Ken McRae

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.

On the nature and scope of featural representations of word meaning.

Behavioral experiments and a connectionist model were used to explore the use of featural representations in the computation of word meaning. The research focused on the role of correlations among features, and differences between speeded and untimed tasks with respect to the use of featural information. The results indicate that featural representations are used in the initial computation of word meaning (as in an attractor network), patterns of feature correlations differ between artifacts and living things, and the degree to which features are intercorrelated plays an important role in the organization of semantic memory. The studies also suggest that it may be possible to predict semantic priming effects from independently motivated featural theories of semantic relatedness. Implications for related behavioral phenomena such as the semantic impairments associated with Alzheimers disease (AD) are discussed.

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.

The basis of consistency effects in word naming

Abstract Spelling-sound consistency effects have played an important role in recent theories of word recognition and naming. However, these effects have not proven to be robust, calling into question whether mechanisms that deal with consistency effects must be incorporated into theories of naming. We describe four experiments in which words with inconsistent spelling-sound correspondences yielded longer naming latencies than words with consistent correspondences. The studies also examined the computational basis of these effects; they depend on the degree of consistency, which is mainly determined by the properties of a words neighborhood, specifically the relative frequencies of “friends” and “enemies.” Consistency effects did not occur with the lexical decision task, suggesting that they are genuinely phonological effects. The results are interpreted within current models of naming in which both frequency and consistency of spelling-sound correspondences affect performance.

A common neural substrate for perceiving and knowing about color.

Functional neuroimaging research has demonstrated that retrieving information about object-associated colors activates the left fusiform gyrus in posterior temporal cortex. Although regions near the fusiform have previously been implicated in color perception, it remains unclear whether color knowledge retrieval actually activates the color perception system. Evidence to this effect would be particularly strong if color perception cortex was activated by color knowledge retrieval triggered strictly with linguistic stimuli. To address this question, subjects performed two tasks while undergoing fMRI. First, subjects performed a property verification task using only words to assess conceptual knowledge. On each trial, subjects verified whether a named color or motor property was true of a named object (e.g., TAXI-yellow, HAIR-combed). Next, subjects performed a color perception task. A region of the left fusiform gyrus that was highly responsive during color perception also showed greater activity for retrieving color than motor property knowledge. These data provide the first evidence for a direct overlap in the neural bases of color perception and stored information about object-associated color, and they significantly add to accumulating evidence that conceptual knowledge is grounded in the brains modality-specific systems.

An Attractor Model of Lexical Conceptual Processing: Simulating Semantic Priming

An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning.

Verb aspect and the activation of event knowledge.

The authors show that verb aspect influences the activation of event knowledge with 4 novel results. First, common locations of events (e.g., arena) are primed following verbs with imperfective aspect (e.g., was skating) but not verbs with perfect aspect (e.g., had skated). Second, people generate more locative prepositional phrases as completions to sentence fragments with imperfective than those with perfect aspect. Third, the amplitude of the N400 component to location nouns varies as a function of aspect and typicality, being smallest for imperfective sentences with highly expected locations and largest for imperfective sentences with less expected locations. Fourth, the amplitude of a sustained frontal negativity spanning prepositional phrases is larger following perfect than following imperfective aspect. Taken together, these findings suggest a dynamic interplay between event knowledge and the linguistic stream.

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.

Activating event knowledge

An increasing number of results in sentence and discourse processing demonstrate that comprehension relies on rich pragmatic knowledge about real-world events, and that incoming words incrementally activate such knowledge. If so, then even outside of any larger context, nouns should activate knowledge of the generalized events that they denote or typically play a role in. We used short stimulus onset asynchrony priming to demonstrate that (1) event nouns prime people (sale-shopper) and objects (trip-luggage) commonly found at those events; (2) location nouns prime people/animals (hospital-doctor) and objects (barn-hay) commonly found at those locations; and (3) instrument nouns prime things on which those instruments are commonly used (key-door), but not the types of people who tend to use them (hose-gardener). The priming effects are not due to normative word association. On our account, facilitation results from event knowledge relating primes and targets. This has much in common with computational models like LSA or BEAGLE in which one word primes another if they frequently occur in similar contexts. LSA predicts priming for all six experiments, whereas BEAGLE correctly predicted that priming should not occur for the instrument-people relation but should occur for the other five. We conclude that event-based relations are encoded in semantic memory and computed as part of word meaning, and have a strong influence on language comprehension.

The mechanics of embodiment: a dialog on embodiment and computational modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamoring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensorimotor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialog between two fictional characters: Ernest, the “experimenter,” and Mary, the “computational modeler.” The dialog consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modeling.