Information Processing Ability Research Articles

MoS2/ZnO-heterostructured optoelectronic synapse for multiwavelength optical information-based sensing, memory, and processing

The rapid development of artificial intelligence and humanoid robots requires hardware-level revolution of synapses-based bionic vision system. However, current optoelectronic synapses featuring multiwavelength sensing and multifunctional processing necessitate intricate integrations of multi-materials and floating gate configurations, which induces complex fabrication process and limited integration density. Here, we propose a novel two-terminal optoelectronic synaptic device based on MoS2/ZnO heterostructure which integrates sense and memory functions and realizes multifunctional optical information processing while circumvents the need of complex configurations. By simultaneously exploiting the photo-adsorption oxygen dissociation property of ZnO and the persistent photoconductivity effect of MoS2, the device demonstrates synaptic capability of four-color perception with a single synaptic event consuming a mere 2.55×10−9 J. Different from the traditional power-density sensitive nociceptor, for the first time photon energy-sensitive nociceptors is developed, which can mimic the process of UV irradiation to the retina leading to fear of visible light. Diverse information processing ability, such as photonic information encryption, associative learning, and logic calculation, was demonstrated in a single device, indicating a practical strategy for facile design for future high integration intensity neuromorphic devices.

Study on the relationship between fixation characteristics and hit rate in psychological procedure training of free throw.

Free throw is an important means of scoring in basketball games. With the improvement of basketball competition level and the enhancement of confrontation degree, the number of free throws in the game gradually increases, so the score of free throw will have an important impact on the result of the game. The purpose of this study is to explore the relationship between visual attention characteristics and hit rate of basketball players in free throw psychological procedure training, so as to provide scientific basis for basketball teaching and training. Forty players with similar free throw abilities were randomly assigned to the experimental group (10 males, 10 females) and control group (10 males, 10 females). The experimental group was free throw psychological procedure training, while the control group was trained with routine training, Eye movement indices (number of fixations, fixation duration, and pupil dilation) and the free throw hit rate and analyzed before and after the experiment. Group differences were examined using t-tests, while paired sample t-tests were conducted to compare pre- and post-test results within each group. The training time and training times of the two groups were the same. There were significant differences in fixation duration, number of fixations, pupil diameter and free throw hit rate between pre-test and post-test in the experimental group (P < 0.05). Post-test, there were significant differences in number of fixations, fixation duration, pupil diameter and free throw hit rate between the two groups (P < 0.05). There was a significant positive correlation between number of fixations and free throw hit rate in top (P < 0.01), and there was a significant positive correlation between fixation duration and hit rate in front (P < 0.01). The psychological procedure training can improve the visual information search strategy and information processing ability of free throw, and significantly improve the free throw hit rate. There was a positive correlation between the front fixation time and the free throw hit rate, and there was a positive correlation between the top number of fixations and the free throw hit rate.

Quảng cáo lan truyền: Phản ứng cảm xúc, thái độ và dự định chia sẻ của người xem

This research applied Lang’s model of limited intermediate information processing ability (2000, 2006, 2009) to test the impact of different emotional reactions to attitudes towards viral video advertising, brand attitude and sharing intention of Vietnamse viewer after watching viral video ads. Experimental Research Design is used to test the relationships between independent variables and dependent variables. Research results show that viral advertising brings a pleasant emotional response that will positively influence viewers’ attitudes toward the brand and their intention to share it with others. On the contrary, with unpleasant emotional reactions (unpleasant), viral advertising will make the viewer’s attitude towards the brand more negative and the intention to share will therefore be very low. For the rest, with viral video ads that bring a neutral feeling (coactive), its impact on viewer attitudes, brands as well as sharing intentions is only average, relatively. The research results will contribute to marketing knowledge studying emotions and attitudinal responses to advertising by conceptualizing emotional tone in a multidimensional perspective that allows for not only positive or negative but also mixed cooperative emotional tone.

A Study of Major Depressive Disorder Based on Resting-State Multilayer EEG Function Network

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$86.88\% \pm 9.25 \%$</tex-math> </inline-formula> . More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

Does corporate digital transformation restrain ESG decoupling? Evidence from China

This paper empirically examines the efficacy of corporate digital transformation on a firm’s environmental, social, and governance (ESG) decoupling. Adopting the text analysis method and using a sample of Chinese A-share listed firms from 2010 to 2019, this paper finds that digital transformation can significantly alleviate ESG decoupling, and this relationship persists after robustness tests. Mechanism analysis reveals that digital transformation reduces ESG decoupling by improving information processing ability and relieving information asymmetry. The relationship between corporate digital transformation and ESG decoupling is stronger among companies in eastern China and firms that do not follow GRI guidance. The economic consequence analysis suggests that corporate digital transformation promotes firms’ high-quality development by reducing ESG decoupling. This study helps reveal corporate digital transformation’s empowering role and governance role in ESG decoupling and contributes to the growing literature on ESG decoupling and corporate digital transformation.

