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We performed 2 Group: training vs. The means were calculated using only trials in which T1 was identified correctly. Since the group differences were not affected by Lag, it indicates that the improvement of the training group from pre-test to post-test was similar in both the long and the short lag Figure 5. This suggests that the training group showed improvements in the identification of T2 across both lags.

Figure 5. Proportion of correctly reported T2 T1 for both lags in pre-test and in post-test for the training group and the control group.

Performance scores of the participants who attended the RAPM-test in pre-test as well as in post-test were submitted to a 2 Group: training vs. Probably, this finding can be attributed to a general ceiling effect in the training group, which performed very well in both the pre- and the post-test sessions.

Therefore, due to its relatively low performance level in the pre-test session, the control group had more space for an improvement of the RAPM values in the post-test session, relative to the training group. The purpose of this study was to investigate, which improvements in executive control functions achieved through WM training can generalize beyond the training task and situation. Within three weeks of training with a demanding WM task, the dual n -back, participants improved their performance significantly from the first to the last session.

A control group that did not undergo training, performed on an equal level in post-test as compared with its pre-test performance three weeks earlier. The improvement of the training group generalized to three untrained tasks: a VS WM updating task, task switching, and an AB task. The nearest transfer occurred to the VS WM updating task. Both the dual n -back and the updating task share the requirement to constantly update WM contents.

However, there are crucial differences between the tasks that must be noted. First of all, there are dissimilarities between the stimuli of the two tasks blue squares vs. Furthermore, the presentation time of the stimuli in the transfer task is different from that of the training task. Most importantly, the two tasks engage different processes: the n -back requires recognition of stimuli, whereas in the updating task correct stimuli have to be recalled from WM. With these aspects in mind, it can be concluded that the training paradigm indeed enhanced the ability to update WM contents, independent of the trained material.

Interestingly, this transfer effect was only seen in the VS modality and spared the AV modality. There are two—not mutually exclusive—possibilities to explain this observation.

Firstly, it is plausible that the auditory WM system is more rehearsed or automatized as a result of everyday auditory experiences, because remembering auditory information demands effective rehearsal processes for example to understand speech Baddeley, Thus, there could be less space to improvement as compared with the visual WM, which for its part is not as strained in daily life Baddeley, b.

According to our results, auditory WM updating is not insensitive to improvements related to task repetition, since we did see an improvement for both groups from pre-test to post-test in the AV WM updating task.

But, to induce an effect of training on skill-level, a more demanding task than the current auditory part of the dual n -back task would probably be required. It might therefore be that the training task indeed rehearsed a central executive mechanism; but, since such mechanism is more closely tied to VS WM processes than to auditory ones, the current transfer effect was more pronounced in the VS updating task.

As for task switching, we found a transfer effect that was reflected in mixing costs but not in switching costs. It, therefore, seems that the transfer effect did not tap transient processes related to task switching i. To calculate the magnitude of mixing costs, we compared performance in repetition trials to that in single-task trials.

Even though these two trial types both require the performance of the same task from one trial to the next, they differ from each other in one critical aspect.

In repetition trials, one has to maintain two task sets in WM, while in single-task trials only one task set is sufficient. The observation of a transfer effect on mixing costs i. It is also congruent with the results from the WM updating task, in that an improvement in WM updating was observed only in the VS task and the stimuli in our task switching paradigm were also presented visually.

With respect to switch costs, they have been described to be—at least partly—a measure of interference from the preceding task set Allport et al.

Thus, it is conceivable that our trained participants showed no reduction in switch costs since the training task did not encourage inhibiting one or the other task: participants were explicitly instructed that only successful performance of both the AV and the VS task would make them advance to the next n -level. Thus, concentrating on only one of the tasks and therefore having to inhibit the information from the other task would not have led to a performance improvement.

This interpretation would to that end also be in accordance with the lack of transfer to the dual-modality updating task see above , which in turn specifically required inhibition of the irrelevant task modality at the response phase. Finally, we found a transfer effect to the AB task, such that T2 identification was improved after training. Also T1 accuracy improved from pre-test to post-test, excluding the possibility that the improvement in T2 identification was a sole consequence of the participants simply attending more to T2 at the expense of T1.

