Predicting and Explaining Human Semantic Search in a Cognitive Model

TL;DR

This paper introduces a cognitively plausible model that incrementally updates a semantic network and uses simple random walks to replicate human semantic search patterns, considering language acquisition constraints.

Contribution

The model uniquely combines incremental network updates with semantic search, aligning with human patterns and accounting for language learning constraints.

Findings

The model replicates human semantic fluency patterns.

Structural and semantic features correlate with human performance.

Incremental updates improve cognitive plausibility.

Abstract

Recent work has attempted to characterize the structure of semantic memory and the search algorithms which, together, best approximate human patterns of search revealed in a semantic fluency task. There are a number of models that seek to capture semantic search processes over networks, but they vary in the cognitive plausibility of their implementation. Existing work has also neglected to consider the constraints that the incremental process of language acquisition must place on the structure of semantic memory. Here we present a model that incrementally updates a semantic network, with limited computational steps, and replicates many patterns found in human semantic fluency using a simple random walk. We also perform thorough analyses showing that a combination of both structural and semantic features are correlated with human performance patterns.