Evidence from large-scale studies (Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) suggests that semantic richness, a multidimensional construct reflecting the extent of variability in the information associated with a word's meaning, facilitates visual word recognition. Specifically, recognition is better for words that (1) have more semantic neighbors, (2) possess referents with more features, and (3) are associated with more contexts. The present study extends Pexman et al. (2008) by examining how two additional measures of semantic richness, number of senses and number of associates (Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007), influence lexical decision, speeded pronunciation, and semantic classification performance, after controlling for an array of lexical and semantic variables. We found that number of features and contexts consistently facilitated word recognition but that the effects of semantic neighborhood density and number of associates were less robust. Words with more senses also elicited faster lexical decisions but less accurate semantic classifications. These findings point to how the effects of different semantic dimensions are selectively and adaptively modulated by task-specific demands.The majority of visual word recognition research has examined how lexical-level properties such as word frequency and number of letters influence performance,using tasks such as lexical decision (word/nonword discrimination), speeded pronunciation (naming words aloud), and semantic classification (e.g., classifying a word as animate or inanimate). However, there is substantial evidence that meaning-level characteristics such as imageability also affect word recognition, even after correlated lexical variables are controlled for

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There is considerable evidence (e.g., Pexman et al., 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using mixed effects analyses to control for the influence of correlated lexical variables, we considered the impact of number of features, number of senses, semantic neighborhood density, imageability, and body–object interaction across five visual word recognition tasks: standard lexical decision, go/no-go lexical decision, speeded pronunciation, progressive demasking, and semantic classification. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing.

In some contexts, concrete words (CARROT) are recognized and remembered more readily than abstract words (TRUTH). This concreteness effect has historically been explained by two theories of semantic representation: dual-coding [Paivio, A. Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255-287, 1991] and context-availability [Schwanenflugel, P. J. Why are abstract concepts hard to understand? In P. J. Schwanenflugel (Ed.), The psychology of word meanings (pp. 223-250). Hillsdale, NJ: Erlbaum, 1991]. Past efforts to adjudicate between these theories using functional magnetic resonance imaging have produced mixed results. Using event-related functional magnetic resonance imaging, we reexamined this issue with a semantic categorization task that allowed for uniform semantic judgments of concrete and abstract words. The participants were 20 healthy adults. Functional analyses contrasted activation associated with concrete and abstract meanings of ambiguous and unambiguous words. Results showed that for both ambiguous and unambiguous words, abstract meanings were associated with more widespread cortical activation than concrete meanings in numerous regions associated with semantic processing, including temporal, parietal, and frontal cortices. These results are inconsistent with both dual-coding and context-availability theories, as these theories propose that the representations of abstract concepts are relatively impoverished. Our results suggest, instead, that semantic retrieval of abstract concepts involves a network of association areas. We argue that this finding is compatible with a theory of semantic representation such as Barsalou's [Barsalou, L. W. Perceptual symbol systems. Behavioral & Brain Sciences, 22, 577-660, 1999] perceptual symbol systems, whereby concrete and abstract concepts are represented by similar mechanisms but with differences in focal content.

Numerous theories describe how word meanings are represented in the mind (see, e.g., Burgess & Lund, 2000;McRae, de Sa, & Seidenberg, 1997). Concepts differ in the amounts of information they evoke, and a complete theory must capture the effects of semantic richness. There is considerable evidence that responding in visual word recognition tasks is facilitated for words with relatively richer semantic representations, even when other lexical and semantic variables are controlled (for a review, see Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007). In the present study, we compared three measures of semantic richness-number of semantic neighbors (NSN), number of features (NF), and contextual dispersion (CD)-extracted from language corpora or norms. We consider each of these to be measures of semantic richness because they each capture variability in information associated with words' meanings. Our goal was to determine whether these three measures of semantic richness predicted shared or unique response times (RTs) and error variances in responses to concrete words in a lexical decision task (LDT) and to categorization of words as concrete or abstract in a semantic categorization task (SCT). Number of Semantic NeighborsBuchanan, Westbury, and Burgess ( 2001) proposed that semantic richness can be quantified according to how words are used in language. In a high-dimensional model of semantic space (Burgess & Lund, 2000), words that co-occur or are used in similar lexical contexts cluster together as semantic neighbors. In the database constructed by Durda, Buchanan, and Caron (2006), words' semantic neighborhoods were extracted from a corpus of English text. A word's global neighbors are most likely to occur in similar lexical contexts (e.g., book and movie) and thus have similar histories of usage in the language corpus. NSN measures the number of global neighbors detected for each word within a specified radius of semantic space. High-NSN words share lexical contexts with many other words (e.g., 28 semantic neighbors for bed, 24 for celery), whereas low-NSN words share lexical contexts with few other words (e.g., 3 semantic neighbors for door, 2 for carrot). Buchanan et al. found that high-NSN words generated faster responses than low-NSN words in LDTs, and they attributed this advantage to stronger semantic activation.

Many models of memory build in a term for encoding variability, the observation that there can be variability in the richness or extensiveness of processing at encoding, and that this variability has consequences for retrieval. In four experiments, we tested the expectation that encoding variability could be driven by the properties of the to-be-remembered item. Specifically, that concepts associated with more semantic features would be better remembered than concepts associated with fewer semantic features. Using feature listing norms we selected sets of items for which people tend to list higher numbers of features (high NoF) and items for which people tend to list lower numbers of features (low NoF). Results showed more accurate free recall for high NoF concepts than for low NoF concepts in expected memory tasks (Experiments 1–3) and also in an unexpected memory task (Experiment 4). This effect was not the result of associative chaining between study items (Experiment 3), and can be attributed to the amount of item-specific processing that occurs at study (Experiment 4). These results provide evidence that stimulus-specific differences in processing at encoding have consequences for explicit memory retrieval.

Competitive Scrabble is an activity that involves extraordinary word recognition experience. We investigated whether that experience is associated with exceptional behavior in the laboratory in a classic visual word recognition paradigm: the lexical decision task (LDT). We used a version of the LDT that involved horizontal and vertical presentation and a concreteness manipulation. In Experiment 1, we presented this task to a group of undergraduates, as these participants are the typical sample in word recognition studies. In Experiment 2, we compared the performance of a group of competitive Scrabble players with a group of agematched nonexpert control participants. The results of a series of cognitive assessments showed that the Scrabble players and control participants differed only in Scrabble-specific skills (e.g., anagramming). Scrabble expertise was associated with two specific effects (as compared to controls): vertical fluency (relatively less difficulty judging lexicality for words presented in the vertical orientation) and semantic deemphasis (smaller concreteness effects for word responses). These results suggest that visual word recognition is shaped by experience, and that with experience there are efficiencies to be had even in the adult word recognition system.

Some concepts have richer semantic representations than others. That is, when considering the meaning of concepts, subjects generate more information (more features, more associates) for some concepts than for others. This variability in semantic richness influences responses in speeded tasks that involve semantic processing, such as lexical decision and semantic categorization tasks. It has been suggested that concepts with richer semantic representations build stronger attractors in semantic space, allowing faster settling of activation patterns and thus faster responding. Using event-related functional magnetic resonance imaging, we examined the neural activation associated with semantic richness by contrasting activation for words with high and low numbers of associates in a semantic categorization task. Results were consistent with faster semantic settling for words with richer representations: Words with a low number of semantic associates produced more activation than words with a high number of semantic associates in a number of regions, including left inferior frontal and inferior temporal gyri.