Human Cognitive System Research Articles

Valence sound symbolism facilitates classification of vowels and emotional facial expressions.

Research on iconicity or sound symbolism chronicles associations between aspects of word meaning and word form. In valence sound symbolism, specifically, vowels were demonstrated to be associated with valence, so that the vowel /i/ (as in English sea) is judged to fit positively valenced stimuli, whereas the vowel /o/ (as in German Boot) is judged to fit negatively valenced stimuli. However, because previous findings were based on judgments or name invention tasks, they may reflect deliberate strategies. The present research aims to demonstrate the automaticity of valence sound symbolism in online language processing by means of a speeded response task. A total of 163 participants performed a speeded categorization task in which they were requested to classify one of two simultaneously presented stimuli, pseudowords containing the vowels /i/ or /o/ and pictures with positive or negative emotional facial expressions. Thus, stimulus pairings could be either congruent (/i/-words and positive expressions; /o/-words and negative expressions) or incongruent (/i/-words and negative expressions; /o/-words and positive expressions). We observed faster and more accurate responses for congruent over incongruent combinations (the simple effects were significant for positive but not for negative expressions). Thus, sound symbolic congruency (vs. incongruency) of an irrelevant stimulus facilitated responses, which accords with an automatic process contributing to valence sound symbolism and indicates that valence sound symbolism is deeply engrained in the human cognitive system. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

GEXMERT: Geometrically enhanced cross-modality encoder representations from transformers inspired by higher-order visual percepts

Owing to the rapid progress made in transformer-based models and multi-modal fusion strategies, the performance of multi-modal networks has remarkably improved in recent years. However, intrinsic connections among individual modalities are crucial in multi-modal fusion strategies, presenting substantial opportunities for further enhancement of multi-modal perception in this field. Taking inspiration from the biological mechanisms of the human multi-modal cognitive system, we proposed a novel cross-modality fusion network. This network leverages the lateral occipital complex, which is a region responsible for object-shape perception in human vision and integrates an auxiliary modality derived using the conventional multi-stream model. This integration for learning complementary information across image and language modalities effectively captures the biological mechanisms of human processing of higher-order visual percepts through parameter updates of the intermediate modality to optimize the suitability for further fusion. To evaluate the effectiveness of our approach, we conducted experiments under various settings. Our statistical analyses demonstrate a considerable enhancement in the performance of vision-language tasks, thereby validating biological plausibility of our model.

Deep Learning model based on CNN using Keras and TensorFlow to determine real time melting point of chemical substances

Deep learning is a subset of machine learning that uses artificial neural networks inspired by human cognitive systems. Although this is a newly approach recently it became very popular and effective. In many applications deep learning become most successful approach where machine learning has been successful at certain rates. In the succession of these the proposed deep learning model is suitable for melting point detection apparatus which determine melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classify images of chemical’s state (Solid or Liquid) by deep neural network (DNN) it consists of TensorFlow framework, libraries like Keras and activation function like ReLu, sigmoid, MaxPool and Flatten to determine melting point of chemical substances. The proposed model enables to TensorFlow architecture, which can determine the melting point of chemicals in real time on a single board computer. This use python as a programming language, TensorFlow framework and keras library. The input image data mainly focuses on chemical’s state, there are 2 categories of chemical’s state either solid or liquid. The Deep Neural Network (DNN) chosen as the best practice for the training process because it provides high accuracy. The results discussed in terms of the image classification accuracy in percentage. The images from two class label gets maximum accuracy is 99.72% and maximum validation accuracy is 99.37% same as liquid’s image and the average value of accuracy 84.17% or higher after certain epochs.

Human cognitive system privileges processing over short-term storage: Asymmetry in working memory limitations.

