A critical review of habit learning and the Basal Ganglia - PubMed (original) (raw)

A critical review of habit learning and the Basal Ganglia

Carol A Seger et al. Front Syst Neurosci. 2011.

Abstract

The current paper briefly outlines the historical development of the concept of habit learning and discusses its relationship to the basal ganglia. Habit learning has been studied in many different fields of neuroscience using different species, tasks, and methodologies, and as a result it has taken on a wide range of definitions from these various perspectives. We identify five common but not universal, definitional features of habit learning: that it is inflexible, slow or incremental, unconscious, automatic, and insensitive to reinforcer devaluation. We critically evaluate for each of these how it has been defined, its utility for research in both humans and non-human animals, and the evidence that it serves as an accurate description of basal ganglia function. In conclusion, we propose a multi-faceted approach to habit learning and its relationship to the basal ganglia, emphasizing the need for formal definitions that will provide directions for future research.

Keywords: automaticity; basal ganglia; habit learning; reward.

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Figures

Figure 1

Figure 1

The fractionation of long-term memory proposed by Squire and Zola-Morgan. Redrawn based on Squire and Zola-Morgan (1991).

Figure 2

Figure 2

Corticostriatal loops.

Figure 3

Figure 3

Comparison of various possible criteria for habit learning that develop across learning on the basis of approximate point in training at which learning becomes habitual. Note that only criteria that can develop across training are included; criteria that are required across the entire time course of learning (unconscious, inflexible) are not. Top section: Qualitative criteria including two operational definitions of automaticity, and reinforcer devaluation. Middle and Bottom section: possible operational definitions of slow or incremental. Middle: Criteria based on computational modeling. This approach is illustrated using reinforcement-learning measures (reward prediction error, and value, or reward prediction) although other approaches can also be used. Bottom: Commonly used simple behavioral measures of learning.

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