NoA’s ark: Influence of the number of associates in visual word recognition

The main aim of this study was to explore the extent to which the number of associates of a word (NoA) influences lexical access, in four tasks that focus on different processes of visual word recognition: lexical decision, reading aloud, progressive demasking, and online sentence reading. Results consistently showed that words with a dense associative neighborhood (high-NoA words) were processed faster than words with a sparse neighborhood (low-NoA words), extending previous findings from English lexical decision and categorization experiments. These results are interpreted in terms of the higher degree of semantic richness of high-NoA words as compared with low-NoA words. 2008 Psychonomic Society, Inc. Author Note     

Grasping the invisible: Semantic processing of abstract words

The problem of how abstract word meanings are represented has been a challenging one. In the present study, we extended the semantic richness approach (e.g., Yap, Tan, Pexman, & Hargreaves in Psychonomic Bulletin & Review 18:742–750, 2011) to abstract words, examining the effects of six semantic richness variables on lexical–semantic processing for 207 abstract nouns. The candidate richness dimensions were context availability (CA), sensory experience rating (SER), valence, arousal, semantic neighborhood (SN), and number of associates (NoA). The behavioral tasks were lexical decision (LDT) and semantic categorization (SCT). Our results showed that the semantic richness variables were significantly related to both LDT and SCT latencies, even after lexical and orthographic factors were controlled. The patterns of richness effects varied across tasks, with CA effects in the LDT, and SER and valence effects in the SCT. These results provide new insight into how abstract meanings may be grounded, and are consistent with a dynamic, multidimensional framework for semantic processing.     

The impact of feedback semantics in visual word recognition: number-of-features effects in lexical decision and naming tasks

The notion of feedback activation from semantics to both orthography and phonology has recently been used to explain a number of semantic effects in visual word recognition, including polysemy effects (Hino & Lupker, 1996; Pexman & Lupker, 1999) and synonym effects (Pecher, 2001). In the present research, we tested an account based on feedback activation by investigating a new semantic variable: number of features (NOF). Words with high NOF (e.g., LION) should activate richer semantic representations than do words with low NOF (e.g., LIME). As a result, the feedback activation from semantics to orthographic and phonological representations should be greater for high-NOF words, which should produce superior lexical decision task (LDT) and naming task performance. The predicted facilitory NOF effects were observed in both LDT and naming.     

Is more always better? Effects of semantic richness on lexical decision, speeded pronunciation, and semantic classification

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.     

There are many ways to be rich: Effects of three measures of semantic richness on visual word recognition

Previous studies have reported that semantic richness facilitates visual word recognition (see, e.g., Buchanan, Westbury, & Burgess, 2001; Pexman, Holyk, & Monfils, 2003). We compared three semantic richness measures--number of semantic neighbors (NSN), the number of words appearing in similar lexical contexts; number of features (NF), the number of features listed for a word's referent; and contextual dispersion (CD), the distribution of a word's occurrences across content areas-to determine their abilities to account for response time and error variance in lexical decision and semantic categorization tasks. NF and CD accounted for unique variance in both tasks, whereas NSN accounted for unique variance only in the lexical decision task. Moreover, each measure showed a different pattern of relative contribution across the tasks. Our results provide new clues about how words are represented and suggest that word recognition models need to accommodate each of these influences.     

The benefits of sensorimotor knowledge: body-object interaction facilitates semantic processing

This article examined the effects of body–object interaction (BOI) on semantic processing. BOI measures perceptions of the ease with which a human body can physically interact with a word's referent. In Experiment 1, BOI effects were examined in 2 semantic categorization tasks (SCT) in which participants decided if words are easily imageable. Responses were faster and more accurate for high BOI words (e.g., mask ) than for low BOI words (e.g., ship ). In Experiment 2, BOI effects were examined in a semantic lexical decision task (SLDT), which taps both semantic feedback and semantic processing. The BOI effect was larger in the SLDT than in the SCT, suggesting that BOI facilitates both semantic feedback and semantic processing. The findings are consistent with the embodied cognition perspective (e.g., Barsalou's, 1999 , Perceptual Symbols Theory), which proposes that sensorimotor interactions with the environment are incorporated in semantic knowledge.     

义符在中文名词和动词分类中的作用

通过3个实验,考察了义符在中文名词和动词分类中的作用。实验1表明,义符提供了重要的语法种类信息,在中文名词和动词分类中有重要作用。当义符与词类一致时分类快,与词类相反时对分类起干扰作用。实验2表明,高频词分类时间短,错误率亦低。义符和词频有显著的交互作用,义符对低频词分类作用更大。实验3表明,具体性高的词分类时间短,错误率亦低。义符和具体性之间无显著的交互作用。整个研究表明,义符不仅是结构的“块”,也是语义的“块”,还是语法的“块”。所以如此,与中文名词和动词的结构特点有关。中文名词大多用义符标记事物的物质组成,动词大多用义符标记动作发出的器官或完成动作的工具。义符的语法意义的发现丰富了中文和中文认知的理论,对中文教学也有重要启示     

Red-hot: How colour and semantic temperature processing interact in a Stroop-like paradigm

