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Unlocking new insights into the somatic marker hypothesis with multilevel logistic models
Full text linkThe Somatic Marker Hypothesis, an influential neurobiological account of decision-making, states that emotional somatic markers (e.g., skin conductance responses) influence decision-making processes. Despite its prominence, the hypothesis remains controversial partly due to inconsistent results stemming from inappropriate statistical methods. Tasks designed to assess decision-making often use repeated measures designs, such as the Iowa Gambling Task (IGT), which requires participants to maximize profits by selecting 100 cards among four decks offering varying win-loss contingencies. Researchers often aggregate repeated measures into a single averaged value to simplify analyses, potentially committing an ecological fallacy by erroneously generalizing results obtained from aggregated data (i.e., interindividual effects) to individual repeated measurements (i.e., intraindividual effects). This paper addresses this issue by demonstrating how to analyze concurrent repeated measures of both independent and dependent variables using multilevel logistic models. First, the principles of logistic multilevel models are explained. Then, simulated and empirical IGT data are analyzed to compare the performance of traditional statistical approaches (i.e., general linear models) with multilevel logistic models. Our proposed multilevel logistic analyses address critical methodological gaps in decision-making research, ensuring more accurate interpretations of repeated measures data. This approach not only advances the study of the Somatic Marker Hypothesis but also provides a robust framework for similar research protocols, ultimately enhancing the reliability and validity of findings
Évaluation écologique momentanée de l'activité physique et des expériences affectives : une revue de la portée
Full text linkÉvaluation de la faisabilité d'une approche d’évaluation écologique momentanée pour explorer l’activité physique, les besoins psychologiques fondamentaux et le bien-être affectif chez des étudiants universitaires
Full text linkL’expression du corps comme outil d’apprentissage des émotions chez les enfants : regard sur les pratiques des personnes qui interviennent auprès des tout-petits.
No full textEvaluating a GPT-4 and Retrieval-Augmented Generation-Based Conversational Agent to Enhance Learning Experience in a MOOC
Full text linkMassive Open Online Courses (MOOCs) face significant challenges due to low completion rates, primarily caused by insufficient personalized support for learners. To address this, we developed a pedagogical AI-powered conversational agent enhanced with Retrieval-Augmented Generation (RAG) to provide real-time, contextually relevant support. Our evaluation with 25 learners demonstrated a statistically significant knowledge gain in the experimental group compared to the control group. Additionally, the agent achieved a high System Usability Scale (SUS) score. These findings highlight the potential of AI technologies to enhance online learning environments and inform future research on their role as learning companions in distance education
Projet de recherche sur le développement d’une littératie numérique par le jeu
No full textPiloté par Sophie Marier, professeure, développeuse et chercheuse en enseignement de l’anglais, langue seconde, au collégial, et financé par l’Entente Canada-Québec, le projet de recherche intitulé «Recherche interordres et interétablissements sur le développement d'une littératie numérique anglophone» vise à évaluer l'acquisition de 37 concepts clés de littératie numérique (réf.: Cadre de référence de la compétence numérique). Cet objectif est en pleine cohérence avec le besoin identifié par le Conseil de l’innovation du Québec dans son récent rapport Prêt pour l’IA (2024). Dans un monde où le numérique et l’intelligence artificielle cohabitent avec les humains, toute action qui permettra «[...] de renforcer la littératie numérique et en IA [...] des étudiants [...]» (p. XV) favorisera une saine humanisation des demains. La présentation mettra en lumière les fondements théoriques, la méthodologie, les développements innovants du projet ainsi que la richesse des collaborations interordres et interétablissements qui soutiennent ce projet ambitieux. Les co-chercheur.se.s Maude Bonenfant, professeure titulaire du Département de communication sociale et publique de l'UQAM, et Patrick Plante, professeur en formation à distance et technologies éducatives à l'Université TÉLUQ, feront également partie de l'équipe en charge de la présentation
Faster Positional-Population Counts for AVX2, AVX-512, and ASIMD
Full text linkThe positional population count operation pospopcnt() counts for an array of w-bit words how often each of the w bits was set. Various applications in bioinformatics, database engineering, and digital processing exist. Building on earlier work by Klarqvist et al., we show how positional population counts can be rapidly computed using SIMD techniques with good performance from the first byte, approaching memory-bound speeds for input arrays of as little as 4 KiB. Improvements include an improved algorithm structure, better handling of unaligned and very short arrays, as well as faster bit-parallel accumulation of intermediate results. We provide a generic algorithm description as well as implementations for various SIMD instruction set extensions, including Intel AVX2, AVX-512, and ARM ASIMD, and discuss the adaption of our algorithm to other platforms
More on distribution of eigenvalues of smooth Toeplitz matrices
Full text linkIn this article – which, as its title suggests, follows on from previous publications by the same author in
the same field of research – we focus on counting measures that weight absolute values of the eigenvalues,
or absolute values of their real parts
A fit index for latent class analysis of dichotomous scale
Full text linkLatent class analysis (LCA) is a powerful statistical method for identifying unobserved subgroups within a population based on categorical data. However, selecting the optimal number of latent classes remains a challenge and there is no consensus on which fit index to use. Based on the properties of dichotomous variables, this paper introduces a new fit index that capitalizes on the recovery of the model implied covariance matrix from the response probabilities to measure its discrepancy with the sample covariance matrix S. Based on the pattern matrix
Based on the pattern X where each row represents one of the 2^I binary I-tuples, such as
X=[■(x_1,1&⋯&x_(1,I)@⋮&⋱&⋮@x_(2^I,I)&⋯&x_(2^I,I) )],
where x_(i,j)∈{0,1} ∀i∈{1,2,…,2^I },j∈{1,2,…,I}, I is the number of item, the pattern probabilities are
P_i=∑_(k=1)^K▒∏_(j=1)^I▒〖p(x_(i,j)^((k)) ) c_k 〗,
where K is the number of classes and c the class probability, we derived the implied covariance matrix
S(θ)=(XP)^' X-MM^',
where M_j=∑_(i=1)^(2^I)▒〖X_(i,j) P_i 〗.
Using the square difference of the Fisher transform of both covariance matrices, we derived a pseudo χ^2statistic. A Monte Carlo simulation was carried out to compare the accuracy and bias of three versions of this fit index with nine usual fit indices (AIC, BIC, saBIC, χ^2, CAIC, AIC3, Lo-Mendell-Rubin, Vuong-Lo-Mendell-Rubin, and the bootstrap LRT). The simulation shows new among the three versions tested, two had very good properties: less bias and more accurate than other indices. The other one had very good accuracy but tended to narrowly miss the correct number of classes leading excessive over-extraction when it failed. Future developments are discussed, i.e., investigating the asymptotic properties of the underlying pseudo-χ^2 distribution, improving the current criteria and extending the index for ordinal scales