Since our AB task tapped both the visual and the auditory modality, this is the first time that a training-related effect to a cross-modal AB task is shown; note that previous studies have shown effects only within the visual modality Green and Bavelier, ; Slagter et al.

In the present study, participants showed an improvement in T2 accuracy in both the short and the long lag. Therefore, we cannot infer that there was a specific decrease in the trained participants' AB, but only that they could report T2 more correctly in general. However, a closer inspection of our data shows that participants still seemed to manifest an AB at the long lag i. In that event, it could be that our long lag may have not been long enough for the T2 to surpass the effect of AB.

Assuming that the AB was indeed decreased and that we missed it because of the properties of our task, this finding would suggest that the improvement in temporal dividing of attentional resources was transferable beyond the training task. This would be in accordance with a previous study by Oberauer , in which it was suggested that WM training specifically on the n -back task leads to a speed up in attentional processes within WM, rather than to a pure increase in WM capacity.

Theories of AB generally address the magnitude of AB to be dependent on the amount of attentional capture by T1 and on the efficiency of T1 processing Shapiro et al. It is thus possible that the improvement in the auditory T2 identification in our paradigm came about by a reduced limitation of T2 encoding due to an improvement in the processing of the visual T1.

This would particularly be consistent with the already reported effects of transfer to tasks in the visual modality i. In fact, in a study by Slagter and colleagues , a decreased AB after meditation training was explained by more efficient processing of T1. This was evident in their electrophysiological EEG data as a smaller P3b-component for T1 after training. As the P3b-component generally reflects the allocation of attentional resources, Slagter and colleagues suggested that meditation training improved the participants' control over the distribution of attentional resources: they were more efficient in deploying resources to T1, thus leading to an increased T2 accuracy.

Consistent with our interpretation of improved division of attentional resources in time are also the findings by Green and Bavelier In their study, participants trained action video-game playing. Following training, the T2 accuracy was improved, such that the trained participants recovered faster than non-trainers from the effects of AB. There is, however, another study by Boot and colleagues that did not find transfer after video-game training to AB. We believe that this discrepancy could be due to general differences between the studies.

For example, the AB task itself was somewhat different between these studies. In the Boot and colleagues' study the task was to identify T1 and to detect whether T2 appeared or not; whereas in our study the task was to identify both T1 and T2, and T2 also appeared in every trial. It is thus possible that our AB task was more sensitive to the type of training we implemented.

Yet another critical difference between these studies is that the collection of the transfer tasks in the study by Boot and colleagues was different from the present study: while in the former study participants performed 12 different tasks, in the latter study participants performed only four different tasks. Thus, it is possible that the larger number of transfer tasks in the study by Boot and colleagues, compared with the number of transfer tasks in the present study four tasks and in the study by Green and Bavelier three tasks counteracted a possible manifestation of transfer in the AB task.

This would be consistent with findings of Schmeichel , who has shown that engaging in one task including an executive function component can have a debilitating effect on the performance in other executive function tasks. Interestingly, training did not transfer to dual-task coordination skills, as revealed by a lack of training-related improvements in the PRP-paradigm.

Although we initially expected an improvement in dual-task abilities following training, the observation of lacking transfer to the PRP-task may not be surprising for two reasons.

First, a key element of the training task was indeed the demand to efficiently update WM contents, which was not essential for the transfer situation in the PRP dual-task.

Second, the training task did not require speeded processing and execution of appropriate stimulus-response mappings, which is an essential characteristic for dual-task processing of the PRP task type Schubert, , Thus, the lack of commonalities between the dual-task processing in the trained dual n -back task and the transfer PRP dual-task situation may have avoided the appearance of specific transfer effects between both task situations.

