Explicit Information Processing Research Articles

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

Neural Coupling between Interhemispheric and Frontoparietal Functional Connectivity during Semantic Processing.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas, owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

A Novel Channel Selection Method for BCI Classification Using Dynamic Channel Relevance

Brain-Computer Interface (BCI) provides a direct communicating pathway between the human brain and the external environment. In the BCI systems, electroencephalography (EEG) signals are used to represent different cognitive patterns corresponding to various limb movements or motor imagery (MI) activities. However, EEG signals are multichannel in nature that require explicit information processing to alleviate the computational complexity of the BCI system. This paper represents a novel channel selection method to find effective EEG channels using Dynamic Channel Relevance (DCR) score. The proposed approach explores the minimum redundancy maximum relevance paradigm for selecting pertinent channels from raw ones. Firstly, the EEG signals are preprocessed using the Savitzky-Golay filter, and a sliding window approach is used to decompose them into chunks of fixed length. Further, three priorly known channels are used as candidate solutions for detecting new correlated channels. The selected channels are used to extract spatial-temporal features using the Multivariate Empirical Mode Decomposition (MEMD) method. The extracted features are used to discriminate four MI tasks (left hand, right hand, tongue, and foot) using the Support Vector Machine (SVM). The experiment is validated on three public EEG datasets (BCI Competition IV- 2008 – IIA, BCI Competition IV- dataset 1, BCI competition III - dataset IVa). The results show that the proposed method achieved a superior classification accuracy (85.4% on dataset 1, 80.33% on dataset 2, and 85.20% on dataset 3) with a lesser number of channels compared to state-of-the-art methods. In addition, our method significantly reduced the computation time compared to other published results without compromising the classification accuracy. Topographical mapping between the selected channels and cognitive regions showed that the central, frontal, and parietal lobe contributes to the execution of various MI tasks during physical activities.

A Novel Integrated Information Processing Model of Presence

Abstract The objective of this article is to conduct a narrative literature review on multisensory integration and propose a novel information processing model of presence in virtual reality (VR). The first half of the article introduces basic multisensory integration (implicit information processing) and the integration of coherent stimuli (explicit information processing) in the physical environment, offering an explanation for people's reactions during VR immersions and is an important component of our model. To help clarify these concepts, examples are provided. The second half of the article addresses multisensory integration in VR. Three models in the literature examine the role that multisensory integration plays in inducing various perceptual illusions and the relationship between embodiment and presence in VR. However, they do not relate specifically to presence and multisensory integration. We propose a novel model of presence using elements of these models and suggest that implicit and explicit information processing lead to presence. We refer to presence as a perceptual illusion that includes a plausibility illusion (the feeling that the scenario in the virtual environment is actually occurring) and a place illusion (the feeling of being in the place depicted in the virtual environment), based on efficient and congruent multisensory integration.

Official sports sponsorship fortress vs ambush marketing attack

PurposeThe purpose of this paper is to assess the effectiveness of event-related sports sponsorship and ambushing activity using social media video advertising that aim to affect spectators’ implicit and explicit brand information processing.Design/methodology/approachA dual model of brand knowledge is used that considers the implicit and explicit information processing of marketing-induced brand messages. A web study was conducted prior to the 2014 FIFA World Cup. Each participant implicitly and explicitly evaluated either one sponsor brand or one ambush brand before and after watching the video advertisement (within-subject design). A Wilcoxon signed-rank test was used to evaluate each change of the pre-post testing scores.FindingsImplicit and explicit brand associations as well as brand behavior were partially affected by the short contact with the advertisements of sponsor brands and ambush brands. In this regard, the implicit association measurements were more sensitive to reveal changes in the brand knowledge structure than their explicit counterparts. Furthermore, sponsorship advertising was slightly more effective than ambush advertising.Originality/valueThe current exploratory study evaluated for the first time the performance of event-related video advertisements that were originally released on social media of sponsor brands and ambush brands. The findings emphasize the necessary requirement of evaluating the implicit processing in addition to the explicit processing of sponsorship information to ensure a holistic evaluation of consumers’ memory with regard to the effectiveness of a sponsorship activity.

