Word meaning in word identification during reading: Co-occurrence-based semantic neighborhood density effects

Abstract

Identifying individual words is an essential part of the reading process that should occur first so that understanding the structural relations between words and comprehending the sentence as a whole may take place. Therefore, lexical processing (or word identification) has received much attention in the literature, with many researchers exploring the effects of different aspects of word representation (orthographic, phonological, and semantic information of words) in word identification. While the influence of many orthographic and phonological factors in normal reading are well researched and understood (Rayner, 1998, 2009), the effect of semantic characteristics of a word in its identification has received relatively less attention. A complete account of lexical processing during normal reading requires understanding the role of word meaning in lexical processing. Currently, little is understood about whether and how the meaning of an individual word is extracted during early stages of word identification. This article primarily focuses on how word meaning contributes to the process of word identification.

A cross-linguistic comparison of Spanish and English semantic norms: Looking at core features

“…In this framework, statistical data about the frequency of occurrence and the probability of feature co-occurrence are proposed as the fundamental organizational principles of cognitive models of semantic memory. This data allows for the development of precise quantitative theories about the organization of conceptual knowledge to be explored by means of computational models (Al Farsi, 2018;Baroni & Lenci, 2010;Cree et al, 2006;Lenci, 2018;Riordan & Jones, 2011). It is worth mentioning that we agree with Riordan and Jones (2011) that featural and distributional models should not be conceptualized as competing theories; the focus should rather be on understanding the cognitive mechanisms human beings employ to integrate the two sources.…”

Complexity Science in Human Change

“…In this way, each cluster can be considered indicative of a thematic content active in the textual corpus and characterized semantically by the pattern of cooccurring lemmas making those ECUs similar to each other (for details, see [108,109]). The number of clusters in which the text is segmented is established in accordance with an iterative algorithm [110,111]; the procedure of clustering stops when further partitions produce no significant improvement of the inter-/intra-cluster ratio, which means that increasing the number of clusters does not produce an appreciable increment of information. In the current analysis, the LClA generated 5 clusters/thematic contents as optimal partitions.…”

“…ACASM can be implemented by several kinds of software. The current analysis used T-Lab [110,111]. T-Lab is appliable to any text based on the Latin alphabet.…”

Mapping Semantic Space: Exploring the Higher-order Structure of Word Meaning

“…We used a test-based AoA measure derived from Dale and O'Rourke (1981) and updated by Brysbaert and Biemiller (2017). We also included a measure of the average semantic distance between a word and its semantic neighbors (henceforth average neighborhood similarity; ANS) (Shaoul & Westbury, 2010) because this property influences lexical processing (for a review, see Farsi, 2018). In addition, we included a measure of the extent to which words appear in more semantically diverse contexts, termed semantic diversity (SemD; Hoffman et al, 2013), which captures aspects of semantic ambiguity and affects lexical-semantic performance (Hoffman & Woollams, 2015).…”