Human Cognitive Capacities Research Articles

Scaffolded Rumination: The Case of Problematic Smartphone Use

AbstractDebates in 4E and scaffolded cognition have been centered on cases in which human cognitive or affective capacities are enhanced through technology. Recently some authors have noted that this may not always be the case: the technologies and environment around us can negatively impact cognitive and affective abilities. In the first part, I elaborate on this change of perspective in the debate. In the second part, I discuss a case of technology scaffolding maladaptive psychological processes. More in detail, I will focus on the role of smartphones in scaffolding ruminative processes. To illustrate this claim, I introduce four core features of rumination and discuss how they may be supported by problematic smartphone use in light of existing evidence.

The Future Of Human-Ai Collaboration In Software Development: A Narrative Exploration

Investigating how artificial intelligence technologies are changing conventional development methods and the role of human developers, this study investigates the changing dynamics of human-AI collaboration in software development. The research explores the opportunities and difficulties brought about by the incorporation of AI tools into software development processes using a thorough investigation of secondary data sources and the body of current literature. The results show that AI is going beyond simple automation in software development, creating a mutually beneficial partnership in which developers become strategic managers of AI-driven workflows and AI enhances human creativity and cognitive capacities. The transition from routine coding to higher-order problem-solving, the rise of new development models, and the increasing significance of ethical considerations in AI implementation are some of the major themes identified in the study. Important issues are also covered, such as the need for improved developer education in AI literacy, bias prevention, and transparency in AI decision-making. The study concludes that effective human-AI cooperation necessitates a well-rounded strategy that makes use of AI's processing capacity while preserving human supervision and originality. Among the suggestions include funding developer education, creating transparent AI systems, encouraging teamwork, and establishing moral standards for integrating AI. This study advances our knowledge of how artificial intelligence will influence software development in the future by indicating that the way forward is to establish an environment in which human knowledge and AI capabilities work in tandem to promote creativity and productivity in software development.

Development and Validation of the Tablet-Based ERAL Nonverbal Intelligence Test 5-17 (ERAL-NIT)

Intelligence has been extensively explored across various disciplines such as psychology, cognitive science, and neurology. Countless scholars have delved into understanding why certain individuals exhibit higher mental acuity and knowledge. Consequently, numerous studies aim to unveil the essence of intelligence and gauge human cognitive capacity. This study introduces and validates the ERAL Nonverbal Intelligence Test (ERAL-NIT) for children aged 5 to 17. Unlike many existing tests, ERAL-NIT is nonverbal, accommodating diverse linguistic backgrounds. Moreover, it incorporates parental assessments, enhancing its holistic approach. Administered via tablet with specialized software, ERAL-NIT provides comprehensive reports to practitioners and families. Its development integrates analyses of hemispheric specialization and executive functions of different brain areas. Content validity is ensured through expert input, while criterion validity is established by comparing results with established assessments and medical diagnoses. ERAL-NIT yields insights into pervasive developmental disorders, specific learning disabilities, attention deficits, and language disorders, thereby enriching diagnostic practices.

Callousness, exploitativeness, and tracking of cooperation incentives in the human default network

Human cognitive capacities that enable flexible cooperation may have evolved in parallel with the expansion of frontoparietal cortical networks, particularly the default network. Conversely, human antisocial behavior and trait antagonism are broadly associated with reduced activity, impaired connectivity, and altered structure of the default network. Yet, behaviors like interpersonal manipulation and exploitation may require intact or even superior social cognition. Using a reinforcement learning model of decision-making on a modified trust game, we examined how individuals adjusted their cooperation rate based on a counterpart's cooperation and social reputation. We observed that learning signals in the default network updated the predicted utility of cooperation or defection and scaled with reciprocal cooperation. These signals were weaker in callous (vs. compassionate) individuals but stronger in those who were more exploitative (vs. honest and humble). Further, they accounted for associations between exploitativeness, callousness, and reciprocal cooperation. Separately, behavioral sensitivity to prior reputation was reduced in callous but not exploitative individuals and selectively scaled with responses of the medial temporal subsystem of the default network. Overall, callousness was characterized by blunted behavioral and default network sensitivity to cooperation incentives. Exploitativeness predicted heightened sensitivity to others' cooperation but not social reputation. We speculate that both compassion and exploitativeness may reflect cognitive adaptations to social living, enabled by expansion of the default network in anthropogenesis.

