Not Require Full Attention

Predictive coding accounts of perception state that the brain generates perceptual predictions in the service of processing incoming sensory data. These predictions are hypothesized to be afforded by the brain's ability to internalize useful patterns, that is, statistical regularities, from the environment. We have previously argued that the N300 ERP component serves as an index of the brain's use of representations of (real-world) statistical regularities. However, we do not yet know whether overt attention is necessary in order for this process to engage. We addressed this question by presenting stimuli of either high or low real-world statistical regularity in terms of their representativeness (good/bad exemplars of natural scene categories) to participants who either fully attended the stimuli or were distracted by another task (attended/distracted conditions). Replicating past work, N300 responses were larger to bad than to good scene exemplars, and furthermore, we demonstrate minimal impacts of distraction on N300 effects. Thus, it seems that overtly focused attention is not required to maintain the brain's sensitivity to real-world statistical regularity. Furthermore, in an exploratory analysis, we showed that providing additional, artificial regularities, formed by altering the proportions of good and bad exemplars within blocks, further enhanced the N300 effect in both attended and distracted conditions, shedding light on the relationship between statistical regularities learned in the real world and those learned within the context of an experiment.

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification

BACKGROUND

In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.

NEW METHOD

This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.

RESULTS

The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.

COMPARISON WITH EXISTING METHODS

In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.

CONCLUSION

The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.

Modulation effects of repeated transcranial direct current stimulation on the dorsal attention and frontal parietal networks and its association with placebo and nocebo effects

Literature suggests that attention is a critical cognitive process for pain perception and modulation and may play an important role in placebo and nocebo effects. Here, we investigated how repeated transcranial direct current stimulation (tDCS) applied at the dorsolateral prefrontal cortex (DLPFC) for three consecutive days can modulate the brain functional connectivity (FC) of two networks involved in cognitive control: the frontoparietal network (FPN) and dorsal attention network (DAN), and its association with placebo and nocebo effects. 81 healthy subjects were randomized to three groups: anodal, cathodal, and sham tDCS. Resting state fMRI scans were acquired pre- and post- tDCS on the first and third day of tDCS. An Independent Component Analysis (ICA) was performed to identify the FPN and DAN. ANCOVA was applied for group analysis. Compared to sham tDCS, 1) both cathodal and anodal tDCS increased the FC between the DAN and right parietal operculum; cathodal tDCS also increased the FC between the DAN and right postcentral gyrus; 2) anodal tDCS led to an increased FC between the FPN and right parietal operculum, while cathodal tDCS was associated with increased FC between the FPN and left superior parietal lobule/precuneus; 3) the FC increase between the DAN and right parietal operculum was significantly correlated to the placebo analgesia effect in the cathodal group. Our findings suggest that both repeated cathodal and anodal tDCS could modulate the FC of two important cognitive brain networks (DAN and FPN), which may modulate placebo / nocebo effects.

Semantic Expectation Effects on Object Detection: Using Figure Assignment to Elucidate Mechanisms

Recent evidence suggesting that object detection is improved following valid rather than invalid labels implies that semantics influence object detection. It is not clear, however, whether the results index object detection or feature detection. Further, because control conditions were absent and labels and objects were repeated multiple times, the mechanisms are unknown. We assessed object detection via figure assignment, whereby objects are segmented from backgrounds. Masked bipartite displays depicting a portion of a mono-oriented object (a familiar configuration) on one side of a central border were shown once only for 90 or 100 ms. Familiar configuration is a figural prior. Accurate detection was indexed by reports of an object on the familiar configuration side of the border. Compared to control experiments without labels, valid labels improved accuracy and reduced response times (RTs) more for upright than inverted objects (Studies 1 and 2). Invalid labels denoting different superordinate-level objects (DSC; Study 1) or same superordinate-level objects (SSC; Study 2) reduced accuracy for upright displays only. Orientation dependency indicates that effects are mediated by activated object representations rather than features which are invariant over orientation. Following invalid SSC labels (Study 2), accurate detection RTs were longer than control for both orientations, implicating conflict between semantic representations that had to be resolved before object detection. These results demonstrate that object detection is not just affected by semantics, it entails semantics.

Connecting and considering: Electrophysiology provides insights into comprehension

The ability to rapidly and systematically access knowledge stored in long-term memory in response to incoming sensory information-that is, to derive meaning from the world-lies at the core of human cognition. Research using methods that can precisely track brain activity over time has begun to reveal the multiple cognitive and neural mechanisms that make this possible. In this article, I delineate how a process of connecting affords an effortless, continuous infusion of meaning into human perception. In a relatively invariant time window, uncovered through studies using the N400 component of the event-related potential, incoming sensory information naturally induces a graded landscape of activation across long-term semantic memory, creating what might be called "proto-concepts". Connecting can be (but is not always) followed by a process of further considering those activations, wherein a set of more attentionally demanding "active comprehension" mechanisms mediate the selection, augmentation, and transformation of the initial semantic representations. The result is a limited set of more stable bindings that can be arranged in time or space, revised as needed, and brought to awareness. With this research, we are coming closer to understanding how the human brain is able to fluidly link sensation to experience, to appreciate language sequences and event structures, and, sometimes, to even predict what might be coming up next.

Neurophysiology of embodied mental rotation: Event-related potentials in a mental rotation task with human bodies as compared to alphanumeric stimuli

The present study examines if the neural signature of information processing in mental rotation tasks is moderated by stimulus characteristics (e.g., body-related vs. non-body-related stimuli). In the present experiment, stimulus sets of human figures (back view; left vs. right arm abduction) and alphanumeric characters ('R'; normal vs. mirrored view) were scrutinized with event-related potentials (ERPs) in the electroencephalography (EEG). Participants had to judge parity between an upright (0° orientation) and a comparison stimulus (stimulus disparity; 0°, 45°, 90°, 135° or 180°). There was a main effect of stimulus disparity for the behavioural (response time and error rates), as well as for the neural data (rotation-related negativity, RRN). The interaction of stimulus disparity and stimulus type was significant for the RRN, but not for the response time. Lower RRN amplitudes for letters indicate a more pronounced use of alternative processes (e.g., memory retrieval), which seems to be reflected in higher N350 amplitudes. Moreover, the increase of the RRN amplitude and the increase in response time as a function of disparity were positively correlated. Task differences were evident for several ERP components (i.e., N150, P150 and N250), being independent of disparity, which might reflect differences in early and late object cognition prior to the mental rotation process itself. This might be associated with the task-dependent activation of embodied cognition processes in mental rotation tasks.