Experimental demonstration of a photonic reservoir computing system based on Fabry Perot laser for multiple tasks processing

Abstract Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a simple hardware implementation method of RC, has attracted widespread attention. In this paper, we present and experimentally demonstrate a time delay RC system based on a Fabry Perot (FP) laser for multiple tasks processing. Here, the various tasks are attempted to perform in parallel in the multiple longitudinal modes of the FP laser. It is found that the time delay RC system based on the FP laser can successfully handle different tasks across multiple longitudinal modes simultaneously. The experimental results demonstrate the potential of the time delay RC system based on the FP laser to achieve multiple tasks processing, providing various possibilities for improving the information processing ability of neural morphology RC systems, and promoting the development of RC systems.

Discrimination of Second Language Vowel Contrasts and the Role of Phonological Short-Term Memory and Nonverbal Intelligence

Although extensive research has focused on the perceptual abilities of second language (L2) learners, a significant gap persists in understanding how cognitive functions like phonological short-term memory (PSTM) and nonverbal intelligence (IQ) impact L2 speech perception. This study sets out to investigate the discrimination of L2 English monophthongal vowel contrasts and to assess the effect of PSTM and nonverbal IQ on L2 speech perception. The participants consisted of adult monolingually-raised Greek speakers, who completed an AX discrimination test, a digit span test, and a nonverbal intelligence test. A control group of English speakers also completed the AX test. Data were analyzed using Bayesian regression models. The results revealed that Greek speakers exhibited below chance discrimination for the majority of L2 vowel contrasts, consistently underperforming in comparison to the control group. Intriguingly, the study did not provide substantial evidence in favor of more accurate discrimination of L2 contrasts by Greek participants with high PSTM compared to those with low PSTM. However, the study yielded compelling evidence indicating that Greek participants with higher IQ demonstrated superior accuracy in discriminating most L2 contrasts compared to their lower IQ counterparts. The limited influence of PSTM on speech perception suggests the need for further exploration, considering the potential impact of test methodologies and the intricate interplay of other confounding factors. Furthermore, the study uncovers a noteworthy relationship between nonverbal IQ and L2 speech perception, likely linked with the association of high IQ with enhanced attentional capacities, information processing abilities, and learning skills—all of which are pivotal for accurate speech perception.

Improved dynamic characteristics of oxide electrolyte-gated transistor for time-delayed reservoir computing

Time-delayed reservoir computing (RC) equipped with prominent superiorities such as easy training and friendly hardware implementation is identified as a high-efficient answer to complex temporal tasks, and thereby draws increasing attention. Oxygen ion-based oxide electrolyte-gated transistor (Ox-EGT) with rich ion dynamic characteristics is deemed as a promising candidate for RC. However, it is still a challenge to produce the required dynamic characteristics for RC implementation. Herein, we develop an Ox-EGT with an oxygen vacancy-electron-coupled electric-double-layer at the electrolyte/channel interface to implement time-delayed RC. Effects of oxygen vacancy concentration on the short-term plasticity are investigated, revealing the optimal concentration range of oxygen vacancies for the dynamic characteristics improvement. The underlying physical mechanism is demonstrated by TCAD simulations. Simulations using the waveform classification and handwritten-digit recognition tasks validate the good information processing ability of the Ox-EGT RC system. These results provide a promising approach to exploit Ox-EGT dynamics for large-scale and energy-efficient neuromorphic computing hardware.

Study on the Automatic Information Processing Strategy in L2 Reading

Reading is currently a prominent topic in L2 acquisition research, serving as an indicator of L2 learners’ overall language proficiency. However, there are still many challenges in reading for L2 learners. For example, there still exist some linguistic and non-linguistic problems, such as insufficient language ability, lack of cultural background knowledge, low reading efficiency, and poor reading habits. Effectively improving the information processing skills of L2 learners in reading posts a major challenge for researchers. This paper explores the effective strategies for L2 learners to obtain the ability of automatic reading information processing through the scale survey. It is found in the study that:1) L2 learners believe that they can reach the level of automatic reading processing through the automatic information processing strategy; 2) The automatic information processing strategy can be applied in L2 reading to effectively improve the level of L2 automatic processing. Various strategies, such as improving language proficiency, increasing cultural background knowledge, utilizing metacognitive strategies, and employing socio-emotional strategies, both linguistic and non-linguistic, have been identified to effectively improve the automatic information processing ability of L2 learners. This study has important theoretical and practical value for improving the reading ability of L2 learners.

