Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks.

Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.

Artificial Intelligence and Human Enhancement: Can AI Technologies Make Us More (Artificially) Intelligent?

This paper discusses two opposing views about the relation between artificial intelligence (AI) and human intelligence: on the one hand, a worry that heavy reliance on AI technologies might make people less intelligent and, on the other, a hope that AI technologies might serve as a form of cognitive enhancement. The worry relates to the notion that if we hand over too many intelligence-requiring tasks to AI technologies, we might end up with fewer opportunities to train our own intelligence. Concerning AI as a potential form of cognitive enhancement, the paper explores two possibilities: (1) AI as extending-and thereby enhancing-people's minds, and (2) AI as enabling people to behave in artificially intelligent ways. That is, using AI technologies might enable people to behave as if they have been cognitively enhanced. The paper considers such enhancements both on the level of individuals and on the level of groups.

Validity and effectiveness of interrogation techniques: A meta-analytic review.

The gathering of information through the use of interrogation techniques in the context of human intelligence (HUMINT) has a long and elusive history within applied settings of law enforcement and the military and civilian intelligence/counterterrorism community. However, psychological research has yet to catch up to systematically address pressing matters regarding the validity and effectiveness of common interrogation methods and a conceptual framework for relevant psychological factors. A promising, comprehensive contribution is the Taxonomy of Interrogation Methods (ToIM), which aims to integrate multiple approaches within the field of interrogation. In this paper, we utilized the ToIM model as a foundation for a meta-analytic review on the validity and effectiveness of interrogation techniques. We systematically integrated the existing evidence from 60 studies in order to determine which techniques from six domains of the ToIM produce valuable information. The results indicate that Rapport and Relationship Building, Presentation of Evidence and Cognitive Facilitation (an additional domain beyond the ToIM) are valid approaches to optimize both the amount of information gathered as well as its accuracy. The evidence is insufficient to conclude the effectiveness of techniques from the other four domains. Overall, the results are in line with the general notion in the field that a positive relationship with a suspect/source is the key to gather valuable information.

An integrative view of human hippocampal function: Differences with other species and capacity considerations.

We describe an integrative model that encodes associations between related concepts in the human hippocampal formation, constituting the skeleton of episodic memories. The model, based on partially overlapping assemblies of "concept cells," contrast markedly with the well-established notion of pattern separation, which relies on conjunctive, context dependent single neuron responses, instead of the invariant, context independent responses found in the human hippocampus. We argue that the model of partially overlapping assemblies is better suited to cope with memory capacity limitations, that the finding of different types of neurons and functions in this area is due to a flexible and temporary use of the extraordinary machinery of the hippocampus to deal with the task at hand, and that only information that is relevant and frequently revisited will consolidate into long-term hippocampal representations, using partially overlapping assemblies. Finally, we propose that concept cells are uniquely human and that they may constitute the neuronal underpinnings of cognitive abilities that are much further developed in humans compared to other species.

The impact of rapport on intelligence yield: police source handler telephone interactions with covert human intelligence sources.

Covert Human Intelligence Sources (CHIS) provide unique access to criminals and organised crime groups, and their collection of intelligence is vital to understanding England and Wales' threat picture. Rapport is essential to the establishment and maintenance of effective professional relationships between source handlers and their CHIS. Thus, rapport-based interviewing is a fundamental factor to maximising intelligence yield. The present research gained unprecedented access to 105 real-life audio recorded telephone interactions between England and Wales police source handlers and CHIS. This research quantified both the rapport component behaviours (e.g., attention, positivity, and coordination) displayed by the source handler and the intelligence yielded from the CHIS, in order to investigate the frequencies of these rapport components and their relationship to intelligence yield. Overall rapport, attention and coordination significantly correlated with intelligence yield, while positivity did not. Attention was the most frequently used component of rapport, followed by positivity, and then coordination.

Inside the shadows: a survey of UK human source intelligence (HUMINT) practitioners, examining their considerations when handling a covert human intelligence source (CHIS).

Law enforcement agencies in the UK are embracing evidence-based policing and recognise the importance of human source intelligence (HUMINT) in the decision-making process. A review of the literature identified six categories likely to impact the handling of a covert human intelligence source (CHIS) or an informant: (a) handler personality traits; (b) informant motivation; (c) rapport; (d) gaining cooperation; (e) obtaining information, and (f) detecting deception. This study sought to identify which of these categories current HUMINT practitioners considered the most when planning and conducting a meeting with an informant. A bespoke online survey was designed and disseminated to 34 practitioners using purposive and snowball sampling. Directed content analysis and thematic content analysis were conducted. Results indicate that practitioners appear most concerned with gaining co-operation (d) and detecting deception (f). Results also found an inter-connectivity between the six categories, with informant handlers often having to balance competing requirements. Implications for future research are discussed.

