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

Abstract

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 Alzheimer's disease (AD) are discussed.

Distinctiveness and Correlation in Conceptual Structure: Behavioral and Computational Studies.

“…) begin to be activated, until a stable state, corresponding to the ELEPHANT concept, is reached. Following McRae et al (1997), we assume that some features are activated more quickly than others and that this rate of activation is affected by correlational structure. Importantly, McRae et al demonstrated that correlational structure determined participants' performance in a speeded feature verification task but not in an untimed saliency rating task, suggesting that correlation affects initial activation rate but not necessarily the final level of activation of features once the stable state has been achieved.…”

“…Other correlation-based models, such as Devlin et al (1998) and McRae et al (1997), emphasize that living things have more shared correlated properties than do nonliving things but do not specifically predict a difference in the occurrence of correlations among the distinctive properties of the two domains. Similarity-based accounts, including exemplar models (e.g., Lamberts & Shapiro, 2002), capture the greater overlap among living things but also have little to say about the predicted patterns of correlation among distinctive features.…”

“…Therefore, we have adhered to the neuropsychological convention and focused on visual and functional properties and the patterns of correlation among them. activated by healthy participants (McRae, Cree, Westmacott, & de Sa, 1999;McRae et al, 1997). In a series of feature verification tasks, participants were asked to verify features as true or false of a concept.…”

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

“…Perhaps most important, the living-thing distinctive features were substantially lower in production frequency as measured by Randall et al's norms (an average of 9 of 45 participants listed the distinctive feature for the relevant living thing concepts) than the nonliving-thing distinctive features (22 of 45), with the living and nonliving shared features falling in the middle (13 and 16, respectively). Ashcraft (1978) and McRae et al (1997) found that production frequency is a strong predictor of feature verification latency. In the Randall et al data, the pattern of production frequency mirrored the pattern found in the verification latencies.…”

“…Next, the concept's word form representation was activated (activation = 1.0) for each of 60 ticks, and the activations of the distinctive and shared features were logged. Thus, activations of target units were a function of the net input from the word form units and the other feature units.As in McRae et al (1997McRae et al ( , 1999, we assumed that verification latency is monotonically related to the activation of the distinctive or shared feature. Note that it is not obvious how to simulate the decision component of the feature verification task.…”