What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach.

Michael S Vitevitch,Gavin J D Mullin

doi:10.3390/brainsci11121628

Abstract

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

Highlights

Various metaphors have been used to increase our understanding of the mind, with the computer perhaps being the most well-known and fundamental metaphor in CognitivePsychology [1]
Given the variety of dependent measures used in the various behavioral experiments that we attempted to simulate in the present study, and the different activation levels in TRACE and the cognitive network model, we attempted in the simulations reported here to replicate only qualitatively the findings from each of the behavioral experiments
In the cognitive network model implemented on spreadr, we found that words with a lower clustering coefficient had higher activation levels indicating they were identified more accurately than words with a higher clustering coefficient

Summary

Introduction

Various metaphors have been used to increase our understanding of the mind, with the computer perhaps being the most well-known and fundamental metaphor in CognitivePsychology [1]. An early use of the network metaphor in Cognitive Psychology is exemplified in the spreading activation theory of semantic memory proposed by [2]. The spreading of activation across the semantic network proposed by Collins and Loftus has been used to understand numerous memory and language phenomena Another use of the network metaphor in Cognitive Psychology is the “artificial neural network” approach exemplified in (localist) connectionist models and in parallel distributed processing (PDP) models. Both types of artificial neural network saw a rise in popularity in the late 1980s and early 1990s [3,4]

Methods

Results

Discussion

Conclusion