A Theory of Thinking and Interpersonal Communication

ABSTRACT This article explores the psychology behind thinking and communicating, subsequently proposing a pragmatic model for improving these processes. It begins from the consideration that we all operate very limited cognitive “machines”: in an average lifetime, the human brain can theoretically store up to 0.0000084% (8.4×10⁻⁸) and consciously process some 0.0000000000512% (5.12×10⁻¹³) of the (digital) information that was ever created, captured or replicated by humans. Such a dismally low ratio between human cognitive capacities on the one side, and the total amount of existing information on the other means that thinking and communicating—especially on “difficult” topics such as warfare, politics or religion—usually involves unilateral or hypolateral thinking. This is defined as people’s general tendency to ignore most of the existing information on any topic; to specialize only on information immediately available and; to use cognitive strategies and other cognitive meta-mechanisms to simplify thinking processes. On the basis of these considerations, the article proposes: (1) a logical proof of why most of our “objective” knowledge is in fact built from emotions and intentions, rather than from rationality and ontological proofs—suggesting that our “inner world” is the ultimate driver of the “outer world” as we perceive it; and (2) a model of hyperlateral or omnilateral thinking, whereby we consciously follow a technique to become aware of the existence of a multitude of perspectives on any one topic, a necessary precondition for meaningful thinking and communication to happen.

Response to commentaries on What Babies Know.

Twenty-five commentaries raise questions concerning the origins of knowledge, the interplay of iconic and propositional representations in mental life, the architecture of numerical and social cognition, the sources of uniquely human cognitive capacities, and the borders among core knowledge, perception, and thought. They also propose new methods, drawn from the vibrant, interdisciplinary cognitive sciences, for addressing these questions and deepening understanding of infant minds.

The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons.

The outstanding human cognitive capacities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many long noncoding RNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the long noncoding RNA repertoire expressed in the developing cerebral cortex and explored putative roles for these long noncoding RNAs in the evolution of the human brain. Using transcriptomics and comparative genomics, we comprehensively annotated the cortical transcriptomes of humans, rhesus macaques, mice, and chickens and classified human cortical long noncoding RNAs into evolutionary groups as a function of their predicted minimal ages. Long noncoding RNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older long noncoding RNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory (GABAergic) neurons. In comparison, younger long noncoding RNAs showed preferential expression in cortical excitatory (glutamatergic) neurons, were enriched in primate and human-specific gene co-expression modules, and were dysregulated in neurodevelopmental disorders. These results suggest different evolutionary routes for older and younger cortical long noncoding RNAs, highlighting old long noncoding RNAs as a possible source of molecular evolution of conserved developmental programs; conversely, we propose that the de novo expression of primate- and human-specific young long noncoding RNAs is a putative source of molecular evolution and dysfunction of cortical excitatory neurons, warranting further investigation.

Freedom of speech or freedom of reach? Strategies for mitigating malicious content in social networks

Malicious content threatens the integrity and quality of content in social networks. Research and practice have experimented with network intervention strategies to curb malicious content propagation. These strategies lack efficiency, target malicious content propagators, and abridge freedom of speech. We draw upon the preferential attachment literature and cognitive load theory to employ the mechanisms of network formation, information sharing, and limited human cognitive capacities to propose an alternative feed management strategy—Preferentiality Dampened Feed Management. We compare and contrast this strategy against other established strategies using an agent-based model that utilizes empirical data from Twitter and findings from the prior literature. The results from our two experiments suggest that our proposed strategy is more effective in curbing malicious content propagation than other established strategies. Our work has important implications for the network interventions literature and practical implications for platform providers, social media users, and society.

Using rare genetic mutations to revisit structural brain asymmetry

Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities.

Can Robots Be Smarter than Human Beings?