The continuous flow of information in which we are immersed obliges our cognitive system to maintain accessible the relevant elements for the time necessary for their processing. The present study investigated how working memory balances the resource demands of this necessary storage in the face of demanding processing. In four experiments using a complex span task, we examined the residual performance in memory and processing of individuals who performed at their best in the other component. Reciprocal dual-task costs pointed toward a resource sharing between the two functions. However, whereas prioritizing processing almost abolished participants' memory performance, more than 60% of their processing capacities were preserved while maintaining memory performance at span. We argue that this asymmetry might be adaptive in nature. Working memory might have evolved as an action-oriented system in which short-term memory capacity is structurally limited to spare the resources needed for processing the information it holds. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Coral bionic e-skin for motion monitoring and intelligent recognition of underwater communication commands assisted by deep learning

Continuous and dependable underwater motion monitoring and communication are crucial for ensuring the safety of swimming, divers and underwater workers. We have developed a coral-inspired piezoresistive electronic skin (Melamine sponge-poly(pyrrole)-polydimethylsiloxane, MS-ppy-PDMS e-skin) for motion monitoring and underwater communication. The microstructure of the coral-like poly(pyrrole) and the PDMS acting as “limestone” make MS-ppy-PDMS e-skin superhydrophobic, stable (up to 80,000 cycles and acid and alkali resistance), and sensitive over a wide pressure sensing range (1–2,000 kPa). Moreover, MS-ppy-PDMS e-skin exhibits remarkable breathability, photothermal antibacterial efficacy, and biocompatibility, thereby increasing the comfort of wear while maintaining its outstanding sensing functionality. MS-ppy-PDMS e-skin exhibited dependable electrical signals output in motion monitoring tests on land and underwater. Most crucially, the combination of underwater communication commands captured by MS-ppy-PDMS e-skin with a multilayer perceptron (MLP) neural network allows for rapid and precise analysis and recognition, achieving a 99.38% accuracy rate in identifying underwater communication commands. This enables the realization of intelligent perception function, and is expected to further advance the development of human brain-like cognitive systems. Overall, the coral bionic MS-ppy-PDMS e-skin demonstrates tremendous application potential in the fields of motion monitoring and underwater communication.

사용자의 사고 작용에 미치는 디자인 심리 연구

This study examined the theory of design psychology affecting users' thinking actions, focusing on the human cognitive system and information processing process. Based on related literature studies such as design, psychology, and brain science, the study was conducted focusing on case analysis of design psychology principles. As a result of the study, first, it was confirmed that the tendency of thinking appeared differently depending on the height of the ceiling, and creativity was promoted in spaces with high ceilings and specific thinking was promoted in spaces with low ceilings. Second, apart from usability, it was found that a design with good aesthetics reinforces users' positive attitudes and explores problem-solving methods with a generous attitude even in the event of a problem. Third, by first presenting positive expressions or experiences before experiencing a situation, it was found to have a positive effect on the user's perception, judgment, behavior, and memory. Fourth, it has been shown that visual stimuli activate physical waves and central nerves, causing psychological changes in human behavior and thoughts. Fifth, design consistency has been shown to affect user behavior and quick decision-making, but family similarity only needs to be interrelated, even if it is not in a fixed form. The significance of this study is to understand that the preference or bias that users judge design is related to design psychology. As a limitation, it failed to deal with the design psychological principles that negatively affect users' thinking formation. Furthermore, it is proposed to be conducted by supplemented and supplemented.

Digital Revolution and Artificial Intelligence as Challenges for Today/Rewolucja cyfrowa i sztuczna inteligencja jako wyzwania współczesności

This article addresses the social impact of the ongoing digital revolution through a critical analysis of the literature on the subject. The aim is to understand the dynamics of change, which is key to dealing with the complexities of the digital age and realising its potential for sustainable development. The information revolution and artificial intelligence pose significant challenges to modern society by marking a profound transformation through self-organising social changes that disrupt existing norms. The digital age has revolutionized information systems, much like Gutenberg's invention of movable-type printing press. While it enables scientific and economic progress, the information revolution raises ethical, moral and legal concerns. Social media have become ”weapons” in a hybrid conflict, where the intangible battleground is human cognitive systems.

