Information Processing Mechanisms Research Articles

Risk Challenges and Coping Strategies of Youth Value Guidance from the Perspective of Algorithm Recommendation

The deep integration of big data and artificial intelligence technology has given rise to the new information configuration paradigm of algorithm recommendation, which has not only profoundly changed the information-imparting model of the youth, but also exerted a far-reaching influence on the values of contemporary youth. At present, algorithm recommendation, with its intelligent information processing and precise information delivery mechanism, has expanded new space and provided new intermediaries for youth value guidance, but it has also aggravated the solidification of value cognition, eliminated the rationality of value judgment, eroded the subjectivity of value choice, and put forward unprecedented challenges to youth value guidance. Therefore, youth value guidance in the era of algorithm recommendation should be based on three aspects, namely, innovating guidance methods, improving media literacy, and activating value subjectivity, to give full play to the positive role of algorithm recommendation in positive value guidance, and effectively guide the youth to set up correct values.

Errors in the Assumptions of Bekesy's Traveling Wave Theory

This article examines the fundamental statements of Bekesy's traveling wave theory in hearing and proposes an updated perspective on auditory information processing mechanisms. It argues that the traditional theory does not adequately account for non-mammalian auditory mechanisms, as many species effectively perceive sound without a basilar membrane or cochlear fluids. It suggests alternative pathways for sound signal reception directly to the receptor, bypassing traditional structures like the basilar membrane. This reevaluation raises significant doubts about the resonance capability and the actual role of the basilar membrane in hearing, suggesting that current understandings of auditory processing may be fundamentally limited and not universally applicable across different species.

Shyness and self-consistency and congruence among Chinese adolescents: mediating role of social comparison orientation and moderating role of self-focused attention.

During the critical period of personality shaping and self-development, adolescents face unique challenges and opportunities. This study, based on Cognitive-Behavioral Theory, explored the relationship between shyness and self-consistency and congruence (hereinafter referred to as SCC), as well as its underlying mechanisms. Through a questionnaire survey on shyness, social comparison orientation, self-focused attention, and SCC among 984 adolescents, the results revealed that (1) Adolescent shyness negatively predicted SCC. (2) Social comparison orientation partially mediated the relationship between shyness and SCC. (3) Self-focused attention moderated the direct pathway of this mediation process, where a high level of self-focused attention exacerbated the negative impact of shyness on SCC. These findings offered a new perspective on understanding SCC and underscored the importance of addressing the information processing mechanisms of social comparison orientation and self-focused attention among shy adolescents in interventions aimed at promoting their psychological harmony and healthy growth.

Repensando a inteligência humana e da máquina sob o determinismo

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. It posits two identical deterministic worlds -- one comprising a human agent and the other a machine agent. These agents exhibit different information processing mechanisms despite their apparent sameness in a causal sense. Providing a conceptual modeling of their difference, this paper resolves what it calls “the vantage point problem” – namely, how to justify an omniscient perspective through which a determinist asserts determinism from within the supposedly deterministic universe.

Repensando a inteligência humana e da máquina sob o determinismo

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. It posits two identical deterministic worlds -- one comprising a human agent and the other a machine agent. These agents exhibit different information processing mechanisms despite their apparent sameness in a causal sense. Providing a conceptual modeling of their difference, this paper resolves what it calls “the vantage point problem” – namely, how to justify an omniscient perspective through which a determinist asserts determinism from within the supposedly deterministic universe.

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA

Exploring the dual routes in influencing sales and adoption in augmented reality retailing: a mixed approach of SEM and FsQCA

Effects of focal cortical cooling on somatosensory evoked potentials in rats

Although the focal brain cooling technique is widely used to examine brain function, the effects of cortical temperature at various levels on sensory information processing and neural mechanisms remain underexplored. To elucidate the mechanisms of temperature modulation in somatosensory processing, this study aimed to examine how P1 and N1 deflections of somatosensory evoked potentials (SEPs) depend on cortical temperature and how excitatory and inhibitory inputs contribute to this temperature dependency. SEPs were generated through electrical stimulation of the contralateral forepaw in anesthetized rats. The SEPs were recorded while cortical temperatures were altered between 17–38 °C either without any antagonists, with a gamma-aminobutyric acid type A (GABAA) receptor antagonist (gabazine), with aminomethylphosphonic acid (AMPA) receptor antagonist (NBQX), or with N-Methyl-D-aspartic acid (NMDA) receptor antagonist ([R]-CPP). The effects of different gabazine concentrations (0, 1, and 10 µM) were examined in the 35–38 °C range. The P1/N1 amplitudes and their peak-to-peak differences plotted against cortical temperature showed an inverted U relationship with a maximum at approximately 27.5 °C when no antagonists were administered. The negative correlation between these amplitudes and temperatures of ≥ 27.5 °C plateaued after gabazine administration, which occurred progressively as the gabazine concentration increased. In contrast, the correlation remained negative after the administration of NBQX and (R)-CPP. These results suggest that GABAergic inhibitory inputs contribute to the negative correlation between SEP amplitude and cortical temperature around the physiological cortical temperature.