Differential Contribution of Cortical Thickness, Surface Area, and Gyrification to Fluid and Crystallized Intelligence.

Human intelligence can be broadly subdivided into fluid (gf) and crystallized (gc) intelligence, each tapping into distinct cognitive abilities. Although neuroanatomical correlates of intelligence have been previously studied, differential contribution of cortical morphologies to gf and gc has not been fully delineated. Here, we tried to disentangle the contribution of cortical thickness, cortical surface area, and cortical gyrification to gf and gc in a large sample of healthy young subjects (n = 740, Human Connectome Project) with high-resolution MRIs, followed by replication in a separate data set with distinct cognitive measures indexing gf and gc. We found that while gyrification in distributed cortical regions had positive association with both gf and gc, surface area and thickness showed more regional associations. Specifically, higher performance in gf was associated with cortical expansion in regions related to working memory, attention, and visuo-spatial processing, while gc was associated with thinner cortex as well as higher cortical surface area in language-related networks. We discuss the results in a framework where "horizontal" cortical expansion enables higher resource allocation, computational capacity, and functional specificity relevant to gf and gc, while lower cortical thickness possibly reflects cortical pruning facilitating "vertical" intracolumnar efficiency in knowledge-based tasks relevant mostly to gc.

Reflections around ethics, human intelligence and artificial intelligence.

On the internet, artificial intelligence has grown to become a program with codes and algorithms that learn and reprogram themselves to carry out pre-established tasks with greater efficiency; although this translates into improvements, the scope of the results and reprogramming are unknown to the programmer. Given the risk of deviation from pre-established objectives and ethical regulations, filters must be installed at the beginning, during and at the end of the process, as alarms for detecting deviations with bioethical implications. The interaction of human intelligence with artificial intelligence has had negative and positive disagreements. Initially, adapting regulations, labor laws and human rights was enough; now it is necessary for ethical standards to be established, such as those formulated in the Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe.

En internet ha crecido la inteligencia artificial hasta convertirse en un programa con códigos y algoritmos que aprenden y se reprograman para efectuar tareas preestablecidas con mayor eficiencia; si bien lo anterior se traduce en mejoría, el programador desconoce los alcances de los resultados y de la reprogramación. Ante el riesgo de desviación de los objetivos preestablecidos y de los reglamentos éticos, se tienen que implementar filtros al inicio, durante y al final del proceso, como alarmas cuando existan desviaciones con implicación bioética. La interacción de la inteligencia humana con la inteligencia artificial ha tenido desencuentros negativos y positivos. Al principio, bastó con adecuar normas, leyes laborales y derechos humanos; ahora se requiere establecer normas éticas, como las formuladas en la Declaración de Barcelona para el Adecuado Desarrollo y Uso de la Inteligencia Artificial en Europa.

Dark control: The default mode network as a reinforcement learning agent.

The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.

An Alternative Molecular View of Evolution: How DNA was Altered over Geological Time.

Four natural phenomena are cited for their defiance of conventional neo-Darwinian analysis: human intelligence; cat domesticity; the Cambrian explosion; and convergent evolution. 1. Humans are now far more intelligent than needed in their hunting-gathering days >10,000 years ago. 2. Domestic cats evolved from wildcats via major genetic and physical changes, all occurring in less than 12,000 years. 3. The Cambrian explosion refers to the remarkable expansion of species that mystifies evolutionists, as there is a total lack of fossil evidence for precursors of this abundant new life. 4. Convergent evolution often involves formation of complex, multigene traits in two or more species that have no common ancestor. These four evolutionary riddles are discussed in terms of a proposed "preassembly" mechanism in which genes and gene precursors are collected silently and randomly over extensive time periods within huge non-coding sections of DNA. This is followed by epigenetic release of the genes, when the environment so allows, and by natural selection. In neo-Darwinism, macroevolution of complex traits involves multiple mutation/selections, with each of the resulting intermediates being more favorable to the species than the previous one. Preassembly, in contrast, invokes natural selection only after a partially or fully formed trait is already in place. Preassembly does not supplant neo-Darwinism but, instead, supplements neo-Darwinism in those important instances where the classical theory is wanting.