This finding is consistent with the study by Jaeggi and colleagues , which used the same training paradigm and found no transfer to the RAPM after eight sessions of training. However, another study by Jaeggi and colleagues did find transfer to RAPM after 20 sessions of dual n -back training. There is a critical difference between the ways how the RAPM were administered in the present study and in those other studies: Jaeggi and colleagues , applied the test with a time restriction 20 min , whereas in our study the test was conducted according to the standardized procedure Raven, , which instructs to give participants a sufficient amount of time to finish the test.

It seems plausible to explain the observation of a training-induced improvement of Gf in a speeded version of the RAPM by the proposed hypothesis that the current WM training optimizes specifically the efficiency of attentional processes within WM, as suggested in our AB results.

Therefore, when the test is administered in line with the standardized procedure described in the test manual as it was the case in the present study , potentially improved attentional processes may not decisively contribute to the performance level in the Gf test. As a consequence, the improvement in attentional processing does not reflect in the Gf level results of the current type of the RAPM test administration. It has already been suggested elsewhere, that the link between Gf and WM is a common attentional control mechanism Gray et al.

Other studies using a different WM training paradigm but that have administered the RAPM similarly to the present study i. In the present study, some participants were not available for the post-test on the RAPM. Thus, the sample size in this test was fairly small, and the lack of power might have contributed to the non-significant transfer effect. Consistent with this idea, the present power analysis demonstrated that even the original sample size of 38 participants would not have been sufficient to lead to a significant training advantage from pre- to post-test.

Summarizing our results, we found transfer to a VS WM updating task, to a task switching situation as measured by mixing costs as well as to the AB task. The diversity of these transfer effects corresponds to the findings of Chein and Morrison , who found transfer effects from a complex WM span task to a variety of other tasks, for example the Stroop-task and reading comprehension, and who proposed training of a domain-general mechanism as a prerequisite for transfer effects.

The observations in the present study are also consistent with the assumption that cognitive enhancements from our training paradigm may have affected not only a specific but also a more domain-general mechanism involved in various executive processes. A strong candidate for such a more general mechanism would be, according to Chein and Morrison, the mechanism of attentional control.

Attentional control processes are strongly present in all of the processes to which we observed transfer: in WM updating as detaching attention from irrelevant items and attending to new relevant items similarly to our training task , in task switching mixing costs as the requirement to control attention between the two task sets Braver et al.

Notably, regarding WM updating, we found transfer only to the VS task. This is worthy of mentioning in reference to theories, which propose that executive attentional mechanisms are more closely related to VS WM than auditory WM processes Baddeley, b ; Miyake et al. Alternatively, it is possible, that our transfer effects were the consequence of improvements in the separate processes that were recruited by the training task and tapped by our transfer tasks.

At last, there are certain limitations in the present study that should be acknowledged and discussed. In controlled cognitive training studies, one general practice has been to compare the performance of the training group to that of a control group, which does not attend any intervention e. In this way it has been possible to eliminate re-test effects; however, it is still questionable, to what degree performance changes of the training group can be attributed to the training task and not just to the existence of an intervention per se Shipstead et al.

In the current study we did not include an active control group, which might raise the question, how much of the performance improvements of the training group in the transfer tasks were due to our training paradigm and how much can be attributed to rather unspecific effects like e.

Generally, we believe, that had the performance improvements been affected by these factors, we would have observed improvements across all tasks and situations. This was not the case in the present study. In fact, we demonstrated specific transfer effects e.

Of course, one could argue that the transfer tasks were of different difficulty and, therefore, unspecific training effects could occur only in a subset of only the easiest tasks. However, this argument seems not to be valid, as, for example in the updating task, according to the amount of correctly reported sequences across both sessions and groups, the VS task was more difficult than the AV task, whereas the dual-modality task seemed to be the most difficult one.

These observations are also supported by the comments of participants, who reported the VS task to have been more difficult than the AV task and the dual-modality task to have been the most difficult one. Therefore, if the transfer effect was driven by the easiness of the task, we should have observed improvements in the AV task rather than in the VS task.