The difference in positivity of the Russian and English lexicon: The big data approach

Psychological cross-cultural studies have long noted differences in the degree of cognition positivity, or optimism, in various cultures. Herewith, the question whether the difference shows up at the level of the language lexicon remains unexplored. Linguistic positivity bias has been confirmed for a number of languages. The point of it is that most words have a positive connotation in the language. This begs the question: is linguistic positivity bias the same for different languages or not? In a sense, the issue is similar to the hypothesis of linguistic relativity suggesting the language impact on the human cognitive system. The problem has been researched only in one work (Dodds et al. 2015), where data on the positivity bias values are given for different languages and the comparison for each pair of languages is based on merely one pair of dictionaries. In the present study, we radically increase the computational baseline by comparing four English and five Russian dictionaries. We carry out the comparative study both at the level of vocabularies and at the level of texts of different genres. A new, previously untapped idea is to compare positivity ratings of translated texts. Also, English and Russian sentiment dictionaries are compared based on the scores of translation-stable words. The results suggest that the Russian language is somewhat slightly more positive than English at the level of vocabulary.

Modalization and Transcendence of Health in the Coming Decade: Emphasizing the Human Cognitive System in the Stories of Prophets in the Holy Quran.

In this study, the progression and importance of health knowledge is explored, projecting its future path. We do this by comparing the stages of evolution of the human cognitive system from birth to old age (inner intellect) with the stages of intellectual modalization in divine messengers (external intellect), as depicted in the stories of the prophets in the Holy Quran. This comparison aims to articulate the course of evolution, which includes sensory perception, apprehension, imaginalization, intellection, and intuition. Future medicine will consider the influence of spiritual factors (soul) in the form of human cognition and intention, as well as material effects (genes and biology). The seven medical domains of the future will encompass the field of 'sprito-epigenetico psycho-neuro-endocrino-immuno-pharmacology'. This perspective emphasizes the need for a transcendent outlook in health and medicine. This study employed a library research method, including studies in medical journals from the last forty years.

Visual attention methods in deep learning: An in-depth survey

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated into one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey on attention techniques to guide researchers in employing attention in their deep models. Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques, categorizing them by their most prominent features. We initiate our discussion by introducing the fundamental concepts behind the success of the attention mechanism. Next, we furnish some essentials such as the strengths and limitations of each attention category, describe their fundamental building blocks, basic formulations with primary usage, and applications specifically for computer vision. We also discuss the challenges and general open questions related to attention mechanisms. Finally, we recommend possible future research directions for deep attention. All the information about visual attention methods in deep learning is provided at https://github.com/saeed-anwar/VisualAttention

Quantifying resource sharing in working memory.

Several models of working memory (WM), the cognitive system devoted to the temporary maintenance of a small amount of information in view of its treatment, assume that these two functions of storage and processing share a common and limited resource. However, the predictions issued from these models concerning this resource-sharing remain usually qualitative, and at which precise extent these functions are affected by their concurrent implementation remains undecided. The aim of the present study was to quantify this resource sharing by expressing storage and processing performance during a complex span task in terms of the proportion of the highest level of performance each participant was able to reach (i.e., their span) in each component when performed in isolation. Two experiments demonstrated that, despite substantial dual-task decrements, participants managed to preserve half or more of their best performance in both components, testifying for a remarkable robustness of the human cognitive system. The implications of these results for the main WM models are discussed.

Expecting the Future: Entropy-Based Predictions in Time Perception

Abstract What cognitive mechanisms underlie the human experience of time’s directionality, often described as the psychological arrow of time? This study explores this question by examining the role of visual entropy, a measure of disorder within a system, in shaping our perception of time. Grounded in the second law of thermodynamics and predictive processing frameworks, we hypothesised that the human cognitive system expects an increase in visual entropy over time and that violations of this expectation (prediction errors) would lead to a relative overestimation of perceived duration. Participants reproduced the duration of computer-generated animations depicting three-dimensional greyscale shapes shattering from wholes into parts (low to high entropy) or the reverse (high to low entropy) presented at different lengths (500, 1000, 1500, and 2000 milliseconds). The results revealed a significant interaction: while forward animations (low to high entropy) were perceived as longer at the shortest duration (500 ms), backward animations (high to low entropy) were overestimated at longer durations (1500 and 2000 ms). This pattern suggests an initial reliance on high-precision expectations for shorter stimuli, with a shift towards increased weighting of prediction errors for longer durations, leading to the overestimation of entropy-reversed stimuli. These results align with predictive processing frameworks, indicating that the brain dynamically reconciles incoming sensory data with prior expectations of entropy progression, thereby shaping our subjective experience of temporal directionality. This research introduces a novel paradigm for examining time perception and supports the application of predictive processing theories in understanding the cognitive and neural underpinnings of temporal experience.

