Simon De Deyne

Age-of-acquisition effects in semantic processing tasks

In two experiments, we examined whether word age-of-acquisition (AoA) is a reliable predictor of processing times in semantic tasks. In the first task, participants were asked to say the first associate that came to mind when they saw a stimulus word; the second task involved a semantic categorisation between words with a definable meaning and first names. In both tasks, there were significantly faster responses to earlier-acquired than to later-acquired words. On the basis of these results, we argue that age-of-acquisition effects do not originate solely from the speech output system, but from the semantic system as well.

Word associations: Network and semantic properties

A number of properties of word associations, generated in a continuous task, were investigated. First, we investigated the correspondence of word class in association cues and responses. Nouns were the modal word class response, regardless of the word class of the cue, indicating a dominant paradigmatic response style. Next, the word association data were used to build an associative network to investigate the centrality of nodes. The study of node centrality showed that central nodes in the network tended to be highly frequent and acquired early. Small-world properties of the association network were investigated and compared with a large English association network (Steyvers & Tenenbaum, 2005). Networks based on a multiple association procedure showed small-world properties despite being denser than networks based on a discrete task. Finally, a semantic taxonomy was used to investigate the composition of semantic types in association responses. The majority of responses were thematically related situation responses and entity responses referring to parts, shape, or color. Since the association task required multiple responses per cue, the interaction between generation position and semantic role could be investigated and discussed in the framework of recent theories of natural concept representations (Barsalou, Santos, Simmons, & Wilson, in press).

Word associations: norms for 1,424 Dutch words in a continuous task.

This study describes the collection of a large set of word association norms. In a continuous word association task, norms for 1,424 Dutch words were gathered. For each cue, three association responses were obtained per participant. In total, an average of 268 responses were collected for each cue. We investigated the relationship with similar procedures, such as discrete association tasks and exemplar generation tasks. The results show that the use of a continuous task allows the study of weaker associations in comparison with a discrete task. The effects of the continuous tasks were investigated for set size and the availability characteristics of the responses, measured through word frequency, age of acquisition, and imageability. Finally, we compared our findings to those of a semantically constrained version of the association task in which participants generated responses within the domain of a semantic category. Results of this comparison are discussed. The Appendix cited in this article is available at www.psychonomic.org/archive.

Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts

Features are at the core of many empirical and modeling endeavors in the study of semantic concepts. This article is concerned with the delineation of features that are important in natural language concepts and the use of these features in the study of semantic concept representation. The results of a feature generation task in which the exemplars and labels of 15 semantic categories served as cues are described. The importance of the generated features was assessed by tallying the frequency with which they were generated and by obtaining judgments of their relevance. The generated attributes also featured in extensive exemplar by feature applicability matrices covering the 15 different categories, as well as two large semantic domains (that of animals and artifacts). For all exemplars of the 15 semantic categories, typicality ratings, goodness ratings, goodness rank order, generation frequency, exemplar associative strength, category associative strength, estimated age of acquisition, word frequency, familiarity ratings, imageability ratings, and pairwise similarity ratings are described as well. By making these data easily available to other researchers in the field, we hope to provide ample opportunities for continued investigations into the nature of semantic concept representation. These data may be downloaded from the Psychonomic Society’s Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

Dutch norm data for 13 semantic categories and 338 exemplars

A data set is described that includes eight variables gathered for 13 common superordinate natural language categories and a representative set of 338 exemplars in Dutch. The category set contains 6 animal categories (reptiles, amphibians, mammals, birds, fish, andinsects), 3 artifact categories (musical instruments, tools, andvehicles), 2 borderline artifact-natural-kind categories (vegetables andfruit), and 2 activity categories (sports andprofessions). In an exemplar and a feature generation task for the category nouns, frequency data were collected. For each of the 13 categories, a representative sample of 5–30 exemplars was selected. For all exemplars, feature generation frequencies, typicality ratings, pairwise similarity ratings, age-of-acquisition ratings, word frequencies, and word associations were gathered. Reliability estimates and some additional measures are presented. The full set of these norms is available in Excel format at the Psychonomic Society Web archive,www.psychonomic. org/archive/.

