Few studies have addressed action control training. In the current study, participants were trained over 19 days in an adaptive training task that demanded constant switching, maintenance and updating of novel action rules. Participants completed an executive functions battery before and after training that estimated processing speed, working memory updating, set-shifting, response inhibition and fluid intelligence. Participants in the training group showed greater improvement than a no-contact control group in processing speed, indicated by reduced reaction times in speeded classification tasks. No other systematic group differences were found across the different pre-post measurements. Ex-Gaussian fitting of the reaction-time distribution revealed that the reaction time reduction observed among trained participants was restricted to the right tail of the distribution, previously shown to be related to working memory. Furthermore, training effects were only found in classification tasks that required participants to maintain novel stimulus-response rules in mind, supporting the notion that the training improved working memory abilities. Training benefits were maintained in a 10-month follow-up, indicating relatively long-lasting effects. The authors conclude that training improved action-related working memory abilities.

Introduction

"Never mistake motion for action" (Ernest Hemingway).

The ability to control, monitor and execute actions in a goal-directed manner has been the focus of much research [1–4]. Usually, our environment holds multiple action cues, most of which are irrelevant to the internal plan being pursued. Additionally, in everyday life, there is a frequent need to shift between one or more tasks. To move between task-sets in a flexible, goal-directed manner, the cognitive system needs to update and maintain relevant action information in mind while resisting interference from irrelevant environmental cues. Thus, following internal action plans might require the orchestrated operation of distinct control process such as inhibition, shifting and updating of action rules. The ability to exert control over actions was found to have a broad relevance to major issues such as cognitive development [5,6], aging [7,8], attention- deficit/hyperactivity disorder [9–11], depression [12–14], frontal lobe damage [15] and intelligence [16–18]. Thus, finding ways to improve these control processes would potentially have a significant impact. The aim of the current study was to explore a novel training protocol, explicitly targeting action control process.

Although most studies refer to cognitive control functions (or executive functions) as one set of control process, it is unclear whether procedural control processes (which monitor and regulate actions) are distinct from declarative processes (relevant to knowledge and facts). Studies that address executive functions typically use the taxonomy of three control processes: updating of information in working memory, inhibition of a pre-potent response and switching between tasks or mental sets [1]. In this taxonomy, there is no distinction between procedural and declarative sub-systems. Yet, paradigms that tap response inhibition [19,20] or switching [21–23] are usually based on measurements of procedural control (e.g., stopping an already initiated response or switching tasks). In contrast, paradigms that tap working memory updating abilities tend to do so using (almost exclusively) declarative representations. For example, a common working memory updating paradigm is the N-back task [24–26]. In the N-back task, participants are asked to indicate whether a current target stimulus is identical to the target stimulus presented N trials back. Therefore, in this task participants are required to hold and update declarative representations in working memory as procedural demands (remembering the task rules) remain low and do not increase with N. Complex span tests [26–28] are also widely used to measure working memory abilities. In these tests, participants are asked to remember information while performing a distracting task. Although these tasks might be procedurally demanding (switching between a memorizing task and the distraction task), the primary dependent measure is the amount of information successfully remembered, i.e., the declarative aspect. Additionally, the load is manipulated by increasing the number of items that need to be remembered, thus not changing the procedural demands of the task.

Recently, Oberauer [29] suggested a distinction between procedural and declarative working memory, with declarative working memory being responsible for the maintenance of facts and knowledge and procedural working memory being responsible for maintaining representations needed for the execution of the current task. This hypothesis has gained empirical support [30,31]. Specifically, Souza, et al., [30] manipulated declarative and procedural working memory load in a single task and found an under-additive interaction. This result led the authors to conclude that the updating and maintenance of representations in working memory is processed in two distinct sub-systems, procedural and declarative. Notably, there is evidence that the ability to execute complex and novel task rules might be more predictive of fluid intelligence than declarative-based working memory tasks [16,32]. This finding gives some additional support to the distinction between procedural and declarative working memory sub-systems.

In the last decade, much interest has been devoted to the study of cognitive training, exploring the mechanisms that might allow the improvement of cognitive processes using computerized training tasks. Currently, there is a wealth of training studies focusing on working memory updating training, predominantly relying on tasks that tax declarative working memory [33–36]. By contrast, only a limited number of studies have directly examined the trainability of the action control process, and none of the training studies of which we are aware used exceptionally demanding training tasks in terms of procedural working memory [37].

The few studies that did explore the effects of action control training made use of the task-switching paradigm as a training task. The task-switching paradigm [21,23,38–41] has been used extensively to explore action control processes, specifically the ability to organized and follow complex task rules. In this paradigm, participants are asked to switch between two or more tasks, with a cost in performance being observed in trials that demand a task switch compared to trials in which no switching is required (i.e., switch cost). It is important to note that the task switching paradigm is assumed to be demanding not only in terms of switching but also in terms of procedural working memory updating. For example, Mayr and Kliegl [42] argued that task switching requires the retrieval of action rules (i.e., stimulus-response rules) of the upcoming task into working memory. Thus, an important aspect of task switching includes the ability to maintain, update and shield the task rules in working memory [43]. The task switching paradigm is also considered demanding in terms of inhibition. First, each target stimulus in this paradigm conveys information about multiple tasks. Thus, if, for example, the participant is asked to perform Task A, the cognitive system might need to refrain from responding according to information relevant to Task B that is also conveyed by the target stimulus. When the responses conveyed by the target for Task A and Task B mismatch, a cost is observed (i.e., task rule incongruence effect) [44]. Additionally, switching has been claimed to require the ability to inhibit the previous task [45].

