We 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.

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a featurelisting task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

No 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.

In everyday life, human beings can report memories of past events that did not occur or that occurred differently from the way they remember them because memory is an imperfect process of reconstruction and is prone to distortion and errors. In this recognition study using word stimuli, we investigated whether a specific operationalization of semantic similarity among concepts can modulate false memories while controlling for the possible effect of associative strength and word co-occurrence in an old-new recognition task. The semantic similarity value of each new concept was calculated as the mean cosine similarity between pairs of vectors representing that new concept and each old concept belonging to the same semantic category. Results showed that, compared with (new) low-similarity concepts, (new) high-similarity concepts had significantly higher probability of being falsely recognized as old, even after partialling out the effect of confounding variables, including associative relatedness and lexical co-occurrence. This finding supports the feature-based view of semantic memory, suggesting that meaning overlap and sharing of semantic features (which are greater when more similar semantic concepts are being processed) have an influence on recognition performance, resulting in more false alarms for new high-similarity concepts. We propose that the associative strength and word co-occurrence among concepts are not sufficient to explain illusory memories but is important to take into account also the effects of feature-based semantic relations, and, in particular, the semantic similarity among concepts.