Human Cognition Research Articles

Variability and positive selection in FOXP2, a gene associated with the development of language, speech, and cognition

Introduction: the FOXP2 gene has been identified as a key genetic factor in the development of language and human cognition. Mutations in FOXP2 have been associated with language disorders and speech difficulties. Additionally, this gene has been linked to various neuropsychiatric conditions. The objective of this study is to analyze the genetic differentiation of populations in the FOXP2 gene and in the rs10447760, rs1456031, rs2253478 and rs2396753 polymorphisms.Method: data from the "1000 Genomes" Project were used to analyze genetic variability in FOXP2 in 2504 individuals from 26 populations and 5 macro populations. Linkage disequilibrium, Hardy-Weinberg equilibrium and allele frequencies of the SNPs were evaluated. Genetic differentiation was estimated using the FST statistic.Results: a highly differentiated region was identified in intron 3 of FOXP2 between the African macro population and the rest, with a maximum FST of 0,78. This region contains an epigenetic mark H3K27Ac, suggesting a regulatory role. Hardy-Weinberg imbalances were observed in some populations for the SNPs analyzed. Linkage disequilibrium analysis showed that these SNPs have independent effects.Conclusions: the highly differentiated region in FOXP2 suggests a past natural selection event, supporting an adaptive role of this gene in the evolution of language, speech and cognition. Population differences in Hardy-Weinberg equilibrium and genetic variability highlight the importance of considering genetic variation in future association studies with FOXP2

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Polygenic proxies of age-related plasma protein levels reveal TIMP2 role in cognitive performance.

Several studies have identified blood proteins that influence brain aging performance in mice, yet translating these findings to humans remains challenging. Here we found that higher predicted plasma levels of Tissue Inhibitor of Metalloproteinases 2 (TIMP2) were significantly associated with improved global cognition and memory performance in humans. We first identified 12 proteins with aging or rejuvenating effects on murine brains through a systematic review. Using protein quantitative trait loci data for these proteins, we computed polygenic scores as proxies for plasma protein levels and validated their prediction accuracy in two independent cohorts. Association models between genetic proxies and cognitive performance highlighted the significance of TIMP2, also when the models were stratified by sex, APOE -ε4, and Aβ42 status. This finding aligns with TIMP2's brain-rejuvenating role in murine models, suggesting it as a promising therapeutic target for brain aging and age-related brain diseases in humans.

Learning From Aggregated Opinion

The capacity to leverage information from others’ opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others’ testimony. Yet a distinct form of social information—aggregated opinion—increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (N = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants’ judgments and the predictions of the Bayesian model, though some participants’ judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution.

NHi-SEGA: n-Hierarchy SEmantic Guided Attention for few-shot learning

Humans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes.

Efficiency in redundancy

In engineering, redundancy is the duplication of vital systems for use in the event of failure. In studies of human cognition, redundancy often refers to the duplication of the signal. Scores of studies have shown the salutary effects of a combined auditory and visual signal over single modality, the advantage of processing complete faces over facial features, and more recently the advantage of two observers over one. But what if the signal (or the number of observers) is fixed and cannot be altered or augmented? Can people improve the efficiency of information processing by recruiting an additional, redundant system? Here we demonstrate that recruiting a second redundant system can, under reasonable assumptions about human capacity, result in improved performance. Recruiting a second redundant system may come with a higher energy cost, but may be worthwhile in high-stakes situations where processing information accurately is crucial.

Cognitive issues of mobile map design and use

ABSTRACT Internet-connected mobile devices have changed how people access information. Like other information sources, maps have benefited from and been re-envisioned for mobile devices, and they are used in new contexts. However, these new contexts often generate additional cognitive load. We explored in depth two strategies designers could use to mitigate high cognitive loads associated with mobile map use: offloading cognition and reducing cognitive load by improved design to support the allocation of attention between the map and the environment. In reviewing these strategies, we considered their relevance to several mobile map use cases (navigation, individual and collaborative spatial decision making, information enrichment, and entertainment). Next, we identified recent progress in our understanding of how to measure cognitive load and map use context. Finally, we explored the wider implications of mobile maps for human behaviour and cognition. We identified two important cross-cutting research questions: 1) How can mobile maps be designed to reduce cognitive load by providing what is really needed by users to facilitate their cognitive processes?; 2) How can the intrinsic additional cognitive load created by the characteristics of mobile maps be managed and minimised by supporting the distribution of the user's attention between the map and environment?