People can reliably detect action changes and goal changes during naturalistic perception.

As a part of ongoing perception, the human cognitive system segments others' activities into discrete episodes (event segmentation). Although prior research has shown that this process is likely related to changes in an actor's actions and goals, it has not yet been determined whether untrained observers can reliably identify action and goal changes as naturalistic activities unfold, or whether the changes they identify are tied to visual features of the activity (e.g., the beginnings and ends of object interactions). This study addressed these questions by examining untrained participants' identification of action changes, goal changes, and event boundaries while watching videos of everyday activities that were presented in both first-person and third-person perspectives. We found that untrained observers can identify goal changes and action changes consistently, and these changes are not explained by visual change and the onsets or offsets of contact with objects. Moreover, the action and goal changes identified by untrained observers were associated with event boundaries, even after accounting for objective visual features of the videos. These findings suggest that people can identify action and goal changes consistently and with high agreement, that they do so by using sensory information flexibly, and that the action and goal changes they identify may contribute to event segmentation.

Pedagogical interventions and their influences on university-level students learning pharmacology-a realist review

IntroductionThe knowledge complexity and varied delivery formats in pharmacology education can leave students unprepared in essential pharmacotherapy skills. This significantly influences their ways of thinking and working in clinical environments, resulting in a challenging clinical transition. This need demands pedagogical innovations to strengthen pharmacology education and improve learners’ skills and competencies in pharmacotherapy. This evidence-based realist review aimed to examine the contextual factors and program theories or causal mechanisms crucial for effective pedagogical interventions in pharmacology, seeking to answer the question of ‘what works for whom, under what circumstances, how, and why’.MethodThe realist synthesis was initiated after retrieving data from Medline (OVID), Cochrane, EBSCO hosted ERIC, SCOPUS, and Embase (OVID) including other sources for additional records. The preliminary analysis enabled the establishment of context, mechanism, and outcome configurations (CMOC) and formulation and refinement of the initial program theory regarding the pedagogical interventions in pharmacology. Data synthesis iteration helped to identify the relevant context and unravel its relationships with underlying causal mechanisms through which said interventions generate outcomes of interest.ResultsA realist review analyzed 1,217 records and identified 75 articles examining a range of educational interventions from individual efforts to faculty-wide curriculum changes in pharmacology education. The key contexts for pharmacology education were troublesome content, traditional delivery methods, inadequate and limited opportunities for knowledge integration, and application. Active participation in interactive learning, along with enjoyment and motivation, was proposed as a causal mechanism for optimizing cognitive load and achieving positive outcomes. The outcomes of the review include subjective perceptions of improved confidence and satisfaction, objective measurements of high post-test scores.DiscussionPedagogical scaffolding in constructivist learning environments helps students overcome challenges in learning troublesome pharmacology knowledge. Considering the human cognitive system’s processing capacity, these interventions improve learning by effectively using cognitive resources. Innovations that focus on enhancing cognitive load through task construction can also promote positive emotional experiences in students, such as engagement and enjoyment, as explained by flow theory. A constructive learning environment, where the cognitive load is optimized and high flow is achieved, can maximize the impact of pedagogical interventions in pharmacology.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=160441, PROSPERO (CRD42020160441).

VISUALIZING CROSS-LINGUISTIC READING BRAINS: UNIVERSALITY AND INTERACTIVE SPECIFICITIES

The universality and specificity of cross-linguistic reading have always been a topic of interest to researchers. The earliest controversies started with the issue of whether speech recoding is required for reading non-alphabetic scripts, for example, Chinese and Kanji, whether it is possible to directly access the semantic meaning of a word from orthography without speech recoding, and whether reading non-alphabetic scripts relies on the right rather than the left brain. The abatement of these controversies began with a series of experiments by Ovid Tzeng and William Wang which raised the possibility of universality across different languages. To study how humans read, two kinds of operation must be considered: the human cognitive system and the structure of language. In this paper, we will take the original controversy of non-alphabetic scripts being different from alphabetic scripts in the 1970s as the starting point, and use the current research results of cognitive neuroscience to explain what kind of consensus has been reached right now. In addition, we will use tone as an important feature in the study of Chinese reading, and describe the current research results on tone to highlight the special characteristics of Chinese. Finally, we will propose future research directions.

