Persona: Martínez Huertas, José Ángel
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Martínez Huertas
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José Ángel
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Publicación Enhancing topic-detection in computerized assessments of constructed responses with distributional models of language(Elsevier, 2021-12-15) Olmos, Ricardo; León, José A.; Martínez Huertas, José ÁngelUsually, computerized assessments of constructed responses use a predictive-centered approach instead of a validity-centered one. Here, we compared the convergent and discriminant validity of two computerized assessment methods designed to detect semantic topics in constructed responses: Inbuilt Rubric (IR) and Partial Contents Similarity (PCS). While both methods are distributional models of language and use the same Latent Semantic Analysis (LSA) prior knowledge, they produce different semantic representations. PCS evaluates constructed responses using non-meaningful semantic dimensions (this method is the standard LSA assessment of constructed responses), but IR endows original LSA semantic space coordinates with meaning. In the present study, 255 undergraduate and high school students were allocated one of three texts and were tasked to make a summary. A topic- detection task was conducted comparing IR and PCS methods. Evidence from convergent and discriminant validity was found in favor of the IR method for topic-detection in computerized constructed response assessments. In this line, the multicollinearity of PCS method was larger than the one of IR method, which means that the former is less capable of discriminating between related concepts or meanings. Moreover, the semantic representations of both methods were qualitatively different, that is, they evaluated different concepts or meanings. The implications of these automated assessment methods are also discussed. First, the meaningful coordinates of the Inbuilt Rubric method can accommodate expert rubrics for computerized assessments of constructed responses improving computer-assisted language learning. Second, they can provide high-quality computerized feedback accurately detecting topics in other educational constructed response assessments. Thus, it is concluded that: (1) IR method can represent different concepts and contents of a text, simultaneously mapping a considerable variability of contents in constructed responses; (2) IR method semantic representations have a qualitatively different meaning than the LSA ones and present a desirable multicollinearity that promotes the discriminant validity of the scores of distributional models of language; and (3) IR method can extend the performance and the applications of current LSA semantic representations by endowing the dimensions of the semantic space with semantic meanings.Publicación Can personality traits be measured analyzing written language? A meta-analytic study on computational methods(Elsevier, 2021) Moreno, José David; Olmos, Ricardo; Jorge Botana, Guillermo de; Botella, Juan; Martínez Huertas, José ÁngelIn the last two decades, empirical evidence has shown that personality traits could be related to the characteristics of written language. This study describes a meta-analysis that synthesizes 23 independent estimates of the correlations between the Big Five major personality traits, and some computationally obtained indicators from written language. The results show significant combined estimates of the correlations, albeit small to moderate according to Cohen’s conventions to interpret effect sizes, for the five traits (between r = 0.26 for agreeableness and neuroticism, and 0.30 for openness). These estimates are moderated by the type of information in the texts, the use of prediction mechanisms, and the source of publication of the primary studies. Generally, the same effective moderators operate for the five traits. It is concluded that written language analyzed through computational methods could be used to extract relevant information of personality. But further research is still needed to consider it as predictive or explanatory tool for individual differences.Publicación Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging(PsychOpen, 2022-12-22) Olmos, Ricardo; Martínez Huertas, José ÁngelRandom effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.Publicación Analyzing Two Automatic Latent Semantic Analysis (LSA) Assessment Methods (Inbuilt Rubric vs. Golden Summary) in Summaries Extracted from Expository Texts(Colegio Oficial de Psicólogos de Madrid, 2018) Jastrzebska, Olga; Mencu, Adrián; Moraleda, Jessica; Olmos, Ricardo; Antonio León, José Antonio; Martínez Huertas, José ÁngelEl objetivo de este estudio es comparar dos métodos de evaluación automática del análisis semántico latente (LSA): Un nuevo método LSA ( Inbuilt Rubric) y un método LSA tradicional ( Golden Summary). Se analizaron dos condiciones del método Inbuilt Rubric: el número de descriptores léxicos que se utilizan para generar la rúbrica (pocos vs. muchos) y una corrección que penaliza el contenido irrelevante incluido en los resúmenes de los estudiantes (corregido vs. no corregido). Ciento sesenta y seis estudiantes