The rapid advancements in artificial intelligence (AI) and robotics have fuelled speculation about the possibility of machines surpassing human intelligence. This abstract explores the question of whether robots can become smarter than human beings. By analyzing the capabilities of contemporary AI technologies and understanding the fundamental characteristics of human intelligence, we assess the potential for machines to outpace human cognitive abilities. To address this question, we examine the current state of AI development, including machine learning, deep learning, and neural networks. We discuss how these technologies enable robots to process vast amounts of data, learn from it, and make autonomous decisions. Additionally, we explore the concept of "general intelligence" and its significance in comparing human intelligence to AI. While machines excel in narrow domains and specific tasks, achieving true human-like general intelligence remains an ongoing challenge. Furthermore, the abstract delves into the unique aspects of human intelligence that set it apart from current AI capabilities. Human cognition encompasses various complex abilities such as creativity, emotional intelligence, moral reasoning, and common sense understanding, which have proven difficult to replicate in machines. We examine the limitations and challenges faced by AI in these areas and explore the philosophical and ethical implications of creating machines that surpass human cognitive capacities. Ultimately, the abstract concludes that while robots have shown remarkable progress in specific domains, surpassing human intelligence in its entirety is a complex and elusive goal. Human intelligence encompasses a multitude of intricacies that extend beyond pure computational power. However, the abstract acknowledges the potential for future advancements in AI and robotics to challenge the boundaries of human cognitive abilities, warranting continued research and thoughtful consideration of their societal impact.

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Revolutionizing developmental neurotoxicity testing - a journey from animal models to advanced in vitro systems.

Developmental neurotoxicity (DNT) testing has seen enormous progress over the last two decades. Preceding even the publication of the animal-based OECD test guideline for DNT testing in 2007, a series of non-animal technology workshops and conferences (starting in 2005) shaped a community that has delivered a comprehensive battery of in vitro test methods (IVB). Its data interpretation is covered by a very recent OECD test guidance (No. 377). Here, we aim to overview the progress in the field, focusing on the evolution of testing strategies, the role of emerging technologies, and the impact of OECD test guidelines on DNT testing. In particular, this is an example of a targeted development of an animal-free testing approach for one of the most complex hazards of chemicals to human health. These developments started literally from a blank slate, with no proposed alternative methods available. Over two decades, cutting-edge science enabled the design of a testing approach that spares animals and enables throughput for this challenging hazard. While it is evident that the field needs guidance and regulation, the massive economic impact of decreased human cognitive capacity caused by chemical exposure should be prioritized more highly. Beyond this, the claim to fame of DNT in vitro testing is the enormous scientific progress it has brought for understanding the human brain, its development, and how it can be perturbed.

Examining Limits of Small Multiples: Frame Quantity Impacts Judgments with Line Graphs.

Small multiples are a popular visualization method, displaying different views of a dataset using multiple frames, often with the same scale and axes. However, there is a need to address their potential constraints, especially in the context of human cognitive capacity limits. These limits dictate the maximum information our mind can process at once. We explore the issue of capacity limitation by testing competing theories that describe how the number of frames shown in a display, the scale of the frames, and time constraints impact user performance with small multiples of line charts in an energy grid scenario. In two online studies (Experiment 1 n = 141 and Experiment 2 n = 360) and a follow-up eye-tracking analysis (n = 5), we found a linear decline in accuracy with increasing frames across seven tasks, which was not fully explained by differences in frame size, suggesting visual search challenges. Moreover, the studies demonstrate that highlighting specific frames can mitigate some visual search difficulties but, surprisingly, not eliminate them. This research offers insights into optimizing the utility of small multiples by aligning them with human limitations.

The use of AI in physical therapy

Following the invention of the steam engine, human social and economic systems saw unprecedented growth and development. More recently, artificially intelligent machines have emerged, enabling human cognitive capacity to recognize previously hidden patterns within massive data sets. These changes are having an impact on every facet of society, including but not limited to our legal, moral, educational, health, and social systems. In comparison to the steam engine-driven mechanical revolution, these changes may have a greater lasting effect on human progress. Physicians will soon have to deal with massive information networks, which will necessitate the use of artificial intelligence (AI) in patient care. This paper aims to describe the potential impacts and range of applications of AI-based technologies on the field of physiotherapy, as well as the resulting changes that physiotherapy education will need to undergo in order to produce graduates qualified to work in a 21st-century healthcare system.

