Search

Multiple Intelligences (MI) theory assumes that people have a full range of intelligences, and individuals differ. Although there have been many studies conducted related to MI, there are still questions that need answers in the context of students who are learning English as a Foreign Language (EFL) in China. Many teachers believe that identifying these intelligences has a significant impact on students learning skills. Identifying these intelligences, however, is not accessible if observation or identification comes from the teachers. Thus, the aim of this study was to identify Chinese college students' self-perceived MI and check the differences between male and females students' self-perceived intelligences. There were 359 Heilongjiang International University (HIU) students who participated in this study. The 35-item MI questionnaire was adopted to collect the data. The findings indicated that both male and female HIU students had a high self-perceived Musical Intelligence. Moreover, there were differences in terms of their self-perceived MI with three intelligences that male HIU students self-perceived are higher than the females. These are mathematical-logical intelligence, body-kinesthetic intelligence and interpersonal intelligence. The findings are followed by some recommendations for teaching and learning. [ABSTRACT FROM AUTHOR]

Intelligence is a well-studied construct in psychology that has correlational relationships with many educational, employment, and health outcomes. However, prior research indicates that incorrect beliefs about intelligence are widespread. In an effort to discern the degree to which the psychology curriculum is responsible for these inaccuracies, we collected course descriptions and catalog information from 303 American colleges and universities. We found that college courses dedicated to mainstream intelligence science are rare. Because the lack of intelligence education within psychology is a plausible contributor to incorrect beliefs about intelligence, we present an outline for a college-level course on intelligence. We also provide advice for implementing a course, including course readings and advice for handling controversies. [ABSTRACT FROM AUTHOR]

Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines. Abstract and operational definitions for human and artificial intelligence are proposed that emphasize maximal capacity for completing novel goals successfully through respective perceptual-cognitive and computational processes. Additionally, support for considering intelligence, both human and artificial, as consistent with a multidimensional model of capabilities is provided. The implications of current practices in artificial intelligence training and testing are also described, as they can be expected to lead to artificial achievement or expertise rather than artificial intelligence. Paralleling psychometrics, 'AI metrics' is suggested as a needed computer science discipline that acknowledges the importance of test reliability and validity, as well as standardized measurement procedures in artificial system evaluations. Drawing parallels with human general intelligence, artificial general intelligence (AGI) is described as a reflection of the shared variance in artificial system performances. We conclude that current evidence more greatly supports the observation of artificial achievement and expertise over artificial intelligence. However, interdisciplinary collaborations, based on common understandings of the nature of intelligence, as well as sound measurement practices, could facilitate scientific innovations that help bridge the gap between artificial and human-like intelligence. • Proposes unified definitions for human and artificial intelligence. • Distinguishes between artificial achievement/expertise and artificial intelligence. • Advocates for AI metrics to ensure good quality AI system evaluations. • Describes artificial general intelligence (AGI) mirroring human general intelligence. • Evidence currently favours presence of artificial achievement over intelligence. [ABSTRACT FROM AUTHOR]

From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of systems in the literature claiming they realize AGI, several other researchers argue that it is impossible to achieve it. In this paper, we take a different view to the problem. First, we discuss that in order to realize AGI, along with building intelligent machines and programs, an intelligent world should also be constructed which is on the one hand, an accurate approximation of our world and on the other hand, a significant part of reasoning of intelligent machines is already embedded in this world. Then we discuss that AGI is not a product or algorithm, rather it is a continuous process which will become more and more mature over time (like human civilization and wisdom). Then, we argue that pre-trained embeddings play a key role in building this intelligent world and as a result, realizing AGI. We discuss how pre-trained embeddings facilitate achieving several characteristics of human-level intelligence, such as embodiment, common sense knowledge, unconscious knowledge and continuality of learning, by machines. [ABSTRACT FROM AUTHOR]

Abstract Introduction Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task‐based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood. Methods Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large‐scale functional brain network. Results We consistently identified in two independent and publicly accessible resting‐state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p

Nowadays, development in machine vision incorporated with artificial intelligence surpasses the ability of human intelligence and its application expands exponentially with the increasing number of electronic gadgets in our day-to-day life. The explosive revolution in multimedia research leads to the need for expanding the utility of texts in a machine vision environment to promote web search operation. Hence, extracting text from images forms the core aspect of information retrieval-based intelligent system. This article is aimed towards extracting text from unconstrained environments. Here, the significance of the CIE-Lab colour space is analysed over text localisation assisted through Renyi entropy-based thresholding. The proposed algorithm is tested on the MSRA Text Detection 500 dataset (MSRA-TD500) and Street View Text (SVT) datasets, which are challenging datasets. Authors’ proposed Renyi entropy-based text localisation algorithm is successful in identifying blurred texts, texts with different font characteristics and multi-lingual texts with manifold orientations from complex background natural scenes.

