Further evidence for feature correlations in semantic memory.

Abstract

The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, subjects 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.

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

“…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.…”

“…Collins and Loftus (1975) further assume that the strength of a link between any two nodes is determined by the frequency of its usage, and that the strength of a link between a concept node and a feature node is determined by criteriality that corresponds to the definingness, or the necessity of a feature to the concept, as in Smith, Shoben, and Rips's (1974) feature comparison model. If items are chosen appropriately, so that distinctive and shared features are equated for criteriality, then there should be no difference in the length of time it takes to activate the two types of features.We were motivated by these contrasting predictions to test whether a model of semantic memory we have used to simulate other behavioral effects (Cree & McRae, 2001;Cree, McRae, & McNorgan, 1999;McRae, Cree, Westmacott, & de Sa, 1999) (2004) behavioral data. We then tested the predictions of the models in two behavioral experiments of our own.…”

“…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.…”

“…Attractor network simulations predicted that features that are strongly inter-correlated with the other features of a concept should be verified more quickly than matched features that are weakly intercorrelated (distinctiveness was equated in these experiments). This prediction was borne out by the human experiments.It is therefore important to situate the shared features from the present experiments in terms of their degree of intercorrelational strength with respect to the items used in McRae et al (1999). We computed intercorrelational strength for all of the shared features used in Experiments 1 and 2 (distinctive features have an intercorrelational strength of 0 because they do not appear in a sufficient number of concepts to be able to derive valid correlations).…”

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

“…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.…”