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J. Intell., Volume 12, Issue 7 (July 2024) – 8 articles

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12 pages, 330 KiB  
Essay
Do Not Worry That Generative AI May Compromise Human Creativity or Intelligence in the Future: It Already Has
by Robert J. Sternberg
J. Intell. 2024, 12(7), 69; https://doi.org/10.3390/jintelligence12070069 - 19 Jul 2024
Viewed by 186
Abstract
Technology alters both perceptions of human intelligence and creativity and the actual processes of intelligence and creativity. Skills that were once important for human intelligence, for example, computational ones, no longer hold anywhere near the same importance they did before the age of [...] Read more.
Technology alters both perceptions of human intelligence and creativity and the actual processes of intelligence and creativity. Skills that were once important for human intelligence, for example, computational ones, no longer hold anywhere near the same importance they did before the age of computers. The advantage of computers is that they may lead us to focus on what we believe to be more important things than what they have replaced. In the case of penmanship, spelling, or arithmetic computation, such an argument could bear fruit. But in the case of human creativity, the loss of creative skills and attitudes may be a long-term loss to humanity. Generative AI is replicative. It can recombine and re-sort ideas, but it is not clear that it will generate the kinds of paradigm-breaking ideas the world needs right now to solve the serious problems that confront it, such as global climate change, pollution, violence, increasing income disparities, and creeping autocracy. Full article
(This article belongs to the Special Issue Generative AI: Reflections on Intelligence and Creativity)
4 pages, 217 KiB  
Editorial
The Interplay between Motivational, Affective Factors and Cognitive Factors in Learning: Editorial
by Brenda R. J. Jansen
J. Intell. 2024, 12(7), 68; https://doi.org/10.3390/jintelligence12070068 - 19 Jul 2024
Viewed by 192
Abstract
Academic success is assumed to be both the start and outcome of a cycle in which affect, motivation, and effort strengthen each other (Vu et al [...] Full article
22 pages, 1812 KiB  
Article
Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis
by Eduar S. Ramírez, Marcos Jiménez, Víthor Rosa Franco and Jesús M. Alvarado
J. Intell. 2024, 12(7), 67; https://doi.org/10.3390/jintelligence12070067 - 18 Jul 2024
Viewed by 292
Abstract
Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait [...] Read more.
Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson’s Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson’s MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson’s multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees’ response processes. Full article
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9 pages, 283 KiB  
Editorial
Grounding Cognition in Perceptual Experience
by Ivana Bianchi, Rossana Actis-Grosso and Linden J. Ball
J. Intell. 2024, 12(7), 66; https://doi.org/10.3390/jintelligence12070066 - 10 Jul 2024
Viewed by 455
Abstract
The aim of this Special Issue was to put forward a multifaceted reflection on the relevance of perceptual experience in affecting and modeling various aspects of cognitive performance [...] Full article
(This article belongs to the Special Issue Grounding Cognition in Perceptual Experience)
16 pages, 364 KiB  
Article
Do Religiosity and Spirituality Differ in Their Relationship with Crystallized Intelligence? Evidence from the General Social Survey
by Florian Dürlinger, Thomas Goetz and Jakob Pietschnig
J. Intell. 2024, 12(7), 65; https://doi.org/10.3390/jintelligence12070065 - 7 Jul 2024
Viewed by 719
Abstract
Negative associations of religiosity and intelligence are well established in psychological research. However, past studies have shown a substantial heterogeneity in reported effect strengths. Causes that may be able to explain the identified inconsistencies pertain to differing religiosity measurement modalities, participant ages, or [...] Read more.
