Information Processing Mechanisms Research Articles

Comparing retro-cue benefit mechanisms in visual working memory: completely valid vs. highly valid retro-cues

Visual working memory (VWM) plays a crucial role in temporarily maintaining and manipulating visual information. Retro-cue benefit (RCB) refers to the enhancement of memory performance when attention is directed toward a subset of items in VWM after their initial encoding. Our recent electroencephalogram (EEG) studies indicate that cue validity affects the mechanisms underlying RCB formation. However, previous research has not thoroughly examined whether these mechanisms differ between completely valid and highly valid cue conditions. This study investigates the consistency of RCB mechanisms under conditions of complete (100%) and high (80%) retro-cue validity. We manipulated retro-cue validity and examined cognitive processing mechanisms under different validity conditions using EEG. Specifically, we focused on the N2pc component, which reflects attentional resource allocation, and the contralateral delay activity (CDA) component, which reflects the quantity of information retained in VWM. The results, encompassing both behavioral and event-related potential (ERP) findings, show that participants in both the 100% and 80% cue validity conditions exhibit robust RCB. Notably, the degree of RCB remains consistent across these conditions, indicating that participants utilize retro-cues to enhance VWM performance to the same extent. In the 80% cue validity condition, a significant retro-cue cost (RCC) was observed, indicating that participants selectively discarded uncued items from VWM. In invalid trials, response accuracy drops to chance levels, supporting the removal hypothesis. ERP results reveal that attentional resource allocation (N2pc) and the quantity of retained information (CDA) remain uniform across cue validity conditions. The mechanism responsible for RCB formation appears to involve an all-or-nothing process of discarding uncued information rather than a flexible resource allocation strategy. This study provides insights into attention allocation and information-processing mechanisms in VWM, suggesting that conclusions drawn from tasks with completely valid retro-cues can be integrated with findings from highly valid cue tasks. These findings also illuminate the flexibility of internal attentional resource allocation during RCB formation and contribute to our understanding of attention processes in VWM.

Firing dynamics and coupling synchronization of memristive EMR-based Chaivlo neuron utilizing equivalent energy approach.

Firing dynamics and its energy property of neuron are crucial for exploring the mechanism of intricate information processing within the nervous system. However, the energy analysis of discrete neuron is significantly lacking in comparison to the vast literature and mature theory available on continuous neuron, thereby necessitating a focused effort in this underexplored realm. In this paper, we introduce a Chaivlo neuron map by employing a flux-controlled memristor to simulate electromagnetic radiation (EMR), and a detailed analysis of its firing dynamics is conducted based on an equivalent Hamiltonian energy approach. Our observations reveal that a range of energy-based firing behaviors, such as spike firing, coexistence firing, mixed-mode firing, and chaotic bursting firing, can be induced by EMR and injected current. To delve deeper into the synchronous firing dynamics, we establish a Chaivlo network by electrically coupling two memristive EMR-based Chaivlo neurons. Subsequently, we experimentally evaluate the synchronization behavior of this network by quantifying both the synchronization factor and the average difference of equivalent Hamiltonian energy. Our findings conclusively demonstrate that both EMR and coupling strength positively contribute to the network's synchronization ability.

When project outcomes matter: Organizational integration in managing long-term target benefits

We build on the research on managing long-term benefits by focusing on the management of outcomes during project execution through the theoretical lens of organizational integration research. Our research question is as follows: How can organizations arrange integration among organizational levels, functions, and projects to ensure the effective development of project outcomes and long-term target benefits? To address this question, we conducted a single case study with the Finnish Defence Forces (FDF) and four defense system projects as embedded cases. Our study provides new knowledge on project benefits management in three distinct areas: the integration mechanisms connecting projects, executives, and functions throughout project execution; effective information processing mechanisms for managing the development of outcomes; and the rhythmic initiation of new projects based on improved knowledge from ongoing projects. Based on our findings, we develop a model that covers project-to-organization, organization-to-project, and project-to-project interactions.

The Mexican Native Primates from the Comparative Psychology Point of View: State of the Art and Perspectives

Primates have been studied extensively to understand their behavior and the mechanisms that allow information processing. However, when we closely analyze the species studied and the different projects and topics carried out with them, we found that some species have been highly studied, while others have only a few studies. These differences create a significant underrepresentation of some primate species that could be especially problematic when we use the available information to track the evolution and the diversification of behavior and the mechanisms for information processing. With this in mind, this review aims to show the state-of-the-art of different topics studied in Mexican primates and identify topics that could be studied in the future. The review also aims to demonstrate why working with native species is a path that many low- and middle-income countries like Mexico can follow to develop research programs in their homelands.

Neural mechanisms of fear responses to emotional stimuli: a preliminary study combining early posterior negativity and electroencephalogram source network analysis

Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.

LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment

Abstract With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.

Psilocybin increases optimistic engagement over time: computational modelling of behaviour in rats.

Psilocybin has shown promise as a novel pharmacological intervention for treatment of depression, where post-acute effects of psilocybin treatment have been associated with increased positive mood and decreased pessimism. Although psilocybin is proving to be effective in clinical trials for treatment of psychiatric disorders, the information processing mechanisms affected by psilocybin are not well understood. Here, we fit active inference and reinforcement learning computational models to a novel two-armed bandit reversal learning task capable of capturing engagement behaviour in rats. The model revealed that after receiving psilocybin, rats achieve more rewards through increased task engagement, mediated by modification of forgetting rates and reduced loss aversion. These findings suggest that psilocybin may afford an optimism bias that arises through altered belief updating, with translational potential for clinical populations characterised by lack of optimism.