Негативность, связанная с ошибкой, как нейрофизиологический коррелят исполнительных функций: Обзор исследований

<p style="text-align: justify;">Executive functions are a set of high-level cognitive processes that regulate human behavior and cognition, and are necessary for adaptive and goal-directed behavior. The error monitoring process can be considered as a condition preceding the enhancement or activation of executive control. The neural correlate of this condition is the error-related negativity (ERN). This article presents a narrative review of research investigating cognitive and affective aspects of the error monitoring process in the context of executive functions using the ERN. The literature review on the affective aspect revealed a diversity of approaches to influencing the state of participants. Most works take into account information about participants' state or affectively salient aspects of stimuli perception. However, this information may not be sufficient to establish a link to the response to conflict induced by an error in an experimental task. Presumably, the general emotional state has only an indirect influence on the error monitoring process. This may be the reason for the inconsistent results observed in this area of research.</p>

Promoting the Shift From Pixel-Level Correlations to Object Semantics Learning by Rethinking Computer Vision Benchmark Data Sets.

In computer vision research, convolutional neural networks (CNNs) have demonstrated remarkable capabilities at extracting patterns from raw pixel data, achieving state-of-the-art recognition accuracy. However, they significantly differ from human visual perception, prioritizing pixel-level correlations and statistical patterns, often overlooking object semantics. To explore this difference, we propose an approach that isolates core visual features crucial for human perception and object recognition: color, texture, and shape. In experiments on three benchmarks-Fruits 360, CIFAR-10, and Fashion MNIST-each visual feature is individually input into a neural network. Results reveal data set-dependent variations in classification accuracy, highlighting that deep learning models tend to learn pixel-level correlations instead of fundamental visual features. To validate this observation, we used various combinations of concatenated visual features as input for a neural network on the CIFAR-10 data set. CNNs excel at learning statistical patterns in images, achieving exceptional performance when training and test data share similar distributions. To substantiate this point, we trained a CNN on CIFAR-10 data set and evaluated its performance on the "dog" class from CIFAR-10 and on an equivalent number of examples from the Stanford Dogs data set. The CNN poor performance on Stanford Dogs images underlines the disparity between deep learning and human visual perception, highlighting the need for models that learn object semantics. Specialized benchmark data sets with controlled variations hold promise for aligning learned representations with human cognition in computer vision research.

The Principle of Cortical Development and Evolution.

Human's robust cognitive abilities, including creativity and language, are made possible, at least in large part, by evolutionary changes made to the cerebral cortex. This paper reviews the biology and evolution of mammalian cortical radial glial cells (primary neural stem cells) and introduces the concept that a genetically step wise process, based on a core molecular pathway already in use, is the evolutionary process that has molded cortical neurogenesis. The core mechanism, which has been identified in our recent studies, is the extracellular signal-regulated kinase (ERK)-bone morphogenic protein 7 (BMP7)-GLI3 repressor form (GLI3R)-sonic hedgehog (SHH) positive feedback loop. Additionally, I propose that the molecular basis for cortical evolutionary dwarfism, exemplified by the lissencephalic mouse which originated from a larger gyrencephalic ancestor, is an increase in SHH signaling in radial glia, that antagonizes ERK-BMP7 signaling. Finally, I propose that: (1) SHH signaling is not a key regulator of primate cortical expansion and folding; (2) human cortical radial glial cells do not generate neocortical interneurons; (3) human-specific genes may not be essential for most cortical expansion. I hope this review assists colleagues in the field, guiding research to address gaps in our understanding of cortical development and evolution.

Exploring the impact of the interthalamic adhesion on human cognition: insights from healthy subjects and thalamic stroke patients

The interthalamic adhesion (IA) is a structure that connects the median borders of both thalami. Its anatomical variants and functions remain poorly studied. The main objective of this study was to explore the role of the IA on cognition. 42 healthy subjects and 40 patients with chronic isolated thalamic strokes underwent a neuroimaging and a neuropsychological assessment. The presence, absence, or lesion of the IA and its anatomical variants were evaluated. 76% of participants had an IA, with a higher prevalence among women (92%) than men (61%). The presence or absence of an IA did not affect the neuropsychological performance of healthy subjects nor did the type of IA variant. Across all the tests and when compared to healthy subjects using a Bayesian rmANOVA, patients exhibiting more cognitive impairments were those without an IA (n = 10, BF10 = 10,648), while those with an IA were more preserved (n = 18, BF10 = 157). More specifically, patients without an IA performed more poorly in verbal memory or the Stroop task versus healthy subjects. This was not explained by age, laterality of the infarct, volume or localization of the lesion. Patients with a lesioned IA (n = 12) presented a similar trend to patients without an IA, which could however be explained by a greater volume of lesions. The IA does not appear to play a major role in cognition in healthy subjects, but could play a compensatory role in patients with thalamic lesions.

