Evocation of functional and volumetric gestural knowledge by objects and words.

We distinguish between grasping gestures associated with using an object for its intended purpose (functional) and those used to pick up an object (volumetric) and we develop a novel experimental framework to show that both kinds of knowledge are automatically evoked by objects and by words denoting those objects. Cued gestures were carried out in the context of depicted objects or visual words. On incongruent trials, the cued gesture was not compatible with gestures typically associated with the contextual item. On congruent trials, the gesture was compatible with the item's functional or volumetric gesture. For both gesture types, response latency was longer for incongruent trials indicating that objects and words elicited both functional and volumetric manipulation knowledge. Additional evidence, however, clearly supports a distinction between these two kinds of gestural knowledge. Under certain task conditions, functional gestures can be evoked without the associated activation of volumetric gestures. We discuss the implication of these results for theories of action evoked by objects and words, and for interpretation of functional imaging results.

Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory.

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure account, but consistent with their own model, the authors present empirical evidence that distinctive features of both living and nonliving things do indeed have a privileged role in the computation of word meaning. The authors explain the mechanism through which these effects are produced in their model by presenting an analysis of the weight structure developed in the network during training.

Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns).

Seven trends regarding the categories that tend to be impaired/preserved in category-specific semantic deficits were identified. The authors hypothesized that these trends arise despite the multiple sources of variation in patient testing because numerous factors that structure semantic memory probabilistically converge to make some categories of knowledge more susceptible to damage than others. Analysis of semantic feature norms and corpus data for 541 concepts revealed that differences in the distribution of knowledge types across categories are sufficient to explain 6 of the trends and are necessary to explain loss of knowledge about nonliving things. Feature informativeness, concept confusability, visual complexity, familiarity, and name frequency contributed to this patterning and provide insight into why knowledge about living things is most often impaired.

Further evidence for feature correlations in semantic memory.

The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, participants were faster to verify that a feature (< is juicy >) is part of a concept (grapefruit) if it is strongly rather than weakly intercorrelated with the other features of that concept. Contrasting interactions between feature correlations and SOA were found when the concept versus the feature was presented first. An attractor network model of word meaning that naturally learns and uses feature correlations predicted those interactions. This research provides further evidence that semantic memory includes implicitly learned statistical knowledge of feature relationships, in contrast to theories such as spreading activation networks, in which feature correlations play no role.

Semantic richness, concreteness, and object domain: an electrophysiological study.

Results from previous event-related potential (ERP) studies of semantic richness and concreteness effects have been mixed. Feature production norms have been used to derive one measure of semantic richness, the number of listed semantic features (NOF) for a given concept. Whereas some ERP studies have found evidence for a semantic richness continuum from abstract concepts, to concrete concepts with few features, to concrete concepts with several features, other studies have not. The present study assessed the effects of NOF (within concrete concepts) and concreteness (concrete vs. abstract concepts), on ERP amplitudes and behavioural decision latencies during a concrete/abstract decision task. It is important we also manipulated object domain, which has been found to influence ERP amplitude and topography. High and low NOF concepts were selected from animal and nonliving thing categories and all four conditions were matched on several potential confounds. We show that although decision latencies support a semantic richness continuum, electrophysiological activity does not. Whereas concrete concepts produce a larger negativity than abstract concepts, low NOF concepts are associated with larger negativities than high NOF concepts. We also replicate an increased posterior positivity for processing animal concepts, and report an interaction between object domain and semantic richness such that the NOF effect is larger within animal concepts.

Web-based image norming: how do object familiarity and visual complexity ratings compare when collected in-lab versus online?

It has been demonstrated previously that, for some experimental paradigms, Web-based research can reliably replicate lab-based results. Yet questions remain as to what types of research can be reproduced, and where differences arise when they cannot be. The present article examines the effect of research location (laboratory vs. online) on normative data collection tasks. Specifically, participants were randomly assigned to a laboratory or online condition and were asked to rate 593 photorealistic images on the basis of object familiarity (N=103) and object visual complexity (N=98). Dependent measures were compared across location conditions, including response latencies and image rating agreement. Our results suggest that norming data collected online are reliable, but an interesting interplay between task type and research location was observed. Specifically, we found that participating online (i.e., a more familiar environment) leads to systematically higher familiarity ratings than in the lab (i.e., an unfamiliar environment). These differences are not found when the alternate complexity rating task is used.

Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations.

The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory.

Semantic feature production norms for a large set of living and nonliving things.

Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.