Similarly in the task switching paradigm, we observed transfer to the mixing costs, and this effect was driven by a group-specific improvement in the repetition trials compared with single-task trials, in which there was no training-related improvement. Considering that the RTs in the repetition trials were generally slower than the RTs in the single-task trials, it seems plausible that the repetition trials were more complex than the single-task trials.

On the other hand, we found no transfer to switching costs, although the performance in the repetition and switch trials differed from each other significantly so that the RTs in switch trials were slower than the RTs in repetition trials. If the simplicity of the task underlay the transfer effect, our transfer effects in the task switching paradigm would seem counterintuitive.

Based on this rather unsystematic pattern of transfer effects from the perspective of task difficulty , we believe that the easiness or the simplicity of a transfer task does not determine transfer. Further, a study by Thorell and colleagues has shown that motivational factors as well as pure engagement in an intervention play a rather minor role in cognitive training, as in their study there were no differences in the performances of an active and a passive control group.

Apart from the methodological concerns about a no-contact control group, we would also emphasize that the inclusion of an active control group may not have been critical to the problem setting in our study. Our aim was to investigate transfer effects related to the dual n -back task without thoroughly specifying the components of the training that may underlie transfer.

Another issue pointed as questionable by Shipstead and colleagues is the inclusion of only a single task for each function. We recognize the problem with this approach, as it cannot be unambiguously concluded that there are improvements in a certain function, but rather in an aspect of a function as measured by a single task. With respect to the present study, we emphasize that first of all, on a general level, we investigated transfer effects from WM training to executive functions; and we used not only one but four different executive tasks for this purpose WM updating, dual-task, task switching, AB.

Second, although at first glance it would seem that for each executive function we implemented only one task, we would like to highlight that our transfer tasks did involve also overlapping processes. For example, WM updating is an essential process in our updating task as well as in task switching. Attentional control was required in the updating task, task switching, and in the AB task.

Multitasking was relevant in the dual-task and in the dual-modality part of the updating task. Our results are also in accordance with these overlaps, in that we, for instance, found no transfer to either the dual-task or the dual-modality updating task. The overlapping of processes between our transfer tasks aside, it should be kept in mind that in such comprehensive studies as the present one, one important criterion is not to exhaust the participants by bombarding them with an immense battery of tests.

This assumption is consistent with 1 findings of Schmeichel , who demonstrated effects of exhausting between executive tasks, and 2 the reduced transfer effects in a more exhausting post-test session including 12 transfer tasks Boot et al. We aimed to tap several executive functions, and encourage future studies to broaden the range of measurements in order to clarify the specific effects of WM training.

In sum, in the present study we have provided evidence that complex WM training can produce transfer effects to executive functions. You have completed your 3 brain workouts today Come back tomorrow to unlock your next workout. Click the tab on the top-right corner of each game-card above to check prior scores View all brain workouts ».

You have unlocked the entire MindGamer library! Your Brain Training Progress:. View ». So, now you know the key benefits of brain training games your next challenge is finding one that will challenge you properly. The following brain training games are all highly rated for providing a full range of the benefits listed above:.

Besides, even if you just have a spare 10 minutes or so in the day, playing some brain training games for fun is a much better use of your time than other games, and you might gain some useful mental agility skills that transfer to other parts of your life as an added bonus! Card Calculation : Calculation training of two cards. Select the answer by touching the card. Stray Shape : Touch the one shape which doesn't fit in the holes. Order Making : Input a number or an alphabet to the blank to make the right order.

Silhouette Box : Silhouettes goes in and out. Select one that remained in the box. Pair Shapes : Select a pair of shapes that meets the condition. Concentration : Memorize and select the pair of same cards. Reverse Order : Touch alphabets in reverse order.

Input Arrows : Input all arrows on the screen by touching the D-pad. Pitch of Sound : Listen to sound and answer the pitch. Instant Decision : If "o" appears, touch it quickly. Make 10 : Fill in the blank to make Use bonus x2 block to get high score! This cute app has many nice minigames for practicing important judgement skills.

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Privacy practices may vary, for example, based on the features you use or your age. Learn More. With Family Sharing set up, up to six family members can use this app.