General Intelligence Research Articles

Spatial and temporal pattern of structure-function coupling of human brain connectome with development.

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7-21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Spatial and temporal pattern of structure–function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.

Analysis of the relative impact of emotional intelligence on explaining adolescent academic performance / Análisis del impacto relativo de la inteligencia emocional en la explicación del rendimiento académico adolescente

The purpose of this study is to explore the potential role of emotional intelligence as a significant predictor of academic performance in secondary school students, while concurrently factoring in other pertinent personal factors in the prediction model, such as general intelligence, academic self-efficacy and scholar motivation. A cross-sectional correlational study was carried out with a sample of 427 adolescents aged between 12 and 17 ( M = 13.83; SD = 1.40). The analyses carried out revealed that academic self-efficacy was the foremost predictor of academic performance, followed by general intelligence, performance-inhibiting anxiety and the experiential area of emotional intelligence. This study’s findings conclude that emotional intelligence is a contributory skill in explaining adolescent academic performance.

RELATIONAL DIMENSION VERSUS ARTIFICIAL INTELLIGENCE.

Thirty years ago, we proposed the similarity between the functioning of artificial intelligence and the human psyche, suggesting multiple parallels between the Freudian model proposed in the "Project for Psychology for Neurologists" and the connectionist theories applied in the generation of parallel distributed processing systems (PDP), also known as connectionist models. These models have been and continue to be the foundation of general artificial intelligences like ChatGPT, evolving and gaining prominence in everyday life. From the earliest applications in psychiatry, recreating computationally simulated modes of illnesses, to the use of deep learning models, especially in the field of computer vision for tasks such as image recognition, segmentation, and classification. Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) are employed for tasks involving sequences of data, such as natural language processing, or models based on the Transformer architecture, like BERT and GPT (Generative Pre-trained Transformer), which have revolutionized natural language processing. In this present work, we analyze the significance of the emergence and exponential growth of these types of tools in the field of healthcare, from medical diagnosis and patient care to psychological attention and psychotherapeutic treatment, exploring the changes and transformations in the forms of subjective expression that are arising. We also examine and argue for the importance and validity of the relational dimension proposed by our psychoanalytic approach in contrast to the potential use of these tools as treatment models.

From Large Language Models to Large Multimodal Models: A Literature Review

With the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to summarize the recent progress from LLMs to LMMs in a comprehensive and unified way. First, we start with LLMs and outline various conceptual frameworks and key techniques. Then, we focus on the architectural components, training strategies, fine-tuning guidance, and prompt engineering of LMMs, and present a taxonomy of the latest vision–language LMMs. Finally, we provide a summary of both LLMs and LMMs from a unified perspective, make an analysis of the development status of large-scale models in the view of globalization, and offer potential research directions for large-scale models.

Beyond Algorithmic Disclosure for Generative AI

One of the most commonly recommended policy interventions with respect to algorithms in general and artificial intelligence (“AI”) systems in particular is the need for greater transparency, often focusing on the disclosure of the variables employed by the algorithm and the weights given to those variables. This Essay argues that any meaningful transparency regime must provide information on other critical dimensions as well. For example, any transparency regime must also include key information about the data on which the algorithm was trained, including its source, scope, quality, and inner correlations, subject to constraints imposed by copyright, privacy, and cybersecurity law. Disclosures about pre-release testing also play a critical role in understanding an AI system’s robustness and its susceptibility to specification gaming. Finally, the fact that AI, like all complex systems, tends to exhibit emergent phenomena, such as proxy discrimination, interactions among multiple agents, the impact of adverse environments, and the well-known tendency of generative AI to hallucinate, makes ongoing post-release evaluation a critical component of any system of AI transparency.

Autism Symptom Presentation and Hierarchical Models of Intelligence.

