The multiple effects of practice: skill, habit and reduced cognitive load
Introduction
Acquiring any new motor skill requires practice. It is not enough to simply be instructed how to drive a car or how to play a new video game; many hours of practice are typically needed to achieve proficiency and fluidity. What attributes of performance are improved through practice? Most obviously, being skilled at a task involves being able to select appropriate actions [1, 2], and to execute those actions accurately [3, 4, 5]. However, a further critical aspect of skill relates to the speed at which an appropriate action can be selected. A novice driver will know to press the brake pedal to slow down, but an experienced driver will be far quicker to hit the brakes in the face of an unexpected hazard.
It has long been recognized that practice not only promotes incremental performance gains, but also leads to a qualitative change in how behavior is generated. A given task seems to require less and less cognitive effort the more we practice it [6, 7, 8]. Driving a car for the first time can feel overwhelming but, after sufficient practice, we have no problem talking to a passenger or listening to the radio while we drive. This familiar experience occurs in almost any skill we practice; as our proficiency increases, the cognitive load decreases.
The diminishing cognitive load of a task, has mostly been studied through the use of dual tasks [9]. In this approach, participants are asked to perform the practiced task at the same time as a cognitively demanding secondary task, such as counting the number of vowels heard in a sequence of spoken letters, or counting backwards from 100 in increments of 7. Early in learning, performance on either or both tasks suffers when they are attempted concurrently. After sufficient practice, however, it usually becomes possible to perform both tasks simultaneously just as well as they can be performed in isolation [10, 11, 12].
Another important phenomenon associated with practice is the formation of habits. Habits are most commonly studied in operant learning tasks in which an animal must learn through experience which action to perform to earn a reward (e.g. which lever triggers delivery of a food pellet). In this context, habitual behavior is typically defined as behavior that is insensitive to changes in the goals of a task [13, 14, 15] (and, by definition, opposite from goal-directed behavior). If a rat has repeatedly pressed a particular lever to earn a food reward, it may continue to press it habitually even when it is not hungry. Such habits are often exposed in daily life when the environment changes. For instance, when driving abroad, if the steering wheel is on the opposite side of the car, you may find yourself habitually reaching toward the door when trying to shift gears or pull the handbrake. The habitual nature of skilled typing is similarly unmasked if one tries to type on a foreign keyboard, in which certain symbols might be mapped onto different keys. This key-switch manipulation is in fact often directly used in experiments in humans to assess whether a practiced visuomotor association has become habitual [16, 17, 18••].
Assessing whether or not behavior is habitual can be complicated by the fact that behavior is known to be generated through a combination of habitual and goal-directed processes [14, 19, 20, 21] and a learned habit can be easily masked by more deliberate, goal-directed influences on behavior. One way to reveal latent habitual behavior is to limit the amount of time available to generate a response to a stimulus. For instance, if participants practice distinguishing between different stimuli, or categories of stimuli, by pressing particular buttons over multiple days or weeks, habitual errors following a button-switch are relatively scarce when participants are allowed to respond under self-paced conditions [16]. But if participants are forced to respond very rapidly, habitual errors can be elicited in a majority of trials [18••]. Similar low-latency expression of habits occurs when using hand movements to control an on-screen cursor. If the mapping between hand and cursor is distorted, for example, through a mirror-reversal of the position of the cursor on the screen, participants can quickly learn to generate accurate movements when allowed to take their time before moving. However, rapid corrective responses to a perturbation applied during the movement betray a persistent habitual tendency to generate baseline patterns of correction, even after extensive practice [22, 23, 24]. Thus, habits are most strikingly revealed when actions must be generated very rapidly.
In summary, practice leads to three distinct changes in behavior: First, it improves skill level, including the ability to select actions more rapidly. Second, it permits appropriate actions to be selected with less cognitive effort than before. Third, it leads action selection to become habitual.
Section snippets
Cached computation as a theory of automatic action selection
These three effects of practice, skill, habit and reduced cognitive load, are often viewed as alternative ways of observing a single underlying change in behavior: that it has become automatic [25, 26, 27] (Figure 1a). It has often been assumed that it is sufficient to study any one of these phenomena on its own as a proxy for assessing the assumed underlying property of ‘automaticity’. However, this assumption has remained largely untested; skill, habit and cognitive load have rarely been
Hierarchical representations and intermediate computations
In principle, one can reduce any learned behavior, such as braking at a red light or hitting a tennis ball, to a single cached association between a stimulus and a response (Figure 2a), which will naturally give rise to all three facets of the automaticity ‘syndrome’. More generally, however, the process of determining an appropriate action may involve a more hierarchical process that entails intermediate computational steps. In the case of braking at a red light, seeing the light may prompt us
Conclusions
The principle of caching provides a compelling and parsimonious theory of automaticity following practice. Caching of a simple stimulus-response relationship accounts for the typically observed behavioral effects of practice: faster responding (skill), habitual behavior, and reduced cognitive load. Caching can, however, also be applied to intermediate steps of more complex computations, potentially giving rise to a multitude of different behavioral consequences.
This possibility leads to a
Conflict of interest statement
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.
We confirm that we
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
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