Less intelligent and unaware? Accuracy and Dunning-Kruger effects for self-estimates of different aspects of intelligence

People’s perceptions of their intelligence correlate only moderately with objective intelligence measures. On average, people overestimate themselves. According to the popular Dunning-Kruger effect, this is particularly true for low performers: Across many domains, those in the lowest quantile overestimate their abilities the most. However, recent work using improved statistical approaches found little support for a Dunning-Kruger effect in general intelligence. We investigated the accuracy of and Dunning-Kruger effects in self-estimates of general, verbal, numerical, and spatial intelligence—domains that differed in how well they can be judged in the past. 281 participants completed self-estimates and intelligence measures online. Self-estimates showed mostly moderate correlational accuracy that was slightly higher for numerical intelligence and lower for verbal intelligence. Across domains, participants rated their intelligence as above-average. However, as their intelligence was indeed high, this was not an overestimation. While standard analyses indicated Dunning-Kruger effects in three out of four measures, improved statistical methods only yielded some support for one in verbal intelligence: People with lower verbal intelligence tended to have less self-knowledge about it. The generalizability of these findings is limited to young, highly educated populations. Nevertheless, our results contribute to a growing literature questioning the generality of the Dunning-Kruger effect.

EMOTION AND EMOTIONAL INTELLIGENCE: FROM CONCEPT AND MODELS TO MEASUREMENT AND APPLICATION

This article approaches the emotional aspect of man, by focusing on emotional intelligence and the concepts associated with it according to two levels: Firstly; Invoking some theoretical models of emotional intelligence, as well as the most important models that explain it, with defining its dimensions. Secondly; See the scales used to measure it and its importance in psychological and social balance. It also aims to evoke the neural basis of this intelligence and its mental and nervous processes, and to monitor its relationship with the environment and genetics and its influence on them, to explain the differences between individuals. It has been shown that emotional intelligence plays a major role on the level of individual adaptation in different contexts: socially, scientifically, and academically. Because it is multifaceted, theoretical models have been crystallized for it; It may be cognitive, or models for a group of traits, or mixed models, which helped to build the standards currently known. By measuring emotional intelligence, it is possible to predict the individual's success or failure in social life, and may exceed mental intelligence in this. Keywords: Intelligence; Emotional Intelligence; Emotion, Affect; Emotional Intelligence Measures.

In Search of the Executive Cognitive Processes Proposed by Process-Overlap Theory

Process-Overlap Theory (POT) suggests that measures of cognitive abilities sample from sets of independent cognitive processes. These cognitive processes can be separated into domain-general executive processes, sampled by the majority of cognitive ability measures, and domain-specific processes, sampled only by measures within a certain domain. According to POT, fluid intelligence measures are related because different tests sample similar domain-general executive cognitive processes to some extent. Re-analyzing data from a study by De Simoni and von Bastian (2018), we assessed domain-general variance from executive processing tasks measuring inhibition, shifting, and efficiency of removal from working memory, as well as examined their relation to a domain-general factor extracted from fluid intelligence measures. The results showed that domain-general factors reflecting general processing speed were moderately and negatively correlated with the domain-general fluid intelligence factor (r = −.17–−.36). However, domain-general factors isolating variance specific to inhibition, shifting, and removal showed only small and inconsistent correlations with the domain-general fluid intelligence factor (r = .02–−.22). These findings suggest that (1) executive processing tasks sample only few domain-general executive processes also sampled by fluid intelligence measures, as well as (2) that domain-general speed of processing contributes more strongly to individual differences in fluid intelligence than do domain-general executive processes.

Measuring Intelligence and Growth Rate: Variations on Hibbard’s Intelligence Measure

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard’s idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard’s intelligence measure is based on the latter growth-rate-measuring method, we survey other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs).

Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution.

<div>Artificial Intelligence algorithms are used to identify “progeny” drugs that are similar to the “parents” already being tested against COVID-19. These algorithms assess similarity not only by the molecular make-up of the molecules, but also by the “context” in which specific functional groups are arrangedand/or by three-dimensional distribution of pharmacophores. The parent-progeny relationships span same-indication drugs (mostly antivirals) as well as those in which the “progenies” have different and perhaps less intuitive primary indications (e.g., immunosuppressant or anti-cancer progenies from antiviral parents). The “progenies” are either already approved drugs or medications in advanced clinical trials – should the currently tested “parent” medicines fail in clinical trials, these “progenies” could be, therefore, re-purposed against the COVID-19 on the timescales relevant to the current pandemic.</div>

Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution.

