25 papers · ranked by Valyu relevance
Dhar, Ruchira, Ninell Oldenburg, Anders Soegaard
Benchmarks such as ARC, Raven-inspired tests, and the Blackbird Task are widely used to evaluate the intelligence of large language models (LLMs). Yet, the concept of intelligence remains elusive—lacking a stable definition and failing to predict performance on practical tasks such as question answering, summarization…
Felix M. Schweitzer, Nele M. Lindenberg, Monika Fleischhauer, Sören Enge
In this preregistered multi-level meta-analysis, we aim to clarify the association of need for cognition (NFC) and typical intellectual engagement (TIE) with intelligence and executive functions. Multi-level models with robust variance estimation were specified and risk of bias was assessed with the adapted Risk of…
Rebecca Engler, Christina Stammen, Stefan Arnau, Javier Schneider Penate + 17 more
Intelligence is associated with important life outcomes. Behavioral, genetic, structural, and functional brain correlates of intelligence have been studied for decades, but questions remain as to how genetics are related to trait expression and what intermediary role brain properties play. This study investigated these…
Dan Hendrycks, Dawn Song, Christian Szegedy, Honglak Lee + 31 more
Dan Hendrycks 1 , Dawn Song2,3, Christian Szegedy 4 , Honglak Lee5,6, Yarin Gal 7 , Erik Brynjolfsson 8 , Sharon Li 9 , Andy Zou1,10,11 , Lionel Levine 12 , Bo Han 13 , Jie Fu 14 , Ziwei Liu 15 , Jinwoo Shin 16 , Kimin Lee 16 , Mantas Mazeika 1 , Long Phan 1 , George Ingebretsen 1 , Adam Khoja 1 , Cihang Xie 17 …
Mokgata Alleen Matjie
Emotional quotient (EQ) and emotional intelligence (EI) are often conflated with cognitive intelligence (CI); however, it distinctly refers to the quantifiable assessment of an individual’s emotional competencies and capabilities. A higher EQ is typically indicative of greater emotional proficiency, which is essential…
Chun-Ju Chou, Mark Fiecas, Elisabetta C. del Re, Eero Vuoksimaa + 1 more
Understanding the neural basis of intelligence in humans remains an ongoing scientific pursuit. Early studies with small samples identified potential regions but lacked consistency across findings. Recent large-scale magnetic resonance imaging (MRI) datasets, using intelligence measures focused on verbal-numerical…
Ishanu Chattopadhyay
Can intelligence be measured? We propose that intelligence can be defined as the lawful amplification of rare but valid futures: a system increases the probability of outcomes that would be unlikely under passive dynamics but remain admissible under the constraints of the domain. We start with the premise that an…
Cameron Rouse Turner, Evan M. Russek, Amanda Seed, Emma Suvi McEwen + 3 more
A diversity of intelligences arises from the constraints under which animals evolve. However, characterizing how constraints shape intelligence is challenging because it requires relating the restrictions on cognitive mechanisms to those that affect their evolution. We demonstrate the potentially complex interaction…
Boris Lucero, María Teresa Muñoz-Quezada, Chiara Saracini, Renzo C. Lanfranco + 1 more
Efficient brain functioning is often defined as the ability to achieve high performance with minimal cognitive resources. This study investigated the relationship between intelligence and attentional network efficiency in school-aged children, using electroencephalography (EEG) during the Attention Network Test (ANT).…
Bogdan S. Zadorozhny, K. V. Petrides, Jinyan Yang, Dimitri van der Linden
One of the central questions in differential psychology is the extent to which its various aspects are interconnected. Past research has revealed significant correlations amongst widely different constructs including emotional intelligence, the Big Five personality traits, the Dark Triad, cognitive intelligence, and…
M. Bodea
Biological consciousness is the product of millions of years of evolution, being deeply rooted in the neural architecture of living organisms. It emerges from the interplay of sensory processing, memory, emotion, and metacognition, with the human brain being its most complex known expression. Synthetic consciousness…
Yujing Lin, Robert Plomin
The most highly predictive polygenic scores in the behavioural sciences are for cognitive traits, especially general cognitive ability (g) and educational achievement. We combined polygenic scores derived from genome-wide association studies of adult g and educational attainment, conditioned on their prediction of…
Pietro Morasso
Although cognitive robotics is still a work in progress, the trend is to “free” robots from the assembly lines of the third industrial revolution and allow them to “enter human society” in large numbers and many forms, as forecasted by Industry 4.0 and beyond. Cognitive robots are expected to be intelligent, designed…
Marko Živanović, Jovana Bjekić, Saša R. Filipović
Human cognitive abilities depend on flexible coordination across distributed cognitive control systems within the frontoparietal network, yet the causal architecture linking executive functions (EFs) to higher cognition remains debated. We combined psychometric modeling and neuromodulation to test causal contributions…
Nicholas Davis
Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI, machine learning, and many generative systems, the dominant unit of analysis remains the individual model or autonomous system evaluated through outputs, benchmarks…
Amir Konigsberg
In 1950, Alan Turing proposed replacing the question "Can machines think?" with a behavioral test: if a machine's outputs are indistinguishable from those of a thinking being, the question of whether it truly thinks can be set aside. This paper argues that Turing's move was not only a pragmatic simplification but also…
Marcus Selart, Jia Huang
This article investigates how cognitive style shaped by framing influences rational and intuitive thinking. We extend existing research on cognitive styles, specifically the construct of holistic dependence/independence on framing, and examine its relationship with reasoning performance, self-rated intuition, and…
Don A. Affognon
Longstanding distinctions between “verbal” and “mathematical” minds continue to shape educational assessment, curriculum design, and how learners are categorized by perceived cognitive strengths. Yet plenty of evidence from psychology and cognitive neuroscience points to a more unified model of intelligence that…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, presenting significant implications for STEM disciplines, it is bringing opportunities to enhance how chemistry and chemical engineering are taught and learned. This perspective critically explores the…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
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We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
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Despite $100 billion in investment and more than 100,000 publications, nanomedicine translation fails primarily at biological validation, not at synthesis or characterization. We present the first systematic technology readiness assessment across autonomous nanomedicine components, revealing a critical finding: while…
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Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
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Molecular mechanisms governing initiation steps of the assembly of thousands of endogenous multi-protein complexes (EMCs) remain incompletely understood. Here, multiple lines of observations are reported reflecting the biological functions-aligned initiation sequence of hybrid assembly pathways (HAPs) of EMCs. HAPs…