Negative associations of religiosity and intelligence are well established in psychological research. However, past studies have shown a substantial heterogeneity in reported effect strengths. Causes that may be able to explain the identified inconsistencies pertain to differing religiosity measurement modalities, participant ages, or possibly cohort effects due to changing societal values in terms of being religious. Moreover, little is known about intelligence associations with the religiosity-related yet distinct construct of spirituality. Here, we provide evidence for religiosity and crystallized intelligence, as well as spirituality and crystallized intelligence associations, in 14 cohorts from 1988 to 2022 (N = 35,093) in the General Social Survey data by means of primary data analyses and meta-analytical approaches. As expected, religiosity was non-trivially negatively associated (r = −0.13, p < .001), but spirituality showed no meaningful association with crystallized intelligence (r = 0.03, p < .001). Our results broadly generalized across age groups, cohorts, and analytical approaches, thus suggesting that religiosity and intelligence may possibly be functionally equivalent to a certain extent whilst spirituality represents a distinct construct that is not functionally equivalent. Full article
3 pages, 226 KiB  
Editorial
Learning and Instruction: How to Use Technology to Enhance Students’ Learning Efficacy
by Gyöngyvér Molnár
J. Intell. 2024, 12(7), 64; https://doi.org/10.3390/jintelligence12070064 - 28 Jun 2024
Viewed by 398
Abstract
Due to the rapid development of technology (see, e [...] Full article
(This article belongs to the Special Issue Learning and Instruction)
25 pages, 885 KiB  
Article
Perceptions of Skills Needed for STEM Jobs: Links to Academic Self-Concepts, Job Interests, Job Gender Stereotypes, and Spatial Ability in Young Adults
by Margaret L. Signorella and Lynn S. Liben
J. Intell. 2024, 12(7), 63; https://doi.org/10.3390/jintelligence12070063 - 27 Jun 2024
Viewed by 604
Abstract
Gender gaps in spatial skills—a domain relevant to STEM jobs—have been hypothesized to contribute to women’s underrepresentation in STEM fields. To study emerging adults’ beliefs about skill sets and jobs, we asked college students (N = 300) about the relevance of spatial, [...] Read more.
Gender gaps in spatial skills—a domain relevant to STEM jobs—have been hypothesized to contribute to women’s underrepresentation in STEM fields. To study emerging adults’ beliefs about skill sets and jobs, we asked college students (N = 300) about the relevance of spatial, mathematical, science and verbal skills for each of 82 jobs. Analyses of responses revealed four job clusters—quantitative, basic & applied science, spatial, and verbal. Students’ ratings of individual jobs and job clusters were similar to judgments of professional job analysts (O*NET). Both groups connected STEM jobs to science, math, and spatial skills. To investigate whether students’ interests in STEM and other jobs are related to their own self-concepts, beliefs about jobs, and spatial performance, we asked students in another sample (N = 292) to rate their self-concepts in various academic domains, rate personal interest in each of the 82 jobs, judge cultural gender stereotypes of those jobs, and complete a spatial task. Consistent with prior research, jobs judged to draw on math, science, or spatial skills were rated as more strongly culturally stereotyped for men than women; jobs judged to draw on verbal skills were more strongly culturally stereotyped for women than men. Structural equation modeling showed that for both women and men, spatial task scores directly (and indirectly through spatial self-concept) related to greater interest in the job cluster closest to the one O*NET labeled “STEM”. Findings suggest that pre-college interventions that improve spatial skills might be effective for increasing spatial self-concepts and the pursuit of STEM careers among students from traditionally under-represented groups, including women. Full article
(This article belongs to the Special Issue Spatial Intelligence and Learning)
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21 pages, 2810 KiB  
Article
Overturning Children’s Misconceptions about Ruler Measurement: The Power of Disconfirming Evidence
by Mee-Kyoung Kwon, Eliza Congdon, Raedy Ping and Susan C. Levine
J. Intell. 2024, 12(7), 62; https://doi.org/10.3390/jintelligence12070062 - 22 Jun 2024
Viewed by 416
Abstract
Children have persistent difficulty with foundational measurement concepts, which may be linked to the instruction they receive. Here, we focus on testing various ways to support their understanding that rulers comprise spatial interval units. We examined whether evidence-based learning tools—disconfirming evidence and/or structural [...] Read more.
Children have persistent difficulty with foundational measurement concepts, which may be linked to the instruction they receive. Here, we focus on testing various ways to support their understanding that rulers comprise spatial interval units. We examined whether evidence-based learning tools—disconfirming evidence and/or structural alignment—enhance their understanding of ruler units. Disconfirming evidence, in this context, involves having children count the spatial interval units under an object that is not aligned with the origin of a ruler. Structural alignment, in this context, involves highlighting what a ruler unit is by overlaying plastic unit chips on top of ruler units when an object is aligned with the origin of a ruler. In three experiments employing a pre-test/training/post-test design, a total of 120 second graders were randomly assigned to one of six training conditions (two training conditions per experiment). The training conditions included different evidence-based learning principles or “business-as-usual” instruction (control), with equal allocation to each (N = 20 for each condition). In each experiment, children who did not perform above chance level on the pre-test were selected to continue with training, which resulted in a total of 88 students for the analysis of improvement. The children showed significant improvement in training conditions that included disconfirming evidence, but not in the structural alignment or control conditions. However, an exploratory analysis suggests that improvement occurred more rapidly and was retained better when structural alignment was combined with disconfirming evidence compared to disconfirming evidence alone. Full article
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