Eliciting human intelligence: police source handlers' perceptions and experiences of rapport during covert human intelligence sources (CHIS) interactions.

Rapport is an integral part of interviewing, viewed as fundamental to the success of intelligence elicitation. One collection capability is human intelligence (HUMINT), the discipline charged with eliciting intelligence through interactions with human sources, such as covert human intelligence sources (CHIS). To date, research has yet to explore the perceptions and experiences of intelligence operatives responsible for gathering HUMINT within England and Wales. The present study consisted of structured interviews with police source handlers (N = 24). Rapport was perceived as essential, especially for maximising the opportunity for intelligence elicitation. Participants provided a range of rapport strategies while highlighting the importance of establishing, and maintaining, rapport. The majority of participants believed rapport could be trained to some degree. Thus, rapport was not viewed exclusively as a natural skill. However, participants commonly perceived some natural attributes are required to build rapport that can be refined and developed through training and experience.

The theory of multiple stupidities: education, technology and organisation in Arabia.

Traditional perspectives have envisaged intelligence as one entity dominated by a single set of abilities (i.e. cognitive abilities), whereas modern perspectives have defined intelligence in various shapes (e.g. linguistic, musical and interpersonal intelligences). By the same token, traditional perspectives have examined stupidity as one set of inabilities (i.e. cognitive inabilities). However, it is not clear whether modern perspectives have discussed whether stupidity exists in various forms-in the same way as they have envisaged intelligence. To address this limitation, 257 university members were asked to share what they perceived as being stupid educational and technological practices in their institutions. Analysis of the data suggested three concepts were important to the members: moral, spatial and administrative stupidities. That is, stupidity is perceived to come in the form of failing to meet certain moral, spatial and administrative values. This implies that modern perspectives may conceptualise stupidity differently from traditional perspectives, seeing it as going beyond cognitive inabilities and viewing it as existing in various forms (e.g. moral, spatial and administrative stupidities). Thus, there are multiple stupidities as there are multiple forms of intelligence. A strength of this research is that it views stupidity through an organisational and qualitative lens, although some may traditionally expect such a topic to be examined quantitatively through psychometric and biological approaches.

How Can the Current State of AI Guide Future Conversations of General Intelligence?

Similar to the field of human intelligence, artificial intelligence (AI) has experienced a long history of advances and controversies regarding its definition, assessment, and application. Starting over 70 years ago, AI set out to achieve a single, general-purpose technology that could overcome many tasks in a similar fashion to humans. However, until recently, implementations were based on narrowly defined tasks, making the systems inapplicable to even slight variations of the same task. With recent advances towards more generality, the contemplation of artificial general intelligence (AGI) akin to human general intelligence (HGI) can no longer be easily dismissed. We follow this line of inquiry and outline some of the key questions and conceptual challenges that must be addressed in order to integrate AGI and HGI and to enable future progress towards a unified field of general intelligence.

Investigative fatigue: how sleep-circadian factors shape criminal investigations.

Investigating criminal complaints and identifying culprits to be prosecuted in the court of law is an essential process for law-enforcement and public safety. However, law-enforcement investigators operate under very challenging conditions due to stressful environments, understaffing, and public scrutiny, which factors into investigative errors (e.g. uncleared cases). This paper argues that one contributing factor to investigative failures involves sleep and circadian disruption of investigators themselves, known to be prevalent among law-enforcement. By focusing on investigative interviewing, this analysis illustrates how sleep and circadian disruption could impact investigations by considering three broad phases of (1) preparation, (2) information elicitation, and (3) assessment and corroboration. These phases are organized in a framework that outlines theory-informed pathways in need of empirical attention, with special focus on effort and decision-making processes critical to investigations. While existing evidence is limited, preliminary findings support some elements of investigative fatigue. The paper concludes by placing investigative fatigue in a broader context of investigative work while providing recommendations for future research throughout. This paper is part of the Sleep and Circadian Health in the Justice System Collection.

Digital pathology implementation in cancer diagnostics: towards informed decision-making.

Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in clinical practice provides plenty of benefits but also harbors hidden ethical challenges affecting physician-patient relationships. This paper addresses the ethical obligation to transform the physician-patient relationship for informed and responsible decision-making when using artificial intelligence (AI)-based tools for cancer diagnostics. DP application allows to improve the performance of the Human-AI Team shifting focus from AI challenges towards the Augmented Human Intelligence (AHI) benefits. AHI enhances analytical sensitivity and empowers pathologists to deliver accurate diagnoses and assess predictive biomarkers for further personalized treatment of cancer patients. At the same time, patients' right to know about using AI tools, their accuracy, strengths and limitations, measures for privacy protection, acceptance of privacy concerns and legal protection defines the duty of physicians to provide the relevant information about AHI-based solutions to patients and the community for building transparency, understanding and trust, respecting patients' autonomy and empowering informed decision-making in oncology.