Optogenetic stimulation of medial septal glutamatergic neurons modulates theta-gamma coupling in the hippocampus

Hippocampal cross-frequency theta-gamma coupling (TGC) is a basic mechanism for information processing, retrieval, and consolidation of long-term and working memory. While the role of entorhinal afferents in the modulation of hippocampal TGC is widely accepted, the influence of other main input to the hippocampus, from the medial septal area (MSA, the pacemaker of the hippocampal theta rhythm) is poorly understood. Optogenetics allows us to explore how different neuronal populations of septohippocampal circuits control neuronal oscillations in vivo. Rhythmic activation of septal glutamatergic neurons has been shown to drive hippocampal theta oscillations, but the role of these neuronal populations in information processing during theta activation has remained unclear. Here we investigated the influence of phasic activation of MSA glutamatergic neurons expressing channelrhodopsin II on theta-gamma coupling in the hippocampus. During the experiment, local field potentials of MSA and hippocampus of freely behaving mice were modulated by 470 nm light flashes with theta frequency (2–10) Hz. It was shown that both the power and the strength of modulation of gamma rhythm nested on hippocampal theta waves depend on the frequency of stimulation. The modulation of the amplitude of slow gamma rhythm (30–50 Hz) prevailed over modulation of fast gamma (55–100 Hz) during flash trains and the observed effects were specific for theta stimulation of MSA. We discuss the possibility that phasic depolarization of septal glutamatergic neurons controls theta-gamma coupling in the hippocampus and plays a role in memory retrieval and consolidation.

Enigma of Pyramidal Neurons: Chirality-Centric View on Biological Evolution. Congruence to Molecular, Cellular, Physiological, Cognitive, and Psychological Functions

The mechanism of brain information processing unfolds within spatial and temporal domains inherently linked to the concept of space–time symmetry. Biological evolution, beginning with the prevalent molecular chirality, results in the handedness of human cognitive and psychological functions (the phenomena known as biochirality). The key element in the chain of chirality transfer from the downstream to upstream processes is the pyramidal neuron (PyrN) morphology–function paradigm (archetype). The most apparent landmark of PyrNs is the geometry of the cell soma. However, “why/how PyrN’s soma gains the shape of quasi-tetrahedral symmetry” has never been explicitly articulated. Resolving the above inquiry is only possible based on the broad-view assumption that encoding 3D space requires specific 3D geometry of the neuronal detector and corresponding network. Accordingly, our hypothesis states that if the primary function of PyrNs, at the organism level, is sensory space symmetry perception, then the pyramidal shape of soma is the best evolutionary-selected geometry to support sensory-motor coupling. The biological system’s non-equilibrium (NE) state is fundamentally linked to an asymmetric, non-racemic, steady state of molecular constituents. The chiral theory of pyramidal soma shape conceptually agrees that living systems have evolved as non-equilibrium systems that exchange energy with the environment. The molecular mechanism involved in developing PyrN’s soma is studied in detail. However, the crucial missing element—the reference to the fundamental link between molecular chirality and the function of spatial navigation—is the main obstacle to resolving the question in demand: why did PyrNs’ soma gain the shape of quasi-tetrahedral symmetry?

“It’s too much”: Excessive smartphone use during the COVID-19 crisis, information overload, and infection self-efficacy

During the COVID-19-related lockdowns around the world, individuals received a permanent digital stream of information about the pandemic via their smartphones. We theorize that such excessive COVID-19-related smartphone use can affect information overload, i.e., the perception of being exposed to too much information about COVID-19. We also introduce the notion of infection self-efficacy, the feeling that one is able to control the likelihood of being infected. We conducted a two-wave panel survey among an adult sample in Austria (NT2 = 416) during the first lockdown in 2020. Findings of a metric measurement invariant structural equation model revealed that excessive smartphone use predicts information overload over time. Moreover, information overload resulted in lower infection self-efficacy after one month. Reciprocal effects showed that information overload at T1 also increased excessive smartphone use at T2, suggesting a spiral. However, infection self-efficacy did not predict information overload and excessive smartphone use over time. Our findings contribute to the literature on information processing mechanisms by providing clear evidence for the reciprocal relationship between excessive smartphone use and information overload over time. The findings are also important for health scholarship because they explain individuals’ perceptions about whether they are able to protect themselves from COVID-19.

