Neural Components of Reading Revealed by Distributed and Symbolic Computational Models.

Ryan Staples,William W Graves

doi:10.1162/nol_a_00018

Abstract

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

Highlights

To better understand how the brain carries out a cognitive process, we must have robust approaches to both cognitive models and neural functions
To determine whether the 20 instantiations of the Artificial neural network (ANN) model achieved an accuracy comparable to human performance, they were tested, as described in Materials and Methods, for the 464 words that humans read in the scanner
We used Representational similarity analysis (RSA) to relate neural data obtained during reading to the representations of orthography-to-phonology transforms generated by these two distinct computational models

Summary

Introduction

To better understand how the brain carries out a cognitive process, we must have robust approaches to both cognitive models and neural functions. Cognitive models provide a mechanistic explanation of cognitive function, and computational implementations of these models provide explicit and testable predictions of how these processes interact (Forstmann et al, 2011) These models alone cannot reveal the neural bases or implementation of the modelled processes. The union of computational cognitive models and neuroimaging, implemented by recent advances in methodology, allows for the quantitative comparison of specific, modelgenerated predictions to test their biological plausibility. We used this approach to better determine the neural and cognitive basis of reading

Objectives

Methods

Results

Conclusion