divididos en dos muestras (81 estudiantes universitarios y 85 estudiantes de instituto) participaron en este estudio. Los estudiantes resumieron dos textos expositivos que tenían distinta complejidad (difícil/fácil) y longitud (1,300/500 palabras). Los resultados mostraron que el método Inbuilt Rubric imita las evaluaciones humanas mejor que Golden Summary en todos los casos. La similitud con las evaluaciones humanas fue más alta con Inbuilt Rubric ( r = .78 and r = .79) que con Golden Summary ( r = .67 and r = .47) en ambos textos. Además, la versión de Inbuilt Rubric con menor número de descriptores y con corrección es la que obtuvo mejores resultados.Publicación Redundancy, Isomorphism and Propagative Mechanisms between Emotional and Amodal Representations of Words: A Computational Study(['Springer', 'Psychonomic Society'], 2020-08-20) Jorge Botana, Guillermo de; Olmos, Ricardo; Martínez Huertas, José Ángel; Luzón Encabo, José MaríaSome proposals claim that language acts as a link to propagate emotional and other modal information. Thus, there is an eminently amodal path of emotional propagation in the mental lexicon. Following these proposals, we present a computational model that emulates a linking mechanism (mapping function) between emotional and amodal representations of words using vector space models, emotional feature-based models, and neural networks. We analyzed three central concepts within the embodiment debate (redundancy, isomorphism, and propagative mechanisms) comparing two alternative hypotheses: semantic neighborhood hypothesis vs. specific dimensionality hypothesis. Univariate and multivariate neural networks were trained for dimensional (N=11,357) and discrete emotions (N=2,266), and later we analyzed its predictions in a test set (N=4,167 and N=875, respectively). We showed how this computational model could propagate emotional responses to words without a direct emotional experience via amodal propagation, but no direct relations were found between emotional rates and amodal distances. Thereby, we found that there were clear redundancy and propagative mechanisms, but no isomorphism should be assumed. Results suggested that it was necessary to establish complex links to go beyond amodal distances of vector spaces. In this way, although the emotional rates of semantic neighborhoods could predict the emotional rates of target words, the mapping function of specific amodal features seemed to simulate emotional responses better. Thus, both hypotheses would not be mutually exclusive. We also showed that discrete emotions could have simpler relations between modal and amodal representations than dimensional emotions. All these results and their theoretical implications are discussed.Publicación Model Selection and Model Averaging for Mixed-Effects Models with Crossed Random Effects for Subjects and Items(['Taylor and Francis Group', 'Routledge'], 2021-02-26) Olmos, Ricardo; Ferrer, Emilio; Martínez Huertas, José ÁngelA good deal of experimental research is characterized by the presence of random effects on subjects and items. A standard modeling approach that includes such sources of variability is the mixed-effects models (MEMs) with crossed random effects. However, under-parameterizing or over-parameterizing the random structure of MEMs bias the estimations of the Standard Errors (SEs) of fixed effects. In this simulation study, we examined two different but complementary perspectives: model selection with likelihood-ratio tests, AIC, and BIC; and model averaging with Akaike weights. Results showed that true model selection was constant across the different strategies examined (including ML and REML estimators). However, sample size and variance of random slopes were found to explain true model selection and SE bias of fixed effects. No relevant differences in SE bias were found for model selection and model averaging. Sample size and variance of random slopes interacted with the estimator to explain SE bias. Only the within-subjects effect showed significant underestimation of SEs with smaller number of items and larger item random slopes. SE bias was higher for ML than REML, but the variability of SE bias was the opposite. Such variability can be translated into high rates of unacceptable bias in many replications.Publicación Metacomprehension skills depend on the type of text: An analysis from Differential Item Functioning(Colegio Oficial de Psicólogos del Principado de Asturias, 2019) Antonio León, José Antonio; Olmos, Ricardo; Moreno, José David; Martínez Huertas, José Ángel; Escudero Domínguez, InmaculadaLas habilidades metacomprensivas dependen del tipo de texto: un análisis desde el Funcionamiento Diferencial de los Ítems. Antecedentes: la metacomprensión supone la habilidad que uno mismo posee para juzgar su grado de aprendizaje y comprensión de un texto, adquiriendo una gran importancia en la comprensión lectora. Dado que los procesos de comprensión se encuentran influenciados por las características de los textos, el objetivo de este estudio fue analizar si diferentes tipos de texto afectan de manera significativa a la habilidad metacomprensiva de estudiantes de distintos niveles de Educación Primaria. Método: un total