Human-in-the-loop robotic system is an emerging technique in recent years. Human intelligence as well as machine intelligence are incorporated to accomplish tasks efficiently and effectively. However, grasp-and-lift (GAL) tasks through human–robot interactions are still a problem in an unstructured environment like urban search and rescue. Human assistive GAL tasks enable robots to complete search or rescue procedures quickly and accurately. Brain–machine interface (BMI) controlled robots have demonstrated promising applications in human–robot collaborative manipulations. In this study, an architecture of human–robot team is proposed for performing GAL tasks in BMI-based human–robot systems. The proposed architecture contains several workflows from both human and robot aspects to improve performance. In addition, human brain activities are generally considered as non-stationary signals with varying spatial and temporal distributions. To enhance robustness and stability of brain-controlled robot's GAL tasks, a new method via adaptive boosting mechanism is proposed. The proposed multiple subjects' adaptive boosting is able to suppress noisy data and outliers in multiple subjects’ electroencephalogram signals, and therefore enhance accuracy and robustness of intention and sensation signal classification in GAL tasks. Preliminary results show that the new architecture is feasible with ethical establishment and the proposed method can outperform traditional methods.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

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.

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies.

The linkage between human intelligence and its determinants is a subject of keen interest among the researchers around the world. It is evident from the existing literature that human intelligence is a matter of pre-natal development and its enhancement is a postnatal development. Considering the agencies which form part of the post-natal environment, educational institutions are one. The study considered determinants of human intelligence acknowledging the previous works on the theme of intelligence. It tried to gauge the relative worth of the determinants on improving the human intelligence which is influenced by syllabus in the higher education. Teachers presently serving in Assam University with experience of decades of teaching in one and/or more universities, teaching different subjects were considered for the survey. It was found that enhancing subject knowledge, learning abilities and retaining information were such determinants which were well taken care of by the syllabus whereas determinants like developing motivation, balancing optimism & pessimism, developing a futuristic approach and developing capacity for complex cognitive process need enhancement through syllabus. [ABSTRACT FROM AUTHOR]

It has long been thought that emotional intelligence (EI) involves skillful emotion regulation, but surprisingly little is known about the precise links between EI and emotion regulation. To address this gap in the literature, we examined the relation between EI—operationalised as an ability—and the use of two common emotion regulation strategies—cognitive reappraisal and expressive suppression. Seven hundred and twelve participants from a community sample in Spain were assessed on ability EI (using the MSCEIT) and emotion regulation (using the ERQ). Findings revealed that EI ability was positively associated with cognitive reappraisal and negatively associated with expressive suppression. These relationships were moderated by gender and age. The strength of the association between EI and cognitive reappraisal increased with age for men, while this strength decreased with age for women. Conversely, the strength of the association between EI and suppression decreased with age for men, but increased with age for women. These findings confirm the expectation that EI is associated with greater use of generally adaptive forms of emotion regulation (reappraisal), and lesser use of generally maladaptive forms of emotion regulation (suppression), although effect sizes were quite modest. Observed differences in the strength of associations between EI and emotion regulation may be the result of gender differences in the development of emotional skills along with cultural changes in emotional education and social norms. [ABSTRACT FROM AUTHOR]

This article examines India's historical efforts to spy inside Pakistan from the mid-1960s to the early 1970s. It draws from memoirs of notable Indian spies who were jailed for espionage in Pakistan and spy 'fiction' written by former Indian intelligence and military officers who allege their writing is based on actual cases. The article highlights commonalities among Indian spies using the words of Indian officers to better understand human intelligence efforts inside Pakistan. It finds that Indian spies in these books have initially been Hindus or from multi-religious families, from the Indian-Pakistan border and have been poorly treated by the Indian government and its intelligence services. [ABSTRACT FROM AUTHOR]

• A novel non-invasive method of measuring human intelligence is proposed. • EEG signals obtained from brain classifies subjects intelligence. • A novel scoring scheme is devised to generate a score. The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and we have attempted to quantify using EEG signals. This paper aims at devising a novel non-invasive method of measuring human intelligence. A newly devised scoring scheme is used to ultimately generate a score for the subjects. Wavelet packet transform approach for feature extraction is applied to 5 channel EEG data. This approach uses db-8 as the mother wavelet. Hierarchical extreme learning machine is used for classification of the EEG signals. 80.00% training accuracy and 73.33% testing accuracy was measured for the classifier. The average sensitivity and specificity across all three classes was measured to be 0.8133 and 0.8923 respectively. An aggregate score was determined from the classification of EEG data. The power spectral analysis of the EEG data was conducted and regions of the brain responsible for various activities was confirmed. In the memory test, theta and beta bands exhibit high power, for arithmetic test, alpha and beta bands are strong, whereas in linguistic test, theta, alpha and beta bands are equally strong. The traditional IQ test determines intelligence indirectly, based on the score obtained from Wechsler test. In this paper an attempt is made to measure intelligence based on various brain activities – memory, arithmetic, linguistic. A new method to measure intelligence using direct approach by classifying the EEG signals is proposed. [ABSTRACT FROM AUTHOR]