Cross-frequency coupling in cortico-hippocampal networks supports the maintenance of sequential auditory information in short-term memory.

It has been suggested that cross-frequency coupling in cortico-hippocampal networks enables the maintenance of multiple visuo-spatial items in working memory. However, whether this mechanism acts as a global neural code for memory retention across sensory modalities remains to be demonstrated. Intracranial EEG data were recorded while drug-resistant patients with epilepsy performed a delayed matched-to-sample task with tone sequences. We manipulated task difficulty by varying the memory load and the duration of the silent retention period between the to-be-compared sequences. We show that the strength of theta-gamma phase amplitude coupling in the superior temporal sulcus, the inferior frontal gyrus, the inferior temporal gyrus, and the hippocampus (i) supports the short-term retention of auditory sequences; (ii) decodes correct and incorrect memory trials as revealed by machine learning analysis; and (iii) is positively correlated with individual short-term memory performance. Specifically, we show that successful task performance is associated with consistent phase coupling in these regions across participants, with gamma bursts restricted to specific theta phase ranges corresponding to higher levels of neural excitability. These findings highlight the role of cortico-hippocampal activity in auditory short-term memory and expand our knowledge about the role of cross-frequency coupling as a global biological mechanism for information processing, integration, and memory in the human brain.

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study.

Aberrance in switching from default mode network (DMN) to fronto-parietal network (FPN) is proposed to underlie working memory deficits in subjects with substance use disorders, which can be studied using neuro-imaging techniques during cognitive tasks. The current study used EEG to investigate pre-stimulus microstates during the performance of Sternberg's working memory task in subjects with substance use disorders. 128-channel EEG was acquired and processed in ten age and gender-matched subjects, each with alcohol use disorder, opioid use disorder, and controls while they performed Sternberg's task. Behavioral parameters, pre-stimulus EEG microstate, and underlying sources were analyzed and compared between subjects with substance use disorders and controls. Both alcohol and opioid use disorder subjects had significantly lower accuracy (P < 0.01), while reaction times were significantly higher only in subjects of alcohol use disorder compared to controls (P < 0.01) and opioid use disorder (P < 0.01), reflecting working memory deficits of varying degrees in subjects with substance use disorders. Pre-stimulus EEG microstate revealed four topographic Maps 1-4: subjects of alcohol and opioid use disorder showing significantly lower mean duration of Map 3 (visual processing) and Map 2 (saliency and DMN switching), respectively, compared to controls (P < 0.05). Reduced mean durations in Map 3 and 2 in subjects of alcohol and opioid use disorder can underlie their poorer performance in Sternberg's task. Furthermore, cortical sources revealed higher activity in both groups of substance use disorders in the parahippocampal gyrus- a hub of DMN; superior and middle temporal gyri associated with impulsivity; and insula that maintains balance between executive reflective system and impulsive system. EEG microstates can be used to envisage neural underpinnings implicated for working memory deficits in subjects of alcohol and opioid use disorders, reflected by aberrant switching between neural networks and information processing mechanisms.

Bidirectional Feature Aggregation Network for Stereo Image Quality Assessment Considering Parallax Attention-Based Binocular Fusion

Inspired by the two-path visual information processing mechanism (i.e., a bottom-up path and a top-down path), we propose a bidirectional binocular feature aggregation based stereo image quality assessment (SIQA) network, which considers a two-path visual mechanism and realizes the binocular fusion based on parallax information. To better aggregate binocular features from different levels, a two-path feature aggregation structure, which simulates the bottom-up and top-down mechanism in human visual system (HVS), is proposed. It not only realizes the supplement of low-level detail information to high-level semantic in the bottom-up path, but also realizes the supplement of high-level semantic information to low-level detail in the top-down path. Simultaneously, because feature misalignment exists in binocular features of adjacent levels, a feature alignment module (FAM) based on deformable convolution is designed to integrate the binocular fusion features of adjacent levels. In addition, considering the importance role of parallax in guiding binocular fusion, a binocular fusion module (BFM) based on parallax attention mechanism, which is different with existing binocular fusion methods, is explicitly proposed to achieve the binocular fusion between the left and right view features. Extensive experiments are conducted on LIVE I, LIVE II, WIVC I and WIVC II databases to demonstrate the effectiveness of the proposed method.

