Persona: Martínez Huertas, José Ángel
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Martínez Huertas
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José Ángel
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Publicación Are valence and arousal related to the development of amodal representations of words? A computational study(['Taylor and Francis Group', 'Routledge'], 2023-11-21) Jorge Botana, Guillermo de; Martínez Mingo, Alejandro; Iglesias, Diego; Olmos, Ricardo; Martínez Huertas, José ÁngelIn this study, we analyzed the relationship between the amodal (semantic) development of words and two popular emotional norms (emotional valence and arousal) in English and Spanish languages. To do so, we combined the strengths of semantics from vector space models (vector length, semantic diversity, and word maturity measures), and feature-based models of emotions. First, we generated a common vector space representing the meaning of words at different developmental stages (five and four developmental stages for English and Spanish, respectively) using the Word Maturity methodology to align different vector spaces. Second, we analyzed the amodal development of words through mixed-effects models with crossed random effects for words and variables using a continuous time metric. Third, the emotional norms were included as covariates in the statistical models. We evaluated more than 23,000 words, whose emotional norms were available for more than 10,000 words, in each language separately. Results showed a curve of amodal development with an increasing linear effect and a small quadratic deceleration. A relevant influence on the amodal development of words was found only for emotional valence (not for arousal), suggesting that positive words have an earlier amodal development and a less pronounced semantic change across early lifespan.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 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 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 Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables(['Springer', 'Psychonomic Society'], 2022-01-11) Olmos, Ricardo; Jorge Botana, Guillermo de; León, José A.; Martínez Huertas, José ÁngelIn this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts’ assessment. Specifically, we improved and validated its scores’ performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity riteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.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 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 Modeling personality language use with small semantic vector subspaces(Elsevier, 2024-12-14) Jorge Botana, Guillermo de; Martínez Mingo, Alejandro; Olmos, Ricardo; Martínez Huertas, José Ángel; Moreno Salinas, DavidPublicación 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 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.