There is a substantial history studying the relationship between general intelligence and the core symptoms of autism. However, a gap in knowledge is how dimensional autism symptomatology associates with different components of clinically-relevant hierarchical models of intelligence. We examined correlations between autism diagnostic symptom magnitude (Autism Diagnostic Observational Schedule; ADOS) and a hierarchical statistical model of intelligence. One autistic cohort was tested on the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV; N = 131), and another on the fifth edition (WISC-V; N = 83). We anticipated a convergent pattern of results between cohorts. On WISC-IV, ADOS scores were correlated significantly with g and three out of four intermediate factor scores, which was a broader pattern of correlations than anticipated from the literature. In the WISC-V cohort, only one intermediate factor correlated significantly with the ADOS; correlations with g and the other intermediate factors were less statistically certain. ADOS-factor correlations were larger in the WISC-IV than WISC-V cohort; this difference was significant at the 90% level. WISC-IV shows dimensional relationships with ADOS at multiple points in the hierarchical model of intelligence. Moreover, the current results provide evidence that relationship between core autism symptomatology and the construct of general intelligence may depend on how intelligence is measured. Known cohort effects in the relationship between categorical autism diagnosis and general intelligence have previously been attributed to changes in autism diagnostic practices. To our knowledge, this is the first evidence that differing versions of IQ tests may be implicated.

Impulsivity is a stable, measurable, and predictive psychological trait

Impulsivity is a personality construct frequently employed to explain and predict important human behaviors. Major inconsistencies in its definition and measurement, however, have led some researchers to call for an outright rejection of impulsivity as a psychological construct. We address this highly unsatisfactory state with a large-scale, preregistered study (N = 1,676) in which each participant completed 48 measures of impulsivity derived from 10 self-report scales and 10 behavioral tasks and reported frequencies of seven impulsivity-related behaviors (e.g., impulsive buying and social media usage); a subsample (N = 196) then completed a retest session 3 mo later. We found that correlations between self-report measures were substantially higher than those between behavioral tasks and between self-report measures and behavioral tasks. Bifactor analysis of these measures exacted one general factor of impulsivity I, akin to the general intelligence factor g, and six specific factors. Factor I was related mainly to self-report measures, had high test-retest reliability, and could predict impulsivity-related behaviors better than existing measures. We further developed a scale named the adjustable impulsivity scale (AIMS) to measure I. AIMS possesses excellent psychometric properties that are largely retained in shorter versions and could predict impulsivity-related behaviors equally well as I. These findings collectively support impulsivity as a stable, measurable, and predictive trait, indicating that it may be too early to reject it as a valid and useful psychological construct. The bifactorial structure of impulsivity and AIMS, meanwhile, significantly advance the conceptualization and measurement of construct impulsivity.

Интерфейсы «мозг–компьютер» и «мозг–мозг» в структуре конвергирующих технологий (на основе экспериментальных данных НИТЦ нейротехнологий ЮФУ)

The article presents a conceptual analysis of the main directions and methods of studying the human psyche, consciousness and brain in contemporary domestic and foreign research. The concept of “converging technologies” is considered in context of the creation of Artificial General Intelligence (AGI). It describes programs for the neuroscientific study of organization of psychic processes in the field of visualization of brain processes and the mapping method, which helps to understand how different parts of the brain can be structurally and functionally separated from each other. The publication pays special attention to understanding the possible use of Brain-Computer Interfaces (BCI) and Brain-to-Brain Interfaces (BMI) in the creation of AGI, “hybrid intelligence”. Brain interfaces are presented as fundamentally new channels of communication and control. An example of a biohybrid odor recognition system developed at the Research Center of Neurotechnology of the Southern Federal University is given. We are talking about the implementation of control through the BMI and mental commands of a bio­logical object – a rat. From the position of a body-oriented approach this work reveals the prob­lem of co-evolution of the body, consciousness, technology and social environment in the post-non-classical paradigm. It is concluded that modern neuroscience has a significant impact on understanding the functioning of the human brain, consciousness and cognitive processes. The feasibility of studying the possible use of BCI and BMI technologies in stimulating brain areas to create sensations equivalent to touch, etc. is noted. We are also talking about the use of biohybrid systems controlled through BMI technology as systems that combine the advan­tages of natural and artificial intelligence (with further elaboration of ethical issues).

