Integrating conceptual knowledge within and across representational modalities (original) (raw)

Further evidence for feature correlations in semantic memory

Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 1999

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

Behavior Research Methods, 2005

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.

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

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2006

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 attractorbased 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.

Feature-based perception of semantic concepts

Lecture Notes in Computer Science, 1997

In this paper we shall point to some principles of neural computation as they have been derived from experimental and theoretical studies primarily on vision. We argue that these principles are well suited to explain some characteristics of the linguistic function of semantic concept recognition. Computational models built on these principles have been applied to morphological-grammatical categories (aspect), function words (determiners) and discourse particles in spoken language. We suggest a few ways in which these studies may be extended to include more detail on neural functions into the computational model.

A Synthetic Model of Semantic Memory as a Representable Structure

We use evidence from the artificial general intelligence modeling and cognitive science fields to develop a realistic computational model of semantic memory. The model is centered around a concept system (CS), which is a connectionist network with local representations. Nodes in the network are either atomic or general, maintaining a natural division among concepts which we believe to hold even in humans. In our proposal, the interaction of the CS with a short-term memory (STM), which is similar in structure to the CS but smaller and more volatile, will result in interesting human-like properties being manifest in the agent which the memory is instantiated in. The model operates in a domain corresponding to physical reality, but simplified for ease of understanding and to alleviate the problem of dealing with more complex experience, which is out of the scope of the immediate problem. Lexical tags, which may be simultaneously presented with experience, help to anchor the growth of semantic meaning in the agent, as well as offering 'handles' into the model for later recall undertaken during testing. We present the reasoning behind the model, the model itself, a functioning implementation of a simulation of it, and extensive ideas for improvement in this paper. We also report and discuss some preliminary testing results, and discuss aspects of the model from a philosophical standpoint as further evidence for the validity of the general model.