Age invariance in rapid facial affective reactions to emotionally valenced stimuli.

It has been suggested that an age-related positivity effect may only occur in the context of explicit information processing, but it is unclear whether this bias extends to the processing of rapid facial reactions. In addition, most studies that have looked for evidence of age-related implicit positivity have used attentional (as opposed to sensory) unawareness paradigms, or used broad-based indicators of attentional awareness that do not speak to the nature of the affective response. In the present study, younger and older adults were therefore asked to view non-facial images presented supraliminally (i.e., consciously) as well as outside of sensory awareness (i.e., subliminally) while their facial reactions were indexed using electromyography. The results indicated that both younger and older adults exhibited rapid facial reactions congruent with the emotional valence of non-facial images in both supraliminal and subliminal conditions. Relative to young, older adults did not respond with greater zygomaticus (cheek) activity to positive stimuli or reduced corrugator (brow) activity to negative stimuli in either condition. These data show that rapid facial reactions to emotional stimuli are intact in late adulthood, even in response to stimuli that activate more automatic and implicit forms of emotion processing. However, there is no evidence for any age-related positivity bias in these behavioral responses.

The green brand: Explicit and implicit framing effects of ecolabelling on brand knowledge

An increasing number of brands rely on ecolabelling as associative branding strategy for communicating their sustainability performance. Specifically, associative branding aims to link a brand to an ecolabel to embed sustainability into brand knowledge to provide an added value for consumers. In this regard, the present study applies a combined measurement approach that examines both implicit and explicit information processing to assess consumers’ brand associations related to sustainability. Specifically, a pre/post-testing is conducted to evaluate the framing effect of ecolabel exposure in a brand advertisement on the enhancement of brand knowledge. The results of the current study provide evidence that in particular implicit brand sustainability is a favorable brand association that positively affects consumers’ decision-making and preferences. Furthermore, the results reveal a relevant framing effect of ecolabelling on the enhancement of brand knowledge, on both implicit and explicit levels. In addition, the study results indicate the existence of three efficacy parameters that influence brand knowledge transfer: adequate ecolabel-brand fit, early ecolabel recognition time, and distinct implicit-explicit knowledge acquisition.

Feedback specificity, information processing, and transfer of training

This study examines the effects of feedback specificity on transfer of training and the mechanisms through which feedback can enhance or inhibit transfer. We used concurrent verbal protocol methodology to elicit and operationalize the explicit information processing activities used by 48 trainees performing the Furniture Factory computer simulation. We hypothesized and found support for a moderated mediation model. Increasing feedback specificity influenced the exposure trainees had to different task conditions and negatively affected their levels of explicit information processing. In turn, explicit information processes and levels of exposure to different task conditions interacted to impact transfer of training. Those who received less specific feedback relied more heavily on explicit information processing and had more exposure to the challenging aspects of the task than those who received more specific feedback, which differentially affected what they learned about the task. We discuss how feedback specificity and exposure to different task conditions may prime different learning processes.

Mind Meets Body:

There is compelling evidence that memory is multimodal, with an explicit information processing system of which we are consciously aware, and a parallel, implicit somatosensory system that operates without our awareness. Trauma causes alterations in the production and release of stress-responsive neurochemicals such as norepinephrine and the endogenous opioids, and extreme levels of these neurochemicals disrupt everyday, explicit information processing. The implicit processing system, however, continues to function in the face of trauma and studies of the neurobiological responses to trauma reveal that recovered memories of trauma are best under-stood as implicit, nonconscious, affective and sensorimotor memories. During trauma somatosensory information bypasses integrative higher-order cortical processing, thereby limiting memory to the fragmented affective and sensorimotor memories of the implicit memory system. Further, during memory retrieval the kindling of initially weak memories is hypothesized to ...

Connectionist models and their properties

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise-sensitivity, distributed decision-making, time and sequence problems, and the representation of complex concepts.

Connectionist Models and Their Properties

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and the representation of complex concepts.