1,721,014 research outputs found
Core features: measures and characterization for different languages
Full text linkAccording 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.Fil: Vivas, Leticia Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Psicología Básica, Aplicada y Tecnología. Universidad Nacional de Mar del Plata. Facultad de Psicología. Instituto de Psicología Básica, Aplicada y Tecnología.; ArgentinaFil: Montefinese, Maria. Università di Padova; Italia. University College London; Estados UnidosFil: Bolognesi, Marianna. University of Oxford; Reino UnidoFil: Vivas, Jorge Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Psicología Básica, Aplicada y Tecnología. Universidad Nacional de Mar del Plata. Facultad de Psicología. Instituto de Psicología Básica, Aplicada y Tecnología; Argentin
Semantic similarity between old and new items produces false alarms in recognition memory
No full textA practical primer on processing semantic property norm data
Full text linkSemantic property listing tasks require participants to generate short propositions (e.g., , ) for a specific concept (e.g., DOG). This task is the cornerstone of the creation of semantic property norms which are essential for modeling, stimuli creation, and understanding similarity between concepts. Despite the wide applicability of semantic property norms for a large variety of concepts across different groups of people, the methodological aspects of the property listing task have received less attention, even though the procedure and processing of the data can substantially affect the nature and quality of the measures derived from them. The goal of this paper is to provide a practical primer on how to collect and process semantic property norms. We will discuss the key methods to elicit semantic properties and compare different methods to derive meaningful representations from them. This will cover the role of instructions and test context, property preprocessing (e.g., lemmatization), property weighting, and relationship encoding using ontologies. With these choices in mind, we propose and demonstrate a processing pipeline that transparently documents these steps, resulting in improved comparability across different studies. The impact of these choices will be demonstrated using intrinsic (e.g., reliability, number of properties) and extrinsic measures (e.g., categorization, semantic similarity, lexical processing). This practical primer will offer potential solutions to several long-standing problems and allow researchers to develop new property listing norms overcoming the constraints of previous studies
Semantic significance: A new measure of feature salience
No full textAccording to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation-that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants' performance. The results showed that semantic significance is a good predictor of participants' verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation
Fine‐Grained Concreteness Effects on Word Processing and Representation Across Three Tasks: An ERP Study
Full text linkPeople process concrete words more quickly and accurately than abstract ones—the so-called “concreteness effect.” This advantage also reflects differences in how the brain processes and stores concrete versus abstract words. In this electrophysiological study, we treated word concreteness as a continuous variable and examined its effects on ERPs across three tasks with distinct processing demands (semantic, affective, grammatical). Behavioral results revealed task-dependent concreteness effects: in the semantic task, reaction times were faster for words at both concreteness extremes, and the classical linear advantage emerged for concrete words. Mass univariate ERP analyses revealed distinct spatiotemporal patterns of task-dependent concreteness effects. In the semantic task, we identified three significant clusters reflecting increased parietal N2/P3-like and sustained bilateral fronto-temporal negativity ERPs and decreased central N400-like ERP for abstract words. By contrast, the affective task elicited an increased parietal P600-like ERP for abstract words. Moreover, results from multivariate representational similarity analysis and an intersection analysis revealed that concreteness is encoded in ERP spatiotemporal patterns from 450 ms onwards, regardless of task, suggesting its role not only as an organizational principle in semantic representation, but also as a factor influencing downstream word processing and univariate ERP concreteness effects. Our findings challenge and extend existing theories like the dual coding and context availability ones, highlighting the importance of treating concreteness as a continuous variable and considering task context in word processing studies. This approach, enabled by advanced analytical techniques, provides a more nuanced understanding of how the brain processes and represents words
The adaptation of the Affective Norms for English Words (ANEW) for Italian
No full textWe developed affective norms for 1,121 Italian words in order to provide researchers with a highly controlled tool for the study of verbal processing. This database was developed from translations of the 1,034 English words present in the Affective Norms for English Words (ANEW; Bradley & Lang, 1999) and from words taken from Italian semantic norms (Montefinese, Ambrosini, Fairfield, & Mammarella, Behavior Research Methods, 45, 440-461, 2013). Participants evaluated valence, arousal, and dominance using the Self-Assessment Manikin (SAM) in a Web survey procedure. Participants also provided evaluations of three subjective psycholinguistic indexes (familiarity, imageability, and concreteness), and five objective psycholinguistic indexes (e.g., word frequency) were also included in the resulting database in order to further characterize the Italian words. We obtained a typical quadratic relation between valence and arousal, in line with previous findings. We also tested the reliability of the present ANEW adaptation for Italian by comparing it to previous affective databases and performing split-half correlations for each variable. We found high split-half correlations within our sample and high correlations between our ratings and those of previous studies, confirming the validity of the adaptation of ANEW for Italian. This database of affective norms provides a tool for future research about the effects of emotion on human cognition
What is the right place for atypical exemplars? Commentary on: The right hemisphere contribution to semantic categorization: A TMS study
Full text link- …