Temperature concepts and colour are commonly associated (i.e., red is “hot” and blue is “cold”), although their direction of influence (unidirectional, bidirectional) is unknown. Semantic Stroop effects, whereby words like fire influence colour categorization, suggest automatic semantic processing influences colour processing. The experiential framework of language comprehension indicates abstract concepts like temperature words simulate concrete experiences in their representation, where expressions like “red-hot” suggest colour processing influences conceptual processing. Participants categorized both colour (Experiment 1: red, blue; Experiment 2: red, green, blue) and word-meaning with matched lists of hot and cold meaning words in each colour. In Experiments 1 and 2, semantic categorization showed congruency effects across hot and cold words, while colour categorization showed facilitation only with hot words in Experiment 2. This asymmetry reflects a more consistent influence of colour categorization on semantic categorization than the reverse, suggesting experiential grounding effects may be more robust than the effects of semantic processing on colour processing.     

Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing

Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature‐based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in (distinctiveness/sharedness) and likelihood of co‐occurrence (correlational strength)—determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech‐to‐meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time‐sensitive co‐occurrence‐driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general‐to‐specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation.     

Number-of-features effects and semantic processing

Participants list many semantic features for some concrete nouns (e.g., lion) and fewer for others (e.g., lime; McRae, de Sa, & Seidenberg, 1997). Pexman, Lupker, and Hino (2002) reported faster lexical decision and naming responses for high number of features (NOF) words than for low-NOF words. In the present research, we investigated the impact of NOF on semantic processing. We observed NOF effects in a self-paced reading task when prior context was not congruent with the target word (Experiment 1) and in a semantic categorization task (concrete vs. abstract; Experiment 2A). When we narrowed our stimuli to include high- and low-NOF words from a single category (birds), we found substantial NOF effects that were modulated by the specificity of the categorization task (Experiments 3A, 3B, and 3C). We argue that these results provide support for distributed representation of word meaning.     

Category and letter fluency in semantic dementia, primary progressive aphasia, and Alzheimer's disease

This study examined the impact of various degenerative dementias on access to semantic knowledge and the status of semantic representations. Patients with semantic dementia, primary progressive aphasia, and Alzheimer's disease were compared with elderly controls on tasks of category and letter fluency, with number of words generated, mean lexical frequency and errors recorded. The findings are consistent with the view that category and letter fluency rely on both common and unique cognitive processes. Fluency tasks, with the richness of data obtained, are valuable in distinguishing different dementia syndromes from one another.     

WINDSOR: Windsor improved norms of distance and similarity of representations of semantics

Lexical co-occurrence models of semantic memory form representations of the meaning of a word on the basis of the number of times that pairs of words occur near one another in a large body of text. These models offer a distinct advantage over models that require the collection of a large number of judgments from human subjects, since the construction of the representations can be completely automated. Unfortunately, word frequency, a well-known predictor of reaction time in several cognitive tasks, has a strong effect on the co-occurrence counts in a corpus. Two words with high frequency are more likely to occur together purely by chance than are two words that occur very infrequently. In this article, we examine a modification of a successful method for constructing semantic representations from lexical co-occurrence. We show that our new method eliminates the influence of frequency, while still capturing the semantic characteristics of words.     

The effect of concurrent semantic categorization on delayed serial recall

The influence of semantic processing on the serial ordering of items in short-term memory was explored using a novel dual-task paradigm. Participants engaged in 2 picture-judgment tasks while simultaneously performing delayed serial recall. List material varied in the presence of phonological overlap (Experiments 1 and 2) and in semantic content (concrete words in Experiment 1 and 3; nonwords in Experiments 2 and 3). Picture judgments varied in the extent to which they required accessing visual semantic information (i.e., semantic categorization and line orientation judgments). Results showed that, relative to line-orientation judgments, engaging in semantic categorization judgments increased the proportion of item-ordering errors for concrete lists but did not affect error proportions for nonword lists. Furthermore, although more ordering errors were observed for phonologically similar relative to dissimilar lists, no interactions were observed between the phonological overlap and picture-judgment task manipulations. These results demonstrate that lexical-semantic representations can affect the serial ordering of items in short-term memory. Furthermore, the dual-task paradigm provides a new method for examining when and how semantic representations affect memory performance.     

Distinguishing common and task-specific processes in word identification: a matter of some moment?

The same 500 words were presented in 6 different word identification tasks (Experiment 1: lexical decision, semantic categorization, and 3 speeded naming tasks; Experiment 2: delayed naming). Reaction time (RT) distributions were estimated for each task and analyses tested for the effects of word frequency and animacy on various parameters of the RT distribution. Low frequency words yielded more skewed distributions than high frequency words in all tasks except delayed naming. The differential skew was most marked for tasks that required lexical discrimination. The semantic categorization task yielded highly skewed distributions for all words, but the word frequency effect was due to shifts in the location of the RT distributions rather than changes in skew. The results are used to evaluate the relative contributions of a common lexical access process and task-specific processes to performance in lexical discrimination and naming tasks.     

