Year: 2005
Type: article
Abstract: Humans exhibit savings in skills.A skill is rarely forgotten completely, even if it remains unused for long periods.Also, the reacquisition of a skill to its previous level of competence is faster than initial skill learning.Traditional artificial neural network models of skill learning have been unable to exhibit savings comparable to that seen in humans because they suffer from catastrophic interference.They are commonly trained to perform only one specific task, and when trained on a new task, they forget the original task completely.A number of specialized connectionist architectures and learning rules have been suggested as means to avoid catastrophic interference.Instead of introducing such a new mechanism, we have investigated the degree to which the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework are sufficient to ameliorate catastrophic interference.In particular, this framework includes both fast lateral inhibition and a local synaptic plasticity model that incorporates both Hebbian and error-based dynamics, grounded in known properties of cortical circuits.In this paper, we provide evidence that these fundamental computational properties of neural circuits can support savings during sequential learning of multiple motor skills.
Authors: Ashish Gupta, David C. Noelle
Institution: Vanderbilt University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W53318105