People interpret verbal expressions of probabilities (e.g. ‘very likely’) in different ways, yet words are commonly preferred to numbers when communicating uncertainty. Simply providing numerical translations alongside reports or text containing verbal probabilities should encourage consistency, but these guidelines are often ignored. In an online experiment with 924 participants, we compared four different formats for presenting verbal probabilities with the numerical guidelines used in the US Intelligence Community Directive (ICD) 203 to see whether any could improve the correspondence between the intended meaning and participants’ interpretation (‘in-context’). This extends previous work in the domain of climate science. The four experimental conditions we tested were: 1. numerical guidelines bracketed in text, e.g. X is very unlikely (05–20%), 2. click to see the full guidelines table in a new window, 3. numerical guidelines appear in a mouse over tool tip, and 4. no guidelines provided (control). Results indicate that correspondence with the ICD 203 standard is substantially improved only when numerical guidelines are bracketed in text. For this condition, average correspondence was 66%, compared with 32% in the control. We also elicited ‘context-free’ numerical judgements from participants for each of the seven verbal probability expressions contained in ICD 203 (i.e., we asked participants what range of numbers they, personally, would assign to those expressions), and constructed ‘evidence-based lexicons’ based on two methods from similar research, ‘membership functions’ and ‘peak values’, that reflect our large sample’s intuitive translations of the terms. Better aligning the intended and assumed meaning of fuzzy words like ‘unlikely’ can reduce communication problems between the reporter and receiver of probabilistic information. In turn, this can improve decision making under uncertainty. [ABSTRACT FROM AUTHOR]

Spearman's g is the name for the shared variance across a set of intercorrelating cognitive tasks. For some but not all--theorists, g is defined as general intelligence. While g is robustly observed in Western populations, it is questionable whether g is manifested in cognitive data from other cultural groups. To test whether g is a cross-cultural phenomenon, we searched for correlation matrices or data files containing cognitive variables collected from individuals in non-Western, nonindustrialized nations. We subjected these data to exploratory factor analysis (EFA) using promax rotation and 2 modern methods of selecting the number of factors. Samples that produced more than 1 factor were then subjected to a second-order EFA using the same procedures and a Schmid-Leiman solution. Across 97 samples from 31 countries totaling 52,340 individuals, we found that a single factor emerged unambiguously from 71 samples (73.2%) and that 23 of the remaining 26 samples (88.5%) produced a single second-order factor. The first factor in the initial EFA explained an average of 45.9% of observed variable variance (SD = 12.9%), which is similar to what is seen in Western samples. One sample that produced multiple second-order factors only did so with 1 method of selecting the number of factors in the initial EFA; the alternate method of selecting the number of factors produced a single higher-order factor. Factor extraction in a higher-order EFA was not possible in 2 samples. These results show that g appears in many cultures and is likely a universal phenomenon in humans. [ABSTRACT FROM AUTHOR]

This paper presents a new brain–robot interaction system by fusing human and machine intelligence to improve the real-time control performance. This system consists of a hybrid P300 and steady-state visual evoked potential (SSVEP) mode conveying a human being’s intention, and the machine intelligence combining a fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object of interest via P300, and the classification algorithm transfers the corresponding parameters to an improved fuzzy color extractor for object extraction. A central vision tracking strategy automatically guides the NAO humanoid robot to the destination selected by the subject intentions represented by brainwaves. During this process, human supervises the system at high level, while machine intelligence assists the robot in accomplishing tasks by analyzing image feeding back from the camera, distance monitoring using out-of-gauge alarms from sonars, and collision detecting from bumper sensors. In this scenario, the SSVEP takes over the situations in which the machine intelligence cannot make decisions. The experimental results show that the subjects can control the robot to a destination of interest, with fewer commands than only using a brain–robot interface. Therefore, the fusion of human and machine intelligence greatly alleviates the brain load and enhances the robot executive efficiency of a brain–robot interaction system. [ABSTRACT FROM AUTHOR]

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.

Numerical cognition is a distinctive component of human intelligence such that the observation of its practice provides a window in high-level brain function. The modelling of numerical abilities in artificial cognitive systems can help to confirm existing child development hypotheses and define new ones by means of computational simulations. Meanwhile, new research will help to discover innovative principles for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms (e.g. deep learning) that can be highly effective but difficult to understand for humans. This study promotes new investigation by providing a common resource for researchers with different backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are interested in pursuing scientific collaboration on mutually stimulating research on this topic. The study emphasises the fundamental role of embodiment in the initial development of numerical cognition in children. This strong relationship with the body motivates the cognitive developmental robotics (CDR) approach for new research that can (among others) help standardise data collection and provide open databases for benchmarking computational models. Furthermore, the authors discuss the potential application of robots in classrooms and argue that the CDR approach can be extended to assist educators and favour mathematical education.