On quantum computing for artificial superintelligence

Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution.

Environmental and Hereditary Effects on Intelligence and Learning Capabilities

A person's intelligence is defined by their different cognitive abilities, such as thinking fast, solving problems, developing questions, understanding, and using critical thinking; these abilities are far more important than general intelligence acquired in a school system. The environment may affect a person, such as having no support from one's parents to study or do well in school and develop cognitive skills. Cognitive skills can be improved, such as attention span, which may improve with consistent practice. Everyone learns differently and requires different modules of learning; people who have high IQs and do well in school tend to be because they fit into the learning module that schools provide. This research will talk about adaptive intelligence and how it is essential to solve problems of the moment because a person with this intelligence has the necessary tools to adapt their skills to any situation. It will also cover the understanding of human abilities, the factors that may affect them, the difference between households, and the effect this can have on a person, such as the amount of support and motivation given to a person to learn and understand. Furthermore, this investigation delineates the importance of education and how it is vital in helping people develop their cognitive skills and improve them over time.

Number sense-arithmetic link in Grade 1 and Grade 2: A case of fluency.

Recent research suggested fluent processing as an explanation on why number sense contributes to simple arithmetic tasks-'Fluency hypothesis'. The current study investigates whether number sense contributes to such arithmetic tasks when other cognitive factors are controlled for (including those that mediate the link); and whether this contribution varies as a function of participants' individual maths fluency levels. Four hundred and thirty-seven Chinese schoolchildren (186 females; Mage = 83.49 months) completed a range of cognitive measures in Grade 1 (no previous classroom training) and in Grade 2 (a year later). Number sense, arithmetic (addition and subtraction), spatial ability, visuo-spatial working memory, perception, reaction time, character reading and general intelligence were measured. Our data showed that the link between number sense and arithmetic was weaker in Grade 1 (Beta = .15 for addition and .06 (ns) for subtraction) compared to Grade 2 (.23-.28), but still persisted in children with no previous maths training. Further, math's performance in Grade 1 did not affect the link between number sense and maths performance in Grade 2. Our data extended previous findings by showing that number sense is linked with simple maths task performance even after controlling for multiple cognitive factors. Our results brought some evidence that number sense-arithmetic link is somewhat sensitive to previous formal maths education. Further research is needed, as the differences in effects between grades were quite small, and arithmetic in Grade 1 did not moderate the link at question in Grade 2.

Frequency-Histogram Coarse Graining in Elementary and 2-Dimensional Cellular Automata

Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent complex structures and behaviors be identified? In this work, we systematically investigate a form of dimensionality reduction for 1-dimensional and 2-dimensional cellular automata based on coarse-graining of macrostates into smaller blocks. We discuss selected examples and provide the entire exploration of coarse graining with different filtering levels in the appendix (available also digitally at this link: https://s4nyam.github.io/eca88/. We argue that being able to capture emergent complexity in AI systems may pave the way to open-ended evolution, a plausible path to reach artificial general intelligence.

Cognition in children with arachnoid cysts - A five-year follow-up after microneurosurgical fenestration.