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.

Artificial intelligence (AI) is one of the fastest-growing fields in various industries, including engineering, architecture, medical and clinical research, aerospace, and others. AI, which is a combination of machine learning (ML), deep learning (DL), and human intelligence (HI), is revolutionizing drug discovery and development by making it more cost-effective and efficient. It is also being used in fields such as medicinal chemistry, molecular and cell biology, pharmacology, pharmacokinetics, formulation development, and toxicology. AI plays a crucial role in clinical testing by enhancing patient stratification, patient sample evaluation, and trial design, assisting in the identification of biomarkers, determining efficacy criteria, dose selection, trial length, and target patient population selection. The primary objective of this study is to emphasize the importance of AI in clinical trials and drug development, while also exploring the existing challenges and potential advancements in AI within the healthcare industry. A comprehensive literature review was conducted, covering the period from 1998 to 2023. The Science Direct, PubMed, and Google Scholar databases were searched for relevant information. A variety of publications, including Research Gate, Nature, MDPI, and Springer Link, provided pertinent data. This study aimed to gain a deeper understanding of the use of AI in clinical research and drug development, as well as its potential and limitations. We also discuss the benefits and main data limitations of the traditional trial and drug development approach. AI approaches are currently being used to overcome research obstacles and eliminate conceptual or methodological limitations. After discussing possible obstacles and coping mechanisms, we provide several recommendations to help individuals understand the challenges and difficulties associated with clinical research and drug development. It is essential for pharmaceutical companies to have a cutting-edge AI strategy if AI is to become a routine tool for clinical research and drug development.

Developing a hetero-intelligence methodological framework for sustainable policy-making based on the assessment of large language models.

This work delves into the increasing relevance of Large Language Models (LLMs) in the realm of sustainable policy-making, proposing an innovative hetero-intelligence framework that blends human and artificial intelligence (AI) for tackling modern sustainability challenges. The research methodology includes a hetero-intelligence performance test, which juxtaposes human intelligence with AI in the formulation and implementation of sustainable policies. After testing this hetero-intelligence methodology, seven steps are rigorously described so that it can be replicated in any sustainability planning related context. The results underscore the capabilities and limitations of LLMs, underscoring the critical role of human intelligence in enhancing the efficacy of hetero-intelligence systems. This work fulfils the need of a rigorous methodological framework based on empirical steps that can provide unbiased outcomes to be integrated into sustainable planning and decision-making processes.•Assesses LLMs' limitations and capabilities regarding sustainable planning issues•A replicable methodology is proposed based on the combination of both human and artificial intelligence•It proposes and systematises the integration of a hetero-intelligent approach into the formulation of sustainability policies to be more efficient and effective.

The limit of human intelligence.

In 1998, Fields medallist Stephen Smale [Smale (1998) [1]] proposed his famous eighteen problems to the mathematicians of this century. The statement of his eighteenth problem is simple but very important. The statement of his problem is, "What are the limits of intelligence, both artificial and human?". To answer the limit of human intelligence, in this paper, we introduce cognitive-consequence space and cognitive-consequence topology, and mainly prove that deductive and non-deductive parts of a human mind will never be empty. It proves a human being will continue to think and solve problems using both deductive and non-deductive inferences as long as they are alive. Hence, we conclude that human intelligence is limitless. We also introduce cognitive closure, cognitive similarity distance, cognitive limit point, cognitive-continuous function, consequence ideal, consequence filter, Gödel's incompleteness black hole, and study related properties. We also provide suitable justifications to show that cognitive consequence topological space is not similar to that of any existing topological space because it connects cognitive space and consequence operator in one frame to find the limit of human intelligence. Moreover, we also provide justifications to state that artificial intelligence has limitations. Thus, we conclude that human intelligence will always remain superior to artificial intelligence.

Will the future of pharmacovigilance be more automated?

INTRODUCTION: Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety. AREAS COVERED: This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection. PERSPECTIVE: The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.

Efficient, continual, and generalized learning in the brain - neural mechanism of Mental Schema 2.0.

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs.