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http://dx.doi.org/10.5007/1518-2924.2014v19n40p235 Dos líneas de teorías estudian la organización del conocimiento conceptual: las holísticas y las de rasgos. El objetivo de este trabajo es presentar dos métodos informatizados de captura y análisis de redes semánticas, el Natural Finder y el Definition Finder, que responden a estos modelos respectivamente. El primero es un procedimiento recursivo para capturar a partir de un concepto disparador la red semántica natural de una persona en torno a dicho concepto; el segundo permite obtener el conjunto de atributos ponderados que mejor definen a un concepto para un colectivo social. Se presentan ejemplos de su aplicación.
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... This procedure was performed for every concept. This was done using the software Definition Finder (DF) (Vivas et al., 2014). 2 The Definition Finder's computer model uses a technique designed to be applied to a social group. ...
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It has been suggested that concepts can be recognised based on at least some amount of highly shared features across individuals (i.e. core features). However, the boundaries between those features and less-shared ones remain unclear. Therefore, the main purpose of this paper is to operationalise the different levels of the structure of a concept's meaning and to test the effect of those levels on semantic decision latencies. First, we performed a feature based cued-recall task. Then, we designed two speeded verification tasks in which features were presented before and after the concept (i.e. feature-concept and concept-feature condition, respectively). Results of the cued-recall task allowed us to operationalise core features. Furthermore, we observed that core features were verified faster than partially shared, followed by features, and partially shared features faster than idiosyncratic ones, being features, and that this effect is independent of the order of stimulus presentation.
... con los atributos descriptores para cada uno de los 130 conceptos en ambas poblaciones. Cada vector se construyó con el software Definition Finder (Vivas et al., 2014). Los resultados se expresan en valores numéricos que fluctúan entre 0 y 1, donde 1 es el mayor peso. ...
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En los últimos años se ha producido una profusa investigación en torno al tema de la memoria semántica en la vejez. En ese marco el presente trabajo abordó la producción de atributos semánticos y las variaciones en la fluencia verbal en el envejecimiento cognitivo normal. Existen numerosos estudios sobre fluencia, pero son exiguos los estudios que comparan la distancia semántica entre conceptos en poblaciones de jóvenes y de mayores de una misma región. El objeto de este trabajo es comparar y analizar la distancia y la riqueza semántica por medio de la fluencia, dada para 130 conceptos por 90 adultos mayores de 65 años cognitivamente conservados, con los atributos producidos por 90 adultos jóvenes, tal como se hallan en las Normas de Producción de Atributos Semánticos en Español. Los atributos semánticos fueron empíricamente colectados en forma escrita y corresponden a las categorías de seres vivos y no vivos. Se encuentran diferencias significativas en fluencia verbal y convergencia en la constitución semántica entre los conceptos dados por ambos grupos etarios.Palabras clave: Atributos. Distancia Semántica. Fluencia Verbal. Adultos Mayores. Adultos Jóvenes
... Para su construcción se les solicitó a los participantes que produjeran los atributos que mejor caractericen y describan a determinados conceptos. Luego de un minucioso proceso de unifi cación de las enunciaciones (para mayor detalle véaseVivas et al., 2016) se cargaron los descriptores en el programa Defi nition Finder (Vivas et al., 2014). Este programa, permite obtener un listado de atributos ponderados correspondientes a cada uno de los conceptos según su frecuencia y la ubicación en la que fueron emitidos dentro de todos los atributos brindados por un conjunto de participantes. ...
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Feature taxonomy proposed by sensory-functional theories to differentiate between domains (living or non-living things) received a number of critics: (a) the vagueness of the definition of attribute types; (b) the limitations of the dichotomous taxonomy; (c) the insufficiency of feature production variable to discriminate between domains. This paper aims to contribute to overcome these difficulties by proposing an analysis based on a complex taxonomy supported on empirical data obtained from Semantic Feature Production Norms in Spanish, considering features' frequency and order of production. Data was collected from a sample of 180 Spanish speaking healthy adults who produced defining features for 90 concrete concepts. The results show similarities between domains, in so far as to the primacy of superordinate attributes, and differences in the weight of the dynamic (functional and behavioral) and sensory features. This information is worthy to characterize the compositional structure of the features that constitute the semantic representation of concrete concepts.
... To construct the dataset, participants were instructed to produce the features that best characterize and describe particular concepts. Following a meticulous process to unify the descriptors produced by participants (for the details of this process, seeVivas et al., 2016), these were then loaded to the Defi nition Finder program (Vivas et al., 2014). This program generates a list of features for each concept weighted by frequency and ranking. ...
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Feature taxonomy proposed by sensory-functional theories to differentiate between domains (living or non-living things) received a number of critics: (a) the vagueness of the definition of attribute types; (b) the limitations of the dichotomous taxonomy; (c) the insufficiency of feature production variable to discriminate between domains. This paper aims to contribute to overcome these difficulties by proposing an analysis based on a complex taxonomy supported on empirical data obtained from Semantic Feature Production Norms in Spanish, considering features' frequency and order of production. Data was collected from a sample of 180 Spanish speaking healthy adults who produced defining features for 90 concrete concepts. The results show similarities between domains, in so far as to the primacy of superordinate attributes, and differences in the weight of the dynamic (functional and behavioral) and sensory features. This information is worthy to characterize the compositional structure of the features that constitute the semantic representation of concrete concepts.
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According to the feature-based view of semantic representation, concepts can be represented as distributed networks of semantic features, which contribute with different weights to determine the overall meaning of a concept. The study of semantic features, typically collected in property generation tasks, is enriched with measures indicating the informativeness and distinctiveness of a given feature for the related concepts. However, while these measures have been provided in several languages (e.g. Italian, Spanish and English), they have hardly been applied comparatively across languages. The purpose of this paper is to investigate language-related differences and similarities emerging from the semantic representation of aggregated core features. Features with higher salience for a set of concrete concepts are identified and described in terms of their feature type. Then, comparisons are made between domains (natural vs. artefacts) and languages (Italian, Spanish, and English) and descriptive statistics are provided. These results show that the characterization of concrete concepts is overall fairly stable across languages, although interesting cross-linguistic differences emerged. We will discuss the implications of our findings in relation to the theoretical paradigm of semantic feature norms, as well as in relation to speakers' mutual understanding in multilingual settings.
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Poder de discriminación de los atributos semánticos. Mínima cantidad de descriptores requeridos para identificar conceptos emocionales García, Gisele; Pagnotta, Luciana; Pazgon, Elisa y Vivas, Jorge Introducción Los estudios más completos en la confección de normas de atributos en lengua inglesa son los producidos por Mc Rae y cols. (Mc Rae et al., 2005). En ellos se pone de manifiesto un esfuerzo sustantivo por establecer las propiedades de informatividad provista por los atributos generados por dichas normas. La calidad y cantidad de información provista por los atributos resulta de suma utilidad para caracterizar un concepto y para poder discriminar entre conceptos similares. Por ejemplo, "trompa" permite distinguir elefante de otros animales, mientras que "peludo" no. La cantidad de atributos distintivos será igual al número de aquellos atributos que aparecen solamente en uno o dos conceptos de aquellos que se estarían estudiando. Esta distintividad informa sobre un continuo desde lo muy distinto a lo compartido (Devlin, Gonnerman, Andersen & Seidenberg, 1998; Garrard, Lambon Ralph, Hodges & Patterson (2001). Complementariamente, otras medidas como la clave de validez de un atributo, sería la probabilidad de que un atributo pueda aparecer en un concepto, dividido por la probabilidad de que ese atributo aparezca en todos los conceptos. Varios autores reportan dimensiones complementarias propuestas como descriptores de caracterización semántica; éstas son: a) relevancia semántica (Sartori & Lombardi, 2004; Sartori, Mameli, Polezzi, Lombardi, 2006), b) distintividad (Garrard et al., 2001), c) dominancia (Ashcraft, 1978) y d) distancia semántica (Zannino, Perri, Pasqualetti, Caltagirone y Carlesimo, 2006). La relevancia es definida como una medida de la contribución de las características semánticas al núcleo de significado de un concepto (Sartori et al., 2006) Pocas características semánticas de alta relevancia son suficientes para la recuperación de un concepto target. Cuando la relevancia semántica es baja, la recuperación es inexacta. La literatura señala pues, numerosos trabajos que aplicaron estos análisis a las propiedades de los atributos. Todos ellos, hasta el momento, se han llevado adelante sobre etiquetas verbales referidas a conceptos de tipo concreto, generalmente de nivel base, pero al presente aún no se exploró el comportamiento de estas propiedades en tareas de producción de atributos tomando como base palabras que aludan a conceptos emocionales.
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