Constructing Semantic Models From Words,Images, and Emojis

A number of recent models of semantics combine linguistic information, derived from text corpora, and visual information, derived from image collections, demonstrating that the resulting multimodal models are better than either of their unimodal counterparts, in accounting for behavioral data. Empirical work on semantic processing has shown that emotion also plays an important role especially in abstract concepts; however, models integrating emotion along with linguistic and visual information are lacking. Here, we first improve on visual and affective representations, derived from state-of-the-art existing models, by choosing models that best fit available human semantic data and extending the number of concepts they cover. Crucially then, we assess whether adding affective representations (obtained from a neural network model designed to predict emojis from co-occurring text) improves the model's ability to fit semantic similarity/relatedness judgments from a purely linguistic and linguistic–visual model. We find that, given specific weights assigned to the models, adding both visual and affective representations improves performance, with visual representations providing an improvement especially for more concrete words, and affective representations improving especially the fit for more abstract words.     

What causes the effect of age of acquisition in lexical processing?

Three hypotheses for effects of age of acquisition (AoA) in lexical processing are compared: the cumulative frequency hypothesis (frequency and AoA both influence the number of encounters with a word, which influences processing speed), the semantic hypothesis (early-acquired words are processed faster because they are more central in the semantic network), and the neural network model (early-acquired words are faster because they are acquired when a network has maximum plasticity). In a regression study of lexical decision (LD) and semantic categorization (SC) in Italian and Dutch, contrary to the cumulative frequency hypothesis, AoA coefficients were larger than frequency coefficients, and, contrary to the semantic hypothesis, the effect of AoA was not larger in SC than in LD. The neural network model was supported.     

Word association in early Alzheimer's disease

The hypothesis that Alzheimer's disease (AD) degrades semantic representations predicts that AD qualitatively alters spontaneous thoughts. In two experiments contrasting free associations to words with strong (e.g., bride-groom) versus weak (e.g., body-leg) associates participants with AD produced less common responses (e.g., bride-pretty) than normal controls but only for words with strong associations, and only on the first (but not on second or third) association response. Furthermore, all participants produced fewer semantically related responses to words with weak associates. Because strong associations should be retrieved more easily than weak associations these results are problematic for retrieval-based accounts of AD. Instead we propose that AD entails a semantic deficit, and that strong associations involve more semantic processing than weak associations (in all speakers).     

What causes the effect of age of acquisition in lexical processing?

Three hypotheses for effects of age of acquisition (AoA) in lexical processing are compared: the cumulative frequency hypothesis (frequency and AoA both influence the number of encounters with a word, which influences processing speed), the semantic hypothesis (early-acquired words are processed faster because they are more central in the semantic network), and the neural network model (early-acquired words are faster because they are acquired when a network has maximum plasticity). In a regression study of lexical decision (LD) and semantic categorization (SC) in Italian and Dutch, contrary to the cumulative frequency hypothesis, AoA coefficients were larger than frequency coefficients, and, contrary to the semantic hypothesis, the effect of AoA was not larger in SC than in LD. The neural network model was supported.     

Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect

The concreteness effect in verbal short-term memory (STM) tasks is assumed to be a consequence of semantic encoding in STM, with immediate recall of concrete words benefiting from richer semantic representations. We used the concreteness effect to test the hypothesis that semantic encoding in standard verbal STM tasks is a consequence of controlled, attention-demanding mechanisms of strategic semantic retrieval and encoding. Experiment 1 analysed the effect of presentation rate, with slow presentations being assumed to benefit strategic, time-dependent semantic encoding. Experiments 2 and 3 provided a more direct test of the strategic hypothesis by introducing three different concurrent attention-demanding tasks. Although Experiment 1 showed a larger concreteness effect with slow presentations, the following two experiments yielded strong evidence against the strategic hypothesis. Limiting available attention resources by concurrent tasks reduced global memory performance, but the concreteness effect was equivalent to that found in control conditions. We conclude that semantic effects in STM result from automatic semantic encoding and provide tentative explanations for the interaction between the concreteness effect and the presentation rate.     

The Micro-Category account of analogy

Here, we investigate how activation of mental representations of categories during analogical reasoning influences subsequent cognitive processing. Specifically, we present and test the central predictions of the "Micro-Category" account of analogy. This account emphasizes the role of categories in aligning terms for analogical mapping. In a semantic priming paradigm, a four-word analogy task was compared to two other four-word tasks. Stimuli were identical in all tasks; only the instructions given to participants differed. Participants were instructed to identify analogy relations, category relations, or conventionalized semantic relations in the four-word sets. After each four-word set, a single target word appeared and participants named this word aloud. Target words that referred to category relations in the preceding four-word sets were primed as strongly when participants identified analogies as when participants identified categories, suggesting that activation of category concepts plays an important role in analogical thinking. In addition, priming of category-referent words in the analogy and category tasks was significantly greater than priming of these words when participants identified conventionalized semantic relations. Since identical stimuli were used in all conditions, this finding indicates that it is the activation of category relations, distinct from any effect of basic semantic association, that causes analogical reasoning to prime category-referent words. We delineate how the "Micro-Category" account of analogy predicts these phenomena and unifies findings from diverse areas of research concerning analogical reasoning.