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

Assessment of neurocognitive functions and information processing ability among type 2 diabetes mellitus patients in Central India – A cross-sectional study

Background: Type 2 diabetes mellitus (T2DM) is a common and prevalent metabolic disorder linked to numerous health complications. Recent research has indicated a potential association between T2DM and cognitive impairment, although the underlying mechanisms remain unclear. Aims and Objectives: This cross-sectional study aimed to investigate neurocognitive functions and information processing ability in T2DM patients compared to healthy individuals. Materials and Methods: A total of 160 participants, including 80 T2DM patients under medication and 80 age- and sex-matched healthy controls, were enrolled. Cognitive assessments were conducted using Addenbrooke’s cognitive examination (ACE-III) and audiovisual reaction time tests. Statistical analyses were performed to assess differences and correlations between groups. Results: The study revealed that T2DM patients exhibited delayed neurocognitive functions, including prolonged auditory and visual reaction times, and lower ACE-III scores compared to healthy controls. Notably, a significant positive correlation between HbA1c levels and cognitive impairment was observed in the diabetic group. Conclusion: This study underscores the presence of cognitive deficits in T2DM patients and highlights the importance of routine cognitive assessment during clinical follow-up. The findings suggest that optimizing glycemic control may offer potential benefits in mitigating cognitive decline in individuals with T2DM. These insights contribute to our understanding of the interplay between T2DM and cognitive function, paving the way for improved patient care and interventions.

A study of visual attention patterns of snow and ice athletes based on eye-tracking technology

Abstract The visual system has a strong information processing ability, and visual attention tracking has various applications in various scenes. This paper mainly focuses on the sports scene of ice and snow far mobilization. It constructs a visual attention system model based on eye tracking. It first establishes an eye tracking system framework using deep learning, and improves the gaze estimation by optimizing the feature extraction network. The visual attention system model was constructed using particle filtering based on motion feature cognition. In the eye-tracking visual attention system model experiments, the Accuracy of the improved eye-tracking system in this paper can be significantly improved to 1.13°, and the error of the visual attention system can be kept within 10°. Furthermore, the four ice and snow sports scene types have an average accuracy of 85.47%, and the constructed model performs well. This study offers a guide for effectively combining eye tracking technology and visual attention.

Students' Information Processing Abilities and Cognitive Processes in Biotechnology Learning

The learning outcomes in the independent curriculum expect students to have skills in processing and analyzing data and information. These skills are in line with the information processing abilities developed by Marzano (1993). This research aims to determine the level of students' information processing abilities in biotechnology learning and its relationship with students' cognitive processes. The data in this research were processed using PLS-SEM (Partial Least Square–Structural Equation Model) analysis. Based on the results of the analysis, the instruments used to test information processing abilities, level 1 retrieval and level 2 comprehension cognitive systems, metacognitive systems and self-systems are valid and reliable. However, the level 3 analysis and level 4 knowledge utilization cognitive systems showed invalid and unreliable results so no further evaluation was carried out at these two levels. The results of the inner model analysis show that increasing information processing abilities have a significant and linear relationship to increasing cognitive processes. The relationship between information processing abilities and cognitive processes from the highest respectively is students' comprehension (L2), self-system (L6), metacognitive system (L5), and retrieval (L1).

Coincidence detection and integration behavior in spiking neural networks

AbstractRecently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now some key advances of biological neural networks are still out of reach. Thus, the energy efficiency and the ability to dynamically react and adapt to input stimuli as observed in biological neurons is still difficult to achieve. One neuron model commonly used in SNNs is the leaky-integrate-and-fire (LIF) neuron. LIF neurons already show interesting dynamics and can be run in two operation modes: coincidence detectors for low and integrators for high membrane decay times, respectively. However, the emergence of these modes in SNNs and the consequence on network performance and information processing ability is still elusive. In this study, we examine the effect of different decay times in SNNs trained with a surrogate-gradient-based approach. We propose two measures that allow to determine the operation mode of LIF neurons: the number of contributing input spikes and the effective integration interval. We show that coincidence detection is characterized by a low number of input spikes as well as short integration intervals, whereas integration behavior is related to many input spikes over long integration intervals. We find the two measures to linearly correlate via a correlation factor that depends on the decay time. Thus, the correlation factor as function of the decay time shows a powerlaw behavior, which could be an intrinsic property of LIF networks. We argue that our work could be a starting point to further explore the operation modes in SNNs to boost efficiency and biological plausibility.

Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity.