In recent years there has been a re-evaluation regarding the clinical implications of temporal lobe arachnoid cysts (temporal arachnoid cysts) in children. These cysts have often been considered asymptomatic, or if symptomatic, only causing focal neurological symptoms or signs of increased intracranial pressure. However, several studies have more recently reported on cognitive symptoms improving after surgery. This study aimed to evaluate if reported cognitive improvement after surgery of temporal arachnoid cysts were stable after five years. Ten consecutive children (m = 14.65; range 12.1-19.415 were assessed cognitively five years after micro-neurosurgical fenestration of a temporal arachnoid cyst. Results were compared to results from their pre- and post-surgical evaluations. Evaluations included the Wechsler-scales, Boston Naming Test (BNT), Rey Auditory Verbal Learning Test (RAVLT), verbal fluency test (FAS) and Rey Complex Figure Test (RCFT). The analysis revealed significant postsurgical improvement compared to baseline on the Wechsler-scales measures of general intelligence (FSIQ), verbal abilities (VCI) and processing speed (PSI). Mean differences after surgery were 8.3 for FSIQ, (p = 0.026), 8.5 for VI (p = < .01) and 9.9 for PSI (p = 0.03). There were no significant differences in mean test results when comparing postsurgical scores with scores five years after surgery, indicating long-term stability of improvements. The results indicate that affected cognitive functions in children with temporal arachnoid cysts improve after surgery and that the improvements remain stable five years later. The improvements and long term stability were also consistent with the experience of both parents and children. The findings provide a strong argument for neurosurgical fenestration of temporal arachnoid cysts in children.

What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence

In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.

Prenatal maternal Inflammation, childhood cognition and adolescent depressive symptoms

BackgroundAccumulating evidence indicates that higher prenatal maternal inflammation is associated with increased depression risk in adolescent and adult-aged offspring. Prenatal maternal inflammation (PNMI) may increase the likelihood for offspring to have lower cognitive performance, which, in turn, may heighten risk for depression onset. Therefore, this study explored the potential mediating role of childhood cognitive performance in the relationship between PNMI and adolescent depressive symptoms in offspring. MethodsParticipants included 696 mother-offspring dyads from the Child Health and Development Studies (CHDS) cohort. Biomarkers of maternal inflammation [interleukin (IL)-6, IL-8, IL-1 receptor antagonist (IL-1RA) and soluble TNF receptor-II (sTNF-RII)] were assayed from first (T1) and second trimester (T2) sera. Childhood (ages 9–11) cognitive performance was assessed via standardized Peabody Picture Vocabulary Test (PPVT), a measure of receptive vocabulary correlated with general intelligence. Adolescent (ages 15–17) depressive symptoms were assessed via self-report. ResultsThere were no significant associations between T1 biomarkers and childhood PPVT or adolescent depressive symptoms. Higher T2 IL1-RA was directly associated with lower childhood PPVT (b = -0.21, SE = 0.08, t = -2.55, p = 0.01), but not with adolescent depressive symptoms. T2 IL-6 was not directly associated with childhood PPVT, but higher T2 IL-6 was directly associated at borderline significance with greater depressive symptoms in adolescence (b = 0.05, SE = 0.03, t = 1.96, p = 0.05). Lower childhood PPVT predicted significantly higher adolescent depressive symptoms (b = -0.07, SE = 0.02, t = -2.99, p < 0.01). There was a significant indirect effect of T2 IL-1RA on adolescent depressive symptoms via childhood PPVT (b = 0.03, 95 % CI = 0.002–0.03) indicating a partially mediated effect. No significant associations were found with T2 sTNF-RII nor IL-8. ConclusionsLower childhood cognitive performance, such as that indicated by a lower PPVT score, represents a potential mechanism through which prenatal maternal inflammation contributes to adolescent depression risk in offspring.

Sex/gender differences in general cognitive abilities: an investigation using the Leiter-3.

Research has shown that differences between males and females are not in general intelligence, but only in some specific factors and tasks. We used the Italian standardization of the Leiter-3, which is a completely nonverbal cognitive battery, to investigate the nature of sex/gender (we used sex/gender to reflect the awareness that the effects of biological 'sex' and socially constructed 'gender' cannot be easily separated and that most individuals' identities are informed by both sex and gender) differences. In doing so we used a multigroup confirmatory factor analysis approach. Results confirmed that males and females perform similarly in general intelligence, but present with some specificities. Males perform better on some, but not all, tasks requiring the spatial manipulation of the stimuli, but females consistently outperform males in tasks such as the nonverbal Stroop, requiring inhibition and attention control to a larger extent. The clinical and practical implications of our findings are considerable. The identification of specific cognitive strengths and weaknesses in males and females underscores the importance of tailored approaches in clinical assessments and interventions.

