A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing.

Vivian V Valentin,W Todd Maddox,F Gregory Ashby

doi:10.3389/fpsyg.2014.00643

Abstract

The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning—results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500 ms compared to delays of 0 and 1000 ms, and highly impaired with delays of 2.5 s or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 s. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

Highlights

Learning, by definition, is a process of laying down a new memory trace, or of strengthening an existing trace
GENERAL DISCUSSION We developed a neurobiologically-detailed computational model that successfully accounts for the effects of varying the feedback delay on procedural category learning
The successful fits suggest that the temporal dynamics of our model provide a good estimate of the time course of the postsynaptic events that lead to synaptic plasticity in the striatum during procedural learning

Summary

Introduction

By definition, is a process of laying down a new memory trace, or of strengthening an existing trace. For this reason, learning and memory are inextricably related. In declarative memory systems, feedback plays a facilitative role in the sense that it often improves learning, but is sometimes not necessary at all (e.g., Ashby et al, 1999). Procedural and declarative memory systems differ with respect to their sensitivity to the timing of feedback. Learning in tasks that depend on declarative memory is flexible with regards to feedback timing, in the sense that long timing delays often have no detrimental effect on learning. One of the earliest and most influential of these showed a deficit in conditioning to lever-press with delayed reinforcement (Skinner, 1938; pp. 72–74)

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