A close call: Interference from semantic neighbourhood density and similarity in language production

Abstract

The present study investigated how lexical selection is influenced by the number of semantically related representations (semantic neighbourhood density) and their similarity (semantic distance) to the target in a speeded picture-naming task. Semantic neighbourhood density and similarity as continuous variables were used to assess lexical selection for which competitive and noncompetitive mechanisms have been proposed. Previous studies found mixed effects of semantic neighbourhood variables, leaving this issue unresolved. Here, we demonstrate interference of semantic neighbourhood similarity with less accurate naming responses and a higher likelihood of producing semantic errors and omissions over accurate responses for words with semantically more similar (closer) neighbours. No main effect of semantic neighbourhood density and no interaction between semantic neighbourhood density and similarity was found. We assessed further whether semantic neighbourhood density can affect naming performance if semantic neighbours exceed a certain degree of semantic similarity. Semantic similarity between the target and each neighbour was used to split semantic neighbourhood density into two different density variables: The number of semantically close neighbours versus distant neighbours. The results showed a significant effect of close, but not of distant, semantic neighbourhood density: Naming pictures of targets with more close semantic neighbours led to longer naming latencies, less accurate responses, and a higher likelihood for the production of semantic errors and omissions over accurate responses. The results show that word inherent semantic attributes such as semantic neighbourhood similarity and the number of coactivated close semantic neighbours modulate lexical selection supporting theories of competitive lexical processing.

Semantic richness and density effects on language production: Electrophysiological and behavioral evidence

“…Specifically, as noted above Mirman (2011) andFieder, Wartenburger, andAbdel Rahman (2018) observed inhibitory influences of the number of closely related semantic neighbors (but see Bormann, 2011, for a null effect of overall semantic neighborhood size). Crucially, while this inhibitory influence is naturally predicted by language production theories assuming lexical competition, in the absence of external context stimuli semantic interference effects cannot be explained in terms of blocked articulators.…”

“…In conclusion, the present study focused on investigating the electrophysiological correlates and temporal dynamics of influences of message-inherent attributes of semantic richness and density. While item-inherent semantic influences on language production have recently attracted growing attention (Bormann, 2011;Fieder et al, 2018;Mirman, 2011;Rabovsky et al, 2016), their neural correlates and time courses have not yet been investigated.…”

The Effect of Lexical Cohort Size Is Independent of Semantic Context Effects in a Picture–Word Interference Task: A Combined ERP and sLORETA Study

“…By contrast, Chen and Mirman (2012) suggested that strongly active neighbors exert a net interference effect, and weakly activated neighbors exert a net facilitative effect. Fieder et al (2018), however, found that the lexical cohort size (i.e., semantic neighborhood density) has a detrimental effect on picture naming for only the close semantic neighbors but not for the distant or the category-specific semantic neighbors. Different semantic contexts in different paradigms produce distinct results, and we thus suggest that the activation of the lexical cohort is flexible and dynamic to the message-inherent semantic attributes and the context in which the specific items are created during the experiment (see also Mirman, 2011).…”

Reduced competition between tool action neighbors in left hemisphere stroke

“…Not all semantic neighbors exert equal effects, as close semantic neighbors will compete to a greater extent than distant semantic neighbors. For example, naming a picture of a dog is frequently slower in the presence of a near semantic neighbor (e.g., cat) than a distant semantic neighbor (e.g., whale) or an unrelated distractor (e.g., couch; see Damian et al, 2001;Fieder et al, 2018;Rose et al, 2018;Vieth et al, 2014; but see Mahon, Costa, Peterson, Vargas, & Caramazza, 2007). The semantic properties that influence neighborhood distance may plausibly include shape, size, color, typical location, or other taxonomic or event-related (thematic) features.…”

“…In the domains of language and semantic memory, considerable evidence suggests that the sharing of semantic (e.g., visual or propositional) or phonological features influences competition. For example, the word "robin" may interfere with the production of the word "ostrich" by virtue of the sharing of visual and propositional feature "has wings" (e.g., see Collins & Loftus, 1975;Fieder, Wartenburger, & Abdel Rahman, 2018;Mirman & Magnuson, 2008;Rose, Aristei, Melinger, & Abdel Rahman, 2018;Vieth, McMahon, & de Zubicaray, 2014;Vigliocco, Vinson, Lewis, & Garrett, 2004); as representations with shared features are more likely to compete for selection (Damian, Vigliocco, & Levelt, 2001;Luce & Pisoni, 1998;Magnuson, Dixon, Tanenhaus, & Aslin, 2007;Vigliocco et al, 2004; for review, see Chen & Mirman, 2012). Furthermore, research in patients with language deficits demonstrates that competition among neighboring word representations is exacerbated in the presence of lesions to the left inferior frontal gyrus (IFG), resulting in a greater number of selection-related errors in picture naming tasks (e.g., see Ries, Karzmark, Navarrete, Knight, & Dronkers, 2015;Schnur et al, 2009;Thompson-Schill et al, 1998).…”