No Need to Forget, Just Keep the Balance : Hebbian Neural Networks for Statistical Learning

Tovar, Angel E. and Westermann, Gert (2023) No Need to Forget, Just Keep the Balance : Hebbian Neural Networks for Statistical Learning. Cognition, 230: 105176. ISSN 0010-0277

[thumbnail of Tovar&Westermann-Cognition2022-preprint]
Text (Tovar&Westermann-Cognition2022-preprint)
Tovar_Westermann_Cognition2022_preprint.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (731kB)


Language processing in humans has long been proposed to rely on sophisticated learning abilities including statistical learning. Endress and Johnson (E&J, 2021) recently presented a neural network model for statistical learning based on Hebbian learning principles. This model accounts for word segmentation tasks, one primary paradigm in statistical learning. In this discussion paper we review this model and compare it with the Hebbian model previously presented by Tovar and Westermann (T&W, 2017a; 2017b; 2018) that has accounted for serial reaction time tasks, cross-situational learning, and categorization paradigms, all relevant in the study of statistical learning. We discuss the similarities and differences between both models, and their key findings. From our analysis, we question the concept of “forgetting” in the model of E&J and their suggestion of considering forgetting as the critical ingredient for successful statistical learning. We instead suggest that a set of simple but well-balanced mechanisms including spreading activation, activation persistence, and synaptic weight decay, all based on biologically grounded principles, allow modeling statistical learning in Hebbian neural networks, as demonstrated in the T&W model which successfully covers learning of nonadjacent dependencies and accounts for differences between typical and atypical populations, both aspects that have not been fully demonstrated in the E&J model. We outline the main computational and theoretical differences between the E&J and T&W approaches, present new simulation results, and discuss implications for the development of a computational cognitive theory of statistical learning.

Item Type:
Journal Article
Journal or Publication Title:
Additional Information:
This is the author’s version of a work that was accepted for publication in Cognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Cognition, 230, 2023 DOI: 10.1016/j.cognition.2022.105176
Uncontrolled Keywords:
Data Sharing Template/yes
?? statistical learninghebbian learningartificial neural networkslanguage processingcomputational modelingyeslinguistics and languagecognitive neuroscienceexperimental and cognitive psychologylanguage and linguistics ??
ID Code:
Deposited By:
Deposited On:
17 May 2022 09:30
Last Modified:
03 Jan 2024 10:05