Although many deep learning models-based medical applications are performance-driven, i.e., accuracy-oriented, their explainability is more critical. This is especially the case with neuroimaging, where we are often interested in identifying biomarkers underlying brain development or disorders. Herein we propose an explainable deep learning approach by elucidating the information transmission mechanism between two layers of a deep network with a joint feature selection strategy that considers several shallow-layer explainable machine learning models and sparse learning of the deep network. At the end, we apply and validate the proposed approach to the analysis of dynamic brain functional connectivity (FC) from fMRI in a brain development study. Our approach can identify the differences within and between functional brain networks over age during development. The results indicate that the brain network transits from undifferentiated structures to more specialized and organized ones, and the information processing ability becomes more efficient as age increases. In addition, we detect two developmental patterns in the brain network: the FCs in regions related to visual and sound processing and mental regulation become weakened, while those between regions corresponding to emotional processing and cognitive activities are enhanced.

Activation network improves spatiotemporal modelling of human brain communication processes

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.

Auditory stimulus reconstruction from ECoG with DNN and self-attention modules

Auditory stimulus reconstruction is a technique that predicts sound from neural activities. Decoding speech from human brains using neuronal networks allows us to understand auditory information processing and study hearing abilities effectively. However, current approaches produce low-quality reconstruction of speech which is problematic for applications of the reconstruction technology to practical uses such as evaluation of hearing characteristics of individuals or functional preservation during neurosurgical procedures. To tackle this issue, we used a self-attention (SA) module which are showing promising advances in deep neural network studies. We investigated the effect of including neural activity from a longer span on the reconstruction accuracy, and the learning behavior of self-attention modules. Our results showed that self-attention modules that focus on the presence or absence of auditory stimuli used relationships between features over a longer period (over 1000 ms) and produced better reconstructions than a convoluted neural network (CNN) model using features over a shorter period (300 ms) and a multilayer perceptron (MLP) model not using the features and reconstructed with attention to presence and absence of auditory stimuli. These findings imply that long and sparse relationships of time-series information on neural activities derived from SA modules improve the reconstruction performance.

The association between managerial short-termism and green innovation: the moderating role of firms’ digital transformation

Purpose The purpose of this study is to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation. Design/methodology/approach This study uses data from Chinese A-share listed companies from 2001 to 2021 and employ panel fixed model and moderating effect model to examine the impact of managerial short-termism on green innovation of firms and the moderating role of digital transformation of enterprises in the association between managerial short-termism and green innovation. Findings The findings of this study reveal that managerial short-termism exerts negative influence on green innovation. Digital transformation enables firms to reduce the adverse effect of managerial short-termism on green innovation because digital transformation enhances information processing ability and then improves internal corporate governance and analyst coverage. Moreover, the moderating role of digital transformation is more prominent for firms with lower internal corporate governance, for firms with less analyst coverage and for non-state-owned enterprises. Originality/value This paper intends to address the following two questions: what is the impact of managerial short-termism on green innovation and what is the role of digital transformation in the two variables’ association? By using data of Chinese A-share listed companies from 2001 to 2021 and developing two individual indexes to measure managerial short-termism and digital transformation, the authors empirically test these above two questions. The results of this study indicate that: First, drawn on time-oriented theory and upper echelon theory, managerial short-termism has an adverse effect on firms’ green innovation. Second, digital transformation enables firms to reduce the negative effect of managerial short-termism on green innovation. Furthermore, the moderating mechanism tests show that the corporate governance effects of digital transformation play a supervisory role that impels managers to reduce short-term investments and promote firms’ green R&amp;D investments, which helps to reduce the negative effect of managerial short-termism on green innovation. Additionally, the heterogeneity checks show that the moderating role of digital transformation in the relation between managerial short-termism and green innovation is more prominent for firms with lower internal corporate governance, with less analyst coverage and for non-state-owned enterprises.

Recent advancements in implantable neural links based on organic synaptic transistors

AbstractThe progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Spiking Neural Network with plasticity in the time domain recovers temporal information from a noisy pattern using reference spikes

Neurons in the brain communicate with each other by sending trains of spikes that can encode information using the timings of the spikes. Spiking Neural Networks (SNNs) are biologically plausible neural networks that can model this transfer of information and that, by incorporating plasticity, can detect repeating patterns that may be embedded in a train of spikes with a noisy background. Although existing model networks are capable of learning to detect the occurrence of repeated patterns, they are not tailored to recover the entirety of the time sequence of the spikes in the pattern. Here we present a network that, in addition to typical parameters of plasticity such as weights and time delays, uses a new plasticity on the time domain that can recover most of the spikes in a pattern. This new plasticity acts by modifying the timings of reference spikes that come from a hypothesized upstream network that could potentially encode preexisting memories. The model is shown to be robust under several noise perturbation scenarios, and its overall performance demonstrates the benefits of using reference spikes to improve the temporal information processing ability of SNNs.