WordIllusion: An adversarial text generation algorithm based on human cognitive system

Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are usually generated by finding substitute words in adjacent spaces of keyword vectors. Unlike English, Chinese is more discrete and has a more complex font structure, which words that are closer in vector spaces may differ greatly in physical structure. Therefore, adversarial examples generated by current methods possess lower quality and can be easily perceived by human, or rather, they are not suitable for the human cognitive system. In this paper, we propose the “WordIllusion”, a new detectable black-box algorithm used for generating Chinese adversarial texts. In this method, we create a CKSF evaluation indicator to select the key words of sentences. And then, based on the shape bias of human cognitive system and the rectification understanding to create replacement spaces of key words. To verify the effectiveness of WordIllusion, we experiment with two types of text classification tasks by using six natural language processing models. The result indicates that our method is able to improve the accuracy rate efficiently, and the generated adversarial texts can be very misleading.

Heterogeneous graph-based knowledge tracing with spatiotemporal evolution

Knowledge tracing (KT), in which the future performance of students is estimated by tracing their knowledge states based on their responses to exercises, is widely applied in the field of intelligent education. However, existing mainstream KT models explore the importance of knowledge relations but ignore the key role of cognitive factors. According to the knowledge construction theory, the human cognitive system performs both spatial accommodation and temporal assimilation to internalize knowledge. In this paper, we propose an innovative heterogeneous graph-based Knowledge tracing method with spatiotemporal evolution (TSKT), in which knowledge state evolution is traced along both temporal and spatial dimensions. We construct a heterogeneous graph with multiple exercise attributes, including content, concepts, and difficulty, to obtain a knowledge space with richer exercise representations through hierarchical aggregation. We design a spatial updating module in which each interaction updates the current node’s state of the knowledge space and transfers its influence to its neighbors. We also design a temporal updating module to further update the knowledge state through short-term memory enhancing and long-term memory forgetting. Finally, we stack these modules to obtain deeper features by using alternate spatiotemporal updating. Extensive experiments on three datasets reveal the superiority of the proposed method and its variants in terms of future performance prediction.

Is Syntax Separate or Shared Between Arabic and English? Syntactic Priming in Arabic-English Bilinguals

Syntactic priming refers to the likelihood that a particular structure is repeated when preceded by an utterance with the same or a related syntactic structure. Experimental studies that demonstrate syntactic priming have attracted the attention of scholars because syntactic priming provides insightful information about the way language is processed in the human cognitive system, facilitates dialogue and informs linguistic theories about the knowledge of language that is activated via syntactic priming. In this study, we investigate syntactic priming in 15 Arabic-English bilinguals through the Confederate-Scripted Dialogue Game technique, targeting four structures: active, passive, double-object (DO), and prepositional-object structures (PO). The results of the study indicate that the syntax of Arabic-English bilinguals is separate for each language. Participants tend to use the active structure regardless of the structure of the prime sentence. The findings of this study illustrate how the syntax of two unrelated languages (Arabic and English) is represented and processed in bilingual brains, contributing to the fields of bilingualism, syntax, psycholinguistics, and second language acquisition.

Questions About Quantifiers: Symbolic and Nonsymbolic Quantity Processing by the Brain.

One approach to understanding how the human cognitive system stores and operates with quantifiers such as "some," "many," and "all" is to investigate their interaction with the cognitive mechanisms for estimating and comparing quantities from perceptual input (i.e., nonsymbolic quantities). While a potential link between quantifier processing and nonsymbolic quantity processing has been considered in the past, it has never been discussed extensively. Simultaneously, there is a long line of research within the field of numerical cognition on the relationship between processing exact number symbols (such as "3" or "three") and nonsymbolic quantity. This accumulated knowledge can potentially be harvested for research on quantifiers since quantifiers and number symbols are two different ways of referring to quantity information symbolically. The goal of the present review is to survey the research on the relationship between quantifiers and nonsymbolic quantity processing mechanisms and provide a set of research directions and specific questions for the investigation of quantifier processing.