A connectivity model for semantic processing

Summary A connectivity model for semantic processing, based on two major assumptions, is proposed and tested. The representation assumption states that semantically related features are represented by an interconnected structure of features. A network is termed interconnected if each node is linked to at least two other nodes. According to the processing assumption, indirect activation becomes increasingly effective as the number of interconnected nodes increases. The connectivity model thus predicts that more complex concepts will be processed faster than less complex concepts. Experiment 1 was performed to determine the number of features for a set of 72 words and to show that subordinate concepts share common features with their natural superordinate concept. In Experiments 2 and 3 subjects had to judge whether or not a word belonged to a previously defined natural superordinate concept. The results confirm the connectivity model and show that concepts with many features can be classified faster than concepts with only a few features.This research was supported by a Grant of the Max Kade Foundation