<div>Artificial Intelligence algorithms are used to identify “progeny” drugs that are similar to the “parents” already being tested against COVID-19. These algorithms assess similarity not only by the molecular make-up of the molecules, but also by the “context” in which specific functional groups are arrangedand/or by three-dimensional distribution of pharmacophores. The parent-progeny relationships span same-indication drugs (mostly antivirals) as well as those in which the “progenies” have different and perhaps less intuitive primary indications (e.g., immunosuppressant or anti-cancer progenies from antiviral parents). The “progenies” are either already approved drugs or medications in advanced clinical trials – should the currently tested “parent” medicines fail in clinical trials, these “progenies” could be, therefore, re-purposed against the COVID-19 on the timescales relevant to the current pandemic.</div>

Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution.

<div>Artificial Intelligence algorithms are used to identify “progeny” drugs that are similar to the “parents” already being tested against COVID-19. These algorithms assess similarity not only by the molecular make-up of the molecules, but also by the “context” in which specific functional groups are arrangedand/or by three-dimensional distribution of pharmacophores. The parent-progeny relationships span same-indication drugs (mostly antivirals) as well as those in which the “progenies” have different and perhaps less intuitive primary indications (e.g., immunosuppressant or anti-cancer progenies from antiviral parents). The “progenies” are either already approved drugs or medications in advanced clinical trials – should the currently tested “parent” medicines fail in clinical trials, these “progenies” could be, therefore, re-purposed against the COVID-19 on the timescales relevant to the current pandemic.</div>

Restrictions of Criminal Intelligence Measures in Law Enforcement Directive and Law on Criminal Intelligence of Lithuania

Increasing use of technologies in the last decades has created an unprecedented opportunity to systematically collect and use a wide variety of data (including personal data) for different purposes. Information and data collected and processed with the help of new technologies is used not only for the purposes of natural and legal persons but also for various other purposes. Intelligence services that ensure prevention of crime must perform their functions to ensure safety of public. When doing so, they use various means and methods of information collection, which help them to reach their goals. However, the means applied undermine and intensively restrict a person’s right to private life. Given that two legal interests compete during the application of criminal intelligence measures, i.e. the individual’s right to privacy and ensuring of public security, the Author seeks to analyse their points of contact – restrictions of application of criminal intelligence measures, which in theory are designed to ensure the person’s right to private life. In this article, the Author analyses the restrictions on the application of criminal intelligence measures, which are present in international, Lithuanian legislation and compares them with relevant requirements set forth in the practice of European Union Court of Justice. Pieaugošā tehnoloģiju izmantošana pēdējās desmitgadēs ir radījusi nepieredzētu iespēju sistemātiski ievākt un izmantot ļoti dažādus datus (ieskaitot personas datus) dažādiem mērķiem. Informācija un dati, kas ievākti un apstrādāti ar jauno tehnoloģiju palīdzību, tiek izmantoti ne tikai fizisko un juridisko personu vajadzībām, bet arī dažādiem citiem mērķiem. Izlūkošanas dienestiem, kas nodrošina noziedzības novēršanu, jāveic savas funkcijas, lai nodrošinātu sabiedrības drošību. To darot, viņi izmanto dažādus informācijas vākšanas līdzekļus un metodes, kas viņiem palīdz sasniegt savus mērķus. Tomēr izmantotie līdzekļi nereti grauj un intensīvi ierobežo personu tiesības uz privāto dzīvi. Tā kā kriminālizlūkošanas pasākumu piemērošanā sacenšas divas likumīgas intereses – personas tiesības uz privātumu un sabiedrības drošības nodrošināšana –, autore cenšas analizēt to saskares punktu – kriminālizlūkošanas pasākumu piemērošanas – ierobežojumus, kas teorētiski ir izstrādāti, lai nodrošinātu personas tiesības uz privāto dzīvi. Šajā rakstā autore ir izvēlējusies analizēt kriminālizlūkošanas pasākumu piemērošanas ierobežojumus: (1) obligāta iepriekšēja kontrole (sankcija) noteiktam kriminālizlūkošanas pasākumam, ko veic tiesa vai neatkarīga administratīva vienība; (2) kriminālizlūkošanas pasākumu ilguma ierobežošana; (3) kriminālizlūkošanas pasākumu samērīgums. Rakstā secināts, ka, kaut arī šie kriminālās izlūkošanas piemērošanas ierobežojumi likuma izpildes direktīvā parasti nepastāv, tie ir ietverti Lietuvas Republikas likumā par kriminālo izlūkošanu. Tomēr ar tiem var viegli manipulēt un tie nenodrošina tiesības uz privātās dzīves efektīvu aizsardzību.

A Comparative Review of Team Emotional Intelligence Measures for IT Teams

In today's complex information technology (IT) systems, team task work is highly interdependent, dynamic, and multifaceted. Firms seek ways to make their IT teams work better. Team emotional intelligence (TEI) is an emergent collective skill that has been shown to benefit performance in teams; however, measures for TEI are relatively new, and research is scant for applying TEI measures to examine IT team behaviors. This research presents a comparative review of the TEI construct for use in research.