One leading example of a study in which the task switching paradigm served for training is Karbach & Kray’s [46]. These authors gave three age groups (i.e., children, young adults and older adults) either task-switching training or a control training task across four sessions. Both the training and the control groups performed a two-choice reaction task, requiring participants to make simple perceptual judgments (e.g., big/small, plane/train, etc.). The control group performed each of these tasks separately, in single blocks, and the training group performed mixed blocks, demanding task switching. The authors found beneficial transfer effects in similar switching tasks, with the training group demonstrating better switching abilities. In addition, the results demonstrated a far transfer effect to measurements of working memory, inhibition and even fluid intelligence. In a second study that examined a similar training only with children suffering attention-deficit/hyperactivity disorder, the authors found similar improvements, though with no transfer to fluid intelligence measurements [47]. Yet, the utility of this training protocol has been questioned, particularly given the difficulty to replicate the far transfer effects originally reported by Karbach and Kray [48,49].

Additional evidence for training action control process comes from action video game studies [50]. These studies use commercial action video games as a training task. The games require the player to learn and react according to a complex set of rules and procedures. Thus, action video games can be a real-life approximation of the action control process demanded in laboratory paradigms. Several correlational studies have found that experienced video game players demonstrate lower switching costs than novice players [51–53].

Given the correlational nature of these studies, one cannot rule out the possibility that people who choose to play video games have better switching ability. This interpretation is ruled out given that similar evidence has been found using experimental designs. Strobach et al. [54] examined novice video game participants in a pre-post battery of executive function tasks (i.e., dual task and task switching). Three groups were included in this study: an experimental group, an active control group and a no-contact control group. Participants in the experimental group were trained for 15 hours in an action control-demanding video game (i.e., Medal of Honor). The training game required constant monitoring and switching between multiple game-related actions and was performed under strong time constraints. The active control group was trained for the same amount of hours in a computerized puzzle game (i.e., Tetris). The puzzle game was also demanding in terms of executive function (i.e., mental rotation) but required focusing on only one task. Last, a no-contact comparison control group performed only the pre- and post-test measurements. The results demonstrated improved performance in task switching, reflected by the reduced switch costs for the experimental group compared to both the active and no-contact control groups. Similar improvement in action control was found in the dual-task paradigm. Others have found similar evidence for improvement in the action control process following training in action video games. For example, Green et al. trained novice video game participants in an action video game for approximately 50 hours and found a greater reduction in switch costs following action video games training compared with control training [55].

To conclude, research using task switching training is characterized by fixed (and moderate) levels of task demands. Moreover, although near transfer effects seem replicable, far transfer effects are more difficult to replicate. Video game playing seems more promising in this regard, yet the involvement of executive functions in the video game is rather implicit given that these are commercial games that were not explicitly designed to tap specific aspects of action control.

In this study, we designed a novel training protocol that explicitly targeted critical components of action control. We used a task with an adaptive difficulty level and a high training dosage (i.e., 19 training sessions), based on results from previous studies [56,57]. The training task required participants to randomly switch between two choice-reaction tasks. In each trial, a cue appeared, signaling which task should be performed, followed by the target stimulus. In addition, when the difficulty level increased, the participants were asked to react according to the stimulus (either the cue or the target) that appeared N trials beforehand. For example, participants could have been presented with a cue for Task A but had to perform Task B because this was the required task N trials beforehand. Thus, unlike the N-back training previously used [56], the current N-back element was mostly action-related; it demanded that the participants maintain and update the representation related to the task rules and not merely be able to report a piece of information. The task difficulty was adjusted according to participants’ performance using multiple sets of variables (see Method section), including the N value. Thus, the training task remained very demanding throughout the training. In addition, to prevent as much as possible performance improvement due to the formation of long-term memory traces and to keep the working memory demands high, a new set of stimuli (i.e., task cues and target stimuli) and response keys was introduced in each block of the training task.

The training group was compared to a no-contact control group who underwent just pre-testing and post-testing. We chose to use a no-contact control group for two main reasons. First, this was the first study to test this particular training protocol, and as such the study was regarded as preliminary. Second and not less importantly, a recent meta-analysis of working-memory training in young adults [58] indicated that although control-group type (active vs. no-contact) influenced the Experimental-vs.-Control difference in pretest-to-posttest gain, this control-group type effect was exclusively due to the experimental groups—that is, it was independent of the type of the control group that was studied. Other studies have also demonstrated no substantial difference between passive and active control groups [59]. Thus, we chose a no-contact comparison group that controls for pre-testing effects [60,61]. To be on the safe side, we were extra cautious in hiding group membership information (see below), to prevent any biases due to demand characteristics and other types of expectations.

We predicted that practice would increase pretest-to-posttest in tasks that require executive control (i.e., near transfer). Additionally, we also explored the improvement in fluid intelligence following training (i.e., far transfer). We chose to include measurements of fluid intelligence based on previous reports [35,56,58,59,62] and on Duncan et al. [16,32], who showed that the ability to maintain and execute complex and novel task rules might be more predictive of fluid intelligence than are declarative based tasks. To examine such near and far transfer effects, a battery of executive functions was administered before and after 19 training sessions. The measurement battery was designed to estimate processing speed, switching, response inhibition, and working memory updating (i.e., near transfer), as well as fluid intelligence measures (i.e., far transfer). In addition, participants completed a follow-up measurement session administrated 10 months from the end of training, testing for long-term transfer effects. Aside from testing the stability of the gains, the follow-up session was introduced in order to rule out some alternative accounts of the results of the first posttest.