Analysis of the semantic representations of living and nonliving concepts: A normative study

Abstract

Current feature-based semantic memory models assume that the semantic representations of concepts differ systematically across living and nonliving categories and that such differences account for the emergence of category-specific semantic deficits in brain-damaged people. To assess some of the different models' main assumptions about structural differences at the semantic feature level in the two major semantic domains, we administrated a feature-listing task to normal young volunteers on 64 concepts drawn from living and nonliving semantic categories. We investigated whether feature correlation, a variable with a crucial role in the emergence of category-specific deficits, should be computed as a concept-dependent or as a concept-independent measure, and we chose the former. We also addressed the issue of a psychological counterpart of feature production frequency. Finally, we analysed the database obtained from the feature-listing tasks, looked at cross-domain differences for correlation, feature frequency, distinctiveness, and feature type, and discussed the implications of these findings for current semantic memory models.

Semantic memory: A feature-based analysis and new norms for Italian

“…Interestingly, several of the most commonly used categories in the literature (e.g., in Battig & Montague, 1969;Bueno & Megherbi, 2009;Garrard et al, 2001;McRae et al, 2005;Van Overschelde et al, 2004;Zannino et al, 2006) were among these LR categories-namely <frutta/fruit>, <strumenti musicali/musical instruments>, <armi/weapons>, and <vegetali/vegetables> -since their concepts were listed on average by only a minority of our participants (20.1 %, 11.1 %, 8.5 %, and 6.2 %, respectively).…”

“…Existing norms in the literature are typically based on concepts or categories that either are selected from previously published databases (De Deyne et al, 2008;Garrard et al, 2001;Izura, Hernández-Muñoz, & Ellis, 2005;Kremer & Baroni, 2011;McRae et al, 2005;Ruts et al, 2004;van Dantzig, Cowell, Zeelenberg, & Pecher, 2011;Zannino et al, 2006) or are chosen a priori by researchers (Lynott & Connell, 2009;McRae et al, 2005;Vinson & Vigliocco, 2008). In this manner, participants describe categories or concepts that are only supposed to be representative of semantic memory, without controlling for their actual representativeness, or importance, in the semantic organization.…”

“…We collected normative data by asking native Italian speakers to perform a feature generation task similar to the one that has been used for other norms (De Deyne et al, 2008;Kremer & Baroni, 2011;McRae et al, 2005;Vinson & Vigliocco, 2008;Zannino et al, 2006).…”

“…Many studies have, in fact, used feature representations deriving from feature production norms to account for empirical phenomena, such as semantic priming (Cree, McRae, & McNorgan, 1999;McRae et al, 1997;Vigliocco et al, 2004), feature verification (Ashcraft, 1978, McRae, Cree, Westmacott, & de Sa, 1999Solomon & Barsalou, 2001), categorization (Hampton, 1979;Smith, Shoben, & Rips, 1974), and category learning (Kruschke, 1992), among others (for a more detailed discussion of the aims and limits of feature norms, see McRae, Cree, Seidenberg, & McNorgan, 2005; for a discussion of theories of semantic memory organization that use feature representations to account for category-specific semantic deficits, see Cree & McRae, 2003;Zannino, Perri, Pasqualetti, Caltagirone, & Carlesimo, 2006).…”

Spanish semantic feature production norms for 400 concrete concepts

“…This is shown, for example, in the investigations conducted by Cree and McRae (2003), who studied the factors that determine the pattern of semantic categories observed in category-specific semantic deficits and concluded that, besides knowledge type, other variables, such as feature informativeness, concept confusability, visual complexity, familiarity, and name frequency, also have influences. Moreover, Zannino, Perri, Pasqualetti, Caltagirone, and Carlesimo (2006) observed that the data regarding category composition vary depending on the selection of concept-dependent or concept-independent correlation measures. They also showed that feature dominance plays an important role on the analysis of the feature type that is relevant for either living or nonliving domains.…”