Semantic significance: a new measure of feature salience

Abstract

According 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.

Affective Norms for Italian Words in Older Adults: Age Differences in Ratings of Valence, Arousal and Dominance

“…In particular, the data set included the Italian translation of all the English ANEW words (1,034 words) [12] and those from the Italian semantic norms collected in our laboratory (87 words) [59,60] and tested in behavioral [61,62] and psychophysiological [63] studies.…”

Causal role of the posterior parietal cortex for two-digit mental subtraction and addition: A repetitive TMS study

“…The effect of this covariate (i.e., the factor time), which accounts for potential confounding longitudinal effects of fatigue or familiarization across participants, was modelled by a parameter representing the session number vector zerocentered (cSession) to remove the possible spurious correlation between the by-subjects random intercepts and slopes. We determined the simplest best (final) linear mixed-effect models to fit our dependent variables by using a log-likelihood ratio test (for a detailed description of the procedure, see Montefinese et al, 2014) according to standard procedures (Baayen et al, 2008;Quené and Van den Bergh, 2008). In the model building process the order of entry of successive variables was based on theoretical motivation.…”

Montefinese M, Vinson D, Ambrosini E. Pupil response to false memory modulated by feature similarity (PrePrint)

“…In brief, we fitted participants' responses to unstudied items with a mixed-effects model in which the fixed effect Condition was replaced with the continuous predictor semantic similarity. Moreover, the model included three parameters accounting for the effects of associative relatedness, lexical co-occurrence, and concept familiarity; note that this model was chosen to confirm our previous findings with the present reduced sample (see Montefinese et al, 2014a for details about the choice of the included variables). The analysis confirmed our previous results, revealing that the log odds of (erroneously) evaluating unstudied items as ''old'' was significantly and positively related to semantic similarity between unstudied and studied concepts (b = .31, SE = .13, z = 2.34, one-tailed p = .010) as well as to the familiarity of the unstudied concepts (b = 1.01, SE = .16, z = 6.22,onetailed p < 10 -9 ), but the effect of associative relatedness and lexical co-occurrence were not significant (respectively, b = -.06 and -.21, SE = .12 and .15, z = -.56 and -1.42, p = .578 and .155).…”