COC-UFGAN: Underwater image enhancement based on color opponent compensation and dual-subnet underwater fusion generative adversarial network

Due to the complex underwater environment and the attenuation of light, the underwater image is produced with various distortions such as color loss, low contrast, noise, blur, and haze-like, which bring significant challenges to underwater image applications. To alleviate these problems, a novel color opponent compensation and a dual-subnet underwater fusion generative adversarial network (COC-UFGAN) are proposed by simulating the information processing mechanism in the human visual system (HVS). Specifically, considering the color opponent mechanism of the primary visual cortex and the characteristics of underwater imaging, an opponent domain is constructed consisting of four color opponent channels: green–red, yellow-blue, cyan-red and black-white. Then, a novel local-to-global color opponent compensation method is designed to restore color loss in underwater imaging. Furthermore, considering the complexity of underwater imaging, a dual-subnet underwater fusion generative adversarial network is designed. Finally, experimental results on synthetic and natural underwater images demonstrate the superiority of the proposed COC-UFGAN over the state-of-the-art methods qualitatively and quantitatively.

Advancing brain-inspired computing with hybrid neural networks.

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

A reshaped intelligent autonomous driving by using the influence and progress of human factor engineering concept

In modern society, with the improvement of people's taste of life and the change of people's consumption awareness and lifestyle, cars are no longer just a driving tool or means of transportation. People's requirements for cars are constantly improving, such as the comfort of car seats and the convenience of reversing radar. This paper examines the role of human factor engineering in the intelligent driving and gives some examples of the usage of the human factor engineering, and analyse it by Single-vehicle level analysis and Macro system level analysis. Then, the paper lists different human factor problems affecting intelligent driving. The paper discusses one iHCl system of human factor engineering which based on the Cognitive theory at three levels of perception. The paper analyses two cognizant. For the human users, it has the three-layer scene elements, data-driven, and goal-driven information processing mechanisms utilize dynamic mechanisms for information collection and subsequent response, allowing human users to perceive the updated information obtained in the dynamic environment. For intelligent system, For intelligent systems, it has a "top-down" decision-making processing mechanism. The feedback mechanism is composed of subjects such as goals, plans, and tasks.

Effects of motion type on motion-onset and steady-state visual evoked potentials: rotation vs. flicker.

The underlying mechanisms of information processing for two basic motion types, rotation and flicker, are not fully understood. Rotational and flickering animations at four speeds - 7 frames per second (fps), 8 fps, 11 fps, and 12 fps, respectively - are presented as visual stimuli. The motion-onset visual evoked potentials (VEPs) and steady-state VEPs (SSVEP) elicited by these motion stimuli were compared between the rotation and flicker motion types at time windows of 0-500 ms and 1000-5000 ms post-stimulus, respectively. The standardized low-resolution electromagnetic tomography (sLORETA) source localization was investigated as well. Four motion speeds had no effect on the whole VEP waveform in either the rotation or the flicker groups. Significant differences in motion-onset VEPs and sLORETA source localization were found between the rotation and the flicker motion types at time windows of 200-500 ms post-stimulus. For the time windows of 1000-5000 ms post-stimulus, both the rotation and flicker groups all demonstrated the characteristics of SSVEP, with the peak spectral topographies showing at the four different frequencies, which correspond to the four motion speeds. Additionally, a higher power of spectral topography at each of the four motion speeds was found in the rotation relative to the flicker stimulation. The perceptual and cognitive processes are distinct for two types of motion: rotation and flicker. In terms of motion-onset VEPs and the characteristics of SSVEP, rotating visual stimulation is superior to flicker stimulation and may be more appropriate for clinical and engineering applications.

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.

Languages generated by numerical P systems with thresholds

Numerical P systems with thresholds (NPT systems) are a type of distributive and parallel computing model in the framework of membrane computing, inspired by the mechanism of information processing in cells. In membrane computing, the study of language generating/accepting power of a computing model is essential for understanding its capabilities and limitations, while the language generating/accepting power of NPT systems remains open. In this work, a language hierarchy for NPT systems is established by considering polynomials of degree one as production functions. Specifically, if each production function is a variable with a positive integer coefficient, the NPT systems characterize regular languages. If each production function is a variable with an integer coefficient, the languages generated by the NPT systems are no more than context-sensitive. Further, the NPT systems generate all recursively enumerable languages if each production function is a linear combination of no more than two variables and each term of a production function is a variable with an integer coefficient. The results show that the language generating power of NPT systems is closely related to the number of variables and the range of coefficients in production functions.