“E-waste” Colonialism: Mechanism of Late Modernity in Chen Qiufan’s Waste Tide

Abstract Among numerous literary works on international waste trade, by way of which the toxic waste has been exported basically from developed countries to underdeveloped areas, the Chinese writer Chen Qiufan’s Waste Tide is the only one that examines one of the most toxic waste streams, namely, the electronic waste, or the e-waste. However, this particular novel has not received much critical attention in the anglophone world. The few studies on this science fiction tend to focus on the capital market, the conflict between global and local, or the alienation of human and nature, neglecting its significant thematic concern on the e-waste. In this context, this study seeks to address this gap by analyzing the author’s focus on the neglected issue of e-waste in the system of waste colonialism. By exploring this work from the perspective of waste colonialism, this study attempts to disclose how the e-waste functions as a special symbol of late modernity and in what mechanism late modernity works to exert its effect respectively on economic-political institutions, knowledge system, and human cognition of their own subjectivity. Significantly, this study will argue that, in late modernity, the system of waste colonialism has developed into what this study terms “e-waste” colonialism, promoting the reconsolidation of power structure, the intensification of expert knowledge, and the deconstruction of human subjectivity.

Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain

While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks.

Generative attention based framework for implicit language change detection

Spoken language change detection (LCD) refers to detecting language switching points in a multilingual speech signal. Most approaches in literature use the explicit framework that requires the modeling of intermediate phonemes and Senones to distinguish language. However, such techniques are limited when used with resource scare/ zero resource languages. Hence as an alternative, this study explores implicit frameworks to perform LCD. The focus of this work is to detect language change when a single speaker is speaking two languages. In this direction, a subjective study is performed to analyze the method humans adapt to discriminate languages. The outcome of the subjective study suggests humans require more neighborhood duration to detect language change. The initial observation suggests, that detecting language change is challenging using the baseline implicit unsupervised distance-based approach. Inspired by human cognition, prior language knowledge is integrated into the computational framework through the Gaussian mixture model and universal background model (GMM-UBM), temporal information via attention, and pattern storage using the Generative adversarial network (GAN) to enhance language discrimination. The experimental results on the Microsoft code-switched (MSCS) dataset show, compared to the unsupervised distance-based approach, the performance of the proposed LCD relatively improved by 19.3%, 47.3%, and 50.7% using the GMM-UBM, attention, and GAN-attention based framework, respectively.

A spatial scene reconstruction framework in emergency response scenario

Rapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for emergency management, but existing generative models have limited understanding of spatial relationships and lack high-quality training samples. To address these issues, this paper proposes a novel spatial scene reconstruction framework. Specifically, the BERT based spatial information knowledge graph extraction method is used to encode the input text, label and classify the encoded text, identify spatial objects and relationships in the text, and accurately extract spatial information. Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. In addition, a high-quality training sample set of “text-scene-knowledge graph” was constructed.

Kant and the other of necessity - contingency

Kant’s critical philosophy is, as is well known, based on the question of the possibility of synthetic judgments a priori. This goes hand in hand with the validity claims of judgments in cognition, morality, law, aesthetics and other philosophical areas that can be shown to be necessary. The degree of necessity differs depending on the context. Central to Kant’s epistemology is the objectively necessary validity of causal judgments, which he grounded anew and in distinction to the subjectively necessary judgments that he attributed to David Hume’s causal theory. Necessity, a priori validity of pure or, for the most part, non-pure synthetic judgements a priori in the context of experience are inconceivable without their other, empiricism and contingency. Contingency plays a less marginal role in Kant’s epistemology in the Critique of Pure Reason, than it might seem at first glance. Contingency is of obvious importance in Kant’s teleology, which he elaborates in the second part of the Critique of the Power of Judgement. Nature is characterized by infinite variety and an infinite number of empirical laws, and therefore contingency is predominant for human cognition. With the (apparent) antinomy of teleological judgement, the reciprocal relationship between (necessary) causal mechanism and finality as teleological expediency Kant shows that the two principles are indispensable for knowledge and observation of nature, even if they each have their own tasks.