Better explanations of lexical and semantic cognition using networks derived from continued rather than single word associations

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.

Similarity of fMRI Activity Patterns in Left Perirhinal Cortex Reflects Semantic Similarity between Words

How verbal and nonverbal visuoperceptual input connects to semantic knowledge is a core question in visual and cognitive neuroscience, with significant clinical ramifications. In an event-related functional magnetic resonance imaging (fMRI) experiment we determined how cosine similarity between fMRI response patterns to concrete words and pictures reflects semantic clustering and semantic distances between the represented entities within a single category. Semantic clustering and semantic distances between 24 animate entities were derived from a concept-feature matrix based on feature generation by >1000 subjects. In the main fMRI study, 19 human subjects performed a property verification task with written words and pictures and a low-level control task. The univariate contrast between the semantic and the control task yielded extensive bilateral occipitotemporal activation from posterior cingulate to anteromedial temporal cortex. Entities belonging to a same semantic cluster elicited more similar fMRI activity patterns in left occipitotemporal cortex. When words and pictures were analyzed separately, the effect reached significance only for words. The semantic similarity effect for words was localized to left perirhinal cortex. According to a representational similarity analysis of left perirhinal responses, semantic distances between entities correlated inversely with cosine similarities between fMRI response patterns to written words. An independent replication study in 16 novel subjects confirmed these novel findings. Semantic similarity is reflected by similarity of functional topography at a fine-grained level in left perirhinal cortex. The word specificity excludes perceptually driven confounds as an explanation and is likely to be task dependent.

Norms of age of acquisition and concreteness for 30,000 Dutch words

Word processing studies increasingly make use of regression analyses based on large numbers of stimuli (the so-called megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.

The influence of working memory load on semantic priming

The present research examines the nature of the different processes that have been proposed to underlie semantic priming. Specifically, it has been argued that priming arises as a result of automatic target activation and/or the use of strategies like prospective expectancy generation and retrospective semantic matching. This article investigates the extent that these processes rely on cognitive resources by experimentally manipulating working memory load. To disentangle prospective and retrospective processes, prime-target pairs were selected such that they were symmetrically associated (e.g., answer-question; SYM) or asymmetrically associated in either the forward direction (e.g., panda-bear; FA) or the backward direction (e.g., ball-catch; BA). The results showed that priming for FA pairs completely evaporated under a high working memory load but that it remained stable for BA and SYM pairs. This was taken to mean that prospective processes, which are assumed to cause FA priming, require cognitive resources, whereas retrospective processes, which lead to BA priming, are relatively effortless.

Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer

Goal: For statistical analysis of event-related potentials (ERPs), there are convincing arguments against averaging across stimuli or subjects. Multivariate filters can be used to isolate an ERP component of interest without the averaging procedure. However, we would like to have certainty that the output of the filter accurately represents the component. Methods: We extended the linearly constrained minimum variance (LCMV) beamformer, which is traditionally used as a spatial filter for source localization, to be a flexible spatiotemporal filter for estimating the amplitude of ERP components in sensor space. In a comparison study on both simulated and real data, we demonstrated the strengths and weaknesses of the beamformer as well as a range of supervised learning approaches. Results: In the context of measuring the amplitude of a specific ERP component on a single-trial basis, we found that the spatiotemporal LCMV beamformer is a filter that accurately captures the component of interest, even in the presence of both structured noise (e.g., other overlapping ERP components) and unstructured noise (e.g., ongoing brain activity and sensor noise). Conclusion: The spatiotemporal LCMV beamformer method provides an accurate and intuitive way to conduct analysis of a known ERP component, without averaging across trials or subjects. Significance: Eliminating averaging allows us to test more detailed hypotheses and apply more powerful statistical models. For example, it allows the usage of multilevel regression models that can incorporate between subject/stimulus variation as random effects, test multiple effects simultaneously, and control confounding effects by partial regression.