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WIREs Cogn Sci

Automaticity and multiple memory systems

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A large number of criteria have been proposed for determining when a behavior has become automatic. Almost all of these were developed before the widespread acceptance of multiple memory systems. Consequently, popular frameworks for studying automaticity often neglect qualitative differences in how different memory systems guide initial learning. Unfortunately, evidence suggests that automaticity criteria derived from these frameworks consistently misclassify certain sets of initial behaviors as automatic. Specifically, criteria derived from cognitive science mislabel much behavior still under the control of procedural memory as automatic, and criteria derived from animal learning mislabel some behaviors under the control of declarative memory as automatic. Even so, neither set of criteria make the opposite error—that is, both sets correctly identify any automatic behavior as automatic. In fact, evidence suggests that although there are multiple memory systems and therefore multiple routes to automaticity, there might nevertheless be only one common representation for automatic behaviors. A number of possible cognitive and cognitive neuroscience models of this single automaticity system are reviewed. WIREs Cogn Sci 2012, 3:363–376. doi: 10.1002/wcs.1172

Figure 1.

A few examples of stimuli that might be used in a rule‐based (top) and an information–integration (bottom) category‐learning experiment. Each stimulus is a circular sine‐wave grating that varies across trials in the width and orientation of the dark and light bars. The category boundaries are denoted by the solid lines. Note that a simple verbal rule achieves perfect accuracy with the rule‐based categories (e.g., respond ‘A’ if the bars are narrow and ‘B’ if they are wide), but there is no such simple verbal description of the optimal strategy in the information–integration task.

[ Normal View 106K | Magnified View 392K ]
Figure 2.

A schematic representation of some of the more important anatomical structures and projections thought to play a role in the development and execution of automatic skilled behaviors. Note that not all structures or pathways are shown. For example, all projections out of the striatum pass first to a basal ganglia output structure (e.g., the internal segment of the globus pallidus) and then to the thalamus before reaching cortex. Also note that cortical projections to the caudate nucleus are not shown.

[ Normal View 25K | Magnified View 67K ]

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Konrad Körding

Konrad Körding

Konrad Körding is Assistant Professor of Physiology and Physical Medicine and Rehabilitation at the Rehabilitation Institute of Chicago, part of Northwestern University. Before joining Northwestern in 2006, Professor Körding worked in three different research groups, most recently in 2004-2005 at MIT, studying machine learning and hierarchical Bayesian models.


Professor Körding is a member of the Swiss Society for Neuroscience, the German Society for Neuroscience, the Society for Neuroscience (USA) and the Electronic Frontier Foundation.

Professor Körding’s current research with the Bayesian Behavior group aims to improve rehabilitation procedures through a greater understanding of motor learning. In order to do this the team studies how people move, and how these movements are affected by uncertainty.

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