Personality metatraits and Young's early maladaptive schemas

Introduction: Early maladaptive schemas, as defined by Young, refer to dysfunctional patterns, consisting of memories, emotions, thoughts and bodily sensations, which are the results of the interaction of innate vulnerability and intense and permanent deprivation of basic psychological needs during childhood. The objective of the research presented here was an attempt to identify, represented in metatraits, personality-based determinants for early maladaptive schemas in Young's concept. The basic assumption was that metatraits understood as biologically conditioned, basic forces shaping a person's style of functioning in the sphere of thoughts, feelings and behaviour, explain to a significant extent the intensity of the maladaptive cognitive-emotional schemas. Method: A group of 404 subjects (221 women and 183 men) aged between 18 and 78 years (M = 37; SD = 10.78). Respondents completed the CPM-Q-SF Personality Questionnaire and the YSQ-S3 Young's Schema Questionnaire. Results: The results from the advanced statistical analyses confirmed the assumption that personality metatraits play a sigfnificant role in terms of predicting early maladaptive schemas. It turned out that the metatraits alpha-minus disinhibition and gamma-minus disharmony underlie all five schema areas in Young's approach. Conclusions: By verifying the relationships discussed, it is possible to conclude that metatraits denoting emotional instability, low frustration tolerance, aggressiveness as well as depressiveness, distrust and generally poor psycho-physical condition may represent a biologically determined personality basis for the organisation and development of dysfunctional mental codes that function as information processing mechanisms and motives for maladaptive behavioural reactions. In practical terms, the above-mentioned means that in case of certain personality disorders, working on schemas, referring to childhood experiences, may be the main approach due to the inability to change the biologically determined personality.

Attentional dynamics of evidence accumulation explain why more numerate people make better decisions under risk

In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing—namely, attention allocation, amount of deliberation, and distorted processing of value—which may differ between more and less numerate people and explain differences in decision quality. Computational modeling of an eye-tracking experiment on risky choice demonstrates that numeracy is linked to how people allocate their attention across the options, how much evidence they require before committing to a choice, and also how strongly they distort currently non-attended information during preference formation. Together, especially the latter two mechanisms largely mediate the effect of numeracy on decision quality. Overall, the current work disentangles and quantifies latent aspects of the dynamics of preference formation, explicates how their interplay may give rise to manifest differences in decision quality, and thereby provides a fully formalized, mechanistic explanation for the link between numeracy and decision quality in risky choice.

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.

Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.

Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and a top-down modulator. We first use a common CNN block to form the local pathway that aims to extract fine local features of the input image. Then, we use a transformer encoder to form the global pathway to capture global structural and contextual information among local parts in the input image. Finally, we construct the learnable top-down modulator where fine local features of the local pathway are modulated by global representations of the global pathway. For ease of use, we encapsulate the dual-pathway computation and modulation process into a building block, called the global-local block (GL block), and a CogNet of any depth can be constructed by stacking a necessary number of GL blocks one after another. Extensive experimental evaluations have revealed that the proposed CogNets have achieved the state-of-the-art performance accuracies on all the six benchmark datasets and are very effective for overcoming the "texture bias" and the "semantic confusion" problems faced by many CNN models.

The Myth about Alzheimer’s Disease Incurability is Wrong: Don’t Miss the Opportunity to Change your Mind

Alzheimer’s disease (AD) is a dangerous degenerative disease that affects the central nervous system of elderly people. In recent years, it has become a serious problem facing the modern society. Since the causes of the occurrence and spread of AD are still unknown, scientific research in these matters is of exceptional importance. Its main efforts are directed at the investigation of the mechanisms of the disease’s onset and progression. Among the mechanisms being researched are genetic, epigenetic, molecular, and environmental mechanisms. For unexplainable reasons, this list does not include and does not even mention information processing mechanisms. Why? Information processing is the bulk of brain activity. That is a generally accepted and indisputable frame of mind! The only possible explanation for such a slip can be only one: contemporary neuroscience (neurobiology) knows nothing about what information is and what information processing stands for! The purpose of this article is to correct somehow and perhaps improve this incredible situation.

Uncertainty Tolerance and Information Overload in High-Risk Epidemic Areas: The Mediating Role of Sense of Control

The rapid advancement of information technology has led to a notable rise in information overload, a critical factor impacting the mental well-being of individuals in modern society. This is particularly pronounced in high-risk epidemic zones, where individuals are inundated with a barrage of epidemic-related information. This study aims to investigate the influence of individuals' tolerance for uncertainty on information overload in these high-risk areas, while also examining the intermediary role played by the sense of control. Through a questionnaire survey involving 250 residents of Hubei Province, the data was analyzed using SPSS and PROCESS plug-ins. The findings reveal a significant negative correlation between uncertainty tolerance and information overload, with the sense of control acting as a statistically significant mediator between the two. This underscores the potential of enhancing individuals' uncertainty tolerance and sense of control in alleviating information overload. These insights offer novel perspectives on understanding the psychological mechanisms of information processing in epidemic-prone regions and provide valuable suggestions for optimizing information dissemination strategies by bolstering individuals' psychological resilience.

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 an aminomethylphosphonic acid (AMPA) receptor antagonist (NBQX), or with an 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.