de 823 estudiantes de 4º y 6º de Primaria (9 y 11 años) leyeron tres textos diferentes (narrativo, expositivo y discontinuo) tomados de la prueba estandarizada de comprensión lectora ECOMPLEC.Pri (León, Escudero, y Olmos, 2012). Los estudiantes fueron clasificados por su nivel de metacomprensión obtenido en la prueba. Un Análisis Diferencial del Ítem (DIF) se aplicó para analizar si los procesos de comprensión lectora y de metacomprensión difieren entre tipos de texto y niveles académicos de los participantes. Resultados: los resultados mostraron una notable divergencia en el rendimiento metacognitivo del texto narrativo frente a los textos expositivo y discontinuo. Estas diferencias estaban relacionadas con el nivel académico. Conclusión: el tipo de texto puede tener un gran impacto en las habilidades de metacomprensión y, consecuentemente, en el aprendizaje de textosPublicación Automated summary evaluation with inbuilt rubric method: An alternative to constructed responses and multiple-choice tests assessments(Taylor and Francis Group, 2019-02-09) Jastrzebska, Olga; Olmos, Ricardo; León, José A.; Martínez Huertas, José ÁngelAutomated Summary Evaluation is proposed as an alternative to rubrics and multiple-choice tests in knowledge assessment. Inbuilt rubric is a recent Latent Semantic Analysis (LSA) method that implements rubrics in an artificially-generated semantic space. It was compared with classical LSA’s cosine-based methods assessing knowledge in a within-subjects design regarding two validation sources: a comparison with the results of rubric scores and multiple-choice tests, and the sensitivity of predicting the academic level of the test-taker. Results showed a higher reliability for inbuilt rubric (from Pearson correlation coefficient .81 to .49) over the classical LSA method (from .61 to .34) and a higher sensitivity using binary logistic regressions and effect sizes to predict academic level. It is concluded that inbuilt rubric has a qualitatively higher reliability and validity than classical LSA methods in a way that is complementary to models based on semantic networks. Thus, it is concluded that new Automated Summary Evaluation approaches such as the inbuilt rubric method can be practical in terms of reliability and efficiency, and, thus, they can offer an affordable and valuable form of knowledge assessment in different educational levels.Publicación Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring(Wiley, 2021-07-19) Jorge Botana, Guillermo de; Olmos, Ricardo; Martínez Huertas, José ÁngelWe present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.Publicación Quantum projections on conceptual subspaces: A deeper dive into methodological challenges and opportunities(SAGE, 2024) Martínez Mingo, Alejandro; Olmos, Ricardo; Jorge Botana, Guillermo; Martínez Huertas, José ÁngelIn alignment with the distributional hypothesis of language, the work “Quantum Projections on Conceptual Subspaces” (Martínez-Mingo A, Jorge-Botana G, Martinez-Huertas JÁ, et al. Quantum projections on conceptual subspaces. Cogn Syst Res 2023; 82: 101154) proposed a methodology for generating conceptual subspaces from textual information based on previous work (Martinez-Mingo A, Jorge-Botana G and Olmos R. Quantum approach for similarity evaluation in LSA vector space models. 2020). These subspaces enable the utilization of the quantum model of similarity put forth by Pothos and Busemeyer (Pothos E, Busemeyer J. A quantum probability explanation for violations of symmetry in similarity judgments. In Proceedings of the annual meeting of the cognitive science society, 2011, Vol. 33, No. 33), allowing for the empirical examination of the violations of assumptions concerning symmetry and triangular inequality (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Barque-Duran A, Scerrati E, et al. The triangle inequality constraint in similarity judgments. Prog Biophys Mol Biol 2017; 130: 26–32), as well as the diagnosticity effect (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Pothos EM, Barque-Duran A, et al. Context effects in similarity judgments. J Exp Psychol Gen 2022; 151: 711–717), within a data-driven environment. These psychological biases, deeply studied by authors such as Tversky and Kahneman, inform us about the limitations of modeling psychological similarity measures using tools from classical geometry. This commentary aims to offer methodological clarifications, discuss theoretical and practical implications, and speculate on future directions in this field of research. Concretely, it aims to propose the use of different contours (conceptual or contextual) to generate the subspaces, which lead to subspaces of terms or contexts. Once these contours are defined, a differentiation is proposed between Aggregated Terms Subspaces (ATSs), Aggregated Contexts Subspaces (ACSs), and Aggregated Features Subspaces (AFSs) depending on whether we define the subspaces by grouping the terms or contexts within the contour, or from the latent dimensions of the semantic space obtained in the contour window. Finally, new data is provided on the violation of the triangular inequality assumption through the application of the quantum similarity model to ATSs.