Fluid intelligence (Gf ) refers to the ability to reason and to solve new problems independently of previously acquired knowledge. Gf is critical for a wide variety of cognitive tasks, and it is considered one of the most important factors in learning. Moreover, Gf is closely related to professional and educational success, especially in complex and demanding environments. Although performance on tests of Gf can be improved through direct practice on the tests themselves, there is no evidence that training on any other regimen yields increased Gf in adults. Furthermore, there is a long history of research into cognitive training showing that, although performance on trained tasks can increase dramatically, transfer of this learning to other tasks remains poor. Here, we present evidence for transfer from training on a demanding working memory task to measures of Gf. This transfer results even though the trained task is entirely different from the intelligence test itself. Furthermore, we demonstrate that the extent of gain in intelligence critically depends on the amount of training: the more training, the more improvement in Gf. That is, the training effect is dosage-dependent. Thus, in contrast to many previous studies, we conclude that it is possible to improve Gf without practicing the testing tasks themselves, opening a wide range of applications.cognitive training ͉ transfer ͉ individual differences ͉ executive processes ͉ control processes

Does cognitive training work? There are numerous commercial training interventions claiming to improve general mental capacity; however, the scientific evidence for such claims is sparse. Nevertheless, there is accumulating evidence that certain cognitive interventions are effective. Here we provide evidence for the effectiveness of cognitive (often called "brain") training. However, we demonstrate that there are important individual differences that determine training and transfer. We trained elementary and middle school children by means of a videogame-like working memory task. We found that only children who considerably improved on the training task showed a performance increase on untrained fluid intelligence tasks. This improvement was larger than the improvement of a control group who trained on a knowledge-based task that did not engage working memory; further, this differential pattern remained intact even after a 3-mo hiatus from training. We conclude that cognitive training can be effective and long-lasting, but that there are limiting factors that must be considered to evaluate the effects of this training, one of which is individual differences in training performance. We propose that future research should not investigate whether cognitive training works, but rather should determine what training regimens and what training conditions result in the best transfer effects, investigate the underlying neural and cognitive mechanisms, and finally, investigate for whom cognitive training is most useful.n-back training | training efficacy | long-term effects | motivation P hysical training has an effect not only on skills that are trained, but also on skills that are not explicitly trained. For example, running regularly can improve biking performance (1). More generally, running will improve performance on activities that benefit from an efficient cardiovascular system and strong leg muscles, such as climbing stairs or swimming. This transfer from a trained to an untrained physical activity is, of course, advantageous; we do not have to perform a large variety of different physical activities to improve general fitness. Although the existence of transfer in the physical domain is not surprising to anyone, demonstrating transfer from cognitive training has been difficult (2, 3), but there is accumulating evidence that certain cognitive interventions yield transfer (4-6).Fluid intelligence (Gf), defined as the ability to reason abstractly and solve novel problems (7), is frequently the target of cognitive training because Gf is highly predictive of educational and professional success (8,9). In contrast to crystallized intelligence (Gc) (7), it is highly controversial whether Gf can be altered by experience, and if so, to what degree (10, 11). Nevertheless, it seems that Gf is malleable to a certain extent as indicated by the fact that there are accumulating data showing an increase in Gf-related processes after cognitive training (6). The common feature of most studies showing transfer to Gf is that the tr...

The N-back task is used extensively in literature as a working memory (WM) paradigm and it is increasingly used as a measure of individual differences. However, not much is known about the psychometric properties of this task and the current study aims to shed more light on this issue. We first review the current literature on the psychometric properties of the N-back task. With three experiments using task variants with different stimuli and load levels, we then investigate the nature of the N-back task by investigating its relationship to WM, and its role as an inter-individual difference measure. Consistent with previous literature, our data suggest that the N-back task is not a useful measure of individual differences in WM, partly because of its insufficient reliability. Nevertheless, the task seems to be useful for experimental research in WM and also well predicts inter-individual differences in other higher cognitive functions, such as fluid intelligence, especially when used at higher levels of load.

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Working memory (WM), the ability to store and manipulate information for short periods of time, is an important predictor of scholastic aptitude and a critical bottleneck underlying higher-order cognitive processes, including controlled attention and reasoning. Recent interventions targeting WM have suggested plasticity of the WM system by demonstrating improvements in both trained and untrained WM tasks. However, evidence on transfer of improved WM into more general cognitive domains such as fluid intelligence (Gf) has been more equivocal. Therefore, we conducted a metaanalysis focusing on one specific training program, n-back. We searched PubMed and Google Scholar for all n-back training studies with Gf outcome measures, a control group, and healthy participants between 18 and 50 years of age. In total, we included 20 studies in our analyses that met our criteria and found a small but significant positive effect of nback training on improving Gf. Several factors that moderate this transfer are identified and discussed. We conclude that short-term cognitive training on the order of weeks can result in beneficial effects in important cognitive functions as measured by laboratory tests.

Working memory (WM) training has recently become a topic of intense interest and controversy. Although several recent studies have reported near- and far-transfer effects as a result of training WM-related skills, others have failed to show far transfer, suggesting that generalization effects are elusive. Also, many of the earlier intervention attempts have been criticized on methodological grounds. The present study resolves some of the methodological limitations of previous studies and also considers individual differences as potential explanations for the differing transfer effects across studies. We recruited intrinsically motivated participants and assessed their need for cognition (NFC; Cacioppo & Petty Journal of Personality and Social Psychology 42:116-131, 1982) and their implicit theories of intelligence (Dweck, 1999) prior to training. We assessed the efficacy of two WM interventions by comparing participants' improvements on a battery of fluid intelligence tests against those of an active control group. We observed that transfer to a composite measure of fluid reasoning resulted from both WM interventions. In addition, we uncovered factors that contributed to training success, including motivation, need for cognition, preexisting ability, and implicit theories about intelligence.

Investigators have begun to examine the temporal dynamics of affect in individuals diagnosed with Major Depressive Disorder (MDD), focusing on instability, inertia, and reactivity of emotion. How these dynamics differ between individuals with MDD and healthy controls have not before been examined in a single study. In the present study, 53 adults with MDD and 53 healthy adults carried hand-held electronic devices for approximately seven days and were prompted randomly eight times per day to report their levels of current negative affect (NA), positive affect (PA), and the occurrence of significant events. In terms of NA, compared with healthy controls, depressed participants reported greater instability and greater reactivity to positive events, but comparable levels of inertia and reactivity to negative events. Neither average levels of NA nor NA reactivity to, frequency or intensity of, events accounted for the group difference in instability of NA. In terms of PA, the MDD and control groups did not differ significantly in their instability, inertia, or reactivity to positive or negative events. These findings highlight the importance of emotional instability in MDD, particularly with respect to NA, and contribute to a more nuanced understanding of the everyday emotional experiences of depressed individuals.

Major Depressive Disorder (MDD) is a prevalent disorder involving disturbances in mood. There is still much to understand regarding precisely how emotions are disrupted in individuals with MDD. In this study, we used a network approach to examine the emotional disturbances underlying MDD. We hypothesized that, compared to healthy controls, individuals diagnosed with MDD would be characterized by a denser emotion network, indicating that their emotion system is more resistant to change. Indeed, results from a 7-day experience sampling study revealed that individuals with MDD had a denser overall emotion network than did healthy controls. Moreover, this difference was driven primarily by a denser negative, but not positive, network in MDD participants. These findings suggest that the disruption in emotions that characterizes depressed individuals stems from a negative emotion system that is resistant to change.