Assessing the Performance of ChatGPT 3.5 and ChatGPT 4 in Operative Dentistry and Endodontics: An Exploratory Study

Background: Artificial intelligence is an innovative technology that mimics human cognitive capacities and has gathered the world’s attention through its vast applications in various fields.Aim: This study is aimed at assessing the effects of ChatGPT 3.5 and ChatGPT 4 on the validity, reliability, and authenticity of standard assessment techniques used in undergraduate dentistry education.Methodology: Twenty questions, each requiring a single best answer, were selected from two domains: 10 from operative dentistry and 10 from endodontics. These questions were divided equally, with half presented with multiple choice options and the other half without. Two investigators used different ChatGPT accounts to generate answers, repeating each question three times. The answers were scored between 0% and 100% based on their accuracy. The mean score of the three attempts was recorded, and statistical analysis was conducted.Results: No statistically significant differences were found between ChatGPT 3.5 and ChatGPT 4 in the accuracy of their responses. Additionally, the analysis showed high consistency between the two reviewers, with no significant difference in their assessments.Conclusion: This study evaluated the performance of ChatGPT 3.5 and ChatGPT 4 in answering questions related to endodontics and operative dentistry. The results showed no statistically significant differences between the two versions, indicating comparable response accuracy. The consistency between reviewers further validated the reliability of the assessment process.

Metacognitive phenomena during human–Internet interactions

External information systems often serve as an extended cognitive system and are usually conceived as expansions of the capacity of human cognition. However, the boundaries between our own mind and a powerful cloud mind, like the Internet, are increasingly blurry. This paper discusses recent empirical evidence of various metacognitive phenomena taking place while searching for information on the Internet, against the backdrop of the theory of transactive memory systems and the theory of cognitive offloading. The discussion focuses on the ways our cognitive systems maneuver and adapt their responses to the medium of the Internet by examining its effects on the metacognitive evaluations of oneself as a knower, the novel metacognitive experience of the feeling of findability, and how our metamemory judgments are affected. The conclusion proposes directions for future research and a better understanding of our interaction with the Internet.

Material constraints enabling human cognition

Material constraints enabling human cognition Compared to our closest living relatives, who typically use fewer than 100 words, humans can build vocabularies of tens of hundreds of thousands of words. The ERC-funded Advanced Grant project ‘Material Constraints enabling Human Cognition’, or ‘MatCo’, will find out why. It will use novel insights from human neurobiology. These will be translated into mathematically exact computational models to find new answers to long-standing questions in cognitive science, linguistics and philosophy. The project will also explore how semantic meaning is implemented for gestures and words and, more specifically, for referential and categorical terms. To identify human cognitive capacities, MatCo will develop models replicating structural differences between human and non-human primate brains. The results will shed light on the biologically constrained networks.

Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved.

“Where lies the grail? AI, common sense, and human practical intelligence”

AbstractThe creation of machines with intelligence comparable to human beings—so-called "human-level” and “general” intelligence—is often regarded as the Holy Grail of Artificial Intelligence (AI) research. However, many prominent discussions of AI lean heavily on the notion of human-level intelligence to frame AI research, but then rely on conceptions of human cognitive capacities, including “common sense,” that are sketchy, one-sided, philosophically loaded, and highly contestable. Our goal in this essay is to bring into view some underappreciated features of the practical intelligence involved in ordinary human agency. These features of practical intelligence are implicit in the structure of our first-person experience of embodied and situated agency, deliberation, and human interaction. We argue that spelling out these features and their implications reveals a fundamental distinction between two forms of intelligence in action, or what we call “efficient task-completion” versus “intelligent engagement in activity.” This distinction helps us to see what is missing from some widely accepted ways of thinking about human-level intelligence in AI, and how human common sense is actually tied, conceptually, to the ideal of practical wisdom, or good (normative) judgment about how to act and live well. Finally, our analysis, if sound, also has implications for the important ethical question of what it means to have AI systems that are aligned with human values, or the so-called “value alignment” problem for artificial intelligence.