Impact of next-generation Artificial General Intelligence (AGI) on international relations

The new generation of artificial intelligence technologies occurrence represented by ChatGPT is the beginning of the Artificial General Intelligence (AGI) era. In contrast to the impact of weak AI technologies on international relations discussed in the previous era, AGI is no longer just a tool in the traditional sense. The human-computer integration facilitated by AGI technology will rapidly and deeply penetrate into all areas of social policy and economy. It also may change the mode of social production and economic development, influence international relations and geopolitics by transforming the comprehensive state capacity, and simultaneously force new challenges in global governance. The acknowledgement of a new generation of AGI technological qualitative changes’ impact on future changes in international relations is no longer the fantasy; the underlying AGI changes in labour productivity will certainly lead to a disruptive reconfiguration of future international relations.

The long-term negative impact of childhood stroke on language.

This study aims to investigate the long-term language outcome in children with unilateral childhood stroke in comparison to those with perinatal strokes and typically developing individuals and to explore the impact of lesion-specific modifiers. We examined nine patients with childhood stroke, acquired between 0;2 and 16;1 years (CHILD; 3 female, median = 13.5 years, 6 left-sided), 23 patients with perinatal strokes (PERI; 11 female, median = 12.5 years, 16 left-sided), and 33 age-matched typically developing individuals (CONTROL; 15 female, median = 12.33 years). The language outcome was assessed using age-appropriate tasks of the Potsdam Illinois Test of Psycholinguistic Abilities (P-ITPA) or the Peabody Picture Vocabulary Test (PPVT). For group comparisons, study-specific language z-scores were calculated. Non-verbal intelligence was assessed using the Test of Non-verbal Intelligence (TONI-4), language lateralization with functional MRI, and lesion size with MRI-based volumetry. All four patients with childhood stroke who initially presented with aphasic symptoms recovered from aphasia. Patients with childhood stroke showed significantly lower language scores than those in the control group, but their scores were similar to those of the patients with perinatal stroke, after adjusting for general intelligence (ANCOVA, language z-score CHILD = -0.30, PERI = -0.38, CONTROL = 0.42). Among the patients with childhood stroke, none of the possible modifying factors, including lesion side, correlated significantly with the language outcome. Childhood stroke, regardless of the affected hemisphere, can lead to chronic language deficits, even though affected children show a "full recovery." The rehabilitation of children and adolescents with childhood stroke should address language abilities, even after the usually quick resolution of clear aphasic symptoms.

Cognitive screening in elderly people

Elderly people are exposed to functional losses, which can either be natural transformations (senescence), or pathological (senility). Cognitive screening tests identify cognitive impairment and risk of developing dementia. This article aims to assess the effectiveness of three cognitive screening instruments in detecting cognitive impairment in elderly individuals with limited education. The study involved 920 individuals over the age of 60, who were recruited from driver’s license centers in various Brazilian states. Participants were assessed using the cognitive screening tools: Battery Routes of Attention (BRA), to attentional capacity; Nonverbal General Intelligence Matrices (MIG), to non-verbal intelligence; and Visual Recognition Memory Test (Memore), to short-term memory. The study examined the correlation between the total scores on tests and the participants' profile (mean=69.10 and SD=6.54), according to sex, age, age group, education level, and state of Brazil. The analysis found a positive correlation between the total scores of the cognitive tests and the level of education of the participants, with higher levels of education serving as a protective factor for cognition. There was moderate correlation between visual recognition memory and sex, indicating better mnemonic performances for female participants. Age was found to have a negative correlation with all cognitive screening tests. The use of cognitive screening batteries can provide relevant indicators to plan appropriate interventions with elderly people. Furthermore, these test results emphasize the significance of further research to enhance the credibility of cognitive screening tests and broaden their applicability to elderly populations with diverse cultural backgrounds.