The Integration of Ethnoscience and Technology: A Review

This literature review critically examines the integration of ethnoscience and technology, highlighting the transformative impact of this amalgamation on the field of social sciences. Ethnoscience, with its focus on understanding the diverse ways in which human communities perceive and interact with their world, and ethnography, a methodology for the immersive study of cultures and social interactions, together provide a robust framework for exploring human cognition, behavior, and societal constructs. The review delves into the key principles of ethnoscience, emphasizing the development and significance of ethnography in anthropological research and the methodological flexibility required to address contemporary challenges. This study further explores the integration of technological innovations such as wearables, digital traces, and information and communication technology (ICT) into ethnographic studies, underscoring the opportunities and challenges presented by the digital era. The paper argues that the incorporation of technology not only expands the methodological toolkit available to researchers but also enhances the depth and breadth of ethnographic insights into socio-economic, policy, and environmental factors influencing technology adoption across various sectors. Despite the methodological, socio-economic, and infrastructural hurdles, the integration offers unprecedented opportunities for understanding complex social phenomena, contributing significantly to policy-making, community development, and technological innovation. This review underscores the necessity of evolving ethnographic practices to keep pace with technological advancements, fostering a deeper and more nuanced understanding of the intricacies of human life in the digital age.

Other ethnicity effects in ensemble coding of facial expressions.

Cultural difference in ensemble emotion perception is an important research question, providing insights into the complexity of human cognition and social interaction. Here, we conducted two experiments to investigate how emotion perception would be affected by other ethnicity effects and ensemble coding. In Experiment 1, two groups of Asian and Caucasian participants were tasked with assessing the average emotion of faces from their ethnic group, other ethnic group, and mixed ethnicity groups. Results revealed that participants exhibited relatively accurate yet amplified emotion perception of their group faces, with a tendency to overestimate the weight of the faces from the other ethnic group. In Experiment 2, Asian participants were instructed to discern the emotion of a target face surrounded by faces from Caucasian and Asian faces. Results corroborated earlier findings, indicating that while participants accurately perceived emotions in faces of their ethnicity, their perception of Caucasian faces was noticeably influenced by the presence of surrounding Asian faces. These findings collectively support the notion that the other ethnicity effect stems from differential emotional amplification inherent in ensemble coding of emotion perception.

Cyber influence defense: Applying the DISARM framework to a cognitive hacking case from the Romanian digital space

One of the main lessons learned in the context of Russia’s full-scale invasion of Ukraine starting in February 2022 is that for- eign information manipulation and interference (FIMI) operations are closely coupled with cyber threats. Regardless of whether cyberattacks are followed by an information manipulation compo- nent and vice versa, the merger of the two can be an early indica- tor of the potential for a conflict to escalate from the cyber area to the ground. Our article is premised on the idea that today’s highly technologised information ecosystem is a fertile ground for cyberattacks and information manipulation in the context of FIMI; more specifically, it enables cognitive hacking, meaning hacking the human mind and human cognition altogether through techno- logical disruption and cyber pressure. Starting from this premise, the aim of the article is to highlight the technological determi- nants of cognitive hacking and identify silent or emerging threats that bypass technological sensors and seek to disrupt and manip- ulate the information environment. The empirical part is based on observation as a descriptive method, which is used to analyse a case of cognitive hacking carried out via a YouTube malvertis- ing campaign targeting Romanian users. This case study is anal- ysed qualitatively by matching the DISinformation Analysis & Risk Management (DISARM) framework with evidence collected through Open-Source Intelligence (OSINT) tools, following an innovative analysis structured according to the purposes, actions, results and techniques (PART) model. The extensive analysis of the identified case shows that applying the DISARM framework to cyber-enabled operations can be useful for anticipating and responding to FIMI threats, even when such operations do not appear to have a spe- cific, immediately identifiable purpose.

Polar Lipids Supplementation Enhances Basal Excitatory Synaptic Transmission in Primary Cortical Neuron.

Polar lipids, such as gangliosides and phospholipids, are fundamental structural components that play critical roles in the development and maturation of neurons in the brain. Recent evidence has demonstrated that dietary intakes of polar lipids in early life are associated with improved cognitive outcomes during infancy and adolescence. However, the specific mechanisms through which these lipids impact cognition remain unclear. This study examines the direct physiological impact of polar lipid supplementation, in the form of buttermilk powder, on primary cortical neuron growth and maturation. The changes are measured with postsynaptic current response recordings, immunohistochemical examination of functional synapse localization and numbers, and the biochemical quantification of receptors responsible for neuronal synaptic neurotransmission. Chronic exposure to polar lipids increases primary mouse cortical neuron basal excitatory synapse response strength attributed to enhanced dendritic complexity and an altered expression of the excitatory α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunit 2 (GluR2). The present finding suggests that dietary polar lipids improve human cognition through an enhancement of neuronal maturation and/or function.