Characterizing semantic space: Neighborhood effects in word recognition

Abstract

Object-Based SemanticsMany models of semantics have attempted to address the preceding questions. These models can be categorized A specification of the structural characteristicsof the mental lexicon is a central goal in word recognition research. Of various word-level characteristics,semantics remains the most resistant to this endeavor. Although there are several theoretically distinct models of lexical semantics with fairly clear operational definitions (e.g., in terms of feature sharing, category membership, associations, or cooccurrences), attempts to empirically ad judicate between these different models have been scarce. In this paper, we present several experiments in which we examined the effects of semantic neighborhood size as defined by two models of lexical semantics-one that defines semantics in terms of associations, and another that defines it in terms of global co-occurrences. We present data that address the question of whether these measures can be fruitfully applied to examinations of lexical activation during visual word recognition. The findings demonstrate that semantic neighborhood can predict performance on both lexical decision and word naming.

Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language

“…Consequently, in the text-based adjective analyses, the principle components analyses were based on the correlation matrix of the 88 adjective occurrences across the 1165 participants' essays. Note that the assumptions underlying this approach are congruent with those in latent semantic analysis (Landauer, Foltz, & Laham, 1998), computational modeling of semantic spaces (e.g., Buchanan, Westbury, & Burgess, 2001), and other approaches to explore natural language patterns (e.g., Graesser, Cai, Louwerse, & Daniel, 2006;Graesser, Lu, Jackson, Mitchell, Ventura, Olney, & Louwerse, 2004;Graesser, McNamara, Louwerse, & Cai, 2004). Although we report the results from principal components analyses with varimax rotation, virtually identical results were obtained using principal axis analyses and with promax, oblique, and equamax rotations.…”

Semantic and phonological influences on the processing of words and pseudohomophones

“…Words with many associates (e.g., movie) can thus be identified as having large semantic neighborhoods, whereas small-neighborhood words are those with relatively few associates (e.g., dog). Previous research has shown that in a lexical decision task, words with large semantic neighborhoods are responded to more rapidly than words with sparse semantic neighborhoods (Buchanan, Westbury, & Burgess, 2001;Locker, Simpson, & Yates, 2003).…”

N400 effects of semantic richness can be modulated by task demands.

“…Behavioral studies that quantified semantic neighborhood according to this model have found that words with large neighborhoods are processed more quickly and accurately than words with small neighborhoods. This facilitatory effect has been found in lexical decision (Buchanan et al, 2001;Pexman et al, 2008;Yap et al, 2012;Yap et al, 2011) and naming tasks (Buchanan et al, 2001). However, the effects in semantic categorization tasks are not consistent.…”

“…Muller et al (2010) and Laszlo & Federmeier (2011) found larger N400 amplitudes in words with high NA than words with low NA. In contrast, Rabovsky et al (2012) Finally, NSN is a dimension that has been investigated in a number of behavioral studies (Buchanan et al, 2001;Pexman et al, 2008;Yap et al, 2012;Yap et al, 2011) and there is evidence that its effects are modulated by task demands Yap et al, 2012;Yap et al, 2